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  </channel><item rdf:about="https://www.cambridge.org/core/journals/royal-institute-of-philosophy-supplements/article/abs/mind-as-a-control-system/501BF772FCAADCB00A1F576602E771F9">
    <title>The Mind as a Control System* | Royal Institute of Philosophy Supplements | Cambridge Core</title>
    <dc:date>2026-04-08T17:16:49+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/royal-institute-of-philosophy-supplements/article/abs/mind-as-a-control-system/501BF772FCAADCB00A1F576602E771F9</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This is not a scholarly research paper, but a ‘position paper’ outlining an approach to the study of mind which has been gradually evolving (at least in my mind) since about 1969 when I first become acquainted with work in Artificial Intelligence through Max Clowes. I shall try to show why it is more fruitful to construe the mind as a control system than as a computational system (although computation can play a role in control mechanisms)."

--- Preprint version [https://cogaffarchive.org/Aaron.Sloman_Mind.as.controlsystem/Aaron.Sloman_Mind.as.controlsystem.pdf] gives the following abstract:

"Many people who favour the design-based approach to the study of mind, including the author previously, have thought of the mind as a computational system, though they don’t all agree regarding the forms of computation required for mentality. Because of ambiguities in the notion of ’computation’ and also because it tends to be too closely linked to the concept of an algorithm, it is suggested in this paper that we should rather construe the mind (or an agent with a mind) as a control system involving many interacting control loops of various kinds, most of them implemented in high level virtual machines, and many of them hierarchically organised. (Some of the sub-processes are clearly computational in character, though not necessarily all.) A number of implications are drawn out, including the implication that there are many informational substates, some incorporating factual information, some control information, using diverse forms of representation. The notion of architecture, i.e. functional differentiation into interacting components, is explained, and the conjecture put forward that in order to account for the main characteristics of the human mind it is more important to get the architecture right than to get the mechanisms right (e.g. symbolic vs neural mechanisms). Architecture dominates mechanism"]]></description>
<dc:subject>philosophy_of_mind control_theory_and_control_engineering via:mraginsky in_NB cognitive_science</dc:subject>
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<dc:identifier>https://pinboard.in/u:cshalizi/b:3e58021c932d/</dc:identifier>
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<item rdf:about="https://direct.mit.edu/books/oa-monograph/6002/The-Idealized-MindFrom-Model-Based-Science-to">
    <title>The Idealized Mind: From Model-Based Science to Cognitive Science | Books Gateway | MIT Press</title>
    <dc:date>2026-02-15T16:09:47+00:00</dc:date>
    <link>https://direct.mit.edu/books/oa-monograph/6002/The-Idealized-MindFrom-Model-Based-Science-to</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study nature, including the mind and brain, by building scientific models. In The Idealized Mind, Michael Kirchhoff brings together ideas from the philosophy of cognitive science and the philosophy of science to reconcile scientific realism with model-based science. His defense of scientific realism—the view that one reasonable aim of science is to provide true (or approximately true) descriptions of reality—is based on the role of idealization in the cognitive sciences. Idealization, he claims, is inevitable in cognitive science; at the same time, any understanding of the mind and brain must show how it is possible for scientific models to be reliably used to make truth-conditional assertions about their target phenomena.
"A central error in most theorizing about the mind, Kirchhoff claims, is to confuse the properties of scientific models with those of the system being modeled. But scientific models are, almost exclusively and unavoidably, idealizations of the world we seek to understand. They are descriptions of hypothetical systems, things that do not actually exist in nature. Specifically, Kirchhoff uses insights on idealization in science to assess the status and standing of three foundational issues in cognitive science: neural representation, neural computation, and the prospects for explanatory unification. He also explains why it is a mistake to approach neural representation and neural computation through the metaphysical stances of realism, fictionalism, or eliminativism."]]></description>
<dc:subject>books:noted downloaded philosophy_of_science philosophy_of_mind cognitive_science modeling in_NB</dc:subject>
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    <title>The Singer of Tales - The Center for Hellenic Studies</title>
    <dc:date>2026-01-27T02:52:15+00:00</dc:date>
    <link>https://chs.harvard.edu/book/lord-albert-bates-the-singer-of-tales/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Actual URL of the epub: [https://chs.harvard.edu/wp-content/uploads/2021/02/lord-albert-bates-the-singer-of-tales.epub]]]></description>
<dc:subject>books:recommended poetry cognitive_science re:shoggothim in_NB</dc:subject>
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<item rdf:about="https://direct.mit.edu/books/oa-monograph/6038/DecisionsStudying-and-Supporting-People-Facing">
    <title>Decisions: Studying and Supporting People Facing Hard Choices | Books Gateway | MIT Press</title>
    <dc:date>2025-11-06T15:53:05+00:00</dc:date>
    <link>https://direct.mit.edu/books/oa-monograph/6038/DecisionsStudying-and-Supporting-People-Facing</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A lively, authoritative insider’s account of how we make decisions and how decision-making research has developed over the last half century.
"Decisions describes the evolution of decision science (also called behavioral decision research and related to behavioral economics) through its application to challenging personal and public policy decisions, since the inception of the field.
"Baruch Fischhoff covers all major topics in basic research, including how people create options, determine what matters to them, evaluate their chances of achieving those goals, and engage their emotions. He shows how those processes play out in an exceptionally wide variety of decisions regarding health, safety, the environment, disasters, and national security, among other topics. He also examines how decision-making abilities vary across individuals and across the lifespan, as well as the ethics and politics of how research is conducted and its results are shared and applied."]]></description>
<dc:subject>to:NB books:noted downloaded fischhoff.baruch decision-making cognitive_science behavioral_economics down_the_hall_if_not_quite_kith_and_kin</dc:subject>
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    <title>Language and thought are not the same thing: evidence from neuroimaging and neurological patients - Fedorenko - 2016 - Annals of the New York Academy of Sciences - Wiley Online Library</title>
    <dc:date>2025-08-16T23:06:05+00:00</dc:date>
    <link>https://nyaspubs.onlinelibrary.wiley.com/doi/abs/10.1111/nyas.13046</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Is thought possible without language? Individuals with global aphasia, who have almost no ability to understand or produce language, provide a powerful opportunity to find out. Surprisingly, despite their near-total loss of language, these individuals are nonetheless able to add and subtract, solve logic problems, think about another person's thoughts, appreciate music, and successfully navigate their environments. Further, neuroimaging studies show that healthy adults strongly engage the brain's language areas when they understand a sentence, but not when they perform other nonlinguistic tasks such as arithmetic, storing information in working memory, inhibiting prepotent responses, or listening to music. Together, these two complementary lines of evidence provide a clear answer: many aspects of thought engage distinct brain regions from, and do not depend on, language."]]></description>
<dc:subject>to:NB neuropsychology cognitive_science aphasia via:melanie_mitchell</dc:subject>
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<dc:identifier>https://pinboard.in/u:cshalizi/b:365e0115771f/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2508.05776">
    <title>[2508.05776] Whither symbols in the era of advanced neural networks?</title>
    <dc:date>2025-08-16T13:20:46+00:00</dc:date>
    <link>https://arxiv.org/abs/2508.05776</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Some of the strongest evidence that human minds should be thought about in terms of symbolic systems has been the way they combine ideas, produce novelty, and learn quickly. We argue that modern neural networks -- and the artificial intelligence systems built upon them -- exhibit similar abilities. This undermines the argument that the cognitive processes and representations used by human minds are symbolic, although the fact that these neural networks are typically trained on data generated by symbolic systems illustrates that such systems play an important role in characterizing the abstract problems that human minds have to solve. This argument leads us to offer a new agenda for research on the symbolic basis of human thought."]]></description>
<dc:subject>to:NB cognitive_science artificial_intelligence large_language_models_(so_called) neural_networks via:melanie_mitchell to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:35961d1fced6/</dc:identifier>
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<item rdf:about="https://link.springer.com/chapter/10.1007/978-3-642-77211-5_13">
    <title>Remembering and Planning: A Neuronal Network Model for the Selection of Behaviour and Its Development for Use in Human Language | SpringerLink</title>
    <dc:date>2025-07-28T14:23:55+00:00</dc:date>
    <link>https://link.springer.com/chapter/10.1007/978-3-642-77211-5_13</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A description is given of the frame/content relationship between the various temporal orders of magnitude in behaviour, from long term contexts down to the duration of single synaptic potentials. The time frame model of motor organization, consisting of nested levels operating at different time scales, is based on this description and deals with how long term contexts influence instantaneous motor output. This model can be simulated by a neuronal network based on the Hopfield algorithm. The time frame approach and the same algorithm can also be used for memory, resulting in a general model for remembering and planning actions in animals. Simple elaborations of this model, consisting of replication and new interconnections of existing components, lead to symbolization and increased categorising power and so allow application of the model to human memory and motor organization. These elaborations are both evolutionarily plausible and supported by neurological findings. Further, it is shown that language can also be described in terms of time frames and that words may be organized in the same way as actions. Thus, the model can also be extended to remembering and planning choice of words in language."]]></description>
<dc:subject>to:NB neuroscience neural_control_of_action cognitive_science via:mraginsky</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7348fbb2bbbf/</dc:identifier>
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</item>
<item rdf:about="https://arxiv.org/abs/2502.21098">
    <title>[2502.21098] Re-evaluating Theory of Mind evaluation in large language models</title>
    <dc:date>2025-03-16T19:24:12+00:00</dc:date>
    <link>https://arxiv.org/abs/2502.21098</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The question of whether large language models (LLMs) possess Theory of Mind (ToM) -- often defined as the ability to reason about others' mental states -- has sparked significant scientific and public interest. However, the evidence as to whether LLMs possess ToM is mixed, and the recent growth in evaluations has not resulted in a convergence. Here, we take inspiration from cognitive science to re-evaluate the state of ToM evaluation in LLMs. We argue that a major reason for the disagreement on whether LLMs have ToM is a lack of clarity on whether models should be expected to match human behaviors, or the computations underlying those behaviors. We also highlight ways in which current evaluations may be deviating from "pure" measurements of ToM abilities, which also contributes to the confusion. We conclude by discussing several directions for future research, including the relationship between ToM and pragmatic communication, which could advance our understanding of artificial systems as well as human cognition."]]></description>
<dc:subject>large_language_models_(so_called) measurement cognitive_science in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:00be6d0de42a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/10.1111/cogs.13229">
    <title>Cognitive Science of Augmented Intelligence - Dubova - 2022 - Cognitive Science - Wiley Online Library</title>
    <dc:date>2025-03-10T13:57:43+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/10.1111/cogs.13229</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Cognitive science has been traditionally organized around the individual as the basic unit of cognition. Despite developments in areas such as communication, human–machine interaction, group behavior, and community organization, the individual-centric approach heavily dominates both cognitive research and its application. A promising direction for cognitive science is the study of augmented intelligence, or the way social and technological systems interact with and extend individual cognition. The cognitive science of augmented intelligence holds promise in helping society tackle major real-world challenges that can only be discovered and solved by teams made of individuals and machines with complementary skills who can productively collaborate with each other."]]></description>
<dc:subject>to:NB intelligence_amplification dubova.marina galesic.mirta goldstone.robert_l. cognitive_science re:democratic_cognition to_read tab_closure</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9e72d6a4086a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:intelligence_amplification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dubova.marina"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:galesic.mirta"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:goldstone.robert_l."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:tab_closure"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s11186-024-09574-3">
    <title>Cognitive microfoundations and social interaction dynamics. The implications of complexity for institutional theory | Theory and Society</title>
    <dc:date>2025-03-02T14:46:53+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11186-024-09574-3</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper investigates the intersection of cognitive sciences and social network theory and its counterpart, the complexity sciences, aiming to shed light on the compatibility and potential integration of these frameworks into institutional theory. Institutional scholars have for long selectively adopted notions linked with the cognitive sciences and complexity sciences, such as the notion of path dependence, without exploring the broader implications of systematically integrating such perspectives into institutionalism. This paper aims to advance such a comprehensive theoretical integration, by investigating the effective combination of these approaches and their significant implications. It shows how the complexity sciences contribute to dissolving the barriers between the cognitive and social realms and illustrates how this impacts notions of human agency and reflexivity. Theoretical integration also involves acknowledging considerable diversity in individual human agency, which in turn prompts a reconsideration of how notions of institutional stability, change, diffusion and adaptation are understood. Furthermore, the paper addresses the epistemological challenge presented by the complexity sciences, before it highlights the general relevance of institutional theory in analyzing complex social phenomena. Finally, the paper explores implications for research methodology, proposing that a fusion of institutional theory and the complexity sciences provides a metatheoretical framework for assessing the contextual suitability of different theoretical and methodological approaches."]]></description>
<dc:subject>to:NB institutions cognitive_science complexity re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5cbd7d82367e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3939493">
    <title>Reflexivity and the Market Mind Hypothesis: Why George Soros is Not a Failed Philosopher (and What it Means for Economics, the Economy, and Investing) by Patrick Schotanus, Ron Chrisley, Andy Clark, Duncan Pritchard, Aaron Schurger :: SSRN</title>
    <dc:date>2025-01-22T15:38:12+00:00</dc:date>
    <link>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3939493</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["George Soros is one of the best traders of all time. That is the general consensus. While Soros gladly accepts that compliment he frequently also expressed his frustration that he failed as a philosopher. Specifically, he admits that he was unable to formulate his philosophy of reflexivity from its original abstractions. More importantly, reflexivity—which informed his successful trading—did not get the academic recognition that Soros’s track record suggests it deserves. This paper will discuss the reasons for this, the key one being that reflexivity points to the elephant in economics’s room. This will be highlighted by explaining reflexivity, from its original abstractions, in novel terms provided by cognitive science. In particular, via philosophy of mind this paper will argue why Soros is not a failed philosopher. This leads to the submission that reflexivity deserves proper recognition as an early contribution to the emerging field of cognitive economics, for which the Market Mind Hypothesis is a standard bearer. Moreover, the issues discussed are relevant in the wider context of our economic predicament."]]></description>
<dc:subject>to:NB cognitive_science economics clark.andy via:mraginsky soros.george</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e0c0fef059ee/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:clark.andy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mraginsky"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:soros.george"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/us/universitypress/subjects/psychology/cognition/situated-cognition-human-knowledge-and-computer-representations?format=PB&amp;isbn=9780521448710">
    <title>Situated cognition human knowledge and computer representations | Cognition | Cambridge University Press</title>
    <dc:date>2025-01-09T20:18:21+00:00</dc:date>
    <link>https://www.cambridge.org/us/universitypress/subjects/psychology/cognition/situated-cognition-human-knowledge-and-computer-representations?format=PB&amp;isbn=9780521448710</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This text deals with recent changes in the design of intelligent machines. New computer models of vision and navigation in animals suggest a different way to build machines. Cognition is viewed not just in terms of high-level "expertise," but in the ability to find one's way around the world, to learn new ways of seeing things, and to coordinate activity. This approach is called situated cognition. Situated Cognition differs from other purely philosophical treatises in that Clancey, an insider who has built expert systems for twenty years, explores the limitations of existing computer programs and compares them to human memory and learning capabilities. Clancey examines the implications of "situated action" from the perspective of artificial intelligence specialists interested in building robots."

