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Humans exhibit a remarkable ability to learn relational concepts from a small number of examples. On the Synthetic Visual Reasoning Test (SVRT), a collection of 23 problems that require learning relational concepts, people typically discover the relational rules from a handful of examples. An important question is what learning mechanisms underlie human ability to acquire relational concepts so quickly. Here, we examine whether analogical comparisons serve as an efficient strategy to facilitate learning with small samples. We conducted two experiments that varied the display format and problem order for the SVRT. When problems are presented in an easy-to-hard order, people learn more efficiently when prior examples are displayed in spatially-segregated sets, consistent with the use of analogical mapping as a learning strategy. However, when the problems are presented in a random order, the advantage of spatially-segregated displays is eliminated. We propose that when hard problems are encountered early in a problem sequence, analogical mapping becomes too demanding, causing people to fall back on a less efficient learning strategy that does not require the comparison of multiple examples.


CogSci 2023

Human Relational Concept Learning on the Synthetic Visual Reasoning Test

technical paper

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

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The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

We propose a novel process account for how verb metaphors (e.g., The boat waddled) are understood: they are processed as analogical comparisons between the event denoted by the verb and an event schema activated by the noun. We first review evidence that this account is consistent with findings in analogical reasoning and with both literal and metaphoric sentence comprehension. Next, we report the results of an online study of verb metaphor comprehension that supports our claims. We conclude with a discussion of the implications of our findings for theories of metaphor processing and language change over time.

Verb Metaphors are Processed as Analogies

Commutativity–the fact that changing the order of operands in an operation does not change the results–is a fundamental principle of mathematics. How does its understanding develop in children? While this question has mostly been addressed for addition, here we focus on multiplication. We ask whether children’s formal learning of multiplication commutativity relies on pre-existing non-symbolic intuitions, or whether it is first learnt symbolically and only later elaborated as a general principle that extends to concrete situations. 2nd and 3rd graders received an intervention on commutative multiplication, before and after which they played a number comparison game testing their understanding of commutativity in symbolic and non-symbolic contexts. Our results suggest that the commutative principle of multiplication may not be available to intuition before formal teaching: perceiving that 2 groups of 3 dots and 3 groups of 2 dots contain the same number of dots requires mastering commutativity symbolically first.

Do school-age children learn that 2 x 3 = 3 x 2 relying on previous intuitions?

Infants are born into rich social networks and are faced with the challenge of learning about them. Previous research shows that infants learn about the individuals in observed social interactions, but it is not clear whether they infer their individual dispositions or their relationships to one another. The current studies address this question in 12-month-old infants and 16- to 18-month-old toddlers who observe social interactions involving imitation. In Studies 1 and 3, infants and toddlers expected that imitators, compared to non-imitators, would respond to their social partners’ distress. Likewise, they expected the targets of imitation, compared to non-targets, to respond to their partner’s distress. In Study 2, these expectations did not generalize to interactions with a new partner, providing evidence for learning about the original individuals’ relationship. In Study 3, infants’ expectations did not generalize to responses to laughter, suggesting a focus on established social relationships. Together, these results provide evidence that infants and toddlers learn about the social relationships of unknown individuals by observing interactions involving imitation.


Infants Infer Social Relationships between Individuals who Engage in Imitative Social Interactions

Can one person tell, just from observing how another person’s body moves, what that person is trying to learn? Here, we explore this question through several experiments on “epistemic action recognition”. We filmed volunteers playing a box-shaking game in which they attempted to determine either (a) the number of objects hidden in an opaque box, or (b) the shape of the objects hidden in the opaque box. Then, independent subjects watched these videos and were asked to determine which videos came from which task: Who was shaking for number and who was shaking for shape? Across 3 experiments, observers successfully determined what an actor was trying to learn about the contents of the box, based only on their observed actions (i.e., how they shook it). These results demonstrate that humans have the ability to infer epistemic intent from physical behaviors, adding a new dimension to research on action understanding.

What does learning look Like?: visual Recognition of epistemic intent

Successful social coordination requires being able to predict how the other people that one depends on are likely to behave. One solution to this dilemma is to establish social conventions, which constrain individuals' behavior but make prediction easier. Here, we develop a multi-agent deep reinforcement learning environment to investigate the costs associated with these conventions. In our produce-and-trade task, agents have varying production skills, but their actions must be predictable in order to be rewarded. Stronger norms improve the overall success of the group by improving the average rewards of the majority, but also systematically disadvantage agents whose specialization is in the minority of the group. Critically, this outcome is magnified by population size: as larger groups make it potentially more difficult to develop individualized representations of agents, minority agents become more likely to conform to a norm that is disadvantageous to them.


Unequal Norms Emerge Under Coordination Uncertainty in Multi-Agent Deep Reinforcement Learning

Humans are remarkably effective social learners, with several recent studies formalizing this capacity using computational models. 
However, previous research has often been limited to tasks where observer and demonstrator share the same reward function. In contrast, humans can learn from others who have different preferences, skills, or goals. To study social learning under individual differences, we introduce the socially correlated bandit, where participants have personalized rewards, which are correlated with but not identical to those of others. Social information can still be useful, but not when used verbatim.
We present a model of social generalization that integrates individual and social information into the generalization process, but assumes social information to be noisier and thus less informative. This model out-competes previous models, with it being the dominant strategy in evolutionary simulations.
Our findings expand on previous models of social learning, showing humans can integrate social information more flexibly than previously assumed.


Social learning with a grain of salt

Many cultures share common constellations and common narratives about the stars in the night sky. Previous research has shown that this overlap in asterisms, minimal star groupings inside constellations, is clearly present across 22 distinct culture groups and can be explained in part by properties of individual stars (brightness) and properties of pairs of stars (proximity) (Kemp, Hamacher, Little and Cropper, 2022). The same work, however, found no evidence that properties of triples (angle) and quadruples (good continuation) predicted constellation formation. We developed a behavioural experiment to explore how individuals form constellations under conditions that reduce cultural learning. We found that participants independently selected and connected similar stars, and that their responses were predicted by two properties of triples (angle and even spacing) in addition to the properties of brightness and proximity supported by previous work. Our findings lend further evidence to the theory that commonality of constellations across cultures is not a result of shared human history but rather stems from shared human nature.


Visual perception principles in constellation creation in individuals

Computational models of multiple object tracking (MOT) presuppose the existence of non-conceptual indexes in visual perception, and as a result predict that identification performance on MOT tasks should be no worse than tracking performance for the same stimuli. However, empirical evidence suggests that identification performance is worse than tracking performance in MOT. We propose a computational model of MOT that is able to account for several empirical results related to tracking performance without the use of indexes and thus avoids yoking tracking performance to identification performance. We also test our model empirically and show that it accounts well for the variation in identification performance with increasing number of targets in MOT with visually identical objects. By employing an explicit (rather than an implicit) mechanism for the maintenance of correspondence between attended locations and object identities, our model accounts for the tracking vs ID performance disparity.


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Human Relational Concept Learning on the Synthetic Visual Reasoning Test

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