Multiple Systems Of Category Learning Research Articles

Impaired and Spared Auditory Category Learning in Developmental Dyslexia

Categorization has a deep impact on behavior, but whether category learning is served by a single system or multiple systems remains debated. Here, we designed two well-equated nonspeech auditory category learning challenges to draw on putative procedural (information-integration) versus declarative (rule-based) learning systems among adult Hebrew-speaking control participants and individuals with dyslexia, a language disorder that has been linked to a selective disruption in the procedural memory system and in which phonological deficits are ubiquitous. We observed impaired information-integration category learning and spared rule-based category learning in the dyslexia group compared with the neurotypical group. Quantitative model-based analyses revealed reduced use of, and slower shifting to, optimal procedural-based strategies in dyslexia with hypothesis-testing strategy use on par with control participants. The dissociation is consistent with multiple category learning systems and points to the possibility that procedural learning inefficiencies across categories defined by complex, multidimensional exemplars may result in difficulty in phonetic category acquisition in dyslexia.

Conceptual anchoring dissociates implicit and explicit category learning.

Categorization researchers have long debated the possibility of multiple category-learning systems. The need persists for paradigms that dissociate explicit-declarative category-learning processes (featuring verbalizable category rules) from implicit-procedural processes (featuring stimulus-response associations lying beneath declarative cognition). The authors contribute a new paradigm, using perfectly matched exclusive-or (XOR) category tasks differing only in the availability or absence of easily verbalizable conceptual content. This manipulation transformed learning. The conceptual task alone was learned suddenly, by insightful rule discovery, producing explicit-declarative XOR knowledge. The perceptual task was learned more gradually, consistent with associative-learning processes, producing impoverished declarative knowledge. We also tested participants under regimens of immediate and deferred reinforcement. The conceptual task alone was learned through processes that survive the loss of trial-by-trial reinforcement. All results support the idea that humans have perceptual-associative processes for implicit learning, but also an overlain conceptual system that under the right circumstances constitutes a parallel explicit-declarative category-learning system. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The effect of feedback delay on perceptual category learning and item memory: Further limits of multiple systems.

Delayed feedback during categorization training has been hypothesized to differentially affect 2 systems that underlie learning for rule-based (RB) or information-integration (II) structures. We tested an alternative possibility: that II learning requires more precise item representations than RB learning, and so is harmed more by a delay interval filled with a confusable mask. Experiments 1 and 2 examined the effect of feedback delay on memory for RB and II exemplars, both without and with concurrent categorization training. Without the training, II items were indeed more difficult to recognize than RB items, but there was no detectable effect of delay on item memory. In contrast, with concurrent categorization training, there were effects of both category structure and delayed feedback on item memory, which were related to corresponding changes in category learning. However, we did not observe the critical selective impact of delay on II classification performance that has been shown previously. Our own results were also confirmed in a follow-up study (Experiment 3) involving only categorization training. The selective influence of feedback delay on II learning appears to be contingent on the relative size of subgroups of high-performing participants, and in fact does not support that RB and II category learning are qualitatively different. We conclude that a key part of successfully solving perceptual categorization problems is developing more precise item representations, which can be impaired by delayed feedback during training. More important, the evidence for multiple systems of category learning is even weaker than previously proposed. (PsycINFO Database Record

Practice and Preparation Time Facilitate System-Switching in Perceptual Categorization.

Mounting evidence suggests that category learning is achieved using different psychological and biological systems. While existing multiple-system theories and models of categorization may disagree about the number or nature of the different systems, all assume that people can switch between systems seamlessly. However, little empirical data has been collected to test this assumption, and recent available data suggest that system-switching is difficult. The main goal of this article is to identify factors influencing the proportion of participants who successfully learn to switch between procedural and declarative systems on a trial-by-trial basis. Specifically, we tested the effects of preparation time and practice, two factors that have been useful in task-switching, in a system-switching experiment. The results suggest that practice and preparation time can be beneficial to system-switching (as calculated by a higher proportion of switchers and lower switch costs), especially when they are jointly present. However, this improved system-switching comes at the cost of a larger button-switch interference when changing the location of the response buttons. The article concludes with a discussion of the implications of these findings for empirical research on system-switching and theoretical work on multiple-systems of category learning.

