You must know something I dont: risky behavior implies privileged information

People make sense of each others behavior by assuming that beliefs and desires vary across agents. We propose thatpeople are more conservative when it comes to risk: when an agent takes an extreme risk, we assume they have privilegedinformation rather than high risk tolerance. Participants watched an agent choose either to obtain three guaranteed tokens,or guess which box from a set had four tokens. After watching the agents choice, participants played the game themselves.In Study 1, participants were quicker to imitate an agent who immediately made extremely risky bets than one whostarted out making low-risk bets that became progressively riskier, suggesting that they inferred that risk-seeking agentswere knowledgeable. In Study 2, participants ceased taking risky bets when the anonymous agent did, suggesting thatparticipants choices depend on mental state inferences rather than contagious but mind-blind risk-seeking behavior

From information-seeking actions (and their costs), adults jointly infer both whatothers know, and what they believe they can learn

We face a challenge when inferring what others know. Actions do not transparently reveal epistemic states: ignorantagents routinely ignore information too costly to obtain, and knowledgeable agents often confirm what they already knowwhen its convenient. We hypothesized that epistemic inferences are sensitive both to agents actions, and the underlyingutilities that best explain them. We tested this possibility in a simple task. Adults watched an explorer decide whetherto collect a map before searching an island for treasure. Participants (n=40) were asked to jointly infer how much theexplorer knew about the treasures location, and how much information the explorer believed the map had. Participantjudgments matched a computational model of epistemic inference structured around an expectation that agents rationallytradeoff information gain with information cost (r=0.86; 95%: 0.740.93, p¡.001). Our results suggest that adult Theory ofMind supports nuanced and graded epistemic inferences from observable action

The price of knowledge: Children infer epistemic states and desires fromexplorations cost

When deciding whether to explore, people must consider both their need for information, and the cost of obtaining it.Thus, to judge why someone explores (or decides not to), we must consider not only their actions, but also the cost ofinformation. Do children attend to the cost of agents exploratory choices when inferring what others know or desireto know? In Experiment 1, four- and five-year-olds judged that an agent who rejected an opportunity to gain low-costinformation must have already known it. In Experiment 2, four- and five-year-olds judged that an agent who incurreda greater cost to gain information had a greater epistemic desire. In two control experiments, we show that these resultscannot be explained by a low-level heuristic linking competence with knowledge. Our results suggest that childrens Theoryof Mind includes expectations about how costs interact with epistemic desires to produce action

Mental state inference from indirect evidence through Bayesian eventreconstruction

From childhood, people routinely explain each other’s behav-ior in terms of inferred mental states, like beliefs and desires.In many cases, however, people can also infer the mental statesof agents whose behavior we cannot see, such as when we in-fer that someone was anxious upon encountering a chewed-uppencil, or that someone left in a hurry if they left the door open.Here we present a computational model of mental-state attri-bution that works by reconstructing the actions an agent took,based on the indirect evidence that revealed their presence. Ourmodel quantitatively fits participant judgments, outperforminga simple alternative cue-based account. Our results shed lighton how people infer mental states from minimal indirect evi-dence, and provides further support to the idea that human The-ory of Mind is instantiated as a probabilistic generative modelof how unobservable mental states produce observable action

Children's learning of number words in an indigenous farming-foraging group

We show that children in the Tsimane', a farming-foraging group in the Bolivian rain-forest, learn number words along a similar developmental trajectory to children from industrialized countries. Tsimane' children successively acquire the first three or four number words before fully learning how counting works. However, their learning is substantially delayed relative to children from the United States, Russia, and Japan. The presence of a similar developmental trajectory likely indicates that the incremental stages of numerical knowledge – but not their timing — reflect a fundamental property of number concept acquisition which is relatively independent of language, culture, age, and early education.National Science Foundation (U.S.) (NSF Award 1022684)National Institutes of Health (U.S.) (NIH Ruth L. Kirschstein National Research Service Award (NRSA)

Native Amazonian children forego egalitarianism in merit-based tasks when they learn to count

Cooperation often results in a final material resource that must be shared, but deciding how to distribute that resource is not straightforward. A distribution could count as fair if all members receive an equal reward (egalitarian distributions), or if each member's reward is proportional to their merit (merit-based distributions). Here, we propose that the acquisition of numerical concepts influences how we reason about fairness. We explore this possibility in the Tsimane’, a farming-foraging group who live in the Bolivian rainforest. The Tsimane’ learn to count in the same way children from industrialized countries do, but at a delayed and more variable timeline, allowing us to de-confound number knowledge from age and years in school. We find that Tsimane’ children who can count produce merit-based distributions, while children who cannot count produce both merit-based and egalitarian distributions. Our findings establish that the ability to count – a non-universal, language-dependent, cultural invention – can influence social cognition.National Science Foundation (U.S.). Research and Evaluation on Education in Science and Engineering Program (Grant 1022684)University of Rocheste

The Naïve Utility Calculus unifies spatial and statistical routes to preference

Humans can seamlessly infer what other people like, based onwhat they do. Broadly, two types of accounts have beenproposed to explain different aspects of this ability. A firstaccount focuses on inferences from spatial information:agents choose and move towards things they like. A secondaccount focuses on inferences from statistical information:uncommon choices reveal preferences more clearly comparedto common choices. Here we argue that these two kinds ofinferences can be explained by the assumption that agentsmaximize utilities. We test this idea in a task where adultparticipants infer an agent’s preferences using a combinationof spatial and statistical information. We show that our modelpredicts human answers with higher accuracy than a set ofplausible alternative models