Individual Differences in Category Learning: Rule- Versus Exemplar-Based Strategies

2012 ◽  
Author(s):  
Jeri L. Little ◽  
Mark A. McDaniel ◽  
Michael J. Cahill
Keyword(s):  
Individual Differences ◽  
Category Learning ◽  
Learning Rule

scholarly journals Attention constraints and learning in categories

2020 ◽  
Author(s):  
Rahul Bhui ◽  
Peiran Jiao
Keyword(s):  
Individual Differences ◽  
Category Learning ◽  
Adaptive Response ◽  
Information Sources ◽  
Bayesian Theory ◽  
Time Constraints ◽  
Categorical Structure ◽  
Information Sampling ◽  
Bias Learning

When different stimuli belong to the same category, learning about their attributes should be guided by this categorical structure. Here, we demonstrate how an adaptive response to attention constraints can bias learning toward shared qualities and away from individual differences. In three preregistered experiments using an information sampling paradigm with mousetracking, we find that people preferentially attend to information at the category level when idiosyncratic variation is low, when time constraints are more severe, and when the category contains more members. While attention is more diffuse across all information sources than predicted by Bayesian theory, there are signs of convergence toward this optimal benchmark with experience. Our results thus indicate a novel way in which a focus on categories can be driven by rational principles.

scholarly journals Cognitive Changes in Conjunctive Rule-Based Category Learning: An ERP Approach

2017 ◽  
Author(s):  
Rahel Rabi ◽  
Marc F Joanisse ◽  
Tianshu Zhu ◽  
John Paul Minda
Keyword(s):  
Working Memory ◽  
Category Learning ◽  
Computational Modelling ◽  
Learning Rule ◽  
Event Related Potentials ◽  
Cognitive Changes ◽  
Rule Based ◽  
Differential Processing ◽  
Conjunctive Rule ◽  
Related Potentials

PreprintWhen learning rule-based categories, sufficient cognitive resources are needed to test hypotheses, maintain the currently active rule in working memory, update rules after feedback, and to select a new rule if necessary. Prior research has demonstrated that conjunctive rules are more complex than unidimensional rules and place greater demands on executive functions like working memory. In our study, event-related potentials (ERPs) were recorded while participants performed a conjunctive rule-based category learning task with trial-by-trial feedback. In line with prior research, correct categorization responses resulted in a larger stimulus-locked late positive complex compared to incorrect responses, possibly indexing the updating of rule information in memory. Incorrect trials elicited a pronounced feedback-locked P300 elicited which suggested a disconnect between perception, and the rule-based strategy. We also examined the differential processing of stimuli that were able to be correctly classified by the suboptimal single-dimensional rule (“easy” stimuli) versus those that could only be correctly classified by the optimal, conjunctive rule (“difficult” stimuli). Among strong learners, a larger, late positive slow wave emerged for difficult compared to easy stimuli, suggesting differential processing of category items even though strong learners performed well on the conjunctive category set. Overall, the findings suggest that ERP combined with computational modelling can be used to better understand the cognitive processes involved in rule-based category learning

scholarly journals Emerging native-similar neural representations underlie non-native speech category learning success

2021 ◽  
pp. 1-82
Author(s):  
Gangyi Feng ◽  
Yu Li ◽  
Shen-Mou Hsu ◽  
Patrick C.M. Wong ◽  
Tai-Li Chou ◽  
...  
Keyword(s):  
Individual Differences ◽  
Category Learning ◽  
Brain Regions ◽  
Neural Representations ◽  
Learning Success ◽  
Learning Speed ◽  
Learning Efficacy ◽  
Native Speech ◽  
Cost Efficient ◽  
Mandarin Tone

