Neurobiological Mechanisms for Semantic Feature Extraction and Conceptual Flexibility

Signs and symbols relate to concepts and can be used to speak about objects, actions, and their features. Theories of semantic grounding address the question how the latter two, concepts and real‐world entities, come into play and interlink in symbol learning. Here, a neurobiological model is used to spell out concrete mechanisms of symbol grounding, which implicate the “association” of information about sign and referents and, at the same time, the extraction of semantic features and the formation of abstract representations best described as conjoined and disjoined feature sets that may or may not have a real‐life equivalent. The mechanistic semantic circuits carrying these feature sets are not static conceptual entries, but exhibit rich activation dynamics related to memory, prediction, and contextual modulation. Four key issues in specifying these activation dynamics will be highlighted: (a) the inner structure of semantic circuits, (b) mechanisms of semantic priming, (c) task specificity in semantic activation, and (d) context‐dependent semantic circuit activation in the processing of referential, existential, and universal statements. These linguistic‐semantic examples show that specific mechanisms are required to account for context‐dependent semantic function or conceptual “flexibility.” Static context‐independent concepts as such are insufficient to account for these different semantic functions. Whereas abstract amodal models of concepts did so far not spell out concrete mechanisms for context‐dependent semantic function, neuronal assembly mechanisms offer a workable perspective

Neurobiological mechanisms for language, symbols and concepts: Clues from brain-constrained deep neural networks

Neural networks are successfully used to imitate and model cognitive processes. However, to provide clues about the neurobiological mechanisms enabling human cognition, these models need to mimic the structure and function of real brains. Brain-constrained networks differ from classic neural networks by implementing brain similarities at different scales, ranging from the micro- and mesoscopic levels of neuronal function, local neuronal links and circuit interaction to large-scale anatomical structure and between-area connectivity. This review shows how brain-constrained neural networks can be applied to study in silico the formation of mechanisms for symbol and concept processing and to work towards neurobiological explanations of specifically human cognitive abilities. These include verbal working memory and learning of large vocabularies of symbols, semantic binding carried by specific areas of cortex, attention focusing and modulation driven by symbol type, and the acquisition of concrete and abstract concepts partly influenced by symbols. Neuronal assembly activity in the networks is analyzed to deliver putative mechanistic correlates of higher cognitive processes and to develop candidate explanations founded in established neurobiological principles

Modelling concrete and abstract concepts using brain-constrained deep neural networks

A neurobiologically constrained deep neural network mimicking cortical areas relevant for sensorimotor, linguistic and conceptual processing was used to investigate the putative biological mechanisms underlying conceptual category formation and semantic feature extraction. Networks were trained to learn neural patterns representing specific objects and actions relevant to semantically ‘ground’ concrete and abstract concepts. Grounding sets consisted of three grounding patterns with neurons representing specific perceptual or action-related features; neurons were either unique to one pattern or shared between patterns of the same set. Concrete categories were modelled as pattern triplets overlapping in their ‘shared neurons’, thus implementing semantic feature sharing of all instances of a category. In contrast, abstract concepts had partially shared feature neurons common to only pairs of category instances, thus, exhibiting family resemblance, but lacking full feature overlap. Stimulation with concrete and abstract conceptual patterns and biologically realistic unsupervised learning caused formation of strongly connected cell assemblies (CAs) specific to individual grounding patterns, whose neurons were spread out across all areas of the deep network. After learning, the shared neurons of the instances of concrete concepts were more prominent in central areas when compared with peripheral sensorimotor ones, whereas for abstract concepts the converse pattern of results was observed, with central areas exhibiting relatively fewer neurons shared between pairs of category members. We interpret these results in light of the current knowledge about the relative difficulty children show when learning abstract words. Implications for future neurocomputational modelling experiments as well as neurobiological theories of semantic representation are discussed

Breakdown of category-specific word representations in a brain-constrained neurocomputational model of semantic dementia

The neurobiological nature of semantic knowledge, i.e., the encoding and storage of conceptual information in the human brain, remains a poorly understood and hotly debated subject. Clinical data on semantic deficits and neuroimaging evidence from healthy individuals have suggested multiple cortical regions to be involved in the processing of meaning. These include semantic hubs (most notably, anterior temporal lobe, ATL) that take part in semantic processing in general as well as sensorimotor areas that process specific aspects/categories according to their modality. Biologically inspired neurocomputational models can help elucidate the exact roles of these regions in the functioning of the semantic system and, importantly, in its breakdown in neurological deficits. We used a neuroanatomically constrained computational model of frontotemporal cortices implicated in word acquisition and processing, and adapted it to simulate and explain the effects of semantic dementia (SD) on word processing abilities. SD is a devastating, yet insufficiently understood progressive neurodegenerative disease, characterised by semantic knowledge deterioration that is hypothesised to be specifically related to neural damage in the ATL. The behaviour of our brain-based model is in full accordance with clinical data—namely, word comprehension performance decreases as SD lesions in ATL progress, whereas word repetition abilities remain less affected. Furthermore, our model makes predictions about lesion- and category-specific effects of SD: our simulation results indicate that word processing should be more impaired for object- than for action-related words, and that degradation of white matter should produce more severe consequences than the same proportion of grey matter decay. In sum, the present results provide a neuromechanistic explanatory account of cortical-level language impairments observed during the onset and progress of semantic dementia

