Learning From Aggregated Opinion.

The capacity to leverage information from others' opinions is a hallmark of human cognition. Consequently, past research has investigated how we learn from others' testimony. Yet a distinct form of social information-aggregated opinion-increasingly guides our judgments and decisions. We investigated how people learn from such information by conducting three experiments with participants recruited online within the United States (N = 886) comparing the predictions of three computational models: a Bayesian solution to this problem that can be implemented by a simple strategy for combining proportions with prior beliefs, and two alternatives from epistemology and economics. Across all studies, we found the strongest concordance between participants' judgments and the predictions of the Bayesian model, though some participants' judgments were better captured by alternative strategies. These findings lay the groundwork for future research and show that people draw systematic inferences from aggregated opinion, often in line with a Bayesian solution.

Structural explanations lead young children and adults to rectify resource inequalities.

Decisions about how to divide resources have profound social and practical consequences. Do explanations regarding the source of existing inequalities influence how children and adults allocate new resources? When 3- to 6-year-old children (N = 201) learned that inequalities were caused by structural forces (stable external constraints affecting access to resources) as opposed to internal forces (effort), they rectified inequalities, overriding previously documented tendencies to perpetuate inequality or divide resources equally. Adults (N = 201) were more likely than children to rectify inequality spontaneously; this was further strengthened by a structural explanation but reversed by an effort-based explanation. Allocation behaviors were mirrored in judgments of which allocation choices by others were appropriate. These findings reveal how explanations powerfully guide social reasoning and action from childhood through adulthood.

Inside Ockham's razor: A mechanism driving preferences for simpler explanations.

People often prefer simpler explanations, defined as those that posit the presence of fewer causes (e.g., positing the presence of a single cause, Cause A, rather than two causes, Causes B and C, to explain observed effects). Here, we test one hypothesis about the mechanisms underlying this preference: that people tend to reason as if they are using "agnostic" explanations, which remain neutral about the presence/absence of additional causes (e.g., comparing "A" vs. "B and C," while remaining neutral about the status of B and C when considering "A," or of A when considering "B and C"), even in cases where "atheist" explanations, which specify the absence of additional causes (e.g., "A and not B or C" vs. "B and C and not A"), are more appropriate. Three studies with US-based samples (total N = 982) tested this idea by using scenarios for which agnostic and atheist strategies produce diverging simplicity/complexity preferences, and asking participants to compare explanations provided in atheist form. Results suggest that people tend to ignore absent causes, thus overgeneralizing agnostic strategies, which can produce preferences for simpler explanations even when the complex explanation is objectively more probable. However, these unwarranted preferences were reduced by manipulations that encouraged participants to consider absent causes: making absences necessary to produce the effects (Study 2), or describing absences as causes that produce alternative effects (Study 3). These results shed light on the mechanisms driving preferences for simpler explanations, and on when these mechanisms are likely to lead people astray.

Breaking down (and moving beyond) novelty as a trigger of curiosity.

The Novelty Seeking Model (NSM) places "novelty" at center stage in characterizing the mechanisms behind curiosity. We argue that the NSM's conception of novelty is too broad, obscuring distinct constructs. More critically, the NSM underemphasizes triggers of curiosity that better unify these constructs and that have received stronger empirical support: those that signal the potential for useful learning.

Ask me why, don't tell me why: Asking children for explanations facilitates relational thinking.

Identifying abstract relations is essential for commonsense reasoning. Research suggests that even young children can infer relations such as "same" and "different," but often fail to apply these concepts. Might the process of explaining facilitate the recognition and application of relational concepts? Based on prior work suggesting that explanation can be a powerful tool to promote abstract reasoning, we predicted that children would be more likely to discover and use an abstract relational rule when they were prompted to explain observations instantiating that rule, compared to when they received demonstration alone. Five- and 6-year-olds were given a modified Relational Match to Sample (RMTS) task, with repeated demonstrations of relational (same) matches by an adult. Half of the children were prompted to explain these matches; the other half reported the match they observed. Children who were prompted to explain showed immediate, stable success, while those only asked to report the outcome of the pedagogical demonstration did not. Findings provide evidence that explanation facilitates early abstraction over and above demonstration alone.

How "is" shapes "ought" for folk-biological concepts.

Knowing which features are frequent among a biological kind (e.g., that most zebras have stripes) shapes people's representations of what category members are like (e.g., that typical zebras have stripes) and normative judgments about what they ought to be like (e.g., that zebras should have stripes). In the current work, we ask if people's inclination to explain why features are frequent is a key mechanism through which what "is" shapes beliefs about what "ought" to be. Across four studies (N = 591), we find that frequent features are often explained by appeal to feature function (e.g., that stripes are for camouflage), that functional explanations in turn shape judgments of typicality, and that functional explanations and typicality both predict normative judgments that category members ought to have functional features. We also identify the causal assumptions that license inferences from feature frequency and function, as well as the nature of the normative inferences that are drawn: by specifying an instrumental goal (e.g., camouflage), functional explanations establish a basis for normative evaluation. These findings shed light on how and why our representations of how the natural world is shape our judgments of how it ought to be.

