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1. Not All Procedural Learning Tasks Are Difficult for Adults With Developmental Language Disorder

   https://doi.org/10.1044/2020_JSLHR-20-00548  

   Gerken, LouAnn; Plante, Elena; Goffman, Lisa (March 2021, Journal of Speech, Language, and Hearing Research)

   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. 

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2. Limited but specific engagement of the mature language network during linguistic statistical learning

   https://doi.org/10.1093/cercor/bhae123  

   Schneider, Julie M; Scott, Terri L; Legault, Jennifer; Qi, Zhenghan (April 2024, Cerebral Cortex)

   Abstract Statistical learning (SL) is the ability to detect and learn regularities from input and is foundational to language acquisition. Despite the dominant role of SL as a theoretical construct for language development, there is a lack of direct evidence supporting the shared neural substrates underlying language processing and SL. It is also not clear whether the similarities, if any, are related to linguistic processing, or statistical regularities in general. The current study tests whether the brain regions involved in natural language processing are similarly recruited during auditory, linguistic SL. Twenty-two adults performed an auditory linguistic SL task, an auditory nonlinguistic SL task, and a passive story listening task as their neural activation was monitored. Within the language network, the left posterior temporal gyrus showed sensitivity to embedded speech regularities during auditory, linguistic SL, but not auditory, nonlinguistic SL. Using a multivoxel pattern similarity analysis, we uncovered similarities between the neural representation of auditory, linguistic SL, and language processing within the left posterior temporal gyrus. No other brain regions showed similarities between linguistic SL and language comprehension, suggesting that a shared neurocomputational process for auditory SL and natural language processing within the left posterior temporal gyrus is specific to linguistic stimuli. 

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3. Introducing desirable difficulty in engineering mathematics with spaced retrieval practice

   Bego, Campbell R.; Ralston, Patricia A.; Lyle, Keith B.; Immekus, Jason C. (July 2021, Proceedings of the 128th ASEE Annual Conference and Exposition)

   null (Ed.)

   This study examined the difficulty introduced by spaced retrieval practice in Calculus I for undergraduate engineering students. Spaced retrieval practice is an instructional technique in which students engage in multiple recall exercises on the same topic with intermittent temporal delays in between. Spacing out retrieval practice increases the difficulty of the exercises, reducing student performance on them. However, empirical research indicates that spaced retrieval practice is associated with improvements in students’ long-term memory for the retrieved information. The short-term costs and long-term benefits of spaced retrieval practice is an example of desirable difficulty, when more difficult exercises during the early stages of learning result in longer-lasting memory [1]. With support from the National Science Foundation (NSF), we sought to address: Does spacing decrease performance on retrieval practice exercises in an engineering mathematics course? Results showed that student performance was significantly lower for questions in the spaced condition than questions in the massed condition, indicating that we successfully increased the difficulty of the questions by spacing them out over time. Future work will assess final quiz performance to determine whether spacing improved long-term course performance, i.e., whether the difficulty imposed by spacing was desirable. 

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4. Predictive attractor models

   Mounir, Ramy; Sarkar, Sudeep (December 2024, Curran Associate Inc)

   Sequential memory, the ability to form and accurately recall a sequence of events or stimuli in the correct order, is a fundamental prerequisite for biological and artificial intelligence as it underpins numerous cognitive functions (e.g., language comprehension, planning, episodic memory formation, etc.) However, existing methods of sequential memory suffer from catastrophic forgetting, limited capacity, slow iterative learning procedures, low-order Markov memory, and, most importantly, the inability to represent and generate multiple valid future possibilities stemming from the same context. Inspired by biologically plausible neuroscience theories of cognition, we propose Predictive Attractor Models (PAM), a novel sequence memory architecture with desirable generative properties. PAM is a streaming model that learns a sequence in an online, continuous manner by observing each input only once. Additionally, we find that PAM avoids catastrophic forgetting by uniquely representing past context through lateral inhibition in cortical minicolumns, which prevents new memories from overwriting previously learned knowledge. PAM generates future predictions by sampling from a union set of predicted possibilities; this generative ability is realized through an attractor model trained alongside the predictor. We show that PAM is trained with local computations through Hebbian plasticity rules in a biologically plausible framework. Other desirable traits (e.g., noise tolerance, CPU-based learning, capacity scaling) are discussed throughout the paper. Our findings suggest that PAM represents a significant step forward in the pursuit of biologically plausible and computationally efficient sequential memory models, with broad implications for cognitive science and artificial intelligence research. Illustration videos and code are available on our project page: https://ramymounir.com/publications/pam. 

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5. Impaired and Spared Auditory Category Learning in Developmental Dyslexia

   https://doi.org/10.1177/09567976231151581  

   Gabay, Yafit; Roark, Casey L.; Holt, Lori L. (January 2023, Psychological Science)

   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. 

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