We propose a unified theory of intentions as neural processes that integrate representations of states of affairs, actions, and emotional evaluation. We show how this theory provides answers to philosophical questions about the concept of intention, psychological questions about human behavior, computational questions about the relations between belief and action, and neuroscientific questions about how the brain produces actions. Our theory of intention ties together biologically plausible mechanisms for belief, planning, and motor control. The computational feasibility of these mechanisms is (...) shown by a model that simulates psychologically important cases of intention. (shrink)

In What Is Thought? Eric Baum proposes a computational explanation of thought.

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking (...) machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models. (shrink)

We routinely observe others’ choices and use them to guide our own. Whose choices influence us more, and why? Prior work has focused on the effect of perceived similarity between two individuals, such as the degree of overlap in past choices or explicitly recognizable group affiliations. In the real world, however, any dyadic relationship is part of a more complex social structure involving multiple social groups that are not directly observable. Here we suggest that human learners go beyond dyadic similarities (...) in choice behaviors or explicit group memberships; they infer the structure of social influence by grouping individuals based on choices, and they use these groups to decide whose choices to follow. We propose a computational model that formalizes this idea, and we test the model predictions in a series of behavioral experiments. In Experiment 1, we reproduce a well-established finding that people's choices are more likely to be influenced by someone whose past choices are more similar to their own past choices, as predicted by our model as well as dyadic similarity models. In Experiments 2–5, we test a set of unique predictions of our model by looking at cases where the degree of choice overlap between individuals is equated, but their choices indicate a latent group structure. We then apply our model to prior empirical results on infants’ understanding of others’ preferences, presenting an alternative account of developmental changes. Finally, we discuss how our model relates to classical findings in the social influence literature and the theoretical implications of our model. Taken together, our findings demonstrate that structure learning is a powerful framework for explaining the influence of social information on decision making in a variety of contexts. (shrink)

This article described three heuristics that are employed in making judgements under uncertainty: representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value (...) is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty. (shrink)

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Marr's levels of analysis—computational, algorithmic, and implementation—have served cognitive science well over the last 30 years. But the recent increase in the popularity of the computational level raises a new challenge: How do we begin to relate models at different levels of analysis? We propose that it is possible to define levels of analysis that lie between the computational and the algorithmic, providing a way to build a bridge between computational- and algorithmic-level models. The key idea is to push the (...) notion of rationality, often used in defining computational-level models, deeper toward the algorithmic level. We offer a simple recipe for reverse-engineering the mind's cognitive strategies by deriving optimal algorithms for a series of increasingly more realistic abstract computational architectures, which we call “resource-rational analysis.”. (shrink)

"The best book available for non-mathematicians." — Contemporary Psychology. Superb nontechnical introduction to game theory and related disciplines, primarily as applied to the social sciences. Clear, comprehensive coverage of utility theory, 2-person zero-sum games, 2-person non-zero-sum games, n-person games, individual and group decision-making, much more. Appendixes. Bibliography. Graphs and figures.

One important distinction in psychology is between inferences based on associative memory and inferences based on analysis and rules. Much previous empirical work conceive of associative and analytical processes as two exclusive ways of addressing a judgment task, where only one process is selected and engaged at a time, in an either-or fashion. However, related work indicate that the processes are better understood as being in interplay and simultaneously engaged. Based on computational modeling and brain imaging of spontaneously adopted judgment (...) strategies together with analyses of brain activity elicited in tasks where participants were explicitly instructed to perform similarity-based associative judgments or rule-based judgments ( n = 74), we identified brain regions related to the two types of processes. We observed considerable overlap in activity patterns. The precuneus was activated for both types of judgments, and its activity predicted how well a similarity-based model fit the judgments. Activity in the superior frontal gyrus predicted the fit of a rule-based judgment model. The results suggest the precuneus as a key node for similarity-based judgments, engaged both when overt responses are guided by similarity-based and rule-based processes. These results are interpreted such that similarity-based processes are engaged in parallel to rule-based-processes, a finding with direct implications for cognitive theories of judgment. (shrink)

In Study 1, 49 college students' preferences for different brands of strawberry jams were compared with experts' ratings of the jams. Students who analyzed why they felt the way they did agreed less with the experts than students who did not. In Study 2, 243 college students' preferences for college courses were compared with expert opinion. Some students were asked to analyze reasons; others were asked to evaluate all attributes of all courses. Both kinds of introspection caused people to make (...) choices that, compared with control Ss', corresponded less with expert opinion. Analyzing reasons can focus people's attention on nonoptimal criteria, causing them to base their subsequent choices on these criteria. Evaluating multiple attributes can moderate people's judgments, causing them to discriminate less between the different alternatives. (shrink)

This article is about the analogy between inclusive fitness and utility. In behavioral ecology, it is often assumed that individual organisms behave as if they were “striving” to maximize their inclusive fitness—a measure analogue to the kind of utility function that is used to represent the preferences of rational agents. Here, I explore some conceptual puzzles related to this view and question whether the kind of biological utility posited by the advocates of the “maximizing agent analogy” can be adequately interpreted (...) in inclusive fitness terms. (shrink)