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 dynamic relationships of affective synchrony to perceptions of situations

Most theories of affect predict that affects of opposite valence should be negatively correlated (de-synchronous) or independent (asynchronous) within individuals. Such theories were challenged by the finding that the association between energetic arousal and tense arousal ranged from de-synchrony to synchrony (Rafaeli, Rogers, &; Revelle, 2007). In this paper, we report two experience-sampling studies employing cell-phone text-messaging aimed at further exploring individual differences in affective experience. Results showed that within-person relationships between energetic arousal and tense arousal ranged from de-synchrony to synchrony, but that within-person relationships between Pleasant and Unpleasant affect varied from strong de-synchrony to weak de-synchrony. Individual differences in within-person EA-TA associations were related to perceiving threatening situations as incentives and to interactions between affective traits.     

A place for nouns and a place for verbs? A critical review of neurocognitive data on grammatical-class effects

It is generally held that noun processing is specifically sub-served by temporal areas, while the neural underpinnings of verb processing are located in the frontal lobe. However, this view is now challenged by a significant body of evidence accumulated over the years. Moreover, the results obtained so far on the neural implementation of noun and verb processing appear to be quite inconsistent. The present review briefly describes and critically re-considers the anatomo-correlative, neuroimaging, MEG, TMS and cortical stimulation studies on nouns and verbs with the aim of assessing the consistency of their results, particularly within techniques. The paper also addresses the question as to whether the inconsistency of the data could be due to the variety of the tasks used. However, it emerged that neither the different investigation techniques used nor the different cognitive tasks employed fully explain the variability of the data. In the final section we thus suggest that the main reason for the emergence of inconsistent data in this field is that the cerebral circuits underlying noun and verb processing are not spatially segregated, at least for the spatial resolution currently used in most neuroimaging studies.     

"Binaural rivalry": dichotic listening as a tool for the investigation of the neural correlate of consciousness

Since about two decades neuroscientists have systematically faced the problem of consciousness: the aim is to discover the neural activity specifically related to conscious perceptions, i.e. the biological properties of what philosophers call qualia. In this view, a neural correlate of consciousness (NCC) is a precise pattern of brain activity that specifically accompanies a particular conscious experience. Almost all studies aimed at investigating the NCC have been carried out in the visual system. One of the most promising paradigms is based on sensory stimuli which elicit bistable percepts, as they allow to decouple subjective perception from the characteristics of the physical stimulation. Such kind of perception can be produced in the visual modality by using particular images (e.g. Rubin's vase/face figure) or by presenting two dissimilar stimuli separately to the two eyes (binocular rivalry). The stimuli compete for perceptual dominance and each image is visible in turn for a few seconds, while the other is suppressed. The use of this methodology has led to important findings concerning visual consciousness, which are briefly discussed. For the investigation of auditory consciousness, a similar stimulation paradigm can be achieved by using dichotic listening, consisting in two different stimuli presented each to one ear, which compete for perception (binaural rivalry). The principal aim of the present mini-review is to discuss the few contributes facing the issue of auditory consciousness and to advance the use of dichotic listening and binaural rivalry as valid tools for its investigation.     

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.     

Cost minimization during simulated evolution of paired neural networks leads to asymmetries and specialization

Motivated by specialization (lateralization) that occurs in corresponding left and right regions of the cerebral cortex, several past computational models have studied conditions under which functional specialization can arise during learning due to underlying asymmetries in paired neural networks. However, these past studies have not addressed the basic issue of how such underlying asymmetries arise in the first place. As an initial step in addressing this issue, we investigated the hypothesis that underlying asymmetries will appear in paired neural networks during a simulated evolutionary process when fitness is based not only on maximizing performance, but also on minimizing various ‘costs’ such as energy consumption, neural connection weights, and response times. Simulated evolution under these conditions consistently produced networks with left–right asymmetries in region size, excitability and plasticity. These underlying asymmetries were often synergistic, leading to subsequent functional lateralization during network training. While our computational models are too simple for these results to be directly extrapolated to real nervous systems, they provide support for the hypothesis that brain asymmetries and lateralization in biological nervous systems may be a consequence of cost minimization present during evolution, and are the first computational demonstration of emergent population lateralization.     