Motivated to learn: An account of explanatory satisfaction.

Many explanations have a distinctive, positive phenomenology: receiving or generating these explanations feels satisfying. Accordingly, we might expect this feeling of explanatory satisfaction to reinforce and motivate inquiry. Across five studies, we investigate how explanatory satisfaction plays this role: by motivating and reinforcing inquiry quite generally ("brute motivation" account), or by selectively guiding inquiry to support useful learning about the target of explanation ("aligned motivation" account). In Studies 1-2, we find that satisfaction with an explanation is related to several measures of perceived useful learning, and that greater satisfaction in turn predicts stronger curiosity about questions related to the explanation. However, in Studies 2-4, we find only tenuous evidence that satisfaction is related to actual learning, measured objectively through multiple-choice or free recall tests. In Study 4, we additionally show that perceptions of learning fully explain one seemingly specious feature of explanatory preferences studied in prior research: the preference for uninformative "reductive" explanations. Finally, in Study 5, we find that perceived learning is (at least in part) causally responsible for feelings of satisfaction. Together, these results point to what we call the "imperfectly aligned motivation" account: explanatory satisfaction selectively motivates inquiry towards learning explanatory information, but primarily through fallible perceptions of learning. Thus, satisfaction is likely to guide individuals towards lines of inquiry that support perceptions of learning, whether or not individuals actually are learning.

A functional approach to explanation-seeking curiosity.

Why do some (and only some) observations prompt people to ask "why?" We propose a functional approach to "Explanation-Seeking Curiosity" (ESC): the state that motivates people to seek an explanation. If ESC tends to prompt explanation search when doing so is likely to be beneficial, we can use prior work on the functional consequences of explanation search to derive "forward-looking" candidate triggers of ESC-those that concern expectations about the downstream consequences of pursuing explanation search. Across three studies (N = 867), we test hypotheses derived from this functional approach. In Studies 1-3, we find that ESC is most strongly predicted by expectations about future learning and future utility. We also find that judgments of novelty, surprise, and information gap predict ESC, consistent with prior work on curiosity; however, the role for forward-looking considerations is not reducible to these factors. In Studies 2-3, we find that predictors of ESC form three clusters, expectations about learning (about the target of explanation), expectations about export (to other cases and future contexts), and backward-looking considerations (having to do with the relationship between the target of explanation and prior knowledge). Additionally, these clusters are consistent across stimulus sets that probe ESC, but not fact-seeking curiosity. These findings suggest that explanation-seeking curiosity is aroused in a systematic way, and that people are not only sensitive to the match or mismatch between a given stimulus and their current or former beliefs, but to how they expect an explanation for that stimulus to improve their epistemic state.

Transformations and Transfer: Preschool Children Understand Abstract Relations and Reason Analogically in a Causal Task.

Previous research suggests that preschoolers struggle with understanding abstract relations and with reasoning by analogy. Four experiments find, in contrast, that 3- and 4-year-olds (N = 168) are surprisingly adept at relational and analogical reasoning within a causal context. In earlier studies preschoolers routinely favored images that share thematic or perceptual commonalities with a target image (object matches) over choices that match the target along abstract relations (relational matches). The present studies embed such choice tasks within a cause-and-effect framework. Without causal framing, preschoolers strongly favor object matches, replicating the results of previous studies. But with causal framing, preschoolers succeed at analogical transfer (i.e., choose relational matches). These findings suggest that causal framing facilitates early analogical reasoning.

Can Science Explain the Human Mind? Intuitive Judgments About the Limits of Science.

Can science explain romantic love, morality, and religious belief? We documented intuitive beliefs about the limits of science in explaining the human mind. We considered both epistemic evaluations (concerning whether science could possibly fully explain a given psychological phenomenon) and nonepistemic judgments (concerning whether scientific explanations for a given phenomenon would generate discomfort), and we identified factors that characterize phenomena judged to fall beyond the scope of science. Across six studies, we found that participants were more likely to judge scientific explanations for psychological phenomena to be impossible and uncomfortable when, among other factors, they support first-person, introspective access (e.g., feeling empathetic as opposed to reaching for objects), contribute to making humans exceptional (e.g., appreciating music as opposed to forgetfulness), and involve conscious will (e.g., acting immorally as opposed to having headaches). These judgments about the scope of science have implications for science education, policy, and the public reception of psychological science.

