Information-integration Category Learning Research Articles

The spacing effect in remote information-integration category learning.

The present experiments examined whether the temporal distribution of procedural category learning experiences would impact learning outcomes. Participants completed the remote category learning experiments on a smartphone in one of two learning conditions: massed or distributed. Consistent with expectations, distributed learners in both experiments reached higher accuracy levels than massed learners. In Experiment 1 the effect disappeared after accounting for reaction time differences, suggesting that it was driven by attentional mechanisms. In Experiment 2, the spacing advantage was only present for previously studied items during a post-learning test, suggesting a role of consolidation. In both experiments, it seems likely that temporal spacing helped participants discover the optimal information-integration categorization strategy. These results suggest that adult category learning is facilitated by temporal spacing. Future work may further explore the effects of temporal and contextual distinctiveness of learning experiences on category learning outcomes.

Auditory Category Learning in Children With Dyslexia.

Developmental dyslexia is proposed to involve selective procedural memory deficits with intact declarative memory. Recent research in the domain of category learning has demonstrated that adults with dyslexia have selective deficits in Information-Integration (II) category learning that is proposed to rely on procedural learning mechanisms and unaffected Rule-Based (RB) category learning that is proposed to rely on declarative, hypothesis testing mechanisms. Importantly, learning mechanisms also change across development, with distinct developmental trajectories in both procedural and declarative learning mechanisms. It is unclear how dyslexia in childhood should influence auditory category learning, a critical skill for speech perception and reading development. We examined auditory category learning performance and strategies in 7- to 12-year-old children with dyslexia (n = 25; nine females, 16 males) and typically developing controls (n = 25; 13 females, 12 males). Participants learned nonspeech auditory categories of spectrotemporal ripples that could be optimally learned with either RB selective attention to the temporal modulation dimension or procedural integration of information across spectral and temporal dimensions. We statistically compared performance using mixed-model analyses of variance and identified strategies using decision-bound computational models. We found that children with dyslexia have an apparent selective RB category learning deficit, rather than a selective II learning deficit observed in prior work in adults with dyslexia. These results suggest that the important skill of auditory category learning is impacted in children with dyslexia and throughout development, individuals with dyslexia may develop compensatory strategies that preserve declarative learning while developing difficulties in procedural learning. https://doi.org/10.23641/asha.25148519.

Procedural auditory category learning is selectively disrupted in developmental language disorder.

Speech communication depends on accurate perception and identification of speech sounds, which vary across talkers and word or sentence contexts. The ability to map this variable input onto discrete speech sound representations relies on categorization. Recent research and theoretical models implicate the procedural learning system in the ability to learn novel speech and non-speech categories. This connection is particularly intriguing because several language disorders that demonstrate linguistic impairments are proposed to stem from procedural learning and memory dysfunction. One such disorder, Developmental Language Disorder (DLD), affects 7.5% of children and persists into adulthood. While DLD is associated with general linguistic impairments, it is not yet clear how fundamental perceptual and cognitive processes supporting language are impacted, such as the ability to learn novel auditory categories. We examined auditory category learning in children with DLD and typically developed (TD) children using two well-matched nonspeech auditory category learning challenges to draw upon presumed procedural (information-integration) versus declarative (rule-based) learning systems. We observed impaired information-integration category learning and intact rule-based category learning in the DLD group. Quantitative model-based analyses revealed reduced use of, and slower shifting to, optimal procedural-based strategies in DLD and slower shifting to but similarly efficient use of optimal hypothesis-testing strategies. The dissociation is consistent with the Procedural Deficit Hypothesis of language disorders and supports the theoretical distinction of multiple category learning systems. These findings demonstrate that highly controlled experimental tasks assessing perceptual and cognitive abilities can relate to real-world challenges facing individuals with DLD in forming stable linguistic representations.

