Rule-based Category Learning Research Articles

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.

Nameability supports rule-based category learning in children and adults.

The present study tested the hypothesis that verbal labels support category induction by providing compact hypotheses. Ninety-seven 4- to 6-year-old children (M = 63.2 months; 46 female, 51 male; 77% White, 8% more than one race, 4% Asian, and 3% Black; tested 2018) and 90 adults (M = 20.1 years; 70 female, 20 male) in the Midwestern United States learned novel categories with features that were easy (e.g., "red") or difficult (e.g., "mauve") to name. Adults (d = 1.06) and-to a lesser extent-children (d = 0.57; final training block) learned categories composed of more nameable features better. Children's knowledge of difficult-to-name color words predicted their learning for categories with difficult-to-name features. Rule-based category learning may be supported by the emerging ability to form verbal hypotheses.

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.

Effects of category learning strategies on recognition memory

Extant research has shown that previously acquired categorical knowledge affects recognition memory, and that differences in category learning strategies impact classification accuracy. However, it is unknown whether different learning strategies also have downstream effects on subsequent recognition memory. The present study investigates the effect of two unidimensional rule-based category learning strategies - learning (a) with or without explicit instruction, and (b) with or without supervision - on subsequent recognition memory. Our findings suggest that acquiring categorical knowledge increased both hits (Experiments 1 and 2) and false-alarms (Experiment 1) for category-congruent items regardless of the particular strategy employed in initially learning these categories. There were, however, small processing speed advantages during recognition memory for both explicit instruction and supervised practice relative to neither (Experiment 2). We discuss these findings in the context of how prior knowledge influences recognition memory, and in relation to similar findings showing schematic effects on episodic memory.

The basal ganglia corticostriatal loops and conditional learning.

Brief maneuvering of the literature as to the various roles attributed to the basal ganglia corticostriatal circuits in a variety of cognitive processes such as working memory, selective attention, and category learning has inspired us to investigate the interplay of the two major basal ganglia open-recurrent loops, namely, visual and executive loops specifically the possible involvement of their overlap in conditional learning. We propose that the interaction of the visual and executive loops reflected through their cortical overlap in the dorsolateral prefrontal cortex (DL-PFC), lateral orbitofrontal cortex (LO-PFC), and presupplementary motor area (SMA) plays an instrumental role preliminary first in forming associations between a series of correct responses following similar stimuli and then in shifting, abstracting, and generalizing conditioned responses. The premotor and supplementary motor areas have been shown essential to producing a sequence of movements while the SMA is engaged in monitoring complex movements. In light of the recent studies, we will suggest that the interaction of visual and executive loops could strengthen or weaken learned associations following different reward values. Furthermore, we speculate that the overlap of the visual and executive loops can account for the switching between the associative vs. rule-based category learning systems.

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.

Novel representations that support rule-based categorization are acquired on-the-fly during category learning.

Humans learn categorization rules that are aligned with separable dimensions through a rule-based learning system, which makes learning faster and easier to generalize than categorization rules that require integration of information from different dimensions. Recent research suggests that learning to categorize objects along a completely novel dimension changes its perceptual representation, making it more separable and discriminable. Here, we asked whether such newly learned dimensions could support rule-based category learning. One group received extensive categorization training and a second group did not receive such training. Later, both groups were trained in a task that made use of the category-relevant dimension, and then tested in an analogical transfer task (Experiment 1) and a button-switch interference task (Experiment 2). We expected that only the group with extensive pre-training (with well-learned dimensional representations) would show evidence of rule-based behavior in these tasks. Surprisingly, both groups performed as expected from rule-based learning. A third experiment tested whether a single session (less than 1 h) of training in a categorization task would facilitate learning in a task requiring executive function. There was a substantial learning advantage for a group with brief pre-training with the relevant dimension. We hypothesize that extensive experience with separable dimensions is not required for rule-based category learning; rather, the rule-based system may learn representations "on the fly" that allow rule application. We discuss what kind of neurocomputational model might explain these data best.

Cognitive changes in conjunctive rule-based category learning: An ERP approach.

