Assimilation of exceptions? Examining representations of regular and exceptional category members across development.

Savic, Olivera; Sloutsky, Vladimir M.

doi:10.1037/xge0000611

Cited by 7 publications

(9 citation statements)

References 35 publications

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“…In line with these predictions, infants' ability to learn categories is found to be affected by the level of category coherence, with less coherent categories being more difficult (Gómez & Lakusta, 2004;Younger, 1990;Younger & Gotlieb, 1988). Moreover, young children demonstrate poorer memory for category exceptions than for typical category members (Savic & Sloutsky, 2019).…”

Section: Hippocampal Maturation and Development Of Categorization Abilitiesmentioning

confidence: 56%

A neural network model of hippocampal contributions to category learning

Sučević

Schapiro

2022

Preprint

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In addition to its critical role in encoding individual episodes, the hippocampus is capable of extracting regularities across experiences. This ability is central to category learning, and a growing literature indicates that the hippocampus indeed makes important contributions to this kind of learning. Using a neural network model that mirrors the anatomy of the hippocampus, we investigated the mechanisms by which the hippocampus may support novel category learning. We simulated three category learning paradigms and evaluated the network's ability to categorize and to recognize specific exemplars in each. We found that the trisynaptic pathway within the hippocampus-connecting entorhinal cortex to dentate gyrus, CA3, and CA1-was critical for remembering individual exemplars, reflecting the rapid binding and pattern separation functions of this circuit. The monosynaptic pathway from entorhinal cortex to CA1, in contrast, was responsible for detecting the regularities that define category structure, made possible by the use of distributed representations and a slower learning rate. Together, the simulations provide an account of how the hippocampus and its constituent pathways support novel category learning.

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Section: Hippocampal Maturation and Development Of Categorization Abilitiesmentioning

confidence: 56%

A neural network model of hippocampal contributions to category learning

Sučević

Schapiro

2022

Preprint

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“…Along these lines, Sloutsky and Fisher (2004) have shown that when making inferences about the pictures of animals, children were more oriented to items themselves, rather than their categories, remembering individual items better than adults when asked to think about the objects at the level of the category. Furthermore, children are equally sensitive to features that are diagnostic to category membership and those that are not, whereas adults prioritize diagnostic features when learning categories (Deng & Sloutsky, 2015; Savic & Sloutsky, 2019). In the current study, however, we did not find support for an orientation toward item‐level information in children in the domain of statistical learning.…”

Section: Discussionmentioning

confidence: 99%

Children automatically abstract categorical regularities during statistical learning

Jung

Bernhardt-Walther

Finn

2021

Developmental Science

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Statistical learning allows us to discover myriad structures in our environment, which is saturated with information at many different levels—from items to categories. How do children learn different levels of information—about regularities that pertain to items and the categories they come from—and how does this differ from adults? Studies on category learning and memory have suggested that children may be more focused on items than adults. If this is also the case for statistical learning, children may not extract and learn the multi‐level regularities that adults can. We report three experiments showing that children and adults extract both item‐ and category‐level regularities in statistical learning. In Experiments 1 and 2, we show that both children and adults can learn structure at the item and category levels when they are measured independently. In Experiment 3, we show that both children and adults learn about categories even when exposure does not require this: both are able to generalize their learning from the item to the category level. Results indicate that statistical learning operates across multi‐level structure in children and adults alike, enabling generalization of learning from specific items to exemplars from categories of those items that observers have never seen. Even though children may be more focused on items during other forms of learning, they learn about categories from item‐level input during statistical learning.

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“…The absence of an effect in the TR condition, however, seems more puzzling and could be related to the presence of atypical items in general or less effective reward learning, as it appeared that reward generalization was concurrently absent in the GCM group of the TR condition. However, given the exploratory nature of Study 3, and the ongoing debate regarding the special role of atypical items (or exceptions) in memory and their implications for the representation of categories (e.g., Erickson & Kruschke, 1998; Nosofsky et al, 1994; Poldrack et al, 2001; Poldrack & Foerde, 2008; Savic & Sloutsky, 2019; Schlegelmilch, Wills, & von Helversen, 2018), these explanations should be considered speculative, and further research is needed to disentangle the effects of typicality and item rewards.…”

Section: Discussionmentioning

confidence: 99%

“…This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Nosofsky et al, 1994;Poldrack et al, 2001;Poldrack & Foerde, 2008;Savic & Sloutsky, 2019;Schlegelmilch, Wills, & von Helversen, 2018), these explanations should be considered speculative, and further research is needed to disentangle the effects of typicality and item rewards.…”

Section: Boundary Conditions Rules and Exceptionsmentioning

confidence: 99%

The influence of reward magnitude on stimulus memory and stimulus generalization in categorization decisions.

Schlegelmilch

Helversen²

2020

Journal of Experimental Psychology: General

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Reward magnitude is a central concept in most theories of preferential decision making and learning. However, it is unknown whether variable rewards also influence cognitive processes when learning how to make accurate decisions (e.g., sorting healthy and unhealthy food differing in appeal). To test this, we conducted 3 studies. Participants learned to classify objects with 3 feature dimensions into two categories before solving a transfer task with novel objects. During learning, we rewarded all correct decisions, but specific category exemplars yielded a 10 times higher reward (high vs. low). Counterintuitively, categorization performance did not increase for high-reward stimuli, compared with an equal-reward baseline condition. Instead, performance decreased reliably for low-reward stimuli. To analyze the influence of reward magnitude on category generalization, we implemented an exemplar-categorization model and a cue-weighting model using a Bayesian modeling approach. We tested whether reward magnitude affects (a) the availability of exemplars in memory, (b) their psychological similarity to the stimulus, or (c) attention to stimulus features. In all studies, the evidence favored the hypothesis that reward magnitude affects the similarity gradients of high-reward exemplars compared with the equal-reward baseline. The results from additional reward-judgment tasks (Studies 2 and 3) strongly suggest that the cognitive processes of reward-value generalization parallel those of category generalization. Overall, the studies provide insights highlighting the need for integrating reward-and category-learning theories. AbstractReward magnitude is a central concept in most theories of preferential decision making and learning. However, it is unknown whether variable rewards also influence cognitive processes when learning how to make accurate decisions (e.g., sorting healthy and unhealthy food differing in appeal). To test this, we conducted three studies.Participants learned to classify objects with three feature dimensions into two categories before solving a transfer task with novel objects. During learning, we rewarded correct decisions, but specific category exemplars yielded a 10 times higher reward (high vs.low). Counterintuitively, categorization performance did not increase for high-reward stimuli, compared to an equal-reward baseline condition. Instead, performance decreased reliably for low-reward stimuli. To analyze the influence of reward magnitude on category generalization, we implemented an exemplar-categorization model and a cue-weighting model using a Bayesian modeling approach. We tested whether reward magnitude affects (a) the availability of exemplars in memory, (b) their psychological similarity to the stimulus, or (c) attention to stimulus features. In all studies, the evidence favored the hypothesis that reward magnitude affects the similarity gradients of high-reward exemplars compared to the equal-reward baseline. The results from additional reward-judgment tasks (Studies 2 and 3) strongly su...

show abstract

Assimilation of exceptions? Examining representations of regular and exceptional category members across development.

Cited by 7 publications

References 35 publications

A neural network model of hippocampal contributions to category learning

A neural network model of hippocampal contributions to category learning

Children automatically abstract categorical regularities during statistical learning

The influence of reward magnitude on stimulus memory and stimulus generalization in categorization decisions.

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