The Neurodynamics of Cognition: A Tutorial on Computational Cognitive Neuroscience

Computational Cognitive Neuroscience (CCN) is a new field that lies at the intersection of computational neuroscience, machine learning, and neural network theory (i.e., connectionism). The ideal CCN model should not make any assumptions that are known to contradict the current neuroscience literature and at the same time provide good accounts of behavior and at least some neuroscience data (e.g., single-neuron activity, fMRI data). Furthermore, once set, the architecture of the CCN network and the models of each individual unit should remain fixed throughout all applications. Because of the greater weight they place on biological accuracy, CCN models differ substantially from traditional neural network models in how each individual unit is modeled, how learning is modeled, and how behavior is generated from the network. A variety of CCN solutions to these three problems are described. A real example of this approach is described, and some advantages and limitations of the CCN approach are discussed.     

The thalamic dynamic core theory of conscious experience

I propose that primary conscious awareness arises from synchronized activity in dendrites of neurons in dorsal thalamic nuclei, mediated particularly by inhibitory interactions with thalamic reticular neurons. In support, I offer four evidential pillars: (1) consciousness is restricted to the results of cortical computations; (2) thalamus is the common locus of action of brain injury in vegetative state and of general anesthetics; (3) the anatomy and physiology of the thalamus imply a central role in consciousness; (4) neural synchronization is a neural correlate of consciousness.     

Are imaging and lesioning convergent methods for assessing functional specialisation?: investigations using an artificial neural network

This article presents an investigation of the relationship between lesioning and neuroimaging methods of assessing functional specialisation, using synthetic brain imaging (SBI) and lesioning of a connectionist network of past-tense formation. The model comprised two processing ‘routes’: one was a direct route between layers of input and output units, while the other, indirect, route featured an intermediate layer of processing units. Emergent specialisation within the network was assessed (1) by lesioning either the direct or indirect route and measuring past-tense performance for regular and irregular verbs, and (2) by measuring functional activation in each route when processing each verb type (SBI). SBI and lesioning approaches failed to converge when network activation was summed over each route in our SBI approach. Examination of individual network solutions suggested that the verb types might be using the indirect route differently in terms of the pattern of activation across the route, rather than in terms of gross activation. A subsequent SBI analysis compared patterns of activation in the indirect route and confirmed that these patterns were more similar between regular-type verbs than between regular and irregular verbs. As the spatial and temporal resolution of neuroimaging techniques improves, the results of this investigation suggest that the key to finding functional specialisation will be to distinguish local coding differences across behaviours that are the results of developmental processes. Other analyses suggest that lesioning data may be limited because, with increasing damage, they reveal the resting activations of a computational system rather than a computational specialisation per se.     

Contributions of dynamic systems theory to cognitive development

We examine the contributions of dynamic systems theory to the field of cognitive development, focusing on modeling using dynamic neural fields. After introducing central concepts of dynamic field theory (DFT), we probe empirical predictions and findings around two examples—the DFT of infant perseverative reaching that explains Piaget's A-not-B error and the DFT of spatial memory that explain changes in spatial cognition in early development. Review of the literature around these examples reveals that computational modeling is having an impact on empirical research in cognitive development; however, this impact does not extend to neural and clinical research. Moreover, there is a tendency for researchers to interpret models narrowly, anchoring them to specific tasks. We conclude on an optimistic note, encouraging both theoreticians and experimentalists to work toward a more theory-driven future.     

Models provide specificity: Testing a proposed mechanism of visual working memory capacity development

Numerous studies have established that visual working memory has a limited capacity that increases during childhood. However, debate continues over the source of capacity limits and its developmental increase. Simmering (2008) adapted a computational model of spatial cognitive development, the Dynamic Field Theory, to explain not only the source of capacity limitations but also the developmental mechanism. Capacity is limited by the balance between excitation and inhibition that maintains multiple neural representations simultaneously in the model. Development occurs according to the Spatial Precision Hypothesis, which proposes that excitatory and inhibitory connections strengthen throughout early childhood. These changes in connectivity result in increasing precision and stability of neural representations over development. Here we test this developmental mechanism by probing children's memory in a single-item change detection task. Results confirmed the model's predictions, providing further support for this account of visual working memory capacity development.     

