This paper is a commentary on the target article by Lokendra Shastri & Venkat Ajjanagadde [S&A]: “From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony” in same issue of the journal, pp.417–451. -/- It puts S&A's temporal-synchrony binding method in a broader context, comments on notions of pointing and other ways of associating information - in both computers and connectionist systems - and mentions types of reasoning that are a challenge to (...) S&A's system (amongst many other connectionist systems, recognizing that S&A's is much more capable as regards reasoning than most other contemporary systems). (shrink)

Reasoning, defined as the production and evaluation of reasons, is a central process in science. The dominant view of reasoning, both in the psychology of reasoning and in the psychology of science, is of a mechanism with an asocial function: bettering the beliefs of the lone reasoner. Many observations, however, are difficult to reconcile with this view of reasoning; in particular, reasoning systematically searches for reasons that support the reasoner’s initial beliefs, and it only evaluates these reasons cursorily. By contrast, (...) reasoners are well able to evaluate others’ reasons: accepting strong arguments and rejecting weak ones. The argumentative theory of reasoning accounts for these traits of reasoning by postulating that the evolved function of reasoning is to argue: to find arguments to convince others and to change one’s mind when confronted with good arguments. Scientific reasoning, however, is often described as being at odds with such an argumentative mechanisms: scientists are supposed to reason objectively on their own, and to be pigheaded when their theories are challenged, even by good arguments. In this article, we review evidence showing that scientists, when reasoning, are subject to the same biases as are lay people while being able to change their mind when confronted with good arguments. We conclude that the argumentative theory of reasoning explains well key features of scientists’ reasoning and that differences in the way scientists and laypeople reason result from the institutional framework of science. (shrink)

The purpose of the two studies reported here was to develop an integrated model of the scientific reasoning process. Subjects were placed in a simulated scientific discovery context by first teaching them how to use an electronic device and then asking them to discover how a hitherto unencountered function worked. To do this task, subjects had to formulate hypotheses based on their prior knowledge, conduct experiments, and evaluate the results of their experiments. In the first study, using 20 adult subjects, (...) we identified two main strategies that subjects used to generate new hypotheses. One strategy was to search memory and the other was to generalize from the results of previous experiments. We described the former group as searching an hypothesis space, and the latter as searching an experiment space. In a second study, with 10 adults, we investigated how subjects search the hypothesis space by instructing them to state all the hypotheses that they could think of prior to conducting any experiments. Following this phase, subjects were then allowed to conduct experiments. Subjects who could not think of the correct rule in the hypothesis generation phase discovered the correct rule only by generalizing from the results of experiments in the experimental phase.Both studies provide support for the view that scientific reasoning can be characterized as search in two problem spaces. By extending Simon and Lea's (1974) Generalized Rule Inducer, we present a general model of Scientific Discovery as Dual Search (SDDS) that shows how search in two problem spaces (an hypothesis space and an experiment space) shapes hypothesis generation, experimental design, and the evaluation of hypotheses. The model also shows how these processes interact with each other. Finally, we interpret earlier findings about the psychology of scientific reasoning in terms of the SDDS model. (shrink)

Inquiry, Volume 57, Issue 1, Page 55-96, February 2014.

This essay attempts to clarify the nature and structure of aliefs. First I distinguish between a robust notion of aliefs and a deflated one. A robust notion of aliefs would introduce aliefs into our psychological ontology as a hitherto undiscovered kind, whereas a deflated notion of aliefs would identify aliefs as a set of pre-existing psychological states. I then propose the following dilemma: one the one hand, if aliefs have propositional content, then it is unclear exactly how aliefs differ from (...) psychological states we already countenance, in which case there is no robust notion of aliefs; on the other, if aliefs just contain associative content, then they cannot do the explanatory work set out for them, in which case there is no reason to posit aliefs at all. Thus, it appears that we have little reason to posit the novel category of robust aliefs. (shrink)

