Models of atypical development must also be models of normal development

Functional magnetic resonance imaging studies of developmental disorders and normal cognition that include children are becoming increasingly common and represent part of a newly expanding field of developmental cognitive neuroscience. These studies have illustrated the importance of the process of development in understanding brain mechanisms underlying cognition and including children ill the study of the etiology of developmental disorders

Are developmental disorders like cases of adult brain damage? Implications from connectionist modelling

It is often assumed that similar domain-specific behavioural impairments found in cases of adult brain damage and developmental disorders correspond to similar underlying causes, and can serve as convergent evidence for the modular structure of the normal adult cognitive system. We argue that this correspondence is contingent on an unsupported assumption that atypical development can produce selective deficits while the rest of the system develops normally (Residual Normality), and that this assumption tends to bias data collection in the field. Based on a review of connectionist models of acquired and developmental disorders in the domains of reading and past tense, as well as on new simulations, we explore the computational viability of Residual Normality and the potential role of development in producing behavioural deficits. Simulations demonstrate that damage to a developmental model can produce very different effects depending on whether it occurs prior to or following the training process. Because developmental disorders typically involve damage prior to learning, we conclude that the developmental process is a key component of the explanation of endstate impairments in such disorders. Further simulations demonstrate that in simple connectionist learning systems, the assumption of Residual Normality is undermined by processes of compensation or alteration elsewhere in the system. We outline the precise computational conditions required for Residual Normality to hold in development, and suggest that in many cases it is an unlikely hypothesis. We conclude that in developmental disorders, inferences from behavioural deficits to underlying structure crucially depend on developmental conditions, and that the process of ontogenetic development cannot be ignored in constructing models of developmental disorders

Refractoriness within the semantic system: investigations on the access and the content of semantic memory

The starting purpose of this project was to investigate some issues related to the mechanisms underlying the efficient access to concepts within the semantic memory systems. These issues were mainly related to the role of refractoriness in explaining the comprehension deficits underlying semantic access. The insights derived from this first approach were then used to formulate and test hypotheses about the organization of the contents of the semantic system itself. The first part of the thesis presents an investigation of the semantic abilities of an unselected case-series of patients affected by tumours to either the left or right temporal lobes in order to detect possible semantic access difficulties. Semantic access deficits are typically attributed to the semantic system becoming temporarily refractory to repeated activation. Previous investigations on the topic were mainly based on single case reports, mainly on stroke patients. The rare examples of group studies suggested moreover the possibility that the syndrome might not be functionally unitary. The tasks used in the study were two word-to-picture matching tasks aimed to control for the typical variables held to be able to distinguish semantic access from degradation syndromes (consistency of access, semantic relatedness, word frequency, presentation rate and serial position). In the group of tumour patients tested access deficits were consistently found in patients with high grade tumours in the left posterior superior temporal lobe. However, the patients were overall only weakly affected by the typical temporal factors (presentation rate and serial position) characterizing an access syndrome as refractory. The pattern of deficit, together with the localization data, suggested that the deficit described is qualitatively different from typical semantic access syndromes and possibly caused by the disconnection of posterior temporal lexical input areas from the semantic system. In the second study we tried to answer the question whether semantic access deficits are caused by the co-occurrence of two causes (refractoriness and a lexicalsemantic disconnection) or whether the presence of refractoriness in itself is sufficient to induce all the behavioural effects described in access syndromes. A second aim of the study was moreover to investigate the precise locus of refractory behaviour, since refractory effects have also been reported in naming tasks in which the possibility exists that the interference might be located at a post-semantic lexical stage of processing. To address these issues a series of three behavioural experiments on healthy subjects was conducted. The tasks used were speeded versions of the same word-to picture matching tasks used in the previous study. A speeded paradigm was adopted in order to induce a mild refractory state also in healthy participants. The results showed that it was possible to induce, in the group of subjects tested, a performance similar to that of refractory semantic access patients. Since no post-semantic stage of processing is assumed to be necessary to perform these tasks it was argued that refractoriness arises due to interference occurring between representations within the semantic system itself. In the second part of the project, the finding that refractoriness arises due to interference involving semantic representations themselves, was used to investigate issues related to the organization of the content within the semantic memory. In particular, a second series of behavioural experiments was performed to investigate whether the way an object is manipulated is indeed a feature that defines manipulable objects at a semantic level. The tasks used were speeded word-to-picture matching tasks similar to those previously described. A significantly greater interference was found in the recognition of objects sharing similar manipulation than in the recognition of objects sharing only visual similarity. Moreover the repeated presentation of objects with similar manipulation created a \u2018negative\u2019 serial position effect (with error increasing over presentations), while the repeated presentation of objects sharing only visual similarity created an opposite \u2018positive\u2019 serial position effect (learning). The role of manipulability in the semantic representation of manipulable objects was further investigated in the last study of this work. In a second unselected group of brain tumour patients the ability to name living things and artifacts was investigated. Artifacts were manipulable objects, varying in the degree of their manipulability. Results from both behavioural and Voxel-based Lesion Symptom Mapping (VLSM) analyses showed that the only patients showing a selective deficit in naming artifacts (particularly highly manipulable objects) were patients with lesions in the posterior middle and superior portions of the left temporal lobe, an area lying within the basin of those regions involved in processing object-directed actions and previously linked to the processing of manipulable objects in a wide range of studies. The results of these last two studies support \u2018property-based networks\u2019 accounts of semantic knowledge rather than \u2018undifferentiated network\u2019 accounts. Overall this series of studies represents an attempt to better understand the mechanisms that underlie the access to semantic representations and, indirectly, the structure of representations stored within semantic networks. The insights obtained about the mechanisms of access to stored semantic representations were used as a tool to investigate the structures of the same semantic representations. A combination of different approaches was used (from behavioural speeded interference paradigms on healthy subjects, to neuropsychological case series investigations, as well as Voxel-based Lesion Symptom Mapping technique), to \u2018cross-validate\u2019 the results obtained at any level of analysis