Structure-function fit underlies the evaluation of teleological explanations.

Teleological explanations, which appeal to a function or purpose (e.g., "kangaroos have long tails for balance"), seem to play a special role within the biological domain. We propose that such explanations are compelling because they are evaluated on the basis of a salient cue: structure-function fit, or the correspondence between a biological feature's form (e.g., tail length) and its function (e.g., balance). Across five studies with 852 participants in total, we find support for three predictions that follow from this proposal. First, we find that function information decreases reliance on mechanistic considerations when evaluating explanations (Experiments 1-3), indicating the presence of a salient, function-based cue. Second, we demonstrate that structure-function fit is the best candidate for this cue (Experiments 3-4). Third, we show that scientifically-unwarranted teleological explanations are more likely to be accepted under speeded and unspeeded conditions when they are high in structure-function fit (Experiment 5). Experiment 5 also finds that structure-function fit extends beyond biology to teleological explanations in other domains. Jointly, these studies provide a new account of how teleological explanations are evaluated and why they are often (but not universally) compelling.

Explaining Constrains Causal Learning in Childhood.

Three experiments investigate how self-generated explanation influences children's causal learning. Five-year-olds (N = 114) observed data consistent with two hypotheses and were prompted to explain or to report each observation. In Study 1, when making novel generalizations, explainers were more likely to favor the hypothesis that accounted for more observations. In Study 2, explainers favored a hypothesis that was consistent with prior knowledge. Study 3 pitted a hypothesis that accounted for more observations against a hypothesis consistent with prior knowledge. Explainers were more likely to base generalizations on prior knowledge. Findings suggest that attempts to explain drive children to evaluate hypotheses using features of "good" explanations, or those supporting generalizations with broad scope, as informed by children's prior knowledge and observations.

Wrong or merely prohibited: Special treatment of strict liability in intuitive moral judgment.

Most crimes in America require that the defendant have mens rea, Latin for "guilty mind." However, mens rea is not legally required for strict liability crimes, such as speeding, for which someone is guilty even if ignorant or deceived about her speed. In 3 experiments involving participants responding to descriptive vignettes, we investigated whether the division of strict liability crimes in the law reflects an aspect of laypeople's intuitive moral cognition. Experiment 1 (N = 396; 236 male, 159 female, 1 other; Mage = 30) found evidence that it does: ignorance and deception were less mitigating for strict liability crimes than for "mens rea" crimes. Experiments 2 (N = 413; 257 male, 154 female, 2 other; Mage = 31) and 3 (N = 404; 183 male, 221 female, Mage = 35) revealed that strict liability crimes are not treated as pure moral violations, but additionally as violations of convention. We found that for strict liability crimes, ratings of moral wrongness and punishment were influenced to a greater extent by the fact that a rule had been violated, even when harm was kept constant, mirroring the legal distinction of malum prohibitum (wrong as prohibited) versus malum in se (wrong in itself). Further, we found that rules prohibiting strict liability crimes were judged more arbitrary than corresponding rules for "mens rea" crimes, and that this judgment was related to the role of mental states. Jointly, the findings suggest a surprising correspondence between the law and laypeople's intuitive judgments. (PsycINFO Database Record

Selective effects of explanation on learning during early childhood.

Two studies examined the specificity of effects of explanation on learning by prompting 3- to 6-year-old children to explain a mechanical toy and comparing what they learned about the toy's causal and non-causal properties with children who only observed the toy, both with and without accompanying verbalization. In Study 1, children were experimentally assigned to either explain or observe the mechanical toy. In Study 2, children were classified according to whether the content of their response to an undirected prompt involved explanation. Dependent measures included whether children understood the toy's functional-mechanical relationships, remembered perceptual features of the toy, effectively reconstructed the toy, and (for Study 2) generalized the function of the toy when constructing a new one. Results demonstrate that across age groups, explanation promotes causal learning and generalization but does not improve (and in younger children can even impair) memory for causally irrelevant perceptual details.

Learning by thinking in natural and artificial minds.

Canonical cases of learning involve novel observations external to the mind, but learning can also occur through mental processes such as explaining to oneself, mental simulation, analogical comparison, and reasoning. Recent advances in artificial intelligence (AI) reveal that such learning is not restricted to human minds: artificial minds can also self-correct and arrive at new conclusions by engaging in processes of 'learning by thinking' (LbT). How can elements already in the mind generate new knowledge? This article aims to resolve this paradox, and in so doing highlights an important feature of natural and artificial minds - to navigate uncertain environments with variable goals, minds with limited resources must construct knowledge representations 'on demand'. LbT supports this construction.