--- 1997; not available electronically.]]></description>
<dc:subject>in_NB cognitive_science artificial_intelligence</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:97ffbe07979f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/elements/slime-mould-and-philosophy/8A2DAE326F52566472C6A85C055A3580">
    <title>Slime Mould and Philosophy</title>
    <dc:date>2025-01-09T14:19:41+00:00</dc:date>
    <link>https://www.cambridge.org/core/elements/slime-mould-and-philosophy/8A2DAE326F52566472C6A85C055A3580</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Physarum polycephalum, also known more colloquially as 'the blob', 'acellular slime mould', or just 'slime mould', is a unicellular multinucleate protist that has continued to attract the interest of biologists over the past century because of its complex life cycle, unique physiology, morphology, and behaviour. More recently, attention has shifted to Physarum as a model organism for investigating putative cognitive capacities such as decision making, learning, and memory in organisms without nervous systems. The aim of this Element is to illustrate how Physarum can be used as a valuable tool for approaching various topics in the philosophy of biology. Physarum and its behaviour not only pose a challenge to some of the received views of biological processes but also, I shall argue, provide an opportunity to clarify and appropriately sharpen the concepts underlying such received views."]]></description>
<dc:subject>to:NB slime_mold cognitive_science philosophy books:noted</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8e737c1838e7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:slime_mold"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://philpapers.org/rec/KINBCC">
    <title>David Kinney &amp; Tania Lombrozo, Building Compressed Causal Models of the World - PhilPapers</title>
    <dc:date>2024-12-11T19:55:08+00:00</dc:date>
    <link>https://philpapers.org/rec/KINBCC</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A given causal system can be represented in a variety of ways. How do agents determine which variables to include in their causal representations, and at what level of granularity? Using techniques from Bayesian networks, information theory, and decision theory, we develop a formal theory according to which causal representations reflect a trade-off between compression and informativeness, where the optimal trade-off depends on the decision-theoretic value of information for a given agent in a given context. This theory predicts that, all else being equal, agents prefer causal models that are as compressed as possible. When compression is associated with information loss, however, all else is not equal, and our theory predicts that agents will favor compressed models only when the information they sacrifice is not informative with respect to the agent’s anticipated decisions. We then show, across six studies reported here (N=2,364) and one study reported in the supplemental materials (N=182), that participants’ preferences over causal models are in keeping with the predictions of our theory. Our theory offers a unification of different dimensions of causal evaluation identified within the philosophy of science (proportionality and stability), and contributes to a more general picture of human cognition according to which the capacity to create compressed (causal) representations plays a central role"]]></description>
<dc:subject>to:NB cognitive_science graphical_models causality information_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ebc6286ac206/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s42113-022-00166-x">
    <title>On Logical Inference over Brains, Behaviour, and Artificial Neural Networks | Computational Brain &amp; Behavior</title>
    <dc:date>2024-12-11T16:09:02+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s42113-022-00166-x</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In the cognitive, computational, and neuro-sciences, practitioners often reason about what computational models represent or learn, as well as what algorithm is instantiated. The putative goal of such reasoning is to generalize claims about the model in question, to claims about the mind and brain, and the neurocognitive capacities of those systems. Such inference is often based on a model’s performance on a task, and whether that performance approximates human behavior or brain activity. Here we demonstrate how such argumentation problematizes the relationship between models and their targets; we place emphasis on artificial neural networks (ANNs), though any theory-brain relationship that falls into the same schema of reasoning is at risk. In this paper, we model inferences from ANNs to brains and back within a formal framework — metatheoretical calculus — in order to initiate a dialogue on both how models are broadly understood and used, and on how to best formally characterize them and their functions. To these ends, we express claims from the published record about models’ successes and failures in first-order logic. Our proposed formalization describes the decision-making processes enacted by scientists to adjudicate over theories. We demonstrate that formalizing the argumentation in the literature can uncover potential deep issues about how theory is related to phenomena. We discuss what this means broadly for research in cognitive science, neuroscience, and psychology; what it means for models when they lose the ability to mediate between theory and data in a meaningful way; and what this means for the metatheoretical calculus our fields deploy when performing high-level scientific inference."]]></description>
<dc:subject>to:NB cognitive_science philosophy_of_science via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:179203deeef9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.taylorfrancis.com/chapters/edit/10.4324/9781003322511-50/replication-crisis-embodied-cognition-research-edouard-machery">
    <title>The Replication Crisis in Embodied Cognition Research | 50 | v2 | The</title>
    <dc:date>2024-12-11T15:57:59+00:00</dc:date>
    <link>https://www.taylorfrancis.com/chapters/edit/10.4324/9781003322511-50/replication-crisis-embodied-cognition-research-edouard-machery</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["During the 2010s, psychology has witnessed an unexpected number of failed replications, some of which have cast doubt on whole research programs such as social priming or ego depletion. 1 Some prominent replication failures have implicated findings in embodied cognition research, including foundational ones. This chapter reviews these replication failures and assesses their impact for the prospects of embodied cognition research. I argue that these failures suggest treating the empirical literature with caution."]]></description>
<dc:subject>to:NB cognitive_science replication_crisis machery.edouard</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a03d31ee4c3f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:replication_crisis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machery.edouard"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://carcinisation.com/2023/08/22/against-automaticity/">
    <title>Against Automaticity – Carcinisation</title>
    <dc:date>2024-12-06T14:00:07+00:00</dc:date>
    <link>https://carcinisation.com/2023/08/22/against-automaticity/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["An explanation of why tricks like priming, nudge, the placebo effect, social contagion, the “emotional inception” model of advertising, most “cognitive biases,” and any field with “behavioral” in its name are not real"

