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The ability to generalize previous knowledge to new contexts is a key aspect of human cognition and relational learning. A well-known learning maxim is that breadth of training predicts breadth of transfer. When examples vary in their surface features, this provides evidence that only the common relational structure is relevant. However, there is some evidence suggesting that the above maxim may not apply well in early relational learning. Here, we present a further test whether the maxim holds for young infants. We find that 3-month-old infants perform better with a narrow, perceptually similar training set than with a broad, perceptually variable set. We argue that lower-level perceptual similarities can prompt comparison processes that facilitate relational abstraction. These findings cohere with research arguing that without alignment, relational learning cannot occur.

CogSci 2023

The Less is More Paradox in Relational Learning

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.

Previous work has demonstrated that relational preference in the Relational Match-to-Sample task can be improved compared to baseline by providing people with the opportunity to consider the target item in isolation prior to receiving the full triad. However, it remains unclear whether the benefits of these supports persist in their absence, or can be observed when a prior strategy has already been established to complete the task. To this end, we conducted two experiments using 2 (presentation-type) by 2 (order) mixed designs to examine the efficacy of two previously established task supports: isolated-focus and description. The aims of this work were to gain further insight into the utility of these supports as a means of promoting relational preference both when the supports are present and when they are absent. We discuss the implications for pedagogical practices and extensions of this work to other materials and tasks.


Sustaining Relational Preference in a Repeated Relational Match-to-Sample Task in the Absence of Task Support

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.


Human Relational Concept Learning on the Synthetic Visual Reasoning Test

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.


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The Less is More Paradox in Relational Learning

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Sustaining Relational Preference in a Repeated Relational Match-to-Sample Task in the Absence of Task Support

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