Abstract:Psychological forces in play across individual, group, and organizational levels of analysis increase the likelihood that people in business organizations will engage in misconduct. Therefore, it is argued, we must turn our attention from dominant normative and empirical trends in business ethics, which revolve around boundaries and constraints, and instead concentrate on methods for promoting ethical behavior in practice, exploiting psychological forces conducive to ethical conduct. This calls for a better understanding of how organizations and their inhabitants function, and, in (...) turn, it points to pragmatic solutions. Ethical conduct can be promoted by (1) normatively justifying vivid aims worthy of pursuit alongside economic objectives, and (2) empirically identifying the conditions and practices that advance those aims in firms. This approach challenges us to bring empirical and normative inquiry together—in ways unsettling to both. It pushes us to move beyond an empirical preoccupation with decision making and judgment, and it requires us to cope with political liberalism’s legitimate qualms about discussions of the good. (shrink)

Short abstract (98 words). Reasoning is generally seen as a means to improve knowledge and make better decisions. However, much evidence shows that reasoning often leads to epistemic distortions and poor decisions. This suggests that the function of reasoning should be rethought. Our hypothesis is that the function of reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning so conceived is adaptive given humans’ exceptional dependence on communication and vulnerability to misinformation. A wide range of (...) evidence in the psychology of reasoning and decision making can be reinterpreted and better explained in the light of this hypothesis. (shrink)

The tendency to test outcomes that are predicted by our current theory (the confirmation bias) is one of the best-known biases of human decision making. We prove that the confirmation bias is an optimal strategy for testing hypotheses when those hypotheses are deterministic, each making a single prediction about the next event in a sequence. Our proof applies for two normative standards commonly used for evaluating hypothesis testing: maximizing expected information gain and maximizing the probability of falsifying the current hypothesis. (...) This analysis rests on two assumptions: (a) that people predict the next event in a sequence in a way that is consistent with Bayesian inference; and (b) when testing hypotheses, people test the hypothesis to which they assign highest posterior probability. We present four behavioral experiments that support these assumptions, showing that a simple Bayesian model can capture people's predictions about numerical sequences (Experiments 1 and 2), and that we can alter the hypotheses that people choose to test by manipulating the prior probability of those hypotheses (Experiments 3 and 4). (shrink)

This target article presents a new computational theory of explanatory coherence that applies to the acceptance and rejection of scientific hypotheses as well as to reasoning in everyday life, The theory consists of seven principles that establish relations of local coherence between a hypothesis and other propositions. A hypothesis coheres with propositions that it explains, or that explain it, or that participate with it in explaining other propositions, or that offer analogous explanations. Propositions are incoherent with each other if they (...) are contradictory, Propositions that describe the results of observation have a degree of acceptability on their own. An explanatory hypothesis is acccpted if it coheres better overall than its competitors. The power of the seven principles is shown by their implementation in a connectionist program called ECHO, which treats hypothesis evaluation as a constraint satisfaction problem. Inputs about the explanatory relations are used to create a network of units representing propositions, while coherende and incoherence relations are encoded by excitatory and inbihitory links. ECHO provides an algorithm for smoothly integrating theory evaluation based on considerations of explanatory breadth, simplicity, and analogy. It has been applied to such important scientific cases as Lovoisier's argument for oxygen against the phlogiston theory and Darwin's argument for evolution against creationism, and also to cases of legal reasoning. The theory of explanatory coherence has implications for artificial intelligence, psychology, and philosophy. (shrink)

Forty years of experimentation on class inclusion and its probabilistic relatives have led to inconsistent results and conclusions about human reasoning. Recent research on the conjunction "fallacy" recapitulates this history. In contrast to previous results, we found that a majority of participants adhere to class inclusion in the classic Linda problem. We outline a theoretical framework that attributes the contradictory results to differences in statistical sophistication and to differences in response mode-whether participants are asked for probability estimates or ranks-and propose (...) two precise cognitive algorithms for ranking probabilities. Our framework allows us to make novel predictions about when and why people adhere to class inclusion. Evidence obtained in several studies supports these predictions and demonstrates that the proposed ranking algorithms can account for about three-quarters of participants' inferences in the Linda problem. (shrink)