Predictions built upon belongings

Peer Reviewe

Transformative Learning: The Role of Language in Supporting a Self-Reflective Process in a Context of Crisis

Research has shown that adult learning is a complex and integrative process that requires an interdisciplinary lens of study. Thus, to understand the cognitive dimensions of learning, a multidisciplinary approach is needed. This single case study aimed to examine how the role of language function in self-reflection supports the socio-cognitive and neurobiological processes associated with transformation through a model of neuroeducation that considers the role of language function. Based on a multidisciplinary review of transformative learning through the lenses of cognitive and cultural psychology, cognitive neuroscience, and language function, a reflective semi-structured interview protocol was implemented with six speech-language pathologists working in educational settings during COVID-19. The analysis of the responses demonstrated that the role of language function was associated with supporting relationships, self-reflection, and learning during a context of crisis. The results suggest how the role of language function contributed to the socio-cognitive and neurobiological processes associated with transformative learning. On this basis, it is recommended that organizations design nurturing, culturally and linguistically responsive learning environments that promote language as a tool for transformation

The Functional Contributions of Consciousness

Most existing research programs are occupied with the difficult question of what consciousness is, overlooking what the more interesting and fruitful research question: what does consciousness do? My dissertation develops a philosophical method for identifying the functional capacities that conscious experience contributes to information processing systems. My strategy involves systematically consolidating and interpreting a range of psychological and neuroscientific research in order to compare conscious and unconscious processing in different psychological domains, namely, vision, emotion, and social cognition. I also defend the principle of functional pluralism: given that conscious experiences presumably form a relatively diverse class in the natural world, we should expect them to facilitate a diverse range of functions in different psychological domains. My pluralist account implies that we will be able to amass a collection of functional markers that can guide future ascriptions of experience to all sorts of natural and artificial systems. Understanding consciousness functional profile should also ultimately help us answer the general but elusive question of what consciousness is as a feature of psychological systems. After laying out the general framework and critically evaluating prominent theories of consciousness in the first chapter, I begin the process of identifying FCCs in particular psychological domains. In my second chapter, I identify some candidate functional markers of consciousness in the functionally-complex domain of visual perception, including the processing of semantic information inherent in more informationally-complex visual stimuli, increased spatiotemporal precision, and representational integration over larger spatiotemporal intervals. My third chapter discusses the domain of emotional processing, where I argue that experience facilitates the inhibition of, the conceptualization of, and flexible response to emotionally valenced representational content. In my fourth chapter, I review a range of bias-intervention strategies that explicitly draw on the functional resources of conscious experience. In my final chapter, I draw some conclusions about the nature of consciousness based on my functional analysis. I introduce what I call a Local Workspace Theory, argue that consciousness is at least in part characterized by a high degree of representational complexity afforded by the structural mechanisms that realize it and reflected in the psychological functions that it facilitates

The Functional Contributions of Consciousness

Most existing research programs are occupied with the difficult question of what consciousness is, overlooking what the more interesting and fruitful research question: what does consciousness do? My dissertation develops a philosophical method for identifying the functional capacities that conscious experience contributes to information processing systems. My strategy involves systematically consolidating and interpreting a range of psychological and neuroscientific research in order to compare conscious and unconscious processing in different psychological domains, namely, vision, emotion, and social cognition. I also defend the principle of functional pluralism: given that conscious experiences presumably form a relatively diverse class in the natural world, we should expect them to facilitate a diverse range of functions in different psychological domains. My pluralist account implies that we will be able to amass a collection of functional markers that can guide future ascriptions of experience to all sorts of natural and artificial systems. Understanding consciousness’ functional profile should also ultimately help us answer the general but elusive question of what consciousness is as a feature of psychological systems. After laying out the general framework and critically evaluating prominent theories of consciousness in the first chapter, I begin the process of identifying FCCs in particular psychological domains. In my second chapter, I identify some candidate functional markers of consciousness in the functionally-complex domain of visual perception, including the processing of semantic information inherent in more informationally-complex visual stimuli, increased spatiotemporal precision, and representational integration over larger spatiotemporal intervals. My third chapter discusses the domain of emotional processing, where I argue that experience facilitates the inhibition of, the conceptualization of, and flexible response to emotionally valenced representational content. In my fourth chapter, I review a range of bias-intervention strategies that explicitly draw on the functional resources of conscious experience. In my final chapter, I draw some conclusions about the nature of consciousness based on my functional analysis. I introduce what I call a Local Workspace Theory, argue that consciousness is at least in part characterized by a high degree of representational complexity afforded by the structural mechanisms that realize it and reflected in the psychological functions that it facilitates