--- Thought I'd bookmarked this before?  I suspect it goes too far, because unconscious cognitive processes _are_ real, but it's well-written and at the very least worth thinking against.

by a literal banana]]></description>
<dc:subject>have_read cognitive_science heuristics_and_biases to:blog advertising psychology behavioral_economics re:anti-nudging</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ff65c6754082/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heuristics_and_biases"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:advertising"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:behavioral_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:anti-nudging"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/philosophy-of-science/article/formats-of-cognitive-representation-a-computational-account/DAA745855F0754879B4D83DC698B894A?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Issues">
    <title>The Formats of Cognitive Representation: A Computational Account | Philosophy of Science | Cambridge Core</title>
    <dc:date>2024-10-09T19:47:03+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/philosophy-of-science/article/formats-of-cognitive-representation-a-computational-account/DAA745855F0754879B4D83DC698B894A?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Issues</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Cognitive representations are typically analyzed in terms of content, vehicle, and format. Although current work on formats appeals to intuitions about external representations, such as words and maps, in this article, we develop a computational view of formats that does not rely on intuitions. In our view, formats are individuated by the computational profiles of vehicles, that is, the set of constraints that fix the computational transformations vehicles can undergo. The resulting picture is strongly pluralistic, makes space for a variety of different formats, and is intimately tied to the computational approach to cognition in cognitive science and artificial intelligence."]]></description>
<dc:subject>cognitive_science philosophy_of_mind in_NB representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:53375bff3cda/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.science.org/doi/10.1126/sciadv.adm8470">
    <title>Space is a latent sequence: A theory of the hippocampus | Science Advances</title>
    <dc:date>2024-08-26T14:27:57+00:00</dc:date>
    <link>https://www.science.org/doi/10.1126/sciadv.adm8470</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Fascinating phenomena such as landmark vector cells and splitter cells are frequently discovered in the hippocampus. Without a unifying principle, each experiment seemingly uncovers new anomalies or coding types. Here, we provide a unifying principle that the mental representation of space is an emergent property of latent higher-order sequence learning. Treating space as a sequence resolves numerous phenomena and suggests that the place field mapping methodology that interprets sequential neuronal responses in Euclidean terms might itself be a source of anomalies. Our model, clone-structured causal graph (CSCG), employs higher-order graph scaffolding to learn latent representations by mapping aliased egocentric sensory inputs to unique contexts. Learning to compress sequential and episodic experiences using CSCGs yields allocentric cognitive maps that are suitable for planning, introspection, consolidation, and abstraction. By explicating the role of Euclidean place field mapping and demonstrating how latent sequential representations unify myriad observed phenomena, our work positions the hippocampus in a sequence-centric paradigm, challenging the prevailing space-centric view."

--- Shades of the old Whitehead definition of "spatial point"...]]></description>
<dc:subject>to:NB neuroscience cognitive_science representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f1870a40070c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://direct.mit.edu/books/oa-monograph/5625/Open-MindedSearching-for-Truth-about-the">
    <title>Open MindedSearching for Truth about the Unconscious Mind | Books Gateway | MIT Press</title>
    <dc:date>2024-05-18T19:28:15+00:00</dc:date>
    <link>https://direct.mit.edu/books/oa-monograph/5625/Open-MindedSearching-for-Truth-about-the</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["How much of a role does the unconscious play in our decision making? In Open Minded: Searching for Truth about the Unconscious Mind, authors Ben R. Newell and David R. Shanks would argue: not very much. Behavioral science and public discourse have placed an outsized emphasis on the unconscious mind when it comes to understanding human behavior. Pursuing trails of fraud, intrigue, and claims about the power of unconscious thought, Newell and Shanks scrutinize the science that has contributed to our conventional wisdom and offer an important counterpoint to the ever-stronger traction that the unconscious mind has gained in public debate, such as the now ubiquitous claim that unconscious bias plays a large role in people's decisions and behavior.
"Open Minded is divided into two sections: the first examines the modern understanding of the conscious mind, and the second shifts the focus to how to reform current research. Focusing on the core processes of decision making, Newell and Shanks cut through many questionable claims about unconscious behavior. Then, they delve into the nuts-and-bolts of methodology, challenging not only psychology and the behavioral sciences but also medicine and science more broadly. In this against-the-grain approach, Newell and Shanks chart new possibilities for how we may be more open to understanding how our minds actually work."]]></description>
<dc:subject>to:NB books:noted downloaded philosophy_of_mind cognitive_science color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:85632cb34b33/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41586-023-06668-3">
    <title>Human-like systematic generalization through a meta-learning neural network | Nature</title>
    <dc:date>2023-12-10T02:42:29+00:00</dc:date>
    <link>https://www.nature.com/articles/s41586-023-06668-3</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The power of human language and thought arises from systematic compositionality—the algebraic ability to understand and produce novel combinations from known components. Fodor and Pylyshyn1 famously argued that artificial neural networks lack this capacity and are therefore not viable models of the mind. Neural networks have advanced considerably in the years since, yet the systematicity challenge persists. Here we successfully address Fodor and Pylyshyn’s challenge by providing evidence that neural networks can achieve human-like systematicity when optimized for their compositional skills. To do so, we introduce the meta-learning for compositionality (MLC) approach for guiding training through a dynamic stream of compositional tasks. To compare humans and machines, we conducted human behavioural experiments using an instruction learning paradigm. After considering seven different models, we found that, in contrast to perfectly systematic but rigid probabilistic symbolic models, and perfectly flexible but unsystematic neural networks, only MLC achieves both the systematicity and flexibility needed for human-like generalization. MLC also advances the compositional skills of machine learning systems in several systematic generalization benchmarks. Our results show how a standard neural network architecture, optimized for its compositional skills, can mimic human systematic generalization in a head-to-head comparison."

--- Last tag not because I think there's no way to get neural networks to be compositional (there'd better be!), but on general principles having to do with flashy claims in AI and the tabloids.]]></description>
<dc:subject>to:NB neural_networks cognitive_science color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2bc0f577fca7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://philpapers.org/rec/LANPSO-2">
    <title>Kevin J. Lande, Pictorial Syntax - PhilPapers</title>
    <dc:date>2023-11-16T16:39:16+00:00</dc:date>
    <link>https://philpapers.org/rec/LANPSO-2</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["It is commonly assumed that images, whether in the world or in the head, do not have a privileged analysis into constituent parts. They are thought to lack the sort of syntactic structure necessary for representing complex contents and entering into sophisticated patterns of inference. I reject this assumption. “Image grammars” are models in computer vision that articulate systematic principles governing the form and content of images. These models are empirically credible and can be construed as literal grammars for images. Images can have rich syntactic structure, though of a markedly different form than sentences in language."

--- Image grammars!  Now there's something I've not thought about since the 1990s...]]></description>
<dc:subject>cognitive_science syntax automata_theory to:NB visual_grammars</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:975a4a3907c7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:syntax"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:automata_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_grammars"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2304.13626">
    <title>[2304.13626] The Roles of Symbols in Neural-based AI: They are Not What You Think!</title>
    <dc:date>2023-06-29T15:44:40+00:00</dc:date>
    <link>https://arxiv.org/abs/2304.13626</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose that symbols are first and foremost external communication tools used between intelligent agents that allow knowledge to be transferred in a more efficient and effective manner than having to experience the world directly. But, they are also used internally within an agent through a form of self-communication to help formulate, describe and justify subsymbolic patterns of neural activity that truly implement thinking. Symbols, and our languages that make use of them, not only allow us to explain our thinking to others and ourselves, but also provide beneficial constraints (inductive bias) on learning about the world. In this paper we present relevant insights from neuroscience and cognitive science, about how the human brain represents symbols and the concepts they refer to, and how today's artificial neural networks can do the same. We then present a novel neuro-symbolic hypothesis and a plausible architecture for intelligent agents that combines subsymbolic representations for symbols and concepts for learning and reasoning. Our hypothesis and associated architecture imply that symbols will remain critical to the future of intelligent systems NOT because they are the fundamental building blocks of thought, but because they are characterizations of subsymbolic processes that constitute thought."