Stable, flexible, common, and distinct behaviors support rule-based and information-integration category learning

The ability to organize variable sensory signals into discrete categories is a fundamental process in human cognition thought to underlie many real-world learning problems. Decades of research suggests that two learning systems may support category learning and that categories with different distributional structures (rule-based, information-integration) optimally rely on different learning systems. However, it remains unclear how the same individual learns these different categories and whether the behaviors that support learning success are common or distinct across different categories. In two experiments, we investigate learning and develop a taxonomy of learning behaviors to investigate which behaviors are stable or flexible as the same individual learns rule-based and information-integration categories and which behaviors are common or distinct to learning success for these different types of categories. We found that some learning behaviors are stable in an individual across category learning tasks (learning success, strategy consistency), while others are flexibly task-modulated (learning speed, strategy, stability). Further, success in rule-based and information-integration category learning was supported by both common (faster learning speeds, higher working memory ability) and distinct factors (learning strategies, strategy consistency). Overall, these results demonstrate that even with highly similar categories and identical training tasks, individuals dynamically adjust some behaviors to fit the task and success in learning different kinds of categories is supported by both common and distinct factors. These results illustrate a need for theoretical perspectives of category learning to include nuances of behavior at the level of an individual learner.

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.

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).

Differing effects of gain and loss feedback on rule-based and information-integration category learning.

Although most category-learning studies use feedback for training, little attention has been paid to how individuals use feedback value and framing of feedback as gains or losses to support learning. We compared learning of rule-based (RB) and information-integration (II) categories with point-valued feedback in which participants gained or lost higher point values for more difficult category members (those closer to the decision bound). We implemented point-valued feedback in four different conditions: Gain (earn points for correct answers), Loss (lose points for incorrect answers), Gain+Loss (earn points for correct answers and lose points for incorrect answers), and Control (accuracy feedback only without point gain or loss). Participants were trained until they reached criterion. Overall, point-valued feedback led to better learning than control conditions. However, the patterns differed across category-learning tasks. In the II task participants reached learning criterion fastest when they received both Gains and Losses. This is consistent with the reliance of II learning on reinforcement-based mechanisms and research showing common coding of gains and losses in neural regions underlying II learning. In contrast, in the RB task, participants reached criterion most rapidly when they received either Gains or Losses, but not both Gains and Losses together. The relative impairment in the Gain+Loss condition in RB learning may be due to conflicting executive function demands for interpreting and using the separate Gain and Loss information, and is consistent with reliance of RB learning on explicit hypothesis-testing mechanisms.

Left Inferior Frontal Gyrus Integrates Multisensory Information in Category Learning.

Humans are able to categorize things they encounter in the world (e.g., a cat) by integrating multisensory information from the auditory and visual modalities with ease and speed. However, how the brain learns multisensory categories remains elusive. The present study used functional magnetic resonance imaging to investigate, for the first time, the neural mechanisms underpinning multisensory information-integration (II) category learning. A sensory-modality-general network, including the left insula, right inferior frontal gyrus (IFG), supplementary motor area, left precentral gyrus, bilateral parietal cortex, and right caudate and globus pallidus, was recruited for II categorization, regardless of whether the information came from a single modality or from multiple modalities. Putamen activity was higher in correct categorization than incorrect categorization. Critically, the left IFG and left body and tail of the caudate were activated in multisensory II categorization but not in unisensory II categorization, which suggests this network plays a specific role in integrating multisensory information during category learning. The present results extend our understanding of the role of the left IFG in multisensory processing from the linguistic domain to a broader role in audiovisual learning.

Auditory information-integration category learning in young children and adults

Adults outperform children on category learning that requires selective attention to individual dimensions (rule-based categories) due to their more highly developed working memory abilities, but much less is known about developmental differences in learning categories that require integration across multiple dimensions (information-integration categories). The current study investigated auditory information-integration category learning in 5- to 7-year-old children (n = 34) and 18- to 25-year-old adults (n = 35). Adults generally outperformed children during learning. However, some children learned the categories well and used strategies similar to those of adults, as assessed through decision-bound computational models. The results demonstrate that information-integration learning ability continues to develop throughout at least middle childhood. These results have implications for the development of mechanisms that contribute to speech category learning.

Dissociable learning processes, associative theory, and testimonial reviews: A comment on Smith and Church (2018).

