skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: 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.  more » « less
Award ID(s):
1655126
PAR ID:
10405742
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Psychological Science
ISSN:
0956-7976
Page Range / eLocation ID:
095679762311515
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Models of human categorization predict the prefrontal cortex (PFC) serves a central role in category learning. The dorsolateral prefrontal cortex (dlPFC) and ventromedial prefrontal cortex (vmPFC) have been implicated in categorization; however, it is unclear whether both are critical for categorization and whether they support unique functions. We administered three categorization tasks to patients with PFC lesions (mean age, 69.6 years; 5 men, 5 women) to examine how the prefrontal subregions contribute to categorization. These included a rule-based (RB) task that was solved via a unidimensional rule, an information integration (II) task that was solved by combining information from two stimulus dimensions, and a deterministic/probabilistic (DP) task with stimulus features that had varying amounts of category-predictive information. Compared with healthy comparison participants, both patient groups had impaired performance. Impairments in the dlPFC patients were largest during the RB task, whereas impairments in the vmPFC patients were largest during the DP task. A hierarchical model was fit to the participants’ data to assess learning deficits in the patient groups. PFC damage was correlated with a regularization term that limited updates to attention after each trial. Our results suggest that the PFC, as a whole, is important for learning to orient attention to relevant stimulus information. The dlPFC may be especially important for rule-based learning, whereas the vmPFC may be important for focusing attention on deterministic (highly diagnostic) features and ignoring less predictive features. These results support overarching functions of the dlPFC in executive functioning and the vmPFC in value-based decision-making. 
    more » « less
  2. Abstract Numerous studies have found that selective attention affects category learning. However, previous research did not distinguish between the contribution of focusing and filtering components of selective attention. This study addresses this issue by examining how components of selective attention affect category representation. Participants first learned a rule‐plus‐similarity category structure, and then were presented with category priming followed by categorization and recognition tests. Additionally, to evaluate the involvement of focusing and filtering, we fit models with different attentional mechanisms to the data. In Experiment 1, participants received rule‐based category training, with specific emphasis on a single deterministic feature (D feature). Experiment 2 added a recognition test to examine participants’ memory for features. Both experiments indicated that participants categorized items based solely on the D feature, showed greater memory for the D feature, were primed exclusively by the D feature without interference from probabilistic features (P features), and were better fit by models with focusing and at least one type of filtering mechanism. The results indicated that selective attention distorted category representation by highlighting the D feature and attenuating P features. To examine whether the distorted representation was specific to rule‐based training, Experiment 3 introduced training, emphasizing all features. Under such training, participants were no longer primed by the D feature, they remembered all features well, and they were better fit by the model assuming only focusing but no filtering process. The results coupled with modeling provide novel evidence that while both focusing and filtering contribute to category representation, filtering can also result in representational distortion. 
    more » « less
  3. null (Ed.)
    Purpose The experiment reported here compared two hypotheses for the poor statistical and artificial grammar learning often seen in children and adults with developmental language disorder (DLD; also known as specific language impairment). The procedural learning deficit hypothesis states that implicit learning of rule-based input is impaired, whereas the sequential pattern learning deficit hypothesis states that poor performance is only seen when learners must implicitly compute sequential dependencies. The current experiment tested learning of an artificial grammar that could be learned via feature activation, as observed in an associatively organized lexicon, without computing sequential dependencies and should therefore be learnable on the sequential pattern learning deficit hypothesis, but not on the procedural learning deficit hypothesis. Method Adults with DLD and adults with typical language development (TD) listened to consonant–vowel–consonant–vowel familiarization words from one of two artificial phonological grammars: Family Resemblance (two out of three features) and a control (exclusive OR, in which both consonants are voiced OR both consonants are voiceless) grammar in which no learning was predicted for either group. At test, all participants rated 32 test words as to whether or not they conformed to the pattern in the familiarization words. Results Adults with DLD and adults with TD showed equal and robust learning of the Family Resemblance grammar, accepting significantly more conforming than nonconforming test items. Both groups who were familiarized with the Family Resemblance grammar also outperformed those who were familiarized with the OR grammar, which, as predicted, was learned by neither group. Conclusion Although adults and children with DLD often underperform, compared to their peers with TD, on statistical and artificial grammar learning tasks, poor performance appears to be tied to the implicit computation of sequential dependencies, as predicted by the sequential pattern learning deficit hypothesis. 
    more » « less
  4. Abstract Dyslexia and dysgraphia are two specific learning disabilities (SLDs) that are prevalent among children. To minimize the negative impact these SLDs have on a child’s academic and social-emotional development, it is crucial to identify dyslexia and dysgraphia at an early age, enabling timely and effective intervention. The first step in this process is screening, which helps determine if a child requires further instruction or a more in-depth assessment. Current screening tools are expensive, require additional administration time beyond regular classroom activities, and are designed to screen exclusively for one condition, not for both dyslexia and dysgraphia, which often share some common behavioral characteristics. Most dyslexia screeners focus on speech and oral tasks and exclude writing activities. However, analyzing children’s writing samples for behavioral signs of dyslexia and dysgraphia can offer valuable insights into the screening process, which can be time-consuming. As a solution, we propose a co-designed framework for building artificial intelligence (AI) tools that could boost the efficiency of screening and aid practitioners such as speech-language pathologists (SLPs), occupational therapists, general educators, and special educators by simplifying their tasks. This paper reviews current screening methods employed by practitioners, the use of AI-based systems in identifying dyslexia and dysgraphia, and the handwriting datasets available to train such systems. The paper also outlines a framework for developing an AI-integrated screening tool that can identify writing-based behavioral indicators of dyslexia and dysgraphia in children’s handwriting. This framework can be used in conjunction with current screening tools like the Dysgraphia and Dyslexia Behavioral Indicator Checklist (DDBIC). The paper also proposes a methodology for collecting children’s offline and online handwriting samples to build a valuable dataset for developing AI solutions. The proposed framework and data collection methodology are co-designed with SLPs, occupational therapists (OTs), special educators, and general educators to ensure the tool can provide explainable, actionable information that would be invaluable in a practical setting. 
    more » « less
  5. Abstract ADHD has been associated with cortico-striatal dysfunction that may lead to procedural memory abnormalities. Sleep plays a critical role in consolidating procedural memories, and sleep problems are an integral part of the psychopathology of ADHD. This raises the possibility that altered sleep processes characterizing those with ADHD could contribute to their skill-learning impairments. On this basis, the present study tested the hypothesis that young adults with ADHD have altered sleep-dependent procedural memory consolidation. Participants with ADHD and neurotypicals were trained on a visual discrimination task that has been shown to benefit from sleep. Half of the participants were tested after a 12-h break that included nocturnal sleep (sleep condition), whereas the other half were tested after a 12-h daytime break that did not include sleep (wakefulness condition) to assess the specific contribution of sleep to improvement in task performance. Despite having a similar degree of initial learning, participants with ADHD did not improve in the visual discrimination task following a sleep interval compared to neurotypicals, while they were on par with neurotypicals during the wakefulness condition. These findings represent the first demonstration of a failure in sleep-dependent consolidation of procedural learning in young adults with ADHD. Such a failure is likely to disrupt automatic control routines that are normally provided by the non-declarative memory system, thereby increasing the load on attentional resources of individuals with ADHD. 
    more » « less