--- I have never read a more Vygotskian abstract from someone trained in Good Old-Fashioned AI.  ("Vygotsky", "Luria", "Sperber", "Mercier" do not appear in the paper; it'll be interesting to see what cognitive science they do cite.)]]></description>
<dc:subject>in_NB to_read artificial_intelligence cognitive_science cultural_transmission_of_cognitive_tools mitchell.tom</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c7bf09843d0f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_transmission_of_cognitive_tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mitchell.tom"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://today.yougov.com/topics/politics/articles-reports/2022/03/15/americans-misestimate-small-subgroups-population">
    <title>​​From millionaires to Muslims, small subgroups of the population seem much larger to many Americans | YouGov</title>
    <dc:date>2023-06-15T19:01:21+00:00</dc:date>
    <link>https://today.yougov.com/topics/politics/articles-reports/2022/03/15/americans-misestimate-small-subgroups-population</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Thought I'd bookmarked this already?
--- Incidentally, this bit:
"Black Americans estimate that, on average, Black people make up 52% of the U.S. adult population; non-Black Americans estimate the proportion is roughly 39%, closer to the real figure of 12%. First-generation immigrants we surveyed estimate that first-generation immigrants account for 40% of U.S. adults, while non-immigrants guess it is around 31%, closer to the actual figure of 14%."
seems like it explains a lot of our recent politics.]]></description>
<dc:subject>have_read statistics cognitive_science demography to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:11dc79e7b78b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:demography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.tandfonline.com/doi/abs/10.1080/13569317.2022.2138293?journalCode=cjpi20">
    <title>Mapping ideologies as networks of ideas: Journal of Political Ideologies: Vol 0, No 0</title>
    <dc:date>2023-06-08T22:05:24+00:00</dc:date>
    <link>https://www.tandfonline.com/doi/abs/10.1080/13569317.2022.2138293?journalCode=cjpi20</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Individuals in a non-representative sample of 93 US progressives were asked which social outcomes they valued and then asked about the relationships among these opinions. Did each outcome provide a reason for a different one? Would each outcome cause a different one? If each outcome came to pass, would it make them more likely to support another outcome? Network diagrams derived from these responses represent portions of these individuals’ ideologies, understood as structures of political thought. Scrutiny of the network diagrams and analysis of the aggregate data suggest that most respondents carefully and reasonably identified relationships among their own ideas. Features of their networks predicted their assessments of five prominent politicians. This exploratory study paints a strikingly different picture of the sample than what would emerge from more conventional methods, such as factor analysis. Instead of a group that looks ideologically homogeneous on a unidimensional scale or that exhibits a low level of ideological coherence (because very few of their ideas are correlated), this method displays a collection of people who hold diverse and complex structures of thought. The method should be replicated with representative samples to explore the variation and significance of such structures."]]></description>
<dc:subject>to:NB graphical_models ideology cognitive_science to_read re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d643359afe28/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ideology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://direct.mit.edu/books/monograph/5378/The-Evolution-of-AgencyBehavioral-Organization">
    <title>The Evolution of Agency: Behavioral Organization from Lizards to Humans | Books Gateway | MIT Press</title>
    <dc:date>2023-05-13T17:00:15+00:00</dc:date>
    <link>https://direct.mit.edu/books/monograph/5378/The-Evolution-of-AgencyBehavioral-Organization</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A leading developmental psychologist proposes an evolutionary pathway to human psychological agency.
"Nature cannot build organisms biologically prepared for every contingency they might possibly encounter. Instead, Nature builds some organisms to function as feedback control systems that pursue goals, make informed behavioral decisions about how best to pursue those goals in the current situation, and then monitor behavioral execution for effectiveness. Nature builds psychological agents. In a bold new theoretical proposal, Michael Tomasello advances a typology of the main forms of psychological agency that emerged on the evolutionary pathway to human beings.
"Tomasello outlines four main types of psychological agency and describes them in evolutionary order of emergence. First was the goal-directed agency of ancient vertebrates, then came the intentional agency of ancient mammals, followed by the rational agency of ancient great apes, ending finally in the socially normative agency of ancient humans. Each new form of psychological organization represented increased complexity in the planning, decision-making, and executive control of behavior. Each also led to new types of experience of the environment and, in some cases, of the organism's own psychological functioning, leading ultimately to humans' experience of an objective and normative world that governs all of their thoughts and actions. Together, these proposals constitute a new theoretical framework that both broadens and deepens current approaches in evolutionary psychology."

--- Might be worth thinking about in conjunction with what HF tags as "AI_madness"...]]></description>
<dc:subject>in_NB downloaded cognitive_science cognitive_development evolutionary_psychology freedom_as_self-control tomasello.michael</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:02f6319349c5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_development"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:freedom_as_self-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:tomasello.michael"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1016/0022-2496(66)90020-4">
    <title>The structure of responses to a sequence of binary events - ScienceDirect</title>
    <dc:date>2023-04-24T21:43:30+00:00</dc:date>
    <link>https://doi.org/10.1016/0022-2496(66)90020-4</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A procedure developed by Foulkes for determining the structure of a sequence of binary events was found to be a useful base-line model of structure determination by human subjects. The structure is represented in terms of the subsequences of events (states) which lead to different probabilities of the events. While the subjects' behavior after each state is not given by the Foulkes procedure, their behavior appeared to be largely a function of the probabilities of the events after each state (matching) and the lastest event in the state (positive recency)."

--- The Foulkes (1959) paper lying behind this is truly wild as a flash of genius but doesn't seem to be online anywhere.  (I may rectify this.)]]></description>
<dc:subject>have_read markov_models cognitive_science variable-length_markov_models_aka_context_trees statistical_inference_for_stochastic_processes re:AoS_project cleaning_out_the_filing_cabinet_for_the_first_time_since_2005 re:dissertation prediction in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:851a2a33fb27/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:variable-length_markov_models_aka_context_trees"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_inference_for_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:AoS_project"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cleaning_out_the_filing_cabinet_for_the_first_time_since_2005"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:dissertation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1073/pnas.2300963120">
    <title>Probing the psychology of AI models | PNAS</title>
    <dc:date>2023-04-22T13:57:18+00:00</dc:date>
    <link>https://doi.org/10.1073/pnas.2300963120</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to_read artificial_intelligence cognitive_science mitchell.melanie kith_and_kin large_language_models_(so_called) in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6f167837e04a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mitchell.melanie"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/doi/10.1073/pnas.2218523120">
    <title>Using cognitive psychology to understand GPT-3 | PNAS</title>
    <dc:date>2023-04-22T01:39:18+00:00</dc:date>
    <link>https://www.pnas.org/doi/10.1073/pnas.2218523120</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study GPT-3, a recent large language model, using tools from cognitive psychology. More specifically, we assess GPT-3’s decision-making, information search, deliberation, and causal reasoning abilities on a battery of canonical experiments from the literature. We find that much of GPT-3’s behavior is impressive: It solves vignette-based tasks similarly or better than human subjects, is able to make decent decisions from descriptions, outperforms humans in a multiarmed bandit task, and shows signatures of model-based reinforcement learning. Yet, we also find that small perturbations to vignette-based tasks can lead GPT-3 vastly astray, that it shows no signatures of directed exploration, and that it fails miserably in a causal reasoning task. Taken together, these results enrich our understanding of current large language models and pave the way for future investigations using tools from cognitive psychology to study increasingly capable and opaque artificial agents."]]></description>
<dc:subject>cognitive_science large_language_models_(so_called) in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0ced075c1fd6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2301.06627">
    <title>[2301.06627] Dissociating language and thought in large language models: a cognitive perspective</title>
    <dc:date>2023-01-23T05:40:19+00:00</dc:date>
    <link>https://arxiv.org/abs/2301.06627</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Today's large language models (LLMs) routinely generate coherent, grammatical and seemingly meaningful paragraphs of text. This achievement has led to speculation that these networks are -- or will soon become -- "thinking machines", capable of performing tasks that require abstract knowledge and reasoning. Here, we review the capabilities of LLMs by considering their performance on two different aspects of language use: 'formal linguistic competence', which includes knowledge of rules and patterns of a given language, and 'functional linguistic competence', a host of cognitive abilities required for language understanding and use in the real world. Drawing on evidence from cognitive neuroscience, we show that formal competence in humans relies on specialized language processing mechanisms, whereas functional competence recruits multiple extralinguistic capacities that comprise human thought, such as formal reasoning, world knowledge, situation modeling, and social cognition. In line with this distinction, LLMs show impressive (although imperfect) performance on tasks requiring formal linguistic competence, but fail on many tests requiring functional competence. Based on this evidence, we argue that (1) contemporary LLMs should be taken seriously as models of formal linguistic skills; (2) models that master real-life language use would need to incorporate or develop not only a core language module, but also multiple non-language-specific cognitive capacities required for modeling thought. Overall, a distinction between formal and functional linguistic competence helps clarify the discourse surrounding LLMs' potential and provides a path toward building models that understand and use language in human-like ways."]]></description>
<dc:subject>natural_language_processing large_language_models_(so_called) neural_networks cognitive_science kanwisher.nancy tenenbaum.joshua_b. in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c2f2fa528eb1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:natural_language_processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kanwisher.nancy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:tenenbaum.joshua_b."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.sagepub.com/doi/10.1177/21677026221114315">
    <title>Are Progressives in Denial About Progress? Yes, but So Is Almost Everyone Else - Gregory Mitchell, Philip E. Tetlock, 2022</title>
    <dc:date>2023-01-18T03:16:11+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/10.1177/21677026221114315</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Scott Lilienfeld warned that psychology’s ideological uniformity would lead to premature closure on sensitive topics. He encouraged psychologists to question politically convenient results and did so himself in numerous areas. We follow Lilienfeld’s example and examine the empirical foundation beneath claims that positive illusions about societal change sustain inequalities by inducing apathy and opposition to reform. Drawing on data from a large-scale survey, we find almost the opposite: a pervasive tendency, across ideological and demographic categories, to see things as getting worse than they really are. These results cast doubt on functionalist claims that people mobilize beliefs about societal trends to support political positions and suggest a simpler explanation: Most laypeople do not organize information in ways that provide reliable monitoring of social change over time, which makes their views on progress susceptible to memory distortions and high-profile current events and political rhetoric."]]></description>
<dc:subject>to:NB tetlock.phillip psychology us_culture_wars cognitive_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8c1b03b4c1d2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:tetlock.phillip"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_culture_wars"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/10.1111/cogs.13230">
    <title>Beyond Single‐Mindedness: A Figure-Ground Reversal for the Cognitive Sciences - Dingemanse - 2023 - Cognitive Science - Wiley Online Library</title>
    <dc:date>2023-01-17T05:49:16+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/10.1111/cogs.13230</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A fundamental fact about human minds is that they are never truly alone: all minds are steeped in situated interaction. That social interaction matters is recognized by any experimentalist who seeks to exclude its influence by studying individuals in isolation. On this view, interaction complicates cognition. Here, we explore the more radical stance that interaction co-constitutes cognition: that we benefit from looking beyond single minds toward cognition as a process involving interacting minds. All around the cognitive sciences, there are approaches that put interaction center stage. Their diverse and pluralistic origins may obscure the fact that collectively, they harbor insights and methods that can respecify foundational assumptions and fuel novel interdisciplinary work. What might the cognitive sciences gain from stronger interactional foundations? This represents, we believe, one of the key questions for the future. Writing as a transdisciplinary collective assembled from across the classic cognitive science hexagon and beyond, we highlight the opportunity for a figure-ground reversal that puts interaction at the heart of cognition. The interactive stance is a way of seeing that deserves to be a key part of the conceptual toolkit of cognitive scientists."