Smith and Church (Psychonomic Bulletin & Review, 25, 1565-1584 2018) present a "testimonial" review of dissociable learning processes in comparative and cognitive psychology, by which we mean they include only the portion of the available evidence that is consistent with their conclusions. For example, they conclude that learning the information-integration category-learning task with immediate feedback is implicit, but do not consider the evidence that people readily report explicit strategies in this task, nor that this task can be accommodated by accounts that make no distinction between implicit and explicit processes. They also consider some of the neuroscience relating to information-integration category learning, but do not report those aspects that are more consistent with an explicit than an implicit account. They further conclude that delay conditioning in humans is implicit, but do not report evidence that delay conditioning requires awareness; nor do they present the evidence that conditioned taste aversion, which should be explicit under their account, can be implicit. We agree with Smith and Church that it is helpful to have a clear definition of associative theory, but suggest that their definition may be unnecessarily restrictive. We propose an alternative definition of associative theory and briefly describe an experimental procedure that we think may better distinguish between associative and non-associative processes.

The role of differential outcomes-based feedback on procedural memory.

In categorization tasks, two memory systems may be involved in the learning of categories: one explicit and rule-based system and another implicit and procedure-based system. Learning of rule-based categories relies on some form of explicit reasoning, whereas procedural memory underlies information-integration category-learning tasks, in which performance is maximized only if information of two (or more) dimensions is integrated. The present study aimed at investigating the role of how feedback is administered, whether differential or non-differential, in procedural learning. An information-integration category-learning task was designed, where the to-be-categorized stimuli differed in two dimensions. Participants were randomly assigned to two groups: one group received the reinforcers for correct categorizations differentially, one for each category (the differential outcomes procedure, DOP), and the other group received the reinforcers randomly (the non-differential outcomes procedure, NOP). The participants of the DOP group showed better procedural learning in the categorization task, compared to the NOP group. Moreover, the analysis of learning strategies revealed that more participants developed more optimal strategies in the DOP group than in the NOP group. These results extend the benefits of the differential outcomes-based feedback to non-declarative memory tasks and help better understand the role of feedback in procedural learning.

One-back reinforcement dissociates implicit-procedural and explicit-declarative category learning.

The debate over unitary/multiple category-learning utilities is reminiscent of debates about multiple memory systems and unitary/dual codes in knowledge representation. In categorization, researchers continue to seek paradigms to dissociate explicit learning processes (yielding verbalizable rules) from implicit learning processes (yielding stimulus-response associations that remain outside awareness). We introduce a new dissociation here. Participants learned matched category tasks with a multidimensional, information-integration solution or a one-dimensional, rule-based solution. They received reinforcement immediately (0-Back reinforcement) or after one intervening trial (1-Back reinforcement). Lagged reinforcement eliminated implicit, information-integration category learning but preserved explicit, rule-based learning. Moreover, information-integration learners facing lagged reinforcement spontaneously adopted explicit rule strategies that poorly suited their task. The results represent a strong process dissociation in categorization, broadening the range of empirical techniques for testing the multiple-process theoretical perspective. This and related methods that disable associative learning-fostering a transition to explicit-declarative cognition-could have broad utility in comparative, cognitive, and developmental science.

Differential Impact of Visuospatial Working Memory on Rule-based and Information-integration Category Learning.

Previous studies have indicated that the category learning system is a mechanism with multiple processing systems, and that working memory has different effects on category learning. But how does visuospatial working memory affect perceptual category learning? As there is no definite answer to this question, we conducted three experiments. In Experiment 1, the dual-task paradigm with sequential presentation was adopted to investigate the influence of visuospatial working memory on rule-based and information-integration category learning. The results showed that visuospatial working memory interferes with rule-based but not information-integration category learning. In Experiment 2, the dual-task paradigm with simultaneous presentation was used, in which the categorization task was integrated into the visuospatial working memory task. The results indicated that visuospatial working memory affects information-integration category learning but not rule-based category learning. In Experiment 3, the dual-task paradigm with simultaneous presentation was employed, in which visuospatial working memory was integrated into the category learning task. The results revealed that visuospatial working memory interferes with both rule-based and information-integration category learning. Through these three experiments, we found that, regarding the rule-based category learning, working memory load is the main mechanism by which visuospatial working memory influences the discovery of the category rules. In addition, regarding the information-integration category learning, visual resources mainly operates on the category representation.

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

Observing Prefrontal Cortex Activity During Rule-Based and Information-Integration Category Learning

Observing Prefrontal Cortex Activity During Rule-Based and Information-Integration Category Learning

Trial-by-trial identification of categorization strategy using iterative decision-bound modeling.

