A long series of studies in social psychology have shown that the explanations people give for their own behaviors are fundamentally different from the explanations they give for the behaviors of others. Still, a great deal of uncertainty remains about precisely what sorts of differences one finds here. We offer a new approach to addressing the problem. Specifically, we distinguish between two levels of representation ─ the level of linguistic structure (which consists of the actual series of words used in (...) the explanation) and the level of conceptual structure (which consists of the concepts these words are used to express). We then formulate and test hypotheses both about self-other differences in conceptual structure and about self-other differences in the mapping from conceptual structure to linguistic structure. (shrink)

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 propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...) construct new causal maps and that their learning is consistent with the Bayes net formalism. (shrink)

Can opium's tendency to induce sleep be explained by appeal to a "dormitive virtue"? If the label merely references the tendency being explained, the explanation seems vacuous. Yet the presence of a label could signal genuinely explanatory content concerning the (causal) basis for the property being explained. In Experiments 1 and 2, we find that explanations for a person's behavior that appeal to a named tendency or condition are indeed judged to be more satisfying than equivalent explanations that differ only (...) in omitting the name. In Experiment 3, we find support for one proposal concerning what it is about a name that drives a boost in explanatory satisfaction: named categories lead people to draw an inference to the existence of a cause underlying the category, a cause that is responsible for the behavior being explained. Our findings have implications for theories of explanation and point to the central role of causation in explaining behavior. (shrink)

There has been a probabilistic turn in contemporary cognitive science. Far and away, most of the work in this vein is Bayesian, at least in name. Coinciding with this development, philosophers have increasingly promoted Bayesianism as the best normative account of how humans ought to reason. In this paper, we make a push for exploring the probabilistic terrain outside of Bayesianism. Non-Bayesian, but still probabilistic, theories provide plausible competitors both to descriptive and normative Bayesian accounts. We argue for this general (...) idea via recent work on explanationist models of updating, which are fundamentally probabilistic but assign a substantial, non-Bayesian role to explanatory considerations. (shrink)