Examining the relationship between language and learning: Insights from the serial reaction time task

Examining the relationship between language and learning: Insights from the serial reaction time task

One Giant Leap for Categorizers: One Small Step for Categorization Theory.

We explore humans’ rule-based category learning using analytic approaches that highlight their psychological transitions during learning. These approaches confirm that humans show qualitatively sudden psychological transitions during rule learning. These transitions contribute to the theoretical literature contrasting single vs. multiple category-learning systems, because they seem to reveal a distinctive learning process of explicit rule discovery. A complete psychology of categorization must describe this learning process, too. Yet extensive formal-modeling analyses confirm that a wide range of current (gradient-descent) models cannot reproduce these transitions, including influential rule-based models (e.g., COVIS) and exemplar models (e.g., ALCOVE). It is an important theoretical conclusion that existing models cannot explain humans’ rule-based category learning. The problem these models have is the incremental algorithm by which learning is simulated. Humans descend no gradient in rule-based tasks. Very different formal-modeling systems will be required to explain humans’ psychology in these tasks. An important next step will be to build a new generation of models that can do so.

Deferred Feedback Sharply Dissociates Implicit and Explicit Category Learning

The controversy over multiple category-learning systems is reminiscent of the controversy over multiple memory systems. Researchers continue to seek paradigms to sharply dissociate explicit category-learning processes (featuring category rules that can be verbalized) from implicit category-learning processes (featuring learned stimulus-response associations that lie outside declarative cognition). We contribute a new dissociative paradigm, adapting the technique of deferred-rearranged reinforcement from comparative psychology. Participants learned matched category tasks that had either a one-dimensional, rule-based solution or a multidimensional, information-integration solution. They received feedback either immediately or after each block of trials, with the feedback organized such that positive outcomes were grouped and negative outcomes were grouped (deferred-rearranged reinforcement). Deferred reinforcement qualitatively eliminated implicit, information-integration category learning. It left intact explicit, rule-based category learning. Moreover, implicit-category learners facing deferred-rearranged reinforcement turned by default and information-processing necessity to rule-based strategies that poorly suited their nominal category task. The results represent one of the strongest explicit-implicit dissociations yet seen in the categorization literature.

Differential impact of relevant and irrelevant dimension primes on rule-based and information-integration category learning

Research has identified multiple category-learning systems with each being “tuned” for learning categories with different task demands and each governed by different neurobiological systems. Rule-based (RB) classification involves testing verbalizable rules for category membership while information-integration (II) classification requires the implicit learning of stimulus–response mappings. In the first study to directly test rule priming with RB and II category learning, we investigated the influence of the availability of information presented at the beginning of the task. Participants viewed lines that varied in length, orientation, and position on the screen, and were primed to focus on stimulus dimensions that were relevant or irrelevant to the correct classification rule. In Experiment 1, we used an RB category structure, and in Experiment 2, we used an II category structure. Accuracy and model-based analyses suggested that a focus on relevant dimensions improves RB task performance later in learning while a focus on an irrelevant dimension improves II task performance early in learning.

The cost of selective attention in category learning: Developmental differences between adults and infants

Selective attention plays an important role in category learning. However, immaturities of top-down attentional control during infancy coupled with successful category learning suggest that early category learning is achieved without attending selectively. Research presented here examines this possibility by focusing on category learning in infants (6–8months old) and adults. Participants were trained on a novel visual category. Halfway through the experiment, unbeknownst to participants, the to-be-learned category switched to another category, where previously relevant features became irrelevant and previously irrelevant features became relevant. If participants attend selectively to the relevant features of the first category, they should incur a cost of selective attention immediately after the unknown category switch. Results revealed that adults demonstrated a cost, as evidenced by a decrease in accuracy and response time on test trials as well as a decrease in visual attention to newly relevant features. In contrast, infants did not demonstrate a similar cost of selective attention as adults despite evidence of learning both to-be-learned categories. Findings are discussed as supporting multiple systems of category learning and as suggesting that learning mechanisms engaged by adults may be different from those engaged by infants.

