Simulating phonological and semantic impairment of English tense inflection with linear discriminative learning

This study applies the computational theory of the ‘discriminative lexicon’ (Baayen, Chuang, Blevins, 2019) to the modeling of the production of English verbs in aphasic speech. Under semantic impairment, speakers have been reported to have greater difficulties with irregular verbs, whereas speakers with phonological impairment are described as having greater problems with regulars. Joanisse and Seidenberg (1999) were able to model this dissociation, but only by adding noise to the semantic units of their model. We report two simulation studies in which topographically coherent regions of phonological and semantic networks were selectively damaged. Our model replicated the main findings, including the high variability in the consequences of brain lesions for speech production. Importantly, our model generated these results without having to lesion the semantic system more than the phonological system. The model’s success hinges on the use of a corpus-based distributional vector space for representing verbs’ meanings. Irregular verbs have denser semantic neighborhoods than do regular verbs (Baayen and Moscoso del Prado Martín, 2005). In our model this renders irregular verbs more fragile under semantic impairment. These results provide further support for the central idea underlying the discriminative lexicon: that behavioral patterns can, to a considerable extent, be understood as emerging from the distributional properties of a language and basic principles of human learning.

The neurophysiological architecture of semantic dementia: spectral dynamic causal modelling of a neurodegenerative proteinopathy

The selective destruction of large-scale brain networks by pathogenic protein spread is a ubiquitous theme in neurodegenerative disease. Characterising the circuit architecture of these diseases could illuminate both their pathophysiology and the computational architecture of the cognitive processes they target. However, this is challenging using standard neuroimaging techniques. Here we addressed this issue using a novel technique—spectral dynamic causal modelling—that estimates the effective connectivity between brain regions from resting-state fMRI data. We studied patients with semantic dementia—the paradigmatic disorder of the brain system mediating world knowledge—relative to healthy older individuals. We assessed how the effective connectivity of the semantic appraisal network targeted by this disease was modulated by pathogenic protein deposition and by two key phenotypic factors, semantic impairment and behavioural disinhibition. The presence of pathogenic protein in SD weakened the normal inhibitory self-coupling of network hubs in both antero-mesial temporal lobes, with development of an abnormal excitatory fronto-temporal projection in the left cerebral hemisphere. Semantic impairment and social disinhibition were linked to a similar but more extensive profile of abnormally attenuated inhibitory self-coupling within temporal lobe regions and excitatory projections between temporal and inferior frontal regions. Our findings demonstrate that population-level dynamic causal modelling can disclose a core pathophysiological feature of proteinopathic network architecture—attenuation of inhibitory connectivity—and the key elements of distributed neuronal processing that underwrite semantic memory.

Simulating phonological and semantic impairment of English tense inflection with linear discriminative learning

This study applies the computational theory of the ‘discriminative lexicon’ (Baayen et al., 2019) to the modeling of the production of regular and irregular English verbs in aphasic speech. Under impairment, speakers with memory loss have been reported to have greater difficulties with irregular verbs, whereas speakers with phonological impairment are described as having greater problems with regulars. Joanisse and Seidenberg (1999) were able to model this dissociation, but only by selectively adding noise to the semantic units of their model. We report two simulation studies in which topographically coherent regions of phonological and semantic networks were selectively damaged. Our model replicated the main findings, including the high variability in the consequences of brain lesions for speech production. Importantly, our model generated these results without having to lesion the semantic system more than the phonological system. The model’s success turns out to hinge on the use of a corpus-based distributional vector space for representing verbs’ meanings. Joanisse and Seidenberg (1999) used one-hot encoding for their semantic representation, under the assumption that semantically regular and irregular verbs do not differ in ways relevant to impairment in aphasia. However, irregular verbs have denser semantic neighborhoods than do regular verbs (Baayen and Moscoso del Prado Martín, 2005), and we show that in our model this greater density renders irregular verbs more fragile under semantic impairment. These results provide further support for the central idea underlying the discriminative lexicon: that behavioral patterns can, to a considerable extent, be understood as emerging from the distributional properties of a language and basic principles of human learning.

A unified neurocognitive model of the anterior temporal lobe contributions to semantics, language, social behaviour & face recognition

The anterior temporal lobes (ATL) have become a key brain region of interest in cognitive and clinical neuroscience. Contemporary explorations are founded upon neuropsychological investigations of semantic dementia (SD) that describe the patients’ selective semantic impairment and the variations in their language, behavioural and face recognition abilities. The purpose of this investigation was to generate a single unified model which captures the known cognitive-behavioural variations in SD, and integrates with the considerable database on healthy semantic function and other patient groups. A new analytical approach was able to capture the graded neuropsychological differences and map these to the patients’ distribution of frontotemporal atrophy. Multiple regression and principal component analyses confirmed that the degree of generalised semantic impairment was related to the patients’ total, bilateral ATL atrophy. Verbal production and word-finding abilities were related to total ATL atrophy as well as to the balance of left>right ATL atrophy. Behavioural apathy was found to relate positively to the degree of orbitofrontal atrophy and negatively to total temporal volumes. Disinhibited behaviour was related to right ATL and orbitofrontal atrophy and face recognition to right ATL volumes. Rather than positing mutually-exclusive sub-categories, the data-driven model repositions semantics, language, social behaviour and face recognition into a continuous frontotemporal neurocognitive space.

History of Studying Semantic Aphasia Mechanisms (Based on Materials from Lurian Archive)

The scientific heritage of Alexander Luria is vast and not completely studied. His scientific archive includes a range of documents that academics have never encountered. We analyzed the history of Luria’s research of semantic aphasia, specifically as it is related to the impairment of visuospatial processing. For this purpose, we investigated unpublished archive materials dating back to 1928–1940, particularly the monograph ‘Parietal Aphasia’ from 1940. The literature regarding the history of the issue and neurophysiological and linguistic interpretations of semantic aphasia is discussed in detail by Luria. We also introduce Luria’s diagnostic manualfor the semantic impairment of language and describe the syndrome structure of semantic aphasia. To conclude, we discuss the specificity of semantic aphasia analysis in the pre-war monograph compared to later studies. Keywords: semantic aphasia, visuospatial processing, temporal-parietal-occipital association area, A.R. Luria, neuropsychology, neurolinguistics, history of psychology