Abstract Learning non-native phonetic categories in adulthood is an exceptionally challenging task, characterized by large inter-individual differences in learning speed and outcomes. The neurobiological mechanisms underlying the inter-individual differences in the learning efficacy are not fully understood. Here we examine the extent to which training-induced neural representations of non-native Mandarin tone categories in English listeners (n = 53) are increasingly similar to those of the native listeners (n = 33) who acquired these categories early in infancy. We particularly assess whether the neural similarities in representational structure between non-native learners and native listeners are robust neuromarkers of inter-individual differences in learning success. Using inter-subject neural representational similarity (IS-NRS) analysis and predictive modeling on two functional magnetic resonance imaging (fMRI) datasets, we examined the neural representational mechanisms underlying speech category learning success. Learners’ neural representations that were significantly similar to the native listeners emerged in brain regions mediating speech perception following training; the extent of the emerging neural similarities with native listeners significantly predicted the learning speed and outcome in learners. The predictive power of IS-NRS outperformed models with other neural representational measures. Furthermore, neural representations underlying successful learning are multidimensional but cost-efficient in nature. The degree of the emergent native-similar neural representations was closely related to the robustness of neural sensitivity to feedback in the frontostriatal network. These findings provide important insights into the experience-dependent representational neuroplasticity underlying successful speech learning in adulthood and could be leveraged in designing individualized feedback-based training paradigms that maximize learning efficacy.

scholarly journals Working memory relates to individual differences in speech category learning: Insights from computational modeling and pupillometry

2021 ◽  
Vol 222 ◽  
pp. 105010
Author(s):  
Jacie R. McHaney ◽  
Rachel Tessmer ◽  
Casey L. Roark ◽  
Bharath Chandrasekaran
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Computational Modeling ◽  
Category Learning

Individual differences in category learning: Sometimes less working memory capacity is better than more

Cognition ◽  
2008 ◽  
Vol 107 (1) ◽  
pp. 284-294 ◽  
Author(s):  
Marci S. DeCaro ◽  
Robin D. Thomas ◽  
Sian L. Beilock
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Category Learning ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Better Than

Individual differences in category learning: Memorization versus rule abstraction

2014 ◽  
Vol 43 (2) ◽  
pp. 283-297 ◽  
Author(s):  
Jeri L. Little ◽  
Mark A. McDaniel
Keyword(s):  
Individual Differences ◽  
Category Learning ◽  
Rule Abstraction

scholarly journals Modeling individual differences using Dirichlet processes

2019 ◽  
Author(s):  
Danielle Navarro ◽  
Tom Griffiths ◽  
Mark Steyvers ◽  
Michael David Lee
Keyword(s):  
Individual Differences ◽  
Infinite Number ◽  
Category Learning ◽  
Dirichlet Process ◽  
Data Set ◽  
Fixed Set ◽  
Nonparametric Bayesian Statistics ◽  
The Individual ◽  
Browsing Behavior ◽  
Parameter Distributions

We introduce a Bayesian framework for modeling individual differences, in which subjects are assumed to belong to one of a potentially infinite number of groups. In this model, the groups observed in any particular data set are not viewed as a fixed set that fully explains the variation between individuals, but rather as representatives of a latent, arbitrarily rich structure. As more people are seen, and more details about the individual differences are revealed, the number of inferred groups is allowed to grow. We use the Dirichlet process—a distribution widely used in nonparametric Bayesian statistics—to define a prior for the model, allowing us to learn flexible parameter distributions without overfitting the data, or requiring the complex computations typically required for determining the dimensionality of a model. As an initial demonstration of the approach, we present three applications that analyze the individual differences in category learning, choice of publication outlets, and web browsing behavior.

The challenge of accounting for individual differences in folk-economic beliefs

2018 ◽  
Vol 41 ◽  
Author(s):  
Benjamin C. Ruisch ◽  
Rajen A. Anderson ◽  
David A. Pizarro
Keyword(s):  
Individual Differences ◽  
Individual Variation ◽  
Political Ideologies ◽  
Economic Beliefs ◽  
Existing Data

AbstractWe argue that existing data on folk-economic beliefs (FEBs) present challenges to Boyer & Petersen's model. Specifically, the widespread individual variation in endorsement of FEBs casts doubt on the claim that humans are evolutionarily predisposed towards particular economic beliefs. Additionally, the authors' model cannot account for the systematic covariance between certain FEBs, such as those observed in distinct political ideologies.

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