When 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 with 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.

Familiarization may minimize age-related declines in rule-based category learning.

Being able to categorize promotes cognitive economy by reducing the amount of information that an individual needs to remember. This ability is particularly important in older adulthood, when executive functioning abilities are known to decline. Prior research has shown that older adults can learn simple categories quite well but struggle when learning more complex categories which place a demand on executive function resources. The goal of Experiments 1 to 3 was to assess whether familiarizing older adults with complex rule-based or non-rule-based categories prior to beginning a categorization task would minimize age-related categorization deficits. Both rule-based and non-rule-based category learning improved among older adults following pretraining, but the improvements to rule-based learning were more drastic, suggesting that executive functioning plays a heavier role in rule-based category learning. Findings provide a potential solution for improving the category learning abilities of older adults. (PsycINFO Database Record

The evaluative role of rostrolateral prefrontal cortex in rule-based category learning

Category learning is a critical neurobiological function that allows organisms to simplify a complex world. Rostrolateral prefrontal cortex (rlPFC) is often active in neurobiological studies of category learning; however, the specific role this region serves in category learning remains uncertain. Previous category learning studies have hypothesized that the rlPFC is involved in switching between rules, whereas others have emphasized rule abstraction and evaluation. We aimed to clarify the role of rlPFC in category learning and dissociate switching and evaluation accounts using two common types of category learning tasks: matching and classification. The matching task involved matching a reference stimulus to one of four target stimuli. In the classification task, participants were shown a single stimulus and learned to classify it into one of two categories. Matching and classification are similar but place different demands on switching and evaluation. In matching, a rule can be known with certainty after a single correct answer. In classification, participants may need to evaluate evidence for a rule even after an initial correct response. This critical difference allows isolation of evaluative functions from switching functions. If the rlPFC is primarily involved in switching between representations, it should cease to be active once participants settle on a given rule in both tasks. If the rlPFC is involved in rule evaluation, its activation should persist in the classification task, but not matching. The results revealed that rlPFC activation persisted into correct trials in classification, but not matching, suggesting that it continues to be involved in the evaluations of evidence for a rule even after participants have arrived at the correct rule.

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making.

Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks.

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.

Transfer in Rule-Based Category Learning Depends on the Training Task.

While learning is often highly specific to the exact stimuli and tasks used during training, there are cases where training results in learning that generalizes more broadly. It has been previously argued that the degree of specificity can be predicted based upon the learning solution(s) dictated by the particular demands of the training task. Here we applied this logic in the domain of rule-based categorization learning. Participants were presented with stimuli corresponding to four different categories and were asked to perform either a category discrimination task (which permits learning specific rule to discriminate two categories) or a category identification task (which does not permit learning a specific discrimination rule). In a subsequent transfer stage, all participants were asked to discriminate stimuli belonging to two of the categories which they had seen, but had never directly discriminated before (i.e., this particular discrimination was omitted from training). As predicted, learning in the category-discrimination tasks tended to be specific, while the category-identification task produced learning that transferred to the transfer discrimination task. These results suggest that the discrimination and identification tasks fostered the acquisition of different category representations which were more or less generalizable.

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

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

Dopaminergic Genetic Polymorphisms Predict Rule-based Category Learning.

Dopaminergic genes play an important role in cognitive function. DRD2 and DARPP-32 dopamine receptor gene polymorphisms affect striatal dopamine binding potential, and the Val158Met single-nucleotide polymorphism of the COMT gene moderates dopamine availability in the pFC. Our study assesses the role of these gene polymorphisms on performance in two rule-based category learning tasks. Participants completed unidimensional and conjunctive rule-based tasks. In the unidimensional task, a rule along a single stimulus dimension can be used to distinguish category members. In contrast, a conjunctive rule utilizes a combination of two dimensions to distinguish category members. DRD2 C957T TT homozygotes outperformed C allele carriers on both tasks, and DARPP-32 AA homozygotes outperformed G allele carriers on both tasks. However, we found an interaction between COMT and task type where Met allele carriers outperformed Val homozygotes in the conjunctive rule task, but both groups performed equally well in the unidimensional task. Thus, striatal dopamine binding may play a critical role in both types of rule-based tasks, whereas prefrontal dopamine binding is important for learning more complex conjunctive rule tasks. Modeling results suggest that striatal dopaminergic genes influence selective attention processes whereas cortical genes mediate the ability to update complex rule representations.

