Do the discoveries of cognitive science generalize beyondartificial lab experiments? Or do they have little hope ofhelping us to understand real-world events? Fretting on thisquestion, I bought a copy of the Wall Street Journal andfound that the three front page headlines each connect tomy own research on explanatory reasoning. I report tests ofthe phenomena of inferred evidence, belief digitization, andrevealed truth in real-world contexts derived from theheadlines. If my own corner of cognitive science has suchexplanatory relevance to the real world, then cognitivescience as a whole must be in far better shape yet.

Economic choices depend on our predictions of the future.Yet, at times predictions are not based on all relevantinformation, but instead on the single most likelypossibility, which is treated as though certainly the case—that is, digitally. Two sets of studies test whether thisdigitization bias would occur in higher-stakes economiccontexts. When making predictions about the future assetprices, participants ignored conditional probabilityinformation given relatively unlikely events and reliedentirely on conditional probabilities given the more likelyevents. This effect was found for both financial aggregatesand individual stocks, for binary predictions about thedirection and continuous predictions about expected values,and even when the “unlikely” event explicitly had aprobability as high as 30%; further, it was not moderatedby investing experience. Implications for behavioralfinance are discussed.

Many propositions are not known to be true or false, andmany phenomena are not understood. What determineswhat propositions and phenomena are perceived asknowable or unknowable? We tested whether factorsrelated to scientific methodology (a proposition’sreducibility and falsifiability), its intrinsic metaphysics (themateriality of the phenomena and its scope ofapplicability), and its relation to other knowledge (itscentrality to one’s other beliefs and values) influenceknowability. Across a wide range of naturalistic scientificand pseudoscientific phenomena (Studies 1 and 2), as wellas artificial stimuli (Study 3), we found that reducibilityand falsifiability have strong direct effects on knowability,that materiality and scope have strong indirect effects (viareducibility and falsifiability), and that belief and valuecentrality have inconsistent and weak effects onknowability. We conclude that people evaluate theknowability of propositions consistently with principlesproposed by epistemologists and practicing scientists.

Stereotypes are important simplifying assumptions we usefor navigating the social world, associating traits withsocial categories. These beliefs can be used to infer anindividual’s likely social category from observed traits (adiagnostic inference) or to make inferences about anindividual’s unknown traits based on their putative socialcategory (a predictive inference). We argue that theseinferences rely on the same explanatory logic as other sortsof diagnostic and predictive reasoning tasks, such as causalexplanation. Supporting this conclusion, we demonstratethat stereotype use involves four of the same biases knownto be used in causal explanation: A bias against categoriesmaking unverified predictions (Exp. 1), a bias towardsimple categories (Exp. 2), an asymmetry betweenconfirmed and disconfirmed predictions of potentialcategories (Exp. 3), and a tendency to treat uncertaincategorizations as certainly true or false (Exp. 4).

People often prefer simpler explanations because they havehigher prior probability. However, simpler explanations arenot always normatively superior because they often do notfit the data as well as complex explanations. How dopeople negotiate this trade-off between prior probability(favoring simplicity) and goodness-of-fit (favoringcomplexity)? Here, we argue that people use opponentheuristics—relying on simplicity as a cue to priorprobability but complexity as a cue to goodness-of-fit(Study 1). We also examine factors that lead one or theother heuristic to predominate in a given context. Study 2finds that people have a stronger simplicity preference indeterministic rather than stochastic contexts, while Study 3finds that people have a stronger simplicity preference forphysical rather than social causal systems. Together, weargue that these cues and contextual moderators act aspowerful constraints that help to specify otherwise ill-defined hypothesis comparison problems.

Scientists seek to discover truths that are interesting andimportant. We characterized these notions by askinglaypeople to assess the importance, interestingness,surprisingness, practical value, scientific impact, andcomprehensibility of research reported in the journalsScience and Psychological Science. These judgments wereinterrelated in both samples, with interest predicted bypractical value, surprisingness, and comprehensibility, andimportance predicted mainly by practical value. However,these judgments poorly tracked the academic impact of theresearch, measured by citation counts three and seven yearslater. These results suggest that although people haveinternally reliable notions of what makes scienceinteresting and important, these notions do not trackscientific findings’ actual impact.

Mathematics is critical for making sense of the world. Yet,little is known about how people evaluate mathematicalexplanations. Here, we use an explanatory reasoning taskto investigate the intuitive structure of mathematics. Weshow that people evaluate arithmetic explanations bybuilding mental proofs over the conceptual structure ofintuitive arithmetic, evaluating those proofs using criteriasimilar to those of professional mathematicians.Specifically, we find that people prefer explanationsconsistent with the conceptual order of the operations(“9÷3=3 because 3 ́3=9” rather than “3 ́3=9 because9÷3=3”), and corresponding to simpler proofs (“9÷3=3because 3 ́3=9” rather than “9÷3=3 because 3+3+3=9”).Implications for mathematics cognition and education arediscussed.