Tell me your (cognitive) budget, and I'll tell you what you value.

Consider the following two (hypothetical) generic causal claims: "Living in a neighborhood with many families with children increases purchases of bicycles" and "living in an affluent neighborhood with many families with children increases purchases of bicycles." These claims not only differ in what they suggest about how bicycle ownership is distributed across different neighborhoods (i.e., "the data"), but also have the potential to communicate something about the speakers' values: namely, the prominence they accord to affluence in representing and making decisions about the social world. Here, we examine the relationship between the level of granularity with which a cause is described in a generic causal claim (e.g., neighborhood vs. affluent neighborhood) and the value of the information contained in the causal model that generates that claim. We argue that listeners who know any two of the following can make reliable inferences about the third: 1) the level of granularity at which a speaker makes a generic causal claim, 2) the speaker's values, and 3) the data available to the speaker. We present results of four experiments (N = 1323) in the domain of social categories that provide evidence in keeping with these predictions.

Allow Me to Explain: Benefits of Explaining Extend to Distal Academic Performance.

How does the act of explaining influence learning? Prior work has studied effects of explaining through a predominantly proximal lens, measuring short-term outcomes or manipulations within lab settings. Here, we ask whether the benefits of explaining extend to academic performance over time. Specifically, does the quality and frequency of student explanations predict students' later performance on standardized tests of math and English? In Study 1 (N = 127 5th-6th graders), participants completed a causal learning activity during which their explanation quality was evaluated. Controlling for prior test scores, explanation quality directly predicted both math and English standardized test scores the following year. In Study 2 (N = 20,384 10th graders), participants reported aspects of teachers' explanations and their own. Controlling for prior test scores, students' own explanations predicted both math and English state standardized test scores, and teacher explanations were linked to test performance through students' own explanations. Taken together, these findings suggest that benefits of explaining may result in part from the development of a metacognitive explanatory skill that transfers across domains and over time. Implications for cognitive science, pedagogy, and education are discussed.

Are ethical explanations explanatory? Meta-ethical beliefs shape judgments about explanations for social change.

Why were women given the right to vote? "Because it is morally wrong to deny women the right to vote." This explanation does not seem to fit the typical pattern for explaining an event: rather than citing a cause, it appeals to an ethical claim. Do people judge ethical claims to be genuinely explanatory? And if so, why? In Studies 1 (N = 220) and 2 (N = 293), we find that many participants accept ethical explanations for social change and that this is predicted by their meta-ethical beliefs in moral progress and moral principles, suggesting that these participants treat morality as a directional feature of the world, somewhat akin to a causal force. In Studies 3 (N = 513) and 4 (N = 328), we find that participants recognize this relationship between ethical explanations and meta-ethical commitments, using the former to make inferences about individuals' beliefs in moral progress and moral principles. Together these studies demonstrate that our beliefs about the nature of morality shape our judgments of explanations and that explanations shape our inferences about others' moral commitments.

Psychological freedom, rationality, and the naive theory of reasoning.

To make sense of the social world, people reason about others' mental states, including whether and in what ways others can form new mental states. We propose that people's judgments concerning the dynamics of mental state change invoke a "naive theory of reasoning." On this theory, people conceptualize reasoning as a rational, semi-autonomous process that individuals can leverage, but not override, to form new rational mental states. Across six experiments, we show that this account of people's naive theory of reasoning predicts judgments about others' ability to form rational and irrational beliefs, desires, and intentions, as well as others' ability to act rationally and irrationally. This account predicts when, and explains why, people judge others as psychologically constrained by coercion and other forms of situational pressure. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Explanation and prior knowledge interact to guide learning.

How do explaining and prior knowledge contribute to learning? Four experiments explored the relationship between explanation and prior knowledge in category learning. The experiments independently manipulated whether participants were prompted to explain the category membership of study observations and whether category labels were informative in allowing participants to relate prior knowledge to patterns underlying category membership. The experiments revealed a superadditive interaction between explanation and informative labels, with explainers who received informative labels most likely to discover (Experiments 1 and 2) and generalize (Experiments 3 and 4) a pattern consistent with prior knowledge. However, explainers were no more likely than controls to discover multiple patterns (Experiments 1 and 2), indicating that effects of explanation are relatively targeted. We suggest that explanation recruits prior knowledge to assess whether candidate patterns are likely to have broad scope (i.e., to generalize within and beyond study observations). This interpretation is supported by the finding that effects of explanation on prior knowledge were attenuated when learners believed prior knowledge was irrelevant to generalizing category membership (Experiment 4). This research provides evidence that explanation can serve as a mechanism for deploying prior knowledge to assess the scope of observed patterns.