With the advent of computers in the experimental labs, dynamic systems have become a new tool for research on problem solving and decision making. A short review of this research is given and the main features of these systems (connectivity and dynamics) are illustrated. To allow systematic approaches to the influential variables in this area, two formal frameworks (linear structural equations and finite state automata) are presented. Besides the formal background, the article sets out how the task demands of system (...) identification and system control can be realised in these environments, and how psychometrically acceptable dependent variables can be derived. (shrink)

How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), we (...) explore fast and frugal heuristics – simple rules in the mind's adaptive toolbox for making decisions with realistic mental resources. These heuristics can enable both living organisms and artificial systems to make smart choices quickly and with a minimum of information by exploiting the way that information is structured in particular environments. In this précis, we show how simple building blocks that control information search, stop search, and make decisions can be put together to form classes of heuristics, including: ignorance-based and one-reason decision making for choice, elimination models for categorization, and satisficing heuristics for sequential search. These simple heuristics perform comparably to more complex algorithms, particularly when generalizing to new data – that is, simplicity leads to robustness. We present evidence regarding when people use simple heuristics and describe the challenges to be addressed by this research program. Key Words: adaptive toolbox; bounded rationality; decision making; elimination models; environment structure; heuristics; ignorance-based reasoning; limited information search; robustness; satisficing; simplicity. (shrink)

Three puzzles about self-deception make this mental phenomenon an intriguing explanatory target. The first relates to how to define it without paradox; the second is about how to make sense of self-deception in light of the interpretive view of the mental that has become widespread in philosophy; and the third concerns why it exists at all. In this paper I address the first and third puzzles. First, I define self-deception. Second, I criticize Robert Trivers' attempt to use adaptionist evolutionary psychology (...) to solve the third puzzle (existence). Third, I sketch a theory to replace that of Trivers. Self-deception is not an adaptation, but a spandrel in the sense that Gould and Lewontin give the term: a byproduct of other features of human (cognitive) architecture. Self-deception is so undeniable a fact of human life that if anyone tried to deny its existence, the proper response would be to accuse this person of it. (Allen Wood, 1988). (shrink)

This paper provides an analysis of the cognitive processes implied in the ego defense known as projection. Projection is first placed in the context of the general cognitive processes of attribution and ascription. Then we address defensive projection, and identify its distinctive features. In particular, whereas general projection consists in the ascription of one's own mental attitudes to others, defensive projection implies one's rejection of the ascribed mental states, and ascription is a means for supporting this rejection. We try to (...) understand how and why ascription is functional to rejection. Then, we examine a few types of defensive projection, and identify the main components and steps in the projective process. Finally, we draw some general conclusions. (shrink)

Rational analysis (Anderson 1990, 1991a) is an empiricalprogram of attempting to explain why the cognitive system isadaptive, with respect to its goals and the structure of itsenvironment. We argue that rational analysis has two importantimplications for philosophical debate concerning rationality. First,rational analysis provides a model for the relationship betweenformal principles of rationality (such as probability or decisiontheory) and everyday rationality, in the sense of successfulthought and action in daily life. Second, applying the program ofrational analysis to research on human reasoning (...) leads to a radicalreinterpretation of empirical results which are typically viewed asdemonstrating human irrationality. (shrink)

Central to many discussions of motivated reasoning is the idea that it runs afoul of epistemic normativity. Reasoning differently about information supporting our prior beliefs versus information contradicting those beliefs, is frequently equated with motivated irrationality. By analyzing the normative status of belief polarization, selective scrutiny, biased assimilation and the myside bias, I show this inference is often not adequately supported. Contrary to what’s often assumed, these phenomena need not indicate motivated irrationality, even though they are instances of belief-consistent information (...) processing. Second, I engage with arguments purporting to show that belief-consistent information processing does not indicate motivated irrationality because of its mere differential treatment of confirming and non-confirming evidence, but rather because it reveals the undermining presence of an irrelevant influence, such as a desire or partisan identity-driven cognition. While linking belief-consistent reasoning to a deeper source of directional motivation to make good on the claim that it indicates motivated irrationality is indeed what’s needed, two prominent such arguments fail. The non-normativity of many reasoning processes often taken to indicate motivated irrationality is not in fact well established. (shrink)