The dynamics of choice among multiple alternatives

We consider neurally based models for decision-making in the presence of noisy incoming data. The two-alternative forced-choice task has been extensively studied, and in that case it is known that mutually inhibited leaky integrators in which leakage and inhibition balance can closely approximate a drift-diffusion process that is the continuum limit of the optimal sequential probability ratio test (SPRT). Here we study the performance of neural integrators in n?2 alternative choice tasks and relate them to a multihypothesis sequential probability ratio test (MSPRT) that is asymptotically optimal in the limit of vanishing error rates. While a simple race model can implement this ‘max-vs-next’ MSPRT, it requires an additional computational layer, while absolute threshold crossing tests do not require such a layer. Race models with absolute thresholds perform relatively poorly, but we show that a balanced leaky accumulator model with an absolute crossing criterion can approximate a ‘max-vs-ave’ test that is intermediate in performance between the absolute and max-vs-next tests. We consider free and fixed time response protocols, and show that the resulting mean reaction times under the former and decision times for fixed accuracy under the latter obey versions of Hick's law in the low error rate range, and we interpret this in terms of information gained. Specifically, we derive relationships of the forms log(n-1), log(n), or log(n+1) depending on error rates, signal-to-noise ratio, and the test itself. We focus on linearized models, but also consider nonlinear effects of neural activities (firing rates) that are bounded below and show how they modify Hick's law.     

The plausibility of spiritual intelligence: spiritual experience,problem solving and neural sites

Australian teachers in Church related schools have begun to use the term ‘spiritual intelligence’ in their educational discourse. Is it accurate to describe spirituality as a form of intelligence? This paper explores whether the notion of spiritual intelligence is plausible. It addresses this firstly by discussing the notion of spiritual experience as a mechanism for problem solving—one of the central themes that underlies the concept of intelligence. Secondly, it examines some of the neural sites of the human brain that have been found to be active in those who apperceive spiritual experience. In light of this discussion, this paper argues that although some concerns prevail in considering spirituality as a form of intelligence, the concept of spiritual intelligence may nonetheless be rendered as plausible.     

A neural implementation of multi-adjoint logic programming

A neural net based implementation of propositional [0,1]-valued multi-adjoint logic programming is presented, which is an extension of earlier work on representing logic programs in neural networks carried out in [A.S. d'Avila Garcez et al., Neural-Symbolic Learning Systems: Foundations and Applications, Springer, 2002; S. Hölldobler et al., Appl. Intelligence 11 (1) (1999) 45–58]. Proofs of preservation of semantics are given, this makes the extension to be well-founded.The implementation needs some preprocessing of the initial program to transform it into a homogeneous program; then, transformation rules carry programs into neural networks, where truth-values of rules relate to output of neurons, truth-values of facts represent input, and network functions are determined by a set of general operators; the net outputs the values of propositional variables under its minimal model.     

Towards learning agents with personality traits: Modeling Openness to Experience

Recent advances in neurosciences and cognitive sciences show us that the human neocortex is not a slave to the experiences from our perception and that the memories stored in hippocampus are goal weighted during the replay of the experiences for the purpose of re-learning from them. Temporal difference reinforcement learning systems that use neural networks as function approximators rely on an experience replay memory structure similar to the hippocampus. We bring forward this similarity and present a novel way of using a goal weighted prioritization of the memory that is biologically inspired. Furthermore, we introduce a novel prioritization criteria called Variety of Experience Index, or VEI, for weighting the selection of the experiences that are stored in the replay memory. Weighting the experiences based on two different extremes of VEI can behaviourally modify the agent’s learning process, generating different types of learning agents that exhibit different personality traits along the dimension of Openness to Experience.