A Neurocomputationial Perspective illustrates the fertility of the concepts and data drawn from the study of the brain and of artificial networks that model the...

Ten years ago, researchers using event-related brain potentials to study language comprehension were puzzled by what looked like a Semantic Illusion: Semantically anomalous, but structurally well-formed sentences did not affect the N400 component—traditionally taken to reflect semantic integration—but instead produced a P600 effect, which is generally linked to syntactic processing. This finding led to a considerable amount of debate, and a number of complex processing models have been proposed as an explanation. What these models have in common is that they (...) postulate two or more separate processing streams, in order to reconcile the Semantic Illusion and other semantically induced P600 effects with the traditional interpretations of the N400 and the P600. Recently, however, these multi-stream models have been called into question, and a simpler single-stream model has been proposed. According to this alternative model, the N400 component reflects the retrieval of word meaning from semantic memory, and the P600 component indexes the integration of this meaning into the unfolding utterance interpretation. In the present paper, we provide support for this “Retrieval–Integration ” account by instantiating it as a neurocomputational model. This neurocomputational model is the first to successfully simulate the N400 and P600 amplitude in language comprehension, and simulations with this model provide a proof of concept of the single-stream RI account of semantically induced patterns of N400 and P600 modulations. (shrink)

A Neurocomputational Perspective , it comes of age as philosophy of mind as well. This book demands to be read by connectionists who wish to understand the philosophical context and ramifications of their work, and by philosophers who wish to understand connectionism and the nature of mind more generally.

A number of conditioning experiments utilize food as a reward. Hunger is considered to be a critical factor governing the animal's behavior in these experiments. Despite its significance, most theories of animal conditioning fail to take hunger into consideration while analyzing the behavioral data. In this paper, we analyze the neuroscientific data supporting the hypothesis that hunger and food consumption affect the brain's dopamine system, which in turn governs the animal's behavior. According to this hypothesis, chronic hunger results in a (...) decrease in the extra-cellular dopamine levels in the animal's brain. This decrease is believed to trigger a series of reactions that increase the responsivity of the phasic dopamine system. A direct consequence of this is an increased vigor of all dopamine-dependent behaviors. The level of extra-cellular dopamine is also modulated by the process of food consumption via the neurotransmitter Acetylcholine. Food consumption raises the dopamine above the baseline level. This rise depends on the animals' hunger, which when satisfied increases the level of Acetylcholine, which causes the dopamine level to fall back to the baseline. Thus, extra-cellular dopamine governs the response vigor, with an increase in dopamine resulting in a more vigorous response. This paper makes two primary contributions. Firstly, we present an abstract mathematical model based on the above hypothesis. Our mathematical model is able to provide a simple explanation for a number of behavioral findings. Another contribution of this paper is the development of a neurocomputational Leabra model of dopamine and acetylcholine activity in the basal ganglia to incorporate hunger and satiation. The experimental results obtained from this neural model are also largely in agreement with behavioral findings. (shrink)

Current explanatory frameworks for social norms pay little attention to why and how brains might carry out computational functions that generate norm compliance behavior. This paper expands on existing literature by laying out the beginnings of a neurocomputational framework for social norms and social cognition, which can be the basis for advancing our understanding of the nature and mechanisms of social norms. Two neurocomputational building blocks are identified that might constitute the core of the mechanism of norm compliance. They consist (...) of Bayesian and reinforcement learning systems. It is sketched why and how the concerted activity of these systems can generate norm compliance by minimization of three specific kinds of prediction-errors. (shrink)

_Figure 1. Dendrites and cell bodies of schematic neurons connected by dendritic-dendritic gap junctions form a laterally connected input_ _layer (“dendritic web”) within a neurocomputational architecture. Dendritic web dynamics are temporally coupled to gamma synchrony_ _EEG, and correspond with integration phases of “integrate and fire” cycles. Axonal firings provide input to, and output from, integration_ _phases (only one input, and three output axons are shown). Cell bodies/soma contain nuclei shown as black circles; microtubule networks_ _pervade the cytoplasm. According to the (...) Orch OR theory, gamma EEG-synchronized integration phases include quantum computations in_ _microtubule networks which culminate with conscious moments. Insert closeup shows a gap junction through which microtubule quantum_ _states entangle among different neurons, enabling macroscopic quantum states in dendritic webs extending throughout cortex and other_ _brain regions._. (shrink)

