Influence of language on concept formation and perception in a brain-constrained deep neural network model

Whether language influences perception and thought remains a subject of intense debate (1, 2). We address this question in a brain-constrained neurocomputational model (3) of fronto-occipital (extrasylvian) and fronto-temporal (perisylvian) cortex including spiking neurons. The unsupervised neural network was simultaneously presented with word forms (phonological patterns, “labels”) in perisylvian areas and semantic grounding information (sensory-motor patterns, “percepts”) in extrasylvian areas representing either concrete or abstract concepts. Following the approach used in a previous simulation (4), each to-be-learned concept was modeled as a triplet of partly overlapping percepts; the models were trained under two conditions: each instance of a perceptual triplet (patterns in extrasylvian areas) was repeatedly paired with patterns in perisylvian areas consisting of either (a) a corresponding word form (label condition), or (b) noise (no-label condition). We quantified the emergence of neuronal representations for the conceptually-related percepts using dissimilarity (Euclidean distance) of neuronal activation vectors during perceptual stimulation. Category learning was measured as the difference between within- and between concept dissimilarity values (DissimDiff) of perceptual activation patterns. A repeated-measures ANOVA with factors SemanticType (concrete/abstract) and Labelling showed main effects of both SemanticType and Label, and a significant interaction. We also quantified the “label effect” in percentage change from NoLabel to Label conditions, separately for between- and within-category dissimilarities. This showed that the label effect was mainly driven by changes in between-category dissimilarity, was significantly larger for abstract than concrete concepts, and became even larger in the “deeper” layers of the model. Providing a referential verbal label during the acquisition of a new concept significantly improves the cortex’ ability to develop distinct semantic-category representations from partly-overlapping (and non-overlapping) perceptual instances. Crucially, this effect is most pronounced in higher order semantic-hub areas of the network. These results provide the first neurocomputational evidence for a “Whorfian” effect of language on perception and concept formation

Influence of verbal labels on concept formation and perception in a deep unsupervised neural network model

OBJECTIVES/RESEARCH QUESTION: Whether language influences perception and thought remains a subject of intense debate. Would the presence or absence of a linguistic label facilitate or hinder the acquisition of new concepts? We here address this question in a neurocomputational model. METHODS: We used a computational brain model of fronto-occipital (extrasylvian) and fronto-temporal (perisylvian) cortex including spiking neurons. With Hebbian learning, the network was trained to associate word forms (phonological patterns, or “labels”) in perisylvian areas with semantic grounding information (sensory-motor patterns, or “percepts”) in extrasylvian areas. To study the effects of labels on the network’s ability to spontaneously develop distinct semantic representations from the multiple perceptual instances of a concept, we modelled each to-be-learned concept as a triplet of partly overlapping percepts and trained the model under two conditions: each instance of a perceptual triplet (patterns in extrasylvian areas) was repeatedly paired with patterns in perisylvian areas consisting of either (1) a corresponding word form (label condition), or (2) white noise (no-label condition). To quantify the emergence of neuronal representations for the conceptually-related percepts, we measured the dissimilarity (Euclidean distance) of neuronal activation vectors during perceptual stimulation. Category learning performance was measured as the difference between within- and between-concept dissimilarity values (DissimDiff) of perceptual activation patterns. RESULTS: The presence or absence of a linguistic label had a significant main effect on category learning (F=2476, p<0.0001, DissimDiff with labels m=0.92, SD=0.32; no-labels m=0.36, SD=0.21). DissimDiff values were also significantly larger in areas most important for semantic processing, so-called semantic-hubs, than in sensorimotor areas (main effect of centrality, F=2535, p<0.0001). Finally, a significant interaction between centrality and label type (F=711, p<0.0001) revealed that the label-related learning advantage was most pronounced in semantic hubs. CONCLUSION: These results suggest that providing a referential verbal label during the acquisition of a new concept significantly improves the cortex’ ability to develop distinct semantic-category representations from partly-overlapping (and non-overlapping) perceptual instances. Crucially, this effect is most pronounced in higher-order semantic-hub areas of the network. In sum, our results provide the first neurocomputational evidence for a “Whorfian” effect of language on perception and concept formation

Neurocomputational Consequences of Evolutionary Connectivity Changes in Perisylvian Language Cortex