Intention,Emotion, and Action: A Neural Theory Based on Semantic Pointers

We propose a unified theory of intentions as neural processes that integrate representations of states of affairs, actions, and emotional evaluation. We show how this theory provides answers to philosophical questions about the concept of intention, psychological questions about human behavior, computational questions about the relations between belief and action, and neuroscientific questions about how the brain produces actions. Our theory of intention ties together biologically plausible mechanisms for belief, planning, and motor control. The computational feasibility of these mechanisms is shown by a model that simulates psychologically important cases of intention.     

Sex differences in neural efficiency: Are they due to the stereotype threat effect?

The neural efficiency hypothesis postulates a more efficient use of brain resources in more intelligent people as compared to less intelligent ones. However, this relationship was found to be moderated by sex and task content. While the phenomenon of neural efficiency was previously supported for men when performing visuo-spatial tasks it occurred for women only when performing verbal tasks. One possible explanation for this finding could be provided by the well-studied phenomenon called stereotype threat. Stereotype threat arises when a negative stereotype of one’s own group is made salient and can result in behavior that confirms the stereotype. Overall, 32 boys and 31 girls of varying intellectual ability were tested with a mental rotation task, either under a stereotype exposure or a no-stereotype exposure condition while measuring their EEG. The behavioral results show that an activated negative stereotype not necessarily hampers the performance of girls. Physiologically, a confirmation of the neural efficiency phenomenon was only obtained for boys working under a no-stereotype exposure condition. This result pattern replicates previous findings without threat and thus suggests that sex differences in neural efficiency during visuo-spatial tasks may not be due to the stereotype threat effect.     

Computation, reduction, and teleology of consciousness

This paper aims to explore mechanistic and teleological explanations of consciousness. In terms of mechanistic explanations, it critiques various existing views, especially those embodied by existing computational cognitive models. In this regard, the paper argues in favor of the explanation based on the distinction between localist (symbolic) representation and distributed representation (as formulated in the connectionist literature), which reduces the phenomenological difference to a mechanistic difference. Furthermore, to establish a teleological explanation of consciousness, the paper discusses the issue of the functional role of consciousness on the basis of the aforementioned mechanistic explanation. A proposal based on synergistic interaction between the conscious and the unconscious is advanced that encompasses various existing views concerning the functional role of consciousness. This two-step deepening explanation has some empirical support, in the form of a cognitive model and various cognitive data that it captures.     

The importance of feature distribution and correlation for simulating 3 to 4-month-old infants' visual categorization processes

Mareschal, French, and Quinn (2000) and Mareschal, Quinn, and French (2002) have proposed a connectionist model of visual categorization in 3- to 4-month-old infants that simulates and predicts previously unexplained behavioural effects such as the asymmetric categorization effect (French, Mareschal, Mermillod, & Quinn, 2004). In the current paper, we show that the model's ability to simulate the asymmetry depends on the correlational structure of the stimuli. These results are important given that adults (Anderson & Fincham, 1996) as well as infants (Younger & Cohen, 1986) are able to rely on correlation information to perform visual categorization. At a behavioural level, the current paper suggests that pure bottom-up processes, based on the correlational structure of the categories, could explain the disappearance of the asymmetry in older 10-month-old infants (Furrer & Younger, 2005). Moreover, our results also raise new challenges for visual categorization models that attempt to simulate the shift from asymmetric categorization in 3- to 4-month-old to symmetric categorization in 10-month-old infants (Shultz & Cohen, 2004; Westermann & Mareschal, 2004, 2012).     

The Fragility of Evolution: Part Two

This essay argues for a shift in evolutionary metaphor-away from natural selection and the elimination of the less fit, to a focus on the fragility of biological change. Three biological arenas, in which the organism and environment more obviously interpenetrate, will be examined. The vulnerable and exceptionally plastic neural system of organisms makes possible subtle capacities such as learning and memory. Behavior, as it often precedes morphological change, emerges as an evolutionary axis. Synergisms between organisms lead to novelty on a macroevolutionary scale-multicellularity, coloniality, sociality, and eusociality. Although fragile due to competitive tensions, novel cohesions have repeatedly emerged throughout evolution. Ecology involves the flow of energy between organisms and the biophysical realm, which life has increasingly shaped. Radical spatial heterogeneity and varying levels of temporal disturbances serve to enhance biodiversity, and restrict the dominance of any single species. The atypical event of evolution can occur relatively suddenly and on varied scales. Paradoxically, evolution can be prompted by novel stresses and spaces protected from harsh competitive stress. As life moves to a greater total complexity, fragility increases. In conclusion, this essay discusses the protection of "less fit" human populations.