Identifying the strategy that participants use in laboratory experiments is crucial in interpreting the results of behavioral experiments. This article introduces a new modeling procedure called iterative decision-bound modeling (iDBM), which iteratively fits decision-bound models to the trial-by-trial responses generated from single participants in perceptual categorization experiments. The goals of iDBM are to identify: (1) all response strategies used by a participant, (2) changes in response strategy, and (3) the trial number at which each change occurs. The new method is validated by testing its ability to identify the response strategies used in noisy simulated data. The benchmark simulation results show that iDBM is able to detect and identify strategy switches during an experiment and accurately estimate the trial number at which the strategy change occurs in low to moderate noise conditions. The new method is then used to reanalyze data from Ell and Ashby (2006). Applying iDBM revealed that increasing category overlap in an information-integration category learning task increased the proportion of participants who abandoned explicit rules, and reduced the number of training trials needed to abandon rules in favor of a procedural strategy. Finally, we discuss new research questions made possible through iDBM.

Optimal sequencing during category learning: Testing a dual-learning systems perspective

Recent studies demonstrate that interleaving the exemplars of different categories, rather than blocking exemplars by category, can enhance inductive learning—the ability to categorize new exemplars—presumably because interleaving affords discriminative contrasts between exemplars from different categories. Consistent with this view, other studies have demonstrated that decreasing between-category similarity and increasing within-category variability can eliminate or even reverse the interleaving benefit. We tested another hypothesis, one based on the dual-learning systems framework—namely, that the optimal schedule for learning categories should depend on an interaction of the cognitive system that mediates learning and the structure of the particular category being learned. Blocking should enhance rule-based category learning, which is mediated by explicit, hypothesis-testing processes, whereas interleaving should enhance information-integration category learning, which is mediated by an implicit, procedural-based learning system. Consistent with this view, we found a crossover interaction between schedule (blocked vs. interleaved) and category structure (rule-based vs. information-integration).

A Comparison of the neural correlates that underlie rule‐based and information‐integration category learning

The influential competition between verbal and implicit systems (COVIS) model proposes that category learning is driven by two competing neural systems-an explicit, verbal, system, and a procedural-based, implicit, system. In the current fMRI study, participants learned either a conjunctive, rule-based (RB), category structure that is believed to engage the explicit system, or an information-integration category structure that is thought to preferentially recruit the implicit system. The RB and information-integration category structures were matched for participant error rate, the number of relevant stimulus dimensions, and category separation. Under these conditions, considerable overlap in brain activation, including the prefrontal cortex, basal ganglia, and the hippocampus, was found between the RB and information-integration category structures. Contrary to the predictions of COVIS, the medial temporal lobes and in particular the hippocampus, key regions for explicit memory, were found to be more active in the information-integration condition than in the RB condition. No regions were more activated in RB than information-integration category learning. The implications of these results for theories of category learning are discussed. Hum Brain Mapp 37:3557-3574, 2016. © 2016 Wiley Periodicals, Inc.

The role of feedback contingency in perceptual category learning.

Feedback is highly contingent on behavior if it eventually becomes easy to predict, and weakly contingent on behavior if it remains difficult or impossible to predict even after learning is complete. Many studies have demonstrated that humans and nonhuman animals are highly sensitive to feedback contingency, but no known studies have examined how feedback contingency affects category learning, and current theories assign little or no importance to this variable. Two experiments examined the effects of contingency degradation on rule-based and information-integration category learning. In rule-based tasks, optimal accuracy is possible with a simple explicit rule, whereas optimal accuracy in information-integration tasks requires integrating information from 2 or more incommensurable perceptual dimensions. In both experiments, participants each learned rule-based or information-integration categories under either high or low levels of feedback contingency. The exact same stimuli were used in all 4 conditions, and optimal accuracy was identical in every condition. Learning was good in both high-contingency conditions, but most participants showed little or no evidence of learning in either low-contingency condition. Possible causes of these effects, as well as their theoretical implications, are discussed. (PsycINFO Database Record

Commentary: “Poverty impedes cognitive function” and “The poor's poor mental power”

Commentary: “Poverty impedes cognitive function” and “The poor's poor mental power”