The present study examined how exposure to the performance of in-group and out-group members can both exacerbate and minimize the negative effects of stereotype threat. Female participants learned that they would be taking a math test that was either diagnostic or nondiagnostic of their math ability. Prior to taking the test, participants interacted with either an in-group peer (a female college student) or an out-group peer (a male college student) who had just taken the test and learned that the student (...) had either performed well or poorly on the test. Exposure to either an in-group or an out-group peer whose performance was consistent with the negative stereotype (a poor-performing female or a strong-performing male) exacerbated stereotype threat effects. In contrast, exposure to an in-group or an out-group peer whose performance challenged the negative stereotype (a strong-performing female or a poor-performing male) eliminated stereotype threat effects. These findings demonstrate that people can look to both in-group and out-group peers as sources of inspiration in the context of a negative stereotype. (shrink)

The causal view of categories assumes that categories are represented by features and their causal relations. To study the effect of causal knowledge on categorization, researchers have used Bayesian causal models. Within that framework, categorization may be viewed as dependent on a likelihood computation (i.e., the likelihood of an exemplar with a certain combination of features, given the category's causal model) or as a posterior computation (i.e., the probability that the exemplar belongs to the category, given its features). Across three (...) experiments, in combination with computational modeling, we offer evidence that categorization is better accounted for by assuming that people compute posteriors and not likelihoods, though both probabilities are closely related. This result contrasts with existing analyses of causal-based categorization, which assume that likelihood computations give a good approximation of human judgments. We also find that people are able to compute likelihoods in a closely related task that elicits judgments of consistency rather than category membership judgments. Our analyses show that people do use causal probabilistic information as prescribed by a Bayesian model but that they flexibly compute likelihoods or posteriors depending on the task. We discuss our results in relation to the relevant literature on the topic. (shrink)

Confirmation bias is often used as an umbrella term for many related phenomena. Information searches, evidence interpretation, and memory recall are the three main components of the thinking proces...

Delusions are deeply evidence-resistant. Patients with delusions are unmoved by evidence that is in direct conflict with the delusion, often responding to such evidence by offering obvious, and strange, confabulations. As a consequence, the standard view is that delusions are not evidence-responsive. This claim has been used as a key argumentative wedge in debates on the nature of delusions. Some have taken delusions to be beliefs and argued that this implies that belief is not constitutively evidence-responsive. Others hold fixed the (...) evidenceresponsiveness of belief and take this to show that delusions cannot be beliefs. Against this common assumption, I appeal to a large range of empirical evidence to argue that delusions are evidence-responsive in the sense that subjects have the capacity to respond to evidence on their delusion in rationally permissible ways. The extreme evidence-resistance of delusions is a consequence of powerful masking factors on these capacities, such as strange perceptual experiences, motivational factors, and cognitive biases. This view makes room for holding both that belief is constitutively evidence-responsive and that delusions are beliefs, and it has important implications for the study and treatment of delusions. (shrink)

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 argue that these patterns reveal that people aim to optimize a tradeoff between accuracy and informativity when 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 belief, assertion, and the conjunction fallacy—the psychological finding that people sometimes rank a conjunction as more probable than one of its conjuncts. More generally, we suggest that guessing helps explain how boundedly rational agents like us navigate a complex, uncertain world. (shrink)

Although recent trends in politics and media make it appear that conspiracy theories are on the rise, in fact they have always been present, probably because they are sustained by natural dispositions of the human brain. This is also the case with medical conspiracy theories. This article reviews some of the most notorious health-related conspiracy theories. It then approaches the reasons why people believe these theories, using concepts from cognitive science. On the basis of that knowledge, the article makes normative (...) proposals for public health officials and health workers as a whole, to deal with conspiracy theories, in order to preserve some of the fundamental principles of medical ethics. (shrink)

We argue that the evolutionary function of reasoning is to allow us to secure more accurate beliefs and more effective intentions through collective deliberation. This sets our view apart both from traditional intellectualist accounts, which take the evolutionary function to be individual deliberation, and from interactionist accounts such as the one proposed by Mercier and Sperber, which agrees that the function of reasoning is collective but holds that it aims to disseminate, rather than come up with, accurate beliefs. We argue (...) that our collectivized intellectualism offers the best explanation of the range of biases that human reasoning is prone to, and that it does better than interactionism at offering a function of reasoning that would have been adaptive for our distant ancestors who first evolved this capacity. (shrink)

According to rational pedagogy models, learners take into account the way in which teachers generate evidence, and teachers take into account the way in which learners assimilate that evidence. The authors develop a framework for integrating rational pedagogy into models of active exploration, in which agents can take actions to influence the evidence they gather from the environment. The key idea is that a single agent can be both teacher and learner.