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).

Category learning in older adulthood: A study of the Shepard, Hovland, and Jenkins (1961) tasks.

Shepard, Hovland, and Jenkins (1961) examined the categorization abilities of younger adults using tasks involving single-dimensional rule learning, disjunctive rule learning, and family resemblance learning. The current study examined category learning in older adults using this well-known category set. Older adults, like younger adults, found category tasks with a single relevant dimension the easiest to learn. In contrast to younger adults, older adults found complex disjunctive rule-based categories harder to learn than family resemblance based categories. Disjunctive rule-based category learning appeared to be the most difficult for older adults to learn because this category set placed the heaviest demands on working memory, which is known to be a cognitive function that declines with normal aging. The authors discuss why complex rule-based category learning is considered more difficult for older adults to learn relative to younger adults, drawing parallels to developmental research.

The role of age and executive function in auditory category learning

Auditory categorization is a natural and adaptive process that allows for the organization of high-dimensional, continuous acoustic information into discrete representations. Studies in the visual domain have identified a rule-based learning system that learns and reasons via a hypothesis-testing process that requires working memory and executive attention. The rule-based learning system in vision shows a protracted development, reflecting the influence of maturing prefrontal function on visual categorization. The aim of the current study was twofold: (a) to examine the developmental trajectory of rule-based auditory category learning from childhood through adolescence and into early adulthood and (b) to examine the extent to which individual differences in rule-based category learning relate to individual differences in executive function. A sample of 60 participants with normal hearing—20 children (age range=7–12years), 21 adolescents (age range=13–19years), and 19 young adults (age range=20–23years)—learned to categorize novel dynamic “ripple” sounds using trial-by-trial feedback. The spectrotemporally modulated ripple sounds are considered the auditory equivalent of the well-studied “Gabor” patches in the visual domain. Results reveal that auditory categorization accuracy improved with age, with young adults outperforming children and adolescents. Computational modeling analyses indicated that the use of the task-optimal strategy (i.e., a conjunctive rule-based learning strategy) improved with age. Notably, individual differences in executive flexibility significantly predicted auditory category learning success. The current findings demonstrate a protracted development of rule-based auditory categorization. The results further suggest that executive flexibility coupled with perceptual processes play important roles in successful rule-based auditory category learning.

Compensatory processing during rule-based category learning in older adults

ABSTRACTHealthy older adults typically perform worse than younger adults at rule-based category learning, but better than patients with Alzheimer’s or Parkinson’s disease. To further investigate aging’s effect on rule-based category learning, we monitored event-related potentials (ERPs) while younger and neuropsychologically typical older adults performed a visual category-learning task with a rule-based category structure and trial-by-trial feedback. Using these procedures, we previously identified ERPs sensitive to categorization strategy and accuracy in young participants. In addition, previous studies have demonstrated the importance of neural processing in the prefrontal cortex and the medial temporal lobe for this task. In this study, older adults showed lower accuracy and longer response times than younger adults, but there were two distinct subgroups of older adults. One subgroup showed near-chance performance throughout the procedure, never categorizing accurately. The other subgroup reached asymptotic accuracy that was equivalent to that in younger adults, although they categorized more slowly. These two subgroups were further distinguished via ERPs. Consistent with the compensation theory of cognitive aging, older adults who successfully learned showed larger frontal ERPs when compared with younger adults. Recruitment of prefrontal resources may have improved performance while slowing response times. Additionally, correlations of feedback-locked P300 amplitudes with category-learning accuracy differentiated successful younger and older adults. Overall, the results suggest that the ability to adapt one’s behavior in response to feedback during learning varies across older individuals, and that the failure of some to adapt their behavior may reflect inadequate engagement of prefrontal cortex.