We examine whether people evaluate the performance of other agents using only behavioral metrics (intuitive behaviorism) or by also taking into account the program that is driving an agent’s performance (intuitive cognitivism). In an online study, 200 participants, most without programming experience, learned to use a simple block programming language that controls a maze-solving robot. Participants then evaluated which of two programs was “better”, for 18 pairs of robots and programs. The programs varied in 3 metrics. Two of these, action efficiency and representation efficiency, were motivated by work in Reinforcement Learning and philosophy of mind, and the third, semantic generalization, is novel. We found that people’s judgements are in line with intuitive cognitivism, that people are sensitive to the program features, and that people intuitively evaluate and care about which other problems a program solves beyond the given task.

Finding and exploiting loopholes is a familiar facet of fable, law, and everyday life. But cognitive, computational, and empirical work on this behavior remains scarce. Engaging with loopholes requires a nuanced understanding of goals, social ambiguity, and value alignment. We trace loophole behavior to early childhood, and we propose that exploiting loopholes results from a conflict in actors' goals combined with a pressure to cooperate. A survey of 260 parents reporting on 425 children reveals that loophole behavior is prevalent, frequent, and diverse in daily parent-child interactions, emerging around ages five to six and tapering off from around ages nine to ten into adolescence. A further experiment shows that adults consider loophole behavior in children as less costly than non-compliance, and children increasingly differentiate loophole behavior from non-compliance from ages four to ten. We discuss limitations of the current work together with a proposal for a formal framework for loophole behavior.

Decision-making can be automatic (“rote”) or on-the-fly (“reflective”). Are people sensitive to whether others are behaving rotely or reflectively? The rote-versus-reflective inference may be particularly relevant when learning from others: good teachers should be actively considering the learner’s needs. When teachers rely on rote systems, this may “break” the mental state recursion that facilitates learning from pedagogy. This study takes a first step in investigating learning and inference when teachers use rote-versus-reflective reasoning, using uniqueness of feedback as a cue to reflectivity. Adult participants viewed videos of teachers providing either identical, similar, or unique feedback to three different groups of students; participants evaluated that teacher along several metrics. Consistent with our predictions, rote teachers were evaluated as poorer informants than reflective teachers, and students paired with rote teachers were expected to learn less. These results are the first to demonstrate sensitivity to, and impact from, inferences about others’ rote-versus-reflective behaviors.

Machine creativity is on the rise. Recent studies find that large language models achieve human performance on common psychological tests of creativity, which often pose a given problem and ask for novel or unusual solutions. But can AI go beyond producing solutions for given problems, to creatively propose new problems? We present the Task Task, a novel test that asks participants to come up with creative problems. In this test, we assess the ability of humans and GPT-4 to design challenge tasks for a game show. We evaluated proposed tasks using crowdsourced subjective creativity ratings, as well as computational measures of linguistic complexity and semantic content. We found that GPT-4 achieves similar scores as humans on creativity, originality, and judgments of how fun or difficult the tasks are. However, model-generated output tends to be shorter and connect more semantically distant concepts. We discuss implications and future directions for the psychology of creativity.

People engage in intentional misunderstandings to get around direct non-compliance. In other words, they use loopholes. Previous work showed that adults and children consider loophole behavior to be less costly than direct non-compliance (Bridgers, Schulz, & Ullman, 2021), and suggested this is a primary reason for their use: loopholes will land you in less trouble than defiance. However, we propose that this difference between loopholes and defiance will not hold for a specific, important context: moral violations. We replicate the finding that loopholes are less costly in a neutral context but find that engaging in loopholes in a moral context is as bad as non-compliance (Experiment 1, N=360). We then use a direct comparison between loopholes and non-compliance (N=150) to investigate whether in some contexts loopholes will be seen as even worse than non-compliance. We replicate the differential effect of the moral context from Experiment 1, but do not find a reversal. We discuss possible extensions and limitations, and consider why loopholes in moral violations may be uniquely unacceptable.

Eating onion ice cream is improbable, and levitating ice cream is impossible. But scooping ice cream using sadness is not just impossible: it is inconceivable. While prior work has examined the distinction between improbable and impossible events, there has been little empirical research on inconceivability. Here, we report a behavioral and computational study of inconceivability in three parts. First, we find that humans reliably categorize events as inconceivable, separate from probable, improbable, and impossible. Second, we find that we can decode the modal category of a sentence using language-model-derived estimates of subjective event probabilities. Third, we reproduce a recent finding that improbable events yield slowest response times in a possibility judgment task, and show that inconceivable events are faster to judge than impossible and improbable events. Overall, our results suggest that people distinguish the impossible from the inconceivable, and such distinctions may be based on graded rather than discrete judgments.

When we reason about the goals of others, how do we balance the positive outcomes that actions led to, with the potentiallybad ways those actions could have ended? In a four-part experiment, we tested whether and how adults (full study) and6- to 8-year-old children (ongoing study) expect other agents to take account of the ways their goal-directed action couldhave failed. Across 4 different tasks, we found that adults expected others to negatively appraise perilous situations (deeptrenches), to minimize the danger of their actions, and to trade off danger and reward in their action plans. Our preliminarychildrens study shows similar trends. These results suggest that people appeal to peril-how badly things could go if onesactions fail-when explaining and predicting other peoples actions, and also make quantitative inferences that are finelytuned to the degree of peril and reward that others face.

People make fast, spontaneous, and consistent judgementsof social situations, even in complex physical contexts withmultiple-body dynamics (e.g. pushing, lifting, carrying, etc.).What mental computations make such judgments possible? Dopeople rely on low-level perceptual cues, or on abstract con-cepts of agency, action, and force? We describe a new exper-imental paradigm, Flatland, for studying social inference inphysical environments, using automatically generated interac-tive scenarios. We show that human interpretations of events inFlatland can be explained by a computational model that com-bines inverse hierarchical planning with a physical simulationengine to reason about objects and agents. This model out-performs cue-based alternatives based on hand-coded (multi-nomial logistic regression) and learned (LSTM) features. Ourresults suggest that humans could use a combination of intu-itive physics and hierarchical planning to interpret complex in-teractive scenarios encountered in daily life.

Sometimes people ask us to do things we do not want to do. We may try to avoid the aversive consequences of non-compliance by finding a loophole: an interpretation of the request that is consistent with its literal but not intended meaning. Exploiting loopholes requires an integrated understanding of pragmatics, goal alignment, and rational planning. This kind of complex social reasoning may be challenging for people with autism. Here we surveyed parents to study the prevalence and development of loophole behavior in childhood among autistic and non-autistic children. Neither the tendency to produce loopholes nor their developmental trajectory differed between autistic (N = 200) and non-autistic children (N = 200). These results are consistent with previous work suggesting the heterogeneous nature of autism and the difficulty of finding single tasks that distinguish high-functioning children with and without autism; the results also demonstrate that autistic children are capable of this kind of complex social reasoning.

How do people understand the pragmatics of spatial language?We propose a rational-speech act model for spatial reasoning,and apply it to the terms ‘in’ and ‘near’. We examine people’sfine-grain spatial reasoning in this domain by having them lo-cate where an event occurred, given an utterance. Our prag-matic listener model provides a quantitative and qualitative fitto people’s inferences.