In this paper we argue that it is often adaptive to use one's background beliefs when interpreting information that, from a normative point of view, is incomplete. In both of the experiments reported here participants were presented with an item possessing two features and were asked to judge, in the light of some evidence concerning the features, to which of two categories it was more likely that the item belonged. It was found that when participants received evidence relevant to just (...) one of these hypothesised categories they used their background beliefs to interpret this information. In Experiment 2, on the other hand, participants behaved in a broadly Bayesian manner when the evidence they received constituted a completed likelihood ratio. We discuss the circumstances under which participants, when making their judgements, consider the alternative hypothesis. We conclude with a discussion of the implications of our results for an understanding of hypothesis testing, belief revision, and categorisation. (shrink)

People cannot contemplate a proposition without believing that proposition. A model of belief fixation is sketched and used to explain hitherto disparate, recalcitrant, and somewhat mysterious psychological phenomena and philosophical paradoxes. Toward this end I also contend that our intuitive understanding of the workings of introspection is mistaken. In particular, I argue that propositional attitudes are beyond the grasp of our introspective capacities. We learn about our beliefs from observing our behavior, not from introspecting our stock beliefs. -/- The model (...) of belief fixation offered in the dissertation poses a novel dilemma for theories of rationality. One might have thought that the ability to contemplate ideas while withholding assent is a necessary condition on rationality. In short, it seems that rational creatures shouldn‘t just form their beliefs based on whatever they happen to think. However, it seems that we are creatures that automatically and reflexively form our beliefs based on whatever propositions we happen to consider. Thus, either the rational requirement that states that we must have evidence for our beliefs must be jettisoned or we must accept the conclusion that we are necessarily irrational. (shrink)

Emotions are Janus-faced: their focus may switch from how a person is feeling deep inside her, to the busy world of actions, words, or gestures whose perception currently affects her. The intimate relation between the ‘inside’ and the ‘outside’ seems to call for a redrawing of the traditional distinction of mental states between those that can look out to the world, and those that are, supposedly, irredeemably blind.

Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might. Following H. Simon's notion of satisficing, the authors have proposed a family of algorithms based on a simple psychological mechanism: one-reason decision making. These fast and frugal algorithms violate fundamental tenets of classical rationality: They neither look up nor integrate all information. By computer simulation, the (...) authors held a competition between the satisficing "Take The Best" algorithm and various "rational" inference procedures. The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy. This result is an existence proof that cognitive mechanisms capable of successful performance in the real world do not need to satisfy the classical norms of rational inference. (shrink)

[Correction Notice: An erratum for this article was reported in Vol 109 of Psychological Review. Due to circumstances that were beyond the control of the authors, the studies reported in "Models of Ecological Rationality: The Recognition Heuristic," by Daniel G. Goldstein and Gerd Gigerenzer overlap with studies reported in "The Recognition Heuristic: How Ignorance Makes Us Smart," by the same authors and with studies reported in "Inference From Ignorance: The Recognition Heuristic". In addition, Figure 3 in the Psychological Review article (...) was originally published in the book chapter and should have carried a note saying that it was used by permission of Oxford University Press.] One view of heuristics is that they are imperfect versions of optimal statistical procedures considered too complicated for ordinary minds to carry out. In contrast, the authors consider heuristics to be adaptive strategies that evolved in tandem with fundamental psychological mechanisms. The recognition heuristic, arguably the most frugal of all heuristics, makes inferences from patterns of missing knowledge. This heuristic exploits a fundamental adaptation of many organisms: the vast, sensitive, and reliable capacity for recognition. The authors specify the conditions under which the recognition heuristic is successful and when it leads to the counter-intuitive less-is-more effect in which less knowledge is better than more for making accurate inferences. (shrink)

