Preschoolers’ understanding of graded preferences

Having less means wanting more: Children hold an intuitive economic theory of diminishing marginal utility

Judgments surrounding resource acquisition and valuation are ubiquitous in daily life. How do humans decide what something is worth to themselves or someone else? One important cue to value is that of resource quantity. As described by economists, the principle of diminishing marginal utility (DMU) holds that as resource abundance increases, the value placed on each unit decreases; likewise, when resources become more scarce, the value placed on each unit rises. While prior research suggests that adults make judgments that align with this concept, it is unclear whether children do so. In Study 1 (n = 104), children (ages 5 through 8) were presented with scenarios involving losses or gains to others' resources and predicted the actions and emotions of the individuals involved. Participants made decisions that aligned with DMU, e.g., expecting individuals with fewer resources to expend more effort for an additional resource than individuals with greater resources. In Study 2 (n = 104), children incorporated information about preferences when inferring others' resource valuations, showing how quantity and preference are both included in children's inferences about others' utility. Our results indicate the early emergence of an intuitive economic theory that aligns with an important economic principle. Long before formal learning on this topic, children integrate quantity and preference information to sensibly predict others' resource valuations, with implications for economic decision-making, social preferences, and judgments of partner quality across the lifespan.

Understanding preferences in infancy

A preference is defined as a dispositional state that helps explain why a person chooses one option over another. Preference understanding is a significant part of interpreting and predicting others' behavior, which can also help to guide social encounters, for instance, to initiate interactions and even form relationships based on shared preferences. Cognitive developmental research in the past several decades has revealed that infants have relatively sophisticated understandings about others' preferences, as part of investigations into how young children make sense of others' behavior in terms of mental states such as intentions, dispositions including preferences, and epistemic states. In recent years, research on early psychological knowledge expands to including infant understanding of social situations. As such, infants are also found to use their preference understandings in their social life. They treat favorably others who share their own preferences, and they prefer prosocial and similar others (e.g., those who speak their language). In reviewing these results, we point out future directions for research and conclude with further suggestions and recommendations. This article is categorized under: Cognitive Biology > Cognitive Development Psychology > Development and Aging.

A rational model of people's inferences about others' preferences based on response times

There's a difference between someone instantaneously saying "Yes!" when you ask them on a date compared to "…yes." Psychologists and economists have long studied how people can infer preferences from others' choices. However, these models have tended to focus on what people choose and not how long it takes them to make a choice. We present a rational model for inferring preferences from response times, using a drift diffusion model to characterize how preferences influence response time, and Bayesian inference to invert this relationship. We test our model's predictions for three experimental questions. Matching model predictions, participants inferred that a decision-maker preferred a chosen item more if the decision-maker spent less time deliberating (Experiment 1), participants predicted a decision-maker's choice in a novel comparison based on inferring the decision-maker's relative preferences from previous response times and choices (Experiment 2), and participants could incorporate information about a decision-maker's mental state of cautious or careless (Experiments 3, 4A, and 4B).

The Naïve Utility Calculus as a unified, quantitative framework for action understanding

The human ability to reason about the causes behind other people' behavior is critical for navigating the social world. Recent empirical research with both children and adults suggests that this ability is structured around an assumption that other agents act to maximize some notion of subjective utility. In this paper, we present a formal theory of this Naïve Utility Calculus as a probabilistic generative model, which highlights the role of cost and reward tradeoffs in a Bayesian framework for action-understanding. Our model predicts with quantitative accuracy how people infer agents' subjective costs and rewards based on their observable actions. By distinguishing between desires, goals, and intentions, the model extends to complex action scenarios unfolding over space and time in scenes with multiple objects and multiple action episodes. We contrast our account with simpler model variants and a set of special-case heuristics across a wide range of action-understanding tasks: inferring costs and rewards, making confidence judgments about relative costs and rewards, combining inferences from multiple events, predicting future behavior, inferring knowledge or ignorance, and reasoning about social goals. Our work sheds light on the basic representations and computations that structure our everyday ability to make sense of and navigate the social world.

People learn other people's preferences through inverse decision-making

People are capable of learning other people's preferences by observing the choices they make. We propose that this learning relies on inverse decision-making-inverting a decision-making model to infer the preferences that led to an observed choice. In Experiment 1, participants observed 47 choices made by others and ranked them by how strongly each choice suggested that the decision maker had a preference for a specific item. An inverse decision-making model generated predictions that were in accordance with participants' inferences. Experiment 2 replicated and extended a previous study by Newtson (1974) in which participants observed pairs of choices and made judgments about which choice provided stronger evidence for a preference. Inverse decision-making again predicted the results, including a result that previous accounts could not explain. Experiment 3 used the same method as Experiment 2 and found that participants did not expect decision makers to be perfect utility-maximizers.

Learning the Structure of Social Influence

We routinely observe others' choices and use them to guide our own. Whose choices influence us more, and why? Prior work has focused on the effect of perceived similarity between two individuals (self and others), such as the degree of overlap in past choices or explicitly recognizable group affiliations. In the real world, however, any dyadic relationship is part of a more complex social structure involving multiple social groups that are not directly observable. Here we suggest that human learners go beyond dyadic similarities in choice behaviors or explicit group memberships; they infer the structure of social influence by grouping individuals (including themselves) based on choices, and they use these groups to decide whose choices to follow. We propose a computational model that formalizes this idea, and we test the model predictions in a series of behavioral experiments. In Experiment 1, we reproduce a well-established finding that people's choices are more likely to be influenced by someone whose past choices are more similar to their own past choices, as predicted by our model as well as dyadic similarity models. In Experiments 2-5, we test a set of unique predictions of our model by looking at cases where the degree of choice overlap between individuals is equated, but their choices indicate a latent group structure. We then apply our model to prior empirical results on infants' understanding of others' preferences, presenting an alternative account of developmental changes. Finally, we discuss how our model relates to classical findings in the social influence literature and the theoretical implications of our model. Taken together, our findings demonstrate that structure learning is a powerful framework for explaining the influence of social information on decision making in a variety of contexts.

Young children infer preferences from a single action, but not if it is constrained

Inferring others' preferences is socially important and useful. We investigated whether children infer preferences from the minimal information provided by an agent's single action, and whether they avoid inferring preference when the action is constrained. In three experiments, children saw vignettes in which an agent took a worse toy instead of a better one. Experiment 1 shows that this single action influences how young children infer preferences. Children aged three and four were more likely to infer the agent preferred the worse toy when the agent took this toy, compared with when the agent did not take either toy. Experiment 2 then shows that children consider constraints when inferring preferences from a single action. From age 5, children were less likely to avoid inferring a preference for the worse toy when the agent's action was physically constrained. Finally, Experiment 3 provides evidence that children's and adults' sensitivity to constraints, when inferring preferences, is not based on a general notion of constraints, and instead depends on several specific notions. Whereas 5-6-year-olds in this experiment considered physical and socio-moral constraints when inferring preferences, they had difficulty grasping the relevance of epistemic constraints. Adults considered physical and epistemic constraints, but were not influenced by the socio-moral constraint of ownership. Together these findings contribute to a picture of cognitive development in which children are able to infer non-obvious properties on the basis of minimal concrete information, and are also sensitive to subtle changes in this information.