--- The first sentence seems obviously false.  (Some people need to go sit in the woods and meditate in silence.)  But there may be interesting stuff in here nonetheless.]]></description>
<dc:subject>to:NB social_life_of_the_mind cognitive_science color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6be847a337d7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2206.08353">
    <title>[2206.08353] Towards Understanding How Machines Can Learn Causal Overhypotheses</title>
    <dc:date>2022-07-22T14:46:51+00:00</dc:date>
    <link>https://arxiv.org/abs/2206.08353</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence. The extensive literature in cognitive science using the ``blicket detector'' environment shows that children are adept at many kinds of causal inference and learning. We propose to adapt that environment for machine learning agents. One of the key challenges for current machine learning algorithms is modeling and understanding causal overhypotheses: transferable abstract hypotheses about sets of causal relationships. In contrast, even young children spontaneously learn and use causal overhypotheses. In this work, we present a new benchmark -- a flexible environment which allows for the evaluation of existing techniques under variable causal overhypotheses -- and demonstrate that many existing state-of-the-art methods have trouble generalizing in this environment."]]></description>
<dc:subject>in_NB causal_inference causal_discovery artificial_intelligence cognitive_science gopnik.alison</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:92821459c251/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gopnik.alison"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/books/problem-solving/211C576C6C0B33F446C00EC1F5ECB775#fndtn-information">
    <title>Problem Solving</title>
    <dc:date>2022-07-03T03:03:39+00:00</dc:date>
    <link>https://www.cambridge.org/core/books/problem-solving/211C576C6C0B33F446C00EC1F5ECB775#fndtn-information</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Intelligent mental representations of physical, cognitive and social environments allow humans to navigate enormous search spaces, whose sizes vastly exceed the number of neurons in the human brain. This allows us to solve a wide range of problems, such as the Traveling Salesperson Problem, insight problems, as well as mathematics and physics problems. As an area of research, problem solving has steadily grown over time. Researchers in Artificial Intelligence have been formulating theories of problem solving for the last 70 years. Psychologists, on the other hand, have focused their efforts on documenting the observed behavior of subjects solving problems. This book represents the first effort to merge the behavioral results of human subjects with formal models of the causative cognitive mechanisms. The first coursebook to deal exclusively with the topic, it provides a main text for elective courses and a supplementary text for courses such as cognitive psychology and neuroscience."]]></description>
<dc:subject>to:NB books:noted cognitive_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a8da4c24c1cc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2206.07271">
    <title>[2206.07271] Human Heuristics for AI-Generated Language Are Flawed</title>
    <dc:date>2022-06-19T16:52:48+00:00</dc:date>
    <link>https://arxiv.org/abs/2206.07271</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Human communication is increasingly intermixed with language generated by AI. Across chat, email, and social media, AI systems produce smart replies, autocompletes, and translations. AI-generated language is often not identified as such but poses as human language, raising concerns about novel forms of deception and manipulation. Here, we study how humans discern whether one of the most personal and consequential forms of language - a self-presentation - was generated by AI. Across six experiments, participants (N = 4,650) tried to identify self-presentations generated by state-of-the-art language models. Across professional, hospitality, and romantic settings, we find that humans are unable to identify AI-generated self-presentations. Combining qualitative analyses with language feature engineering, we find that human judgments of AI-generated language are handicapped by intuitive but flawed heuristics such as associating first-person pronouns, authentic words, or family topics with humanity. We show that these heuristics make human judgment of generated language predictable and manipulable, allowing AI systems to produce language perceived as more human than human. We conclude by discussing solutions - such as AI accents or fair use policies - to reduce the deceptive potential of generated language, limiting the subversion of human intuition."]]></description>
<dc:subject>natural_born_cyborgs natural_language_processing natural_history_of_truthiness text_mining via:henry_farrell cognitive_science networked_life philip_k_dick_and_the_fake_humans_rules_everything_around_me large_language_models_(so_called) in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8ab7ffaadfd0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:natural_born_cyborgs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:natural_language_processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:natural_history_of_truthiness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:text_mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philip_k_dick_and_the_fake_humans_rules_everything_around_me"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://press.princeton.edu/books/paperback/9780691205717/what-makes-us-smart">
    <title>What Makes Us Smart | Princeton University Press</title>
    <dc:date>2022-02-05T21:05:23+00:00</dc:date>
    <link>https://press.princeton.edu/books/paperback/9780691205717/what-makes-us-smart</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB to_read cognitive_science gershman.sam downloaded books:in_library</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8f78cc196f0b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gershman.sam"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:in_library"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/books/understanding-intelligence/3DEE41441E6A5A817A29AD5335A92021#fndtn-contents">
    <title>Understanding Intelligence</title>
    <dc:date>2022-01-17T06:26:29+00:00</dc:date>
    <link>https://www.cambridge.org/core/books/understanding-intelligence/3DEE41441E6A5A817A29AD5335A92021#fndtn-contents</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB books:noted iq psychology cognitive_science color_me_skeptical re:g_paper</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f5c2c9331663/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:iq"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:g_paper"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-neuro-092920-120559">
    <title>Human Representation Learning | Annual Review of Neuroscience</title>
    <dc:date>2021-07-09T16:37:01+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-neuro-092920-120559</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The central theme of this review is the dynamic interaction between information selection and learning. We pose a fundamental question about this interaction: How do we learn what features of our experiences are worth learning about? In humans, this process depends on attention and memory, two cognitive functions that together constrain representations of the world to features that are relevant for goal attainment. Recent evidence suggests that the representations shaped by attention and memory are themselves inferred from experience with each task. We review this evidence and place it in the context of work that has explicitly characterized representation learning as statistical inference. We discuss how inference can be scaled to real-world decisions by approximating beliefs based on a small number of experiences. Finally, we highlight some implications of this inference process for human decision-making in social environments."]]></description>
<dc:subject>to:NB psychology cognitive_science induction</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:93935a482990/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:induction"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://pubmed.ncbi.nlm.nih.gov/19739881/">
    <title>Conditions for intuitive expertise: a failure to disagree - PubMed</title>
    <dc:date>2021-06-28T03:39:24+00:00</dc:date>
    <link>https://pubmed.ncbi.nlm.nih.gov/19739881/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This article reports on an effort to explore the differences between two approaches to intuition and expertise that are often viewed as conflicting: heuristics and biases (HB) and naturalistic decision making (NDM). Starting from the obvious fact that professional intuition is sometimes marvelous and sometimes flawed, the authors attempt to map the boundary conditions that separate true intuitive skill from overconfident and biased impressions. They conclude that evaluating the likely quality of an intuitive judgment requires an assessment of the predictability of the environment in which the judgment is made and of the individual's opportunity to learn the regularities of that environment. Subjective experience is not a reliable indicator of judgment accuracy."]]></description>
<dc:subject>to:NB heuristics cognitive_science clinical_vs_actuarial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:38fa8cde1442/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:clinical_vs_actuarial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://press.uchicago.edu/ucp/books/book/distributed/O/bo50729800">
    <title>The Origins of Self: An Anthropological Perspective, Edwardes</title>
    <dc:date>2021-06-28T03:22:19+00:00</dc:date>
    <link>https://press.uchicago.edu/ucp/books/book/distributed/O/bo50729800</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The Origins of Self explores the role selfhood plays in defining both human society and each individual in that society. It considers the genetic and cultural origins of self, the role that self plays in socialization and language, and the types of selves we generate in our individual journeys to and through adulthood. Martin P. J. Edwardes argues that other-awareness is a relatively early evolutionary development, present throughout the primate clade and perhaps beyond, but self-awareness is a product of the sharing of social models, something only humans appear to do. The self of which we are aware is not something innate within us, it is a model of our self produced as a response to the models of us offered to us by other people. Edwardes proposes that human construction of selfhood involves seven different types of self. All but one of them are internally generated models, and the only nonmodel, the actual self, is completely hidden from conscious awareness. We rely on others to tell us about our self, and even to let us know we are a self. Developed in relation to a range of subject areas—linguistics, anthropology, genomics, and cognition, as well as sociocultural theory—The Origins of Self is of particular interest to students and researchers studying the origins of language, human origins in general, and the cognitive differences between human and other animal psychologies."]]></description>
<dc:subject>to:NB books:noted cognitive_science cognitive_development human_evolution books:suggest_to_library</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4f42f77a67aa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_development"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:human_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:suggest_to_library"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/abs/10.1207/s15516709cog1402_1">
    <title>Finding Structure in Time - Elman - 1990 - Cognitive Science - Wiley Online Library</title>
    <dc:date>2021-04-19T18:45:54+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/abs/10.1207/s15516709cog1402_1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves: the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands: indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context‐dependent, while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction."]]></description>
<dc:subject>to:NB neural_networks cognitive_science have_read re:paradigm_formation_in_statistical_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:71681b2ec599/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:paradigm_formation_in_statistical_learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/content/118/15/e1912441117">
    <title>A scientific theory of gist communication and misinformation resistance, with implications for health, education, and policy | PNAS</title>
    <dc:date>2021-04-14T17:49:11+00:00</dc:date>
    <link>https://www.pnas.org/content/118/15/e1912441117</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A framework is presented for understanding how misinformation shapes decision-making, which has cognitive representations of gist at its core. I discuss how the framework goes beyond prior work, and how it can be implemented so that valid scientific messages are more likely to be effective, remembered, and shared through social media, while misinformation is resisted. The distinction between mental representations of the rote facts of a message—its verbatim representation—and its gist explains several paradoxes, including the frequent disconnect between knowing facts and, yet, making decisions that seem contrary to those facts. Decision makers can falsely remember the gist as seen or heard even when they remember verbatim facts. Indeed, misinformation can be more compelling than information when it provides an interpretation of reality that makes better sense than the facts. Consequently, for many issues, scientific information and misinformation are in a battle for the gist. A fuzzy-processing preference for simple gist explains expectations for antibiotics, the spread of misinformation about vaccination, and responses to messages about global warming, nuclear proliferation, and natural disasters. The gist, which reflects knowledge and experience, induces emotions and brings to mind social values. However, changing mental representations is not sufficient by itself; gist representations must be connected to values. The policy choice is not simply between constraining behavior or persuasion—there is another option. Science communication needs to shift from an emphasis on disseminating rote facts to achieving insight, retaining its integrity but without shying away from emotions and values."]]></description>
<dc:subject>to:NB epidemiology_of_representations cognitive_science color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:aae83329ff88/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemiology_of_representations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41586-021-03380-y">
    <title>People systematically overlook subtractive changes | Nature</title>
    <dc:date>2021-04-08T14:10:34+00:00</dc:date>
    <link>https://www.nature.com/articles/s41586-021-03380-y</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Improving objects, ideas or situations—whether a designer seeks to advance technology, a writer seeks to strengthen an argument or a manager seeks to encourage desired behaviour—requires a mental search for possible changes1,2,3. We investigated whether people are as likely to consider changes that subtract components from an object, idea or situation as they are to consider changes that add new components. People typically consider a limited number of promising ideas in order to manage the cognitive burden of searching through all possible ideas, but this can lead them to accept adequate solutions without considering potentially superior alternatives4,5,6,7,8,9,10. Here we show that people systematically default to searching for additive transformations, and consequently overlook subtractive transformations. Across eight experiments, participants were less likely to identify advantageous subtractive changes when the task did not (versus did) cue them to consider subtraction, when they had only one opportunity (versus several) to recognize the shortcomings of an additive search strategy or when they were under a higher (versus lower) cognitive load. Defaulting to searches for additive changes may be one reason that people struggle to mitigate overburdened schedules11, institutional red tape12 and damaging effects on the planet13,14."