Computational modelling of interventions for developmental disorders

We evaluate the potential of connectionist models of developmental disorders to offer insights into the efficacy of interventions. Based on a range of computational simulation results, we assess factors that influence the effectiveness of interventions for reading, language, and other cognitive developmental disorders. The analysis provides a level of mechanistic detail that is generally lacking in behavioural approaches to intervention. We review an extended programme of modelling work in four sections. In the first, we consider long-term outcomes and the possibility of compensated outcomes and resolution of early delays. In the second section, we address methods to remediate atypical development in a single network. In the third section, we address interventions to encourage compensation via alternative pathways. In the final section, we consider the key issue of individual differences in response to intervention. Together with advances in understanding the neural basis of developmental disorders and neural responses to training, formal computational approaches can spur theoretical progress to narrow the gap between the theory and practice of intervention

Reading and visual word recognition ability in semantic dementia is not predicted by semantic performance

This paper describes longitudinal testing of two Semantic Dementia (SD) cases. It is common for patients with SD to present with deficits in reading aloud irregular words (i.e. surface dyslexia), and in lexical decision. Theorists from the connectionist tradition (e.g. Woollams et al., 2007) argue that in SD cases with concurrent surface dyslexia, the deterioration of irregular word reading and recognition performance is related to the extent of the deterioration of the semantic system. The Dual Route Cascaded model (DRC; Coltheart et al., 2001) makes no such prediction. We examined this issue using a battery of cognitive tests and two structural scans undertaken at different points in each cases time course. Across both cases, our behavioural testing found little evidence of a key putative link between semantic impairment and the decline of irregular word reading or lexical decision. In addition, our neuroimaging analyses suggested that it may be the emergence of atrophy to key neural regions both inside and outside the anterior temporal lobes that may best capture the emergence of impairments of irregular word reading, and implicated inferior temporal cortex in surface dyslexia

INVESTIGATIONS ON COGNITIVE COMPUTATION AND COMPUTATIONAL COGNITION

This Thesis describes our work at the boundary between Computer Science and Cognitive (Neuro)Science. In particular, (1) we have worked on methodological improvements to clustering-based meta-analysis of neuroimaging data, which is a technique that allows to collectively assess, in a quantitative way, activation peaks from several functional imaging studies, in order to extract the most robust results in the cognitive domain of interest. Hierarchical clustering is often used in this context, yet it is prone to the problem of non-uniqueness of the solution: a different permutation of the same input data might result in a different clustering result. In this Thesis, we propose a new version of hierarchical clustering that solves this problem. We also show the results of a meta-analysis, carried out using this algorithm, aimed at identifying specific cerebral circuits involved in single word reading. Moreover, (2) we describe preliminary work on a new connectionist model of single word reading, named the two-component model because it postulates a cascaded information flow from a more cognitive component that computes a distributed internal representation for the input word, to an articulatory component that translates this code into the corresponding sequence of phonemes. Output production is started when the internal code, which evolves in time, reaches a sufficient degree of clarity; this mechanism has been advanced as a possible explanation for behavioral effects consistently reported in the literature on reading, with a specific focus on the so called serial effects. This model is here discussed in its strength and weaknesses. Finally, (3) we have turned to consider how features that are typical of human cognition can inform the design of improved artificial agents; here, we have focused on modelling concepts inspired by emotion theory. A model of emotional interaction between artificial agents, based on probabilistic finite state automata, is presented: in this model, agents have personalities and attitudes that can change through the course of interaction (e.g. by reinforcement learning) to achieve autonomous adaptation to the interaction partner. Markov chain properties are then applied to derive reliable predictions of the outcome of an interaction. Taken together, these works show how the interplay between Cognitive Science and Computer Science can be fruitful, both for advancing our knowledge of the human brain and for designing more and more intelligent artificial systems

Neural Coordination of Distinct Motor Learning Strategies: Latent Neurofunctional Mechanisms Elucidated via Computational Modeling

In this dissertation, a neurofunctional theory of learning is presented as an extension of functional analysis. This new theory clarifies the distinction— via applied quantitative analysis— between functionally intrinsic (essential) mechanistic structures and irrelevant structural details. This thesis is supported by a review of the relevant literature to provide historical context and sufficient scientific background. Further, the scope of this thesis is elucidated by two questions that are posed from a neurofunctional perspective— (1) how can specialized neuromorphology contribute to the functional dynamics of neural learning processes? (2) Can large-scale neurofunctional pathways emerge via inter-network communication between disparate neural circuits? These questions motivate the specific aims of this dissertation. Each aim is addressed by posing a relevant hypothesis, which is then tested via a neurocomputational experiment. In each experiment, computational techniques are leveraged to elucidate specific mechanisms that underlie neurofunctional learning processes. For instance, the role of specialized neuromorphology is investigated via the development of a computational model that replicates the neurophysiological mechanisms that underlie cholinergic interneurons’ regulation of dopamine in the striatum during reinforcement learning. Another research direction focuses on the emergence of large-scale neurofunctional pathways that connect the cerebellum and basal ganglia— this study also involves the construction of a neurocomputational model. The results of each study illustrate the capability of neurocomputational models to replicate functional learning dynamics of human subjects during a variety of motor adaptation tasks. Finally, the significance— and some potential applications— of neurofunctional theory are discussed