Abstract representations in temporal cortex support generative linguistic processing

Gow, David W.; Avcu, Enes; Schoenhaut, Adriana; Sorensen, David O.; Ahlfors, Seppo P.

doi:10.1080/23273798.2022.2157029

Cited by 4 publications

(2 citation statements)

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“…Posterior MTG and adjacent areas have been specifically implicated in wordform representations that mediate the mapping between sound and meaning (Gow, 2012;Hickok & Poeppel, 2007). Imaging studies have shown that activation in these posterior temporal regions is influenced by wordform properties such as word frequency, lexical neighborhood size, lexical enhancement/suppression, phonological similarity, and word-level structural properties (Biran & Friedmann, 2005;Gow et al, 2022;Graves et al, 2007;Prabhakaran et al, 2006;Righi et al, 2009). In addition, damage to posterior temporal regions has been shown to produce deficits in lexicosemantic processing (Axer et al, 2001;Coslett et al, 1987;Goldstein, 1948;Wernicke, 1969).…”

Section: Discussionmentioning

confidence: 99%

“…Effective connectivity analyses follow our previously published Granger causality analysis approach (Gow & Caplan, 2012), with modifications to integrate the results of the decoding analysis as described in Gow et al (2022). The goal of the modified Granger causality analyses was to identify whether the activation time courses in ROIs that showed decoding could predict (or "Granger cause") the SVM classifier accuracy time courses in other ROIs.…”

Section: Effective Connectivity Analysesmentioning

confidence: 99%

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Neural representation of phonological wordform in bilateral posterior temporal cortex

Sorensen

Avcu

Lynch

et al. 2023

Preprint

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While the neural bases of the earliest stages of speech categorization have been widely explored using neural decoding methods, there is still a lack of consensus on questions as basic as how wordforms are represented and in what way this word-level representation influences downstream processing in the brain. Isolating and localizing the neural representations of wordform is challenging because spoken words evoke activation of a variety of representations (e.g., segmental, semantic, articulatory) in addition to form-based representations. We addressed these challenges through a novel integrated neural decoding and effective connectivity design using region of interest (ROI)-based, source reconstructed magnetoencephalography/electroencephalography (MEG/EEG) data collected during a lexical decision task. To localize wordform representations, we trained classifiers on words and nonwords from different phonological neighborhoods and then tested the classifiers' ability to discriminate between untrained target words that overlapped phonologically with the trained items. Training with either word or nonword neighbors supported decoding in many brain regions during an early analysis window (100-400 ms) reflecting primarily incremental phonological processing. Training with word neighbors, but not nonword neighbors, supported decoding in a bilateral set of temporal lobe ROIs, in a later time window (400-600 ms) reflecting activation related to word recognition. These ROIs included bilateral posterior temporal regions implicated in wordform representation. Effective connectivity analyses among regions within this subset indicated that word-evoked activity influenced the decoding accuracy more than nonword-evoked activity did. Taken together, these results evidence functional representation of wordforms in bilateral temporal lobes isolated from phonemic or semantic representations.

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Section: Discussionmentioning

confidence: 99%

Section: Effective Connectivity Analysesmentioning

confidence: 99%

Neural representation of phonological wordform in bilateral posterior temporal cortex

Sorensen

Avcu

Lynch

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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The developmental cognitive mechanism of learning algebraic rules from the dual‐process theory perspective

Xiao,

Liang,

Sun

et al. 2024

PsyCh Journal

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Rule learning is an important ability that enables human beings to adapt to nature and develop civilizations. There have been many discussions on the mechanism and characteristics of algebraic rule learning, but there are still controversies due to the lack of theoretical guidance. Based on the dual‐process theory, this study discussed the following arguments for algebraic rule learning across human and animal studies: whether algebraic rule learning is simply Type 1 processing, whether algebraic rule learning is a domain‐general ability, whether algebraic rule learning is shared by humans and animals, and whether an algebraic rule is learned consciously. Moreover, we propose that algebraic rule learning is possibly a cognitive process that combines both Type 1 and Type 2 processing. Further exploration is required to establish the essence and neural basis of algebraic rule learning.

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Exploring Abstract Pattern Representation in The Brain and Non-symbolic Neural Networks

Avcu,

Gow

2023

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Human cognitive and linguistic generativity depends on the ability to identify abstract relationships between perceptually dissimilar items. Marcus et al. (1999) found that human infants can rapidly discover and generalize patterns of syllable repetition (reduplication) that depend on the abstract property of identity, but simple recurrent neural networks (SRNs) could not. They interpreted these results as evidence that purely associative neural network models provide an inadequate framework for characterizing the fundamental generativity of human cognition. Here, we present a series of deep long short-term memory (LSTM) models that identify abstract syllable repetition patterns and words based on training with cochleagrams that represent auditory stimuli. We demonstrate that models trained to identify individual syllable trigram words and models trained to identify reduplication patterns discover representations that support classification of abstract repetition patterns. Simulations examined the effects of training categories (words vs. patterns) and pretraining to identify syllables, on the development of hidden node representations that support repetition pattern discrimination. Representational similarity analyses (RSA) comparing patterns of regional brain activity based on MRI-constrained MEG/EEG data to patterns of hidden node activation elicited by the same stimuli showed a significant correlation between brain activity localized in primarily posterior temporal regions and representations discovered by the models. These results suggest that associative mechanisms operating over discoverable representations that capture abstract stimulus properties account for a critical example of human cognitive generativity.

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Abstract representations in temporal cortex support generative linguistic processing

Cited by 4 publications

References 64 publications

Neural representation of phonological wordform in bilateral posterior temporal cortex

Neural representation of phonological wordform in bilateral posterior temporal cortex

The developmental cognitive mechanism of learning algebraic rules from the dual‐process theory perspective

Exploring Abstract Pattern Representation in The Brain and Non-symbolic Neural Networks

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