Efficient learning in children with rapid GABA boosting during and after training.

It is generally thought that children learn more efficiently than adults. One way to accomplish this is to have learning rapidly stabilized such that it is not interfered with by subsequent learning. Although γ-aminobutyric acid (GABA) plays an important role in stabilization, it has been reported that GABAergic inhibitory processing is not fully matured yet in children compared with adults. Does this finding indicate that more efficient learning in children is not due to more rapid stabilization? Here, we measured the concentration of GABA in early visual cortical areas in a time-resolved fashion before, during, and after visual perceptual learning (VPL) within subjects using functional MRS (fMRS) and then compared the concentrations between children (8 to 11 years old) and adults (18 to 35 years old). We found that children exhibited a rapid boost of GABA during visual training that persisted after training ended, whereas the concentration of GABA in adults remained unchanged. Moreover, behavioral experiments showed that children exhibited rapid development of resilience to retrograde interference, which indicates that children stabilize VPL much faster than adults. These results together suggest that inhibitory processing in children's brains is more dynamic and adapts more quickly to stabilize learning than in adults, making learning more efficient in children.

Linear separability, irrelevant variability, and categorization difficulty.

In rule-based (RB) category-learning tasks, the optimal strategy is a simple explicit rule, whereas in information-integration (II) tasks, the optimal strategy is impossible to describe verbally. This study investigates the effects of two different category properties on learning difficulty in category learning tasks-namely, linear separability and variability on stimulus dimensions that are irrelevant to the categorization decision. Previous research had reported that linearly separable II categories are easier to learn than nonlinearly separable categories, but Experiment 1, which compared performance on linearly and nonlinearly separable categories that were equated as closely as possible on all other factors that might affect difficulty, found that linear separability had no effect on learning. Experiments 1 and 2 together also established a novel dissociation between RB and II category learning: increasing variability on irrelevant stimulus dimensions impaired II learning but not RB learning. These results are all predicted by the best available measures of difficulty in RB and II tasks. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

When instructions don't help: Knowing the optimal strategy facilitates rule-based but not information-integration category learning.

Providing verbal or written instructions on how to perform optimally in a task is one of the most common ways to teach beginners. This practice is so widely accepted that scholarship primarily focuses on how to provide instructions, not whether these instructions help or not. Here we investigate the benefits of prior instruction on rule-based (RB) category-learning, in which the optimal strategy is a simple explicit rule, and information-integration (II) category-learning, in which the optimal strategy is similarity-based. Participants (N = 58) learned either RB or II categories, with or without verbal and written instruction about the optimal categorization strategy. Instructions significantly improved performance with RB categories but had no effect with II categories. The theoretical and practical implication of these results is discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Dissociations between rule-based and information-integration categorization are not caused by differences in task difficulty.

In rule-based (RB) category-learning tasks, the optimal strategy is a simple explicit rule, whereas in information-integration (II) tasks, the optimal strategy is impossible to describe verbally. Many studies have reported qualitative dissociations between training and performance in RB and II tasks. Virtually all of these studies were testing predictions of the dual-systems model of category learning called COVIS. The most prominent alternative account to COVIS is that humans have one learning system that is used in all tasks, and that the observed dissociations occur because the II task is more difficult than the RB task. This article describes the first attempt to test this difficulty hypothesis against anything more than a single set of data. First, two novel predictions are derived that discriminate between the difficulty and multiple-systems hypotheses. Next, these predictions are tested against a wide variety of published categorization data. Overall, the results overwhelmingly reject the difficulty hypothesis and instead strongly favor the multiple-systems account of the many RB versus II dissociations.

A difficulty predictor for perceptual category learning.

Predicting human performance in perceptual categorization tasks in which category membership is determined by similarity has been historically difficult. This article proposes a novel biologically motivated difficulty measure that can be generalized across stimulus types and category structures. The new measure is compared to 12 previously proposed measures on four extensive data sets that each included multiple conditions that varied in difficulty. The studies were highly diverse and included experiments with both continuous- and binary-valued stimulus dimensions, a variety of different stimulus types, and both linearly and nonlinearly separable categories. Across these four applications, the new measure was the most successful at predicting the observed rank ordering of conditions by difficulty, and it was also the most accurate at predicting the numerical values of the mean error rates in each condition.

Retinal-specific category learning.

Virtually all cognitive theories of category learning (such as prototype theory1-5 and exemplar theory6-8) view this important skill as a high-level process that uses abstract representations of objects in the world. Because these representations are removed from visual characteristics of the display, such theories suggest that category learning occurs in higher-level (such as association) areas and therefore should be immune to the visual field dependencies that characterize processing of objects mediated by representations in low-level visual areas. Here we challenge that view by describing a fully controlled demonstration of visual-field dependence in category learning. Eye-tracking was used to control gaze while participants either learned rule-based categories known to recruit prefrontal-based explicit reasoning, or information-integration categories known to depend on basal-ganglia-mediated procedural learning9. Results showed that learning was visual-field dependent with information-integration categories, but we found no evidence of visual-field dependence with rule-based categories. A theoretical interpretation of this difference is offered in terms of the underlying neurobiology. Finally, these results are situated within the broad perceptual-learning literature in an attempt to motivate further research on the similarities and differences between category and perceptual learning.