--- That last is a bit of a reach, no?
]]></description>
<dc:subject>to:NB decision-making problem-solving cognitive_science experimental_psychology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8fdee917d180/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:problem-solving"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_psychology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://oxford-universitypressscholarship-com.cmu.idm.oclc.org/view/10.1093/acprof:oso/9780198524021.001.0001/acprof-9780198524021?rskey=9UW923&amp;result=952">
    <title>Causal Cognition: A Multidisciplinary Debate - Oxford Scholarship</title>
    <dc:date>2021-01-23T06:39:53+00:00</dc:date>
    <link>https://oxford-universitypressscholarship-com.cmu.idm.oclc.org/view/10.1093/acprof:oso/9780198524021.001.0001/acprof-9780198524021?rskey=9UW923&amp;result=952</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Dan Sperber, David Premack, and Ann James Premack (eds.)
"An understanding of cause-effect relationships is fundamental to the study of cognition. In this book, chapters based on comparative psychology, social psychology, developmental psychology, anthropology, and philosophy present the newest developments in the study of causal cognition and discuss their different perspectives. They reflect on the role and forms of causal knowledge, both in animal and human cognition, on the development of human causal cognition from infancy, and on the relationship between individual and cultural aspects of causal understanding. This book presents an informative, insightful, and interdisciplinary debate."]]></description>
<dc:subject>to:NB causality cognitive_science psychology books:noted</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:505527fff17a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1093/oso/9780198866282.001.0001">
    <title>Neurocognitive Mechanisms: Explaining Biological Cognition - Oxford Scholarship</title>
    <dc:date>2021-01-16T05:18:10+00:00</dc:date>
    <link>https://doi.org/10.1093/oso/9780198866282.001.0001</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This book provides the foundations for a neurocomputational explanation of cognition based on contemporary cognitive neuroscience. An ontologically egalitarian account of composition and realization, according to which all levels are equally real, is defended. Multiple realizability and mechanisms are explicated in light of this ontologically egalitarian framework. A goal-contribution account of teleological functions is defended, and so is a mechanistic version of functionalism. This provides the foundation for a mechanistic account of computation, which in turn clarifies the ways in which the computational theory of cognition is a multilevel mechanistic theory supported by contemporary cognitive neuroscience. The book argues that cognition is computational at least in a generic sense. The computational theory of cognition is defended from standard objections yet a priori arguments for the computational theory of cognition are rebutted. The book contends that the typical vehicles of neural computations are representations and that, contrary to the received view, neural representations are observable and manipulable in the laboratory. The book also contends that neural computations are neither digital nor analog; instead, neural computations are sui generis. The book concludes by investigating the relation between computation and consciousness, suggesting that consciousness may have a functional yet not wholly computational nature."]]></description>
<dc:subject>to:NB books:noted philosophy_of_mind cognitive_science to_download</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7d2bea48fcb6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_download"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-psych-010419-050807">
    <title>Judging Truth | Annual Review of Psychology</title>
    <dc:date>2021-01-03T19:27:15+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-psych-010419-050807</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Deceptive claims surround us, embedded in fake news, advertisements, political propaganda, and rumors. How do people know what to believe? Truth judgments reflect inferences drawn from three types of information: base rates, feelings, and consistency with information retrieved from memory. First, people exhibit a bias to accept incoming information, because most claims in our environments are true. Second, people interpret feelings, like ease of processing, as evidence of truth. And third, people can (but do not always) consider whether assertions match facts and source information stored in memory. This three-part framework predicts specific illusions (e.g., truthiness, illusory truth), offers ways to correct stubborn misconceptions, and suggests the importance of converging cues in a post-truth world, where falsehoods travel further and faster than the truth."]]></description>
<dc:subject>to:NB psychology cognitive_science deceiving_us_has_become_an_industrial_process epidemiology_of_representations</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9b66494a891f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:deceiving_us_has_become_an_industrial_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemiology_of_representations"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-psych-010419-050747">
    <title>Judgment and Decision Making | Annual Review of Psychology</title>
    <dc:date>2021-01-03T19:24:54+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-psych-010419-050747</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The science of judgment and decision making involves three interrelated forms of research: analysis of the decisions people face, description of their natural responses, and interventions meant to help them do better. After briefly introducing the field's intellectual foundations, we review recent basic research into the three core elements of decision making: judgment, or how people predict the outcomes that will follow possible choices; preference, or how people weigh those outcomes; and choice, or how people combine judgments and preferences to reach a decision. We then review research into two potential sources of behavioral heterogeneity: individual differences in decision-making competence and developmental changes across the life span. Next, we illustrate applications intended to improve individual and organizational decision making in health, public policy, intelligence analysis, and risk management. We emphasize the potential value of coupling analytical and behavioral research and having basic and applied research inform one another."]]></description>
<dc:subject>to:NB decision-making cognitive_science broomell.stephen fischhoff.baruch kith_and_kin</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e06964c371db/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:broomell.stephen"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fischhoff.baruch"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-psych-010418-103358">
    <title>Computational Models of Memory Search | Annual Review of Psychology</title>
    <dc:date>2021-01-03T19:23:42+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-psych-010418-103358</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The capacity to search memory for events learned in a particular context stands as one of the most remarkable feats of the human brain. How is memory search accomplished? First, I review the central ideas investigated by theorists developing models of memory. Then, I review select benchmark findings concerning memory search and analyze two influential computational approaches to modeling memory search: dual-store theory and retrieved context theory. Finally, I discuss the key theoretical ideas that have emerged from these modeling studies and the open questions that need to be answered by future research."]]></description>
<dc:subject>to:NB memory cognitive_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a076e2f69b66/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:memory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-psych-010419-051044">
    <title>Emotional Objectivity: Neural Representations of Emotions and Their Interaction with Cognition | Annual Review of Psychology</title>
    <dc:date>2021-01-03T19:23:17+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-psych-010419-051044</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Recent advances in our understanding of information states in the human brain have opened a new window into the brain's representation of emotion. While emotion was once thought to constitute a separate domain from cognition, current evidence suggests that all events are filtered through the lens of whether they are good or bad for us. Focusing on new methods of decoding information states from brain activation, we review growing evidence that emotion is represented at multiple levels of our sensory systems and infuses perception, attention, learning, and memory. We provide evidence that the primary function of emotional representations is to produce unified emotion, perception, and thought (e.g., “That is a good thing”) rather than discrete and isolated psychological events (e.g., “That is a thing. I feel good”). The emergent view suggests ways in which emotion operates as a fundamental feature of cognition, by design ensuring that emotional outcomes are the central object of perception, thought, and action."]]></description>
<dc:subject>to:NB psychology cognitive_science emotion</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a01402f705cc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emotion"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/episteme/article/abs/what-is-the-function-of-reasoning-on-mercier-and-sperbers-argumentative-and-justificatory-theories/214AA03FC41B89E8861EF85EBFB8E03B">
    <title>What is the Function of Reasoning? On Mercier and Sperber's Argumentative and Justificatory Theories | Episteme | Cambridge Core</title>
    <dc:date>2020-12-16T19:20:03+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/episteme/article/abs/what-is-the-function-of-reasoning-on-mercier-and-sperbers-argumentative-and-justificatory-theories/214AA03FC41B89E8861EF85EBFB8E03B</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper aims to accessibly present, and then critique, Hugo Mercier and Dan Sperber's recent proposals for the evolutionary function of human reasoning. I take a critical look at the main source of experimental evidence that they claim as support for their view, namely the confirmation or “myside” bias in reasoning. I object that Mercier and Sperber did not adequately argue for a claim that their case rests on, namely that it is evolutionarily advantageous for you to get other people to believe whatever you antecedently believe. And I give my own argument that this claim is false. I also critically look at their suggestion that reasoning has a justificatory function, functioning as a kind of reputation management tool. I argue this suggestion does not amount to a plausible evolutionary function."]]></description>
<dc:subject>to:NB reason cognitive_science evolutionary_psychology sperber.dan mercier.hugo</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:031ef99459c5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reason"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sperber.dan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mercier.hugo"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://psyarxiv.com/rybh9/">
    <title>PsyArXiv Preprints | How computational modeling can force theory building in psychological science</title>
    <dc:date>2020-12-03T03:01:39+00:00</dc:date>
    <link>https://psyarxiv.com/rybh9/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Psychology endeavors to develop theories of human capacities and behaviors based on a variety of methodologies and dependent measures. We argue that one of the most divisive factors in our field is whether researchers choose to employ computational modeling of theories (over and above data) during the scientific inference process. Modeling is undervalued, yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us towards better science by forcing us to conceptually analyze, specify, and formalise intuitions which otherwise remain unexamined — what we dub “open theory”. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Herein, we present scientific inference in psychology as a path function, where each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability “crises” and persistent failure at coherent theory-building. This is because without formal modelling we lack open and transparent theorising. We also explain how to formalise, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all."]]></description>
<dc:subject>to:NB psychology cognitive_science modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dcccb0192551/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20191717">
    <title>What Makes a Rule Complex? - American Economic Association</title>
    <dc:date>2020-11-30T16:06:01+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20191717</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study the complexity of rules by paying experimental subjects to implement a series of algorithms and then eliciting their willingness-to-pay to avoid implementing them again in the future. The design allows us to examine hypotheses from the theoretical "automata" literature about the characteristics of rules that generate complexity costs. We find substantial aversion to complexity and a number of regularities in the characteristics of rules that make them complex and costly for subjects. Experience with a rule, the way a rule is represented, and the context in which a rule is implemented (mentally versus physically) also influence complexity."]]></description>
<dc:subject>to:NB complexity_measures learning_in_games experimental_economics decision-making cognitive_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b93c403fcb3b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://philpapers.org/rec/DORGG">
    <title>Kevin Dorst &amp; Matthew Mandelkern, Good Guesses - PhilPapers</title>
    <dc:date>2020-11-29T19:19:38+00:00</dc:date>
    <link>https://philpapers.org/rec/DORGG</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper is about guessing: how people respond to a question when they aren’t certain of the answer. Guesses show surprising and systematic patterns that the most obvious theories don’t explain. We offer a theory that does explain them: we propose that people aim to optimize a tradeoff between accuracy and informativity in forming their guess. After spelling out our theory, we use it to argue that guessing plays a central role in our cognitive lives. In particular, our account of guessing yields new theories of (1) belief, (2) assertion, and (3) the conjunction fallacy—the psychological finding that people sometimes rate conjunctions as more probable than their conjuncts. More generally, we suggest that guessing helps explain how boundedly rational agents like us navigate a complex, uncertain world."]]></description>
<dc:subject>to:NB to_read psychology cognitive_science guessing philosophy_of_mind rationality relevance_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:edac8844225a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:guessing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rationality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:relevance_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-soc-121919-054736">
    <title>Social Networks and Cognition | Annual Review of Sociology</title>
    <dc:date>2020-11-19T22:11:55+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-soc-121919-054736</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social network analysis, now often thought of simply as network science, has penetrated nearly every scientific and many scholarly fields and has become an indispensable resource. Yet, social networks are special by virtue of being specifically social, and our growing understanding of the brain is affecting our understanding of how social networks form, mature, and are exploited by their members. We discuss the expanding research on how the brain manages social information, how this information is heuristically processed, and how network cognitions are affected by situation and circumstance. In the process, we argue that the cognitive turn in social networks exemplifies the modern conception of the brain as fundamentally reprogrammable by experience and circumstance. Far from social networks being dependent upon the brain, we anticipate a modern view in which cognition and social networks coconstitute each other."]]></description>
<dc:subject>to:NB social_networks psychology cognitive_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:597a2cc8000a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1912.11335">
    <title>[1912.11335] A Continuous-Time Dynamic Choice Measurement Model for Problem-Solving Process Data</title>
    <dc:date>2020-11-18T17:16:45+00:00</dc:date>
    <link>https://arxiv.org/abs/1912.11335</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Problem solving has been recognized as a central skill that today's students need to thrive and shape their world. As a result, the measurement of problem-solving competency has received much attention in education in recent years. A popular tool for the measurement of problem solving is simulated interactive tasks, which require students to uncover some of the information needed to solve the problem through interactions with a computer-simulated environment. A computer log file records a student's problem-solving process in details, including his/her actions and the time stamps of these actions. It thus provides rich information for the measurement of students' problem-solving competency. On the other hand, extracting useful information from log files is a challenging task, due to its complex data structure. In this paper, we show how log file process data can be viewed as a marked point process, based on which we propose a continuous-time dynamic choice model. The proposed model can serve as a measurement model for scaling students along the latent traits of problem-solving competency and action speed, based on data from one or multiple tasks. A real data example is given based on data from Program for International Student Assessment 2012."]]></description>
<dc:subject>to:NB stochastic_processes time_series point_processes cognitive_science psychometrics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:941a8ed7087c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:point_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychometrics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/18315/">
    <title>The Math is not the Territory: Navigating the Free Energy Principle - PhilSci-Archive</title>
    <dc:date>2020-10-28T03:11:46+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/18315/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The free energy principle (FEP) has seen extensive philosophical engagement— both from a general philosophy of science perspective and from the perspective of philosophies of specific sciences: cognitive science, neuroscience, and biology. The literature on the FEP has attempted to draw out specific philosophical commitments and entailments of the framework. But the most fundamental questions, from the perspective of philosophy of science, remain open: To what discipline(s) does the FEP belong? Does it make falsifiable claims? What sort of scientific object is it? Is it to be taken as a representation of contingent states of affairs in nature? Does it constitute knowledge? What role is it in- tended to play in relation to empirical research? Does the FEP even properly belong to the domain of science? To the extent that it has engaged with them at all, the extant literature has begged, dodged, dismissed, and skirted around these questions, without ever addressing them head-on. These questions must, I urge, be answered satisfactorily before we can make any headway on the philosophical consequences of the FEP. I take preliminary steps towards answering these questions in this paper, first by examining closely key formal elements of the framework and the implications they hold for its utility, and second, by highlighting potential modes of interpreting the FEP in light of an abundant philosophical literature on scientific modelling."]]></description>
<dc:subject>to:NB cognitive_science philosophy_of_science philosophy_of_mind via:rvenkat</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9d4f8485f5f7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/content/early/2020/10/01/2014505117">
    <title>The logic of universalization guides moral judgment | PNAS</title>
    <dc:date>2020-10-05T18:36:01+00:00</dc:date>
    <link>https://www.pnas.org/content/early/2020/10/01/2014505117</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["To explain why an action is wrong, we sometimes say, “What if everybody did that?” In other words, even if a single person’s behavior is harmless, that behavior may be wrong if it would be harmful once universalized. We formalize the process of universalization in a computational model, test its quantitative predictions in studies of human moral judgment, and distinguish it from alternative models. We show that adults spontaneously make moral judgments consistent with the logic of universalization, and report comparable patterns of judgment in children. We conclude that, alongside other well-characterized mechanisms of moral judgment, such as outcome-based and rule-based thinking, the logic of universalizing holds an important place in our moral minds."