Instance memorization and category influence: Challenging the evidence for multiple systems in category learning

A class of dual-system theories of categorization assumes a categorization system based on actively formed prototypes in addition to a separate instance memory system. It has been suggested that, because they have used poorly differentiated category structures (such as the influential "5-4" structure), studies supporting the alternative exemplar theory reveal little about the properties of the categorization system. Dual-system theories assume that the instance memory system only influences categorization behaviour via similarity to single isolated instances, without generalization across instances. However, we present the results of two experiments employing the 5-4 structure to argue against this. Experiment 1 contrasted learning in the standard 5-4 structure with learning in an even more poorly differentiated 5-4 structure. In Experiment 2, participants memorized the 5-4 structure based on a five minute simultaneous presentation of all nine category instances. Both experiments revealed category influences as reflected by differences in instance learnability and generalization, at variance with the dual-system prediction. These results have implications for the exemplars versus prototypes debate and the nature of human categorization mechanisms.

Posterror slowing predicts rule-based but not information-integration category learning

We examined whether error monitoring, operationalized as the degree to which individuals slow down after committing an error (i.e., posterror slowing), is differentially important in the learning of rule-based versus information-integration category structures. Rule-based categories are most efficiently solved through the application of an explicit verbal strategy (e.g., "sort by color"). In contrast, information-integration categories are believed to be learned in a trial-by-trial, associative manner. Our results indicated that posterror slowing predicts enhanced rule-based but not information-integration category learning. Implications for multiple category-learning systems are discussed.

Human category learning 2.0

During the 1990s and early 2000s, cognitive neuroscience investigations of human category learning focused on the primary goal of showing that humans have multiple category-learning systems and on the secondary goals of identifying key qualitative properties of each system and of roughly mapping out the neural networks that mediate each system. Many researchers now accept the strength of the evidence supporting multiple systems, and as a result, during the past few years, work has begun on the second generation of research questions-that is, on questions that begin with the assumption that humans have multiple category-learning systems. This article reviews much of this second generation of research. Topics covered include (1) How do the various systems interact? (2) Are there different neural systems for categorization and category representation? (3) How does automaticity develop in each system? and (4) Exactly how does each system learn?

Single versus multiple systems of category learning: Reply to Nosofsky and Kruschke (2002)

Nosofsky and Kruschke (2002) argued that the singlesystem ALCOVE model (Kruschke, 1992) can account for the dual-task category learning data reported by Waldron and Ashby (2001). In our reply, we argue that Nosofsky and Kruschke overstated the ability of ALCOVE to account for the Waldron and Ashby results. In fact, ALCOVE has difficulty with these data, and we show that the only versions of ALCOVE that actually fit the Waldron and Ashby accuracy data make incorrect predictions about other previously unreported features of that experiment. We also show that the dual-system COVIS model (Ashby, Alfonso-Reese, Turken, & Waldron, 1998) naturally predicts these results.

A neuropsychological theory of multiple systems in category learning.

A neuropsychological theory is proposed that assumes category learning is a competition between separate verbal and implicit (i.e., procedural-learning-based) categorization systems. The theory assumes that the caudate nucleus is an important component of the implicit system and that the anterior cingulate and prefrontal cortices are critical to the verbal system. In addition to making predictions for normal human adults, the theory makes specific predictions for children, elderly people, and patients suffering from Parkinson's disease, Huntington's disease, major depression, amnesia, or lesions of the prefrontal cortex. Two separate formal descriptions of the theory are also provided. One describes trial-by-trial learning, and the other describes global dynamics. The theory is tested on published neuropsychological data and on category learning data with normal adults.

A neuropsychological theory of multiple systems in category learning.

A neuropsychological theory of multiple systems in category learning.