The human brain sets itself apart from that of its primate relatives by specific neuroanatomical features, especially the strong linkage of left perisylvian language areas (frontal and temporal cortex) by way of the arcuate fasciculus (AF). AF connectivity has been shown to correlate with verbal working memory—a specifically human trait providing the foundation for language abilities— but a mechanistic explanation of any related causal link between anatomical structure and cognitive function is still missing. Here, we provide a possible explanation and link, by using neurocomputational simulations in neuroanatomically structured models of the perisylvian language cortex. We compare networks mimicking key features of cortical connectivity in monkeys and humans, specifically the presence of relatively stronger higher-order “jumping links” between nonadjacent perisylvian cortical areas in the latter, and demonstrate that the emergence of working memory for syllables and word forms is a functional consequence of this structural evolutionary change. We also show that a mere increase of learning time is not sufficient, but that this specific structural feature, which entails higher connectivity degree of relevant areas and shorter sensorimotor path length, is crucial. These results offer a better understanding of specifically human anatomical features underlying the language faculty and their evolutionary selection advantage

The role of valuation and bargaining in optimising transboundary watercourse treaty regimes

In the face of water scarcity, growing water demands, population increase, ecosystem degradation, climate change, and so on transboundary watercourse states inevitably have to make difficult decisions on how finite quantities of water are distributed. Such waters, and their associated ecosystem services, offer multiple benefits. Valuation and bargaining can play a key role in the sharing of these ecosystems services and their associated benefits across sovereign borders. Ecosystem services in transboundary watercourses essentially constitute a portfolio of assets. Whilst challenging, their commodification, which creates property rights, supports trading. Such trading offers a means by which to resolve conflicts over competing uses and allows states to optimise their ‘portfolios’. However, despite this potential, adoption of appropriate treaty frameworks that might facilitate a market-based approach to the discovery and allocation of water-related ecosystem services at the transboundary level remains both a challenge, and a topic worthy of further study. Drawing upon concepts in law and economics, this paper therefore seeks to advance the study of how treaty frameworks might be developed in a way that supports such a market-based approach to ecosystem services and transboundary waters

Retained capacity for perceptual learning of degraded speech in primary progressive aphasia and Alzheimer's disease

This work was supported by the Alzheimer’s Society (AS-PG-16-007), the National Institute for Health Research University College London Hospitals Biomedical Research Centre, the UCL Leonard Wolfson Experimental Neurology Centre (PR/ylr/18575) and the Economic and Social Research Council (ES/K006711/1). Individual authors were supported by the Medical Research Council (PhD Studentship to CJDH and RLB; MRC Clinician Scientist Fellowship to JDR), the Wolfson Foundation (Clinical Research Fellowship to CRM), Alzheimer’s Research UK (ART-SRF2010-3 to SJC) and the Wellcome Trust (091673/Z/10/Z to JDW)

Highway to (verbal) memory: Neurocomputational consequences of specifically human connectivity in perisylvian cortex.

Rich long-distance connectivity in the fronto-temporal perisylvian language areas by way of the dorsal arcuate fasciculus (AF) sets apart humans from other primates and appears to be crucial for verbal memory and thus word learning abilities in humans. But how come that a stronger AF entails better working memory and word learning? To investigate this, we used a neurophysiologically plausible computational model implementing Hebbian learning mechanisms (cf. Garagnani et al. 2008) to simulate major regions and relevant neuroanatomical connections of superior-temporal and inferior-frontal cortex. We compared models with links documented in macaques (monkey model, MM) vs. those plus additional ones of the AF recently reported in humans specifically (human model, HM). The models were presented with spoken ‘words’ coded as concurrent neural activity patterns in auditory and motor cortex. 24 randomly initiated networks (12 of each type), were trained with 14 ‘words’ each and evaluated. Compared with the MM, HM models developed larger circuits with especially high circuit cell densities in the higher-association areas. Crucially, long-lasting memory activity was only seen in the HM circuits, whereas MM circuits lost their activation rapidly, thus giving little evidence of verbal memory processes. These functional differences did not depend on the amount of learning or other potentially relevant parameters. In summary, we show that the specific anatomical features of human cortex, especially the stronger connectivity within central perisylvian areas implicated by the human AF, contribute to better learning of spoken word forms and may explain the specifically human ability of verbal working memory