]]></description>
<dc:subject>moral_psychology cognitive_science psychology in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b74f5209dfea/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:moral_psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/us/academic/subjects/psychology/cognition/frame-it-again-new-tools-rational-decision-making?format=HB">
    <title>Frame it again: new tools for rational decision making | Cognition | Cambridge University Press</title>
    <dc:date>2020-10-01T01:22:57+00:00</dc:date>
    <link>https://www.cambridge.org/us/academic/subjects/psychology/cognition/frame-it-again-new-tools-rational-decision-making?format=HB</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Framing effects are everywhere. An estate tax looks very different to a death tax. Gun safety seems to be one thing and gun control another. Yet, the consensus from decision theorists, finance professionals, psychologists, and economists is that frame-dependence is completely irrational. This book challenges that view. Some of the toughest decisions we face are just clashes between different frames. It is perfectly rational to value the same thing differently in two different frames, even when the decision-maker knows that these are really two sides of the same coin. Frame It Again sheds new light on the structure of moral predicaments, the nature of self-control, and the rationality of co-operation. Framing is a powerful tool for redirecting public discussions about some of the most polarizing contemporary issues, such as gun control, abortion, and climate change. Learn effective problem-solving and decision-making to get the better of difficult dilemmas."]]></description>
<dc:subject>to:NB books:noted rationality decision-making cognitive_science books:suggest_to_library</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8b26bef69c71/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rationality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:suggest_to_library"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-psych-122216-011829">
    <title>Concepts and Compositionality: In Search of the Brain's Language of Thought | Annual Review of Psychology</title>
    <dc:date>2020-09-19T12:36:09+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-psych-122216-011829</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Imagine Genghis Khan, Aretha Franklin, and the Cleveland Cavaliers performing an opera on Maui. This silly sentence makes a serious point: As humans, we can flexibly generate and comprehend an unbounded number of complex ideas. Little is known, however, about how our brains accomplish this. Here we assemble clues from disparate areas of cognitive neuroscience, integrating recent research on language, memory, episodic simulation, and computational models of high-level cognition. Our review is framed by Fodor's classic language of thought hypothesis, according to which our minds employ an amodal, language-like system for combining and recombining simple concepts to form more complex thoughts. Here, we highlight emerging work on combinatorial processes in the brain and consider this work's relation to the language of thought. We review evidence for distinct, but complementary, contributions of map-like representations in subregions of the default mode network and sentence-like representations of conceptual relations in regions of the temporal and prefrontal cortex."]]></description>
<dc:subject>to:NB to_read cognitive_science neuroscience</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d906b04591a8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstor.org/stable/j.ctt17kk7fp">
    <title>A Natural History of Natural Theology: The Cognitive Science of Theology and Philosophy of Religion on JSTOR</title>
    <dc:date>2020-08-20T21:17:46+00:00</dc:date>
    <link>https://www.jstor.org/stable/j.ctt17kk7fp</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>books:noted religion natural_history_of_religion cognitive_science downloaded in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6d47a5b0bab5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:religion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:natural_history_of_religion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://crookedtimber.org/2020/07/24/in-praise-of-negativity/">
    <title>In praise of negativity — Crooked Timber</title>
    <dc:date>2020-07-24T18:55:06+00:00</dc:date>
    <link>https://crookedtimber.org/2020/07/24/in-praise-of-negativity/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Too good to excerpt.]]></description>
<dc:subject>collective_cognition science_as_a_social_process argument cognitive_science defenses_of_liberalism farrell.henry kith_and_kin mercier.hugo sperber.dan</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c20ceeb58b18/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:science_as_a_social_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:argument"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:defenses_of_liberalism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:farrell.henry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mercier.hugo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sperber.dan"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/full/10.1111/cogs.12530">
    <title>Demons of Ecological Rationality - Otworowska - 2018 - Cognitive Science - Wiley Online Library</title>
    <dc:date>2020-07-23T14:22:55+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/full/10.1111/cogs.12530</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["How can resource‐bounded minds like our own make rational or otherwise “good” decisions in an uncertain and complex world (Oaksford & Chater, 1998; Simon, 1957, 1990)? The Adaptive Toolbox theory answers this question by defining human rationality in terms of a degree of adaptation of decision strategies (heuristics) to different environments (Gigerenzer & Todd, 1999; Todd & Gigerenzer, 2012). When heuristics are adapted to the environment and lead to “good enough” (or even high‐quality) decisions, they are said to be ecologically rational . For almost two decades, this theory has been considered a tractable alternative to classical theories of human rationality based on logic or probability theory (Gigerenzer, 2015; Gigerenzer & Todd, 1999). These classical theories have been criticized for postulating intractable (e.g., NP‐hard)1 computations (Arkes, Gigerenzer, & Hertwig, 2016; Gigerenzer, 2008; Oaksford & Chater, 1998), which suggests that humans must possess demonic computational powers in order to make rational decisions (so‐called demons of rationality ; Gigerenzer & Todd, 1999; Goldstein & Gigerenzer, 1999). It is widely assumed that the Adaptive Toolbox theory circumvents the intractability problem that plagues classical accounts of human rationality, because heuristics are by definition tractable. Yet the notion of ecological rationality hinges on the existence of tractable adaptation processes. Here, we present an argument that, contrary to common belief, the Adaptive Toolbox theory has not yet tamed the intractability demon. Rather, the demon is hiding in the theory's cornerstone assumption that ecological rationality is achieved by processes of adaptation, such as evolution, development, or learning."