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...) psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields. 1. Causal Reasoning: An Introduction Michael R. Waldmann Part I: Theories of Causal Cognition 2. Associative Accounts of Causal Cognition Mike E. Le Pelley, Oren Griffiths, and Tom Beesley 3. Rules of Causal Judgment: Mapping Statistical Information Onto Causal Beliefs José C. Perales, Andrés Catena, Antonio Cándido, and Antonio Maldonado 4. The Inferential Reasoning Theory of Causal Learning: Toward a Multi- Process Propositional Account Yannick Boddez, Jan De Houwer, and Tom Beckers 5. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power Patricia W. Cheng and Hongjing Lu 6. The Acquisition and Use of Causal Structure Knowledge Benjamin Margolin Rottman 7. Formalizing Prior Knowledge in Causal Induction Thomas L. Griffiths 8. Causal Mechanisms Samuel G. B. Johnson and Woo-kyoung Ahn 9. Force Dynamics and Causation Phillip Wolff and Robert Thorstad 10. Mental Models and Causation P. N. Johnson- Laird and Sangeet S. Khemlani 11. Pseudocontingencies Klaus Fiedler and Florian Kutzner 12. Singular Causation David Danks 13. Cognitive Neuroscience of Causal Reasoning Joachim T. Operskalski and Aron K. Barbey Part II: Basic Cognitive Functions 14. Visual Impressions of Causality Peter White 15. Goal-Directed Actions Bernhard Hommel 16. Planning and Control Magda Osman 17. Reinforcement Learning and Causal Models Samuel J. Gershman 18. Causation and the Probability of Causal Conditionals David E. Over 19. Causal Models and Conditional Reasoning Mike Oaksford and Nick Chater 20. Concepts as Causal Models: Categorization Bob Rehder 21. Concepts as Causal Models: Induction Bob Rehder 22. Causal Explanation Tania Lombrozo and Nadya Vasilyeva 23. Diagnostic Reasoning Björn Meder and Ralf Mayrhofer 24. Inferring Causal Relations by Analogy Keith J. Holyoak and Hee-Seung Lee 25. Causal Argument Ulrike Hahn, Roland Bluhm, and Frank Zenker 26. Causality in Decision- Making York Hagmayer and Philip M. Fernbach Part III: Domains of Causal Reasoning 27. Intuitive Theories Tobias Gerstenberg and Joshua B. Tenenbaum 28. Space, Time, and Causality Marc J. Buehner 29. Causation in Legal and Moral Reasoning David A. Lagnado and Tobias Gerstenberg 30. The Role of Causal Knowledge in Reasoning About Mental Disorders Woo-kyoung Ahn, Nancy S. Kim, and Matthew S. Lebowitz 31. Causality and Causal Reasoning in Natural Language Torgrim Solstad and Oliver Bott 32. Social Attribution and Explanation Denis Hilton Part IV: Development, Phylogeny, and Culture 33. The Development of Causal Reasoning Paul Muentener and Elizabeth Bonawitz 34. Causal Reasoning in Non-Human Animals Christian Schloegl and Julia Fischer 35. Causal Cognition and Culture Andrea Bender, Sieghard Beller, and Douglas L. Medin. (shrink)

Collecting information that one expects to be useful is a powerful way to facilitate learning. However, relatively little is known about how people decide which information is worth sampling over the course of learning. We describe several alternative models of how people might decide to collect a piece of information inspired by “active learning” research in machine learning. We additionally provide a theoretical analysis demonstrating the situations under which these models are empirically distinguishable, and we report a novel empirical study (...) that exploits these insights. Our model-based analysis of participants' information gathering decisions reveals that people prefer to select items which resolve uncertainty between two possibilities at a time rather than items that have high uncertainty across all relevant possibilities simultaneously. Rather than adhering to strictly normative or confirmatory conceptions of information search, people appear to prefer a “local” sampling strategy, which may reflect cognitive constraints on the process of information gathering. (shrink)