--- I'll need to examine this carefully, but my initial reaction is that this is a bit off-base, at least for population-level adaptation.]]></description>
<dc:subject>to:NB to_read heuristics cognitive_science computational_complexity via:rvenkat adaptive_behavior bounded_rationality</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:592948725e47/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:adaptive_behavior"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bounded_rationality"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/10.1146/annurev-vision-091718-014951">
    <title>Deep Learning: The Good, the Bad, and the Ugly | Annual Review of Vision Science</title>
    <dc:date>2020-06-26T14:24:52+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/10.1146/annurev-vision-091718-014951</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machine vision—giving a sense that the problem of vision is getting closer to being solved. The goal of this review is to provide a comprehensive overview of recent deep learning developments and to critically assess actual progress toward achieving human-level visual intelligence. I discuss the implications of the successes and limitations of modern machine vision algorithms for biological vision and the prospect for neuroscience to inform the design of future artificial vision systems."]]></description>
<dc:subject>to:NB to_read cognitive_science neural_networks via:rvenkat your_favorite_deep_neural_network_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:773a2e1b079f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:your_favorite_deep_neural_network_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://royalsocietypublishing.org/doi/10.1098/rstb.2019.0502">
    <title>Childhood as a solution to explore–exploit tensions | Philosophical Transactions of the Royal Society B: Biological Sciences</title>
    <dc:date>2020-06-18T16:18:29+00:00</dc:date>
    <link>https://royalsocietypublishing.org/doi/10.1098/rstb.2019.0502</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I argue that the evolution of our life history, with its distinctively long, protected human childhood, allows an early period of broad hypothesis search and exploration, before the demands of goal-directed exploitation set in. This cognitive profile is also found in other animals and is associated with early behaviours such as neophilia and play. I relate this developmental pattern to computational ideas about explore–exploit trade-offs, search and sampling, and to neuroscience findings. I also present several lines of empirical evidence suggesting that young human learners are highly exploratory, both in terms of their search for external information and their search through hypothesis spaces. In fact, they are sometimes more exploratory than older learners and adults."]]></description>
<dc:subject>cognitive_development cognitive_science human_evolution learning_theory gopnik.alison via:melanie_mitchell have_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a389686de9df/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_development"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:human_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gopnik.alison"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:melanie_mitchell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s11229-018-1768-x">
    <title>Predictive coding and thought | SpringerLink</title>
    <dc:date>2020-04-17T15:55:00+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11229-018-1768-x</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Predictive processing has recently been advanced as a global cognitive architecture for the brain. I argue that its commitments concerning the nature and format of cognitive representation are inadequate to account for two basic characteristics of conceptual thought: first, its generality—the fact that we can think and flexibly reason about phenomena at any level of spatial and temporal scale and abstraction; second, its rich compositionality—the specific way in which concepts productively combine to yield our thoughts. I consider two strategies for avoiding these objections and I argue that both confront formidable challenges."]]></description>
<dc:subject>to:NB cognitive_science prediction philosophy_of_mind</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:85c7ef8bb1c4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_mind"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.sagepub.com/doi/abs/10.1111/j.0956-7976.2005.01573.x">
    <title>Reasoning From Unfamiliar Premises: A Study With Unschooled Adults - Maria Dias, Antonio Roazzi, Paul L. Harris, 2005</title>
    <dc:date>2020-01-26T01:41:38+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/abs/10.1111/j.0956-7976.2005.01573.x</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A long tradition of research initiated by Luria in the 1930s has established that unschooled adults perform poorly on reasoning tasks. Particularly when the premises are unfamiliar, they adopt an inappropriate empirical bias. However, recent findings show that young children with little or no schooling reason competently if prompted to think of the unfamiliar premises as pertaining to a distant planet. We tested two groups of adults: illiterate, unschooled adults and adults with limited schooling. Both groups received problems that included either a premise with unknown content or a premise contradicting their everyday experience. When given a minimal prompt, both groups manifested the customary empirical bias. By contrast, when explicitly prompted to think of the unfamiliar premises as pertaining to a distant planet, they reasoned accurately and appropriately justified their conclusions in terms of the supplied premises."]]></description>
<dc:subject>to:NB psychology cognitive_science luria.a.r.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6daaebdd680a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:luria.a.r."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.journals.uchicago.edu/doi/10.1086/706879">
    <title>Why Divination?: Evolved Psychology and Strategic Interaction in the Production of Truth | Evolved Psychology and Strategic Interaction in the Production of Truth: Ahead of Print</title>
    <dc:date>2020-01-21T17:03:29+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/10.1086/706879</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Divination is found in most human societies, but there is little systematic research to explain (1) why it is persuasive or (2) why divination is required for important collective decisions in many small-scale societies. Common features of human communication and cooperation may help address both questions. A highly recurrent feature of divination is “ostensive detachment,” a demonstration that the diviners are not the authors of the statements they utter. As a consequence, people spontaneously interpret divination as less likely than other statements to be influenced by anyone’s intentions or interests. This is enough to give divination an epistemic advantage compared with other sources of information, answering question 1. This advantage is all the more important in situations where a diagnosis will create differential costs and benefits, for example, determining who is responsible for someone’s misfortune in a small-scale community. Divinatory statements provide a version of the situation that most participants are motivated to agree with, as it provides a focal point for efficient coordination at a minimal cost for almost all participants, which would answer question 2."

---Exercise for the reader (easy but cynical): Apply this argument to (i) personality tests, (ii) macroeconomic forecasts, (iii) "history will not look kindly on...".]]></description>
<dc:subject>to:NB anthropology divination cognitive_science boyer.pascal evolution_of_cooperation to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:96284ecd6522/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:anthropology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:divination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:boyer.pascal"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolution_of_cooperation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://press.princeton.edu/ideas/what-do-you-really-know-about-gullibility">
    <title>What do you really know about gullibility? | Princeton University Press</title>
    <dc:date>2020-01-09T20:44:13+00:00</dc:date>
    <link>https://press.princeton.edu/ideas/what-do-you-really-know-about-gullibility</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>mercier.hugo cognitive_science psychology apparently_irrational_beliefs persuasion social_influence books:noted via:henry_farrell books:owned books:suggest_to_library</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f7a07fa4ace3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mercier.hugo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:apparently_irrational_beliefs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:persuasion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:suggest_to_library"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s42113-019-00032-3">
    <title>Computational Resource Demands of a Predictive Bayesian Brain | SpringerLink</title>
    <dc:date>2020-01-06T18:29:49+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s42113-019-00032-3</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["There is a growing body of evidence that the human brain may be organized according to principles of predictive processing. An important conjecture in neuroscience is that a brain organized in this way can effectively and efficiently approximate Bayesian inferences. Given that many forms of cognition seem to be well characterized as a form of Bayesian inference, this conjecture has great import for cognitive science. It suggests that predictive processing may provide a neurally plausible account of how forms of cognition that are modeled as Bayesian inference may be physically implemented in the brain. Yet, as we show in this paper, the jury is still out on whether or not the conjecture is really true. Specifically, we demonstrate that each key subcomputation invoked in predictive processing potentially hides a computationally intractable problem. We discuss the implications of these sobering results for the predictive processing account and propose a way to move forward."]]></description>
<dc:subject>to:NB neuroscience neural_coding_and_decoding perception cognitive_science bayesianism computational_complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a8142c60720b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_coding_and_decoding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:perception"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1911.00535">
    <title>[1911.00535] Think-aloud interviews: A tool for exploring student statistical reasoning</title>
    <dc:date>2019-11-18T21:51:44+00:00</dc:date>
    <link>https://arxiv.org/abs/1911.00535</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["As statistics educators revise introductory courses to cover new topics and reach students from more diverse academic backgrounds, they need assessments to test if new teaching strategies and new curricula are meeting their goals. But assessing student understanding of statistics concepts can be difficult: conceptual questions are difficult to write clearly, and students often interpret questions in unexpected ways and give answers for unexpected reasons. Assessment results alone also do not clearly indicate the reasons students pick specific answers.
"We describe think-aloud interviews with students as a powerful tool to ensure that draft questions fulfill their intended purpose, uncover unexpected misconceptions or surprising readings of questions, and suggest new questions or further pedagogical research. We have conducted more than 40 hour-long think-aloud interviews to develop over 50 assessment questions, and have collected pre- and post-test assessment data from hundreds of introductory statistics students at two institutions.
"Think-alouds and assessment data have helped us refine draft questions and explore student misunderstandings. Our findings include previously under-reported statistical misconceptions about sampling distributions and causation. These results suggest directions for future statistics education research and show how think-aloud interviews can be effectively used to develop assessments and improve our understanding of student learning."]]></description>
<dc:subject>to:NB heard_the_talk kith_and_kin statistics cognitive_science education protocol_analysis expertise have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cc522a226b76/</dc:identifier>
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