The rapid development of connectionist models in computer science and of powerful computational tools in neuroscience has encouraged eliminativist materialist philosophers to propose specific alternatives to traditional mentalistic theories of mind. One of the problems associated with such a move is that elimination of the mental would seem to remove access to ideas like truth as the foundations of normative epistemology. Thus, a successful elimination of propositional or sentential theories of mind must not only replace them for purposes of our (...) psychology, it must also replace them for purposes of the evaluation of our theories and explanations, psychological and otherwise. This paper briefly reviews eliminativist arguments for doubting the correctness of sentential accounts of explanation, understanding, and normative evaluation. It then considers Paul Churchland's (1989) proposed alternative norms, which are framed neurocomputationally. The alternative is found wanting in several specific ways. The arguments for eliminating propositionally-based norms are then re-examined and it is suggested that the need for wholesale elimination is overstated. A clear gap in the traditional epistemological story is identified, however, and a more modest set of norms is proposed as a way of filling this gap, rather than as a way of entirely replacing the traditional framework. (shrink)

This article delineates two dimensions along which computational models of face processing may vary, and briefly review three such models, the Dailey and Cottrell model; the O'Reilly and Munakata model; and the Riesenhuber and Poggio. It focuses primarily on one of the models and shows how this model is used to reveal potential mechanisms underlying the neural processing of faces and objects—the development of a specialized face processor, how it could be recruited for other domains, hemispheric lateralization of face processing, (...) facial expression processing, and the development of face discrimination. It turns to the Riesenhuber and Poggio model to describe the elegant way it has been used to predict functional magnetic resonance imaging data on face processing. The overall strategy of these modeling efforts is to sample problems that are constrained by neurophysiological and behavioral data, and to stress the ways in which models can generate novel hypotheses about the way humans process faces. (shrink)

Questions concerning the nature of representation and what representations are about have been a staple of Western philosophy since Aristotle. Recently, these same questions have begun to concern neuroscientists, who have developed new techniques and theories for understanding how the locus of neurobiological representation, the brain, operates. My dissertation draws on philosophy and neuroscience to develop a novel theory of representational content.

Recent developments in the cognitive sciences and artificial intelligence suggest ways of answering the most serious challenge to Peirce's notion of abduction. Either there is no such logical process as abduction or, if abduction is a form of inference, it is essentially unconscious and therefore beyond rational control so that it lacks any normative significance. Peirce himself anticipates and attempts to answer this challenge. Peirce argues that abduction is both a source of creative insight and a form of logical inference (...) subject to a degree of conscious control. In this paper I shall sketch a developing account of abduction that is suggested by the work of Paul Churchland, Paul Thagard, Chris Eliasmith, William Wimsatt, Owen Flanagan, and others. I shall argue that a credible account of abduction will require that we approach the phenomenon from both higher and lower levels as represented by these approaches. (shrink)

The authors present a neurological theory of how cognitive information and emotional information are integrated in the nucleus accumbens during effective decision making. They describe how the nucleus accumbens acts as a gateway to integrate cognitive information from the ventromedial prefrontal cortex and the hippocampus with emotional information from the amygdala. The authors have modeled this integration by a network of spiking artificial neurons organized into separate areas and used this computational model to simulate 2 kinds of cognitive–affective integration. The (...) model simulates successful performance by people with normal cognitive–affective integration. The model also simulates the historical case of Phineas Gage as well as subsequent patients whose ability to make decisions became.. (shrink)

The subject matter of this thesis can be summarized by a triplet of questions and answers. Showing what these questions and answers mean is, in essence, the goal of my project. The triplet goes like this: Q: How can we make progress in our understanding of social norms and norm compliance? A: Adopting a neurocomputational framework is one effective way to make progress in our understanding of social norms and norm compliance. Q: What could the neurocomputational mechanism of social norm (...) compliance be? A: The mechanism of norm compliance probably consists of Bayesian - Reinforcement Learning algorithms implemented by activity in certain neural populations. Q: What could information about this mechanism tell us about social norms and social norm compliance? A: Information about this mechanism tells us that: a1: Social norms are uncertainty-minimizing devices. a2: Social norm compliance is one trick that agents employ to interact coadaptively and smoothly in their social environment. Most of the existing treatments of norms and norm compliance consist in what Cristina Bicchieri refers to as “rational reconstructions.” A rational reconstruction of the concept of social norm “specifies in which sense one may say that norms are rational, or compliance with a norm is rational”. What sets my project apart from these types of treatments is that it aims, first and foremost, at providing a description of some core aspects of the mechanism of norm compliance. The single most original idea put forth in my project is to bring an alternative explanatory framework to bear on social norm compliance. This is the framework of computational cognitive neuroscience. The chapters of this thesis describe some ways in which central issues concerning social norms can be fruitfully addressed within a neurocomputational framework. In order to qualify and articulate the triplet above, my strategy consists firstly in laying down the beginnings of a model of the mechanism of norm compliance behaviour, and then zooming in on specific aspects of the model. Such a model, the chapters of this thesis argue, explains apparently important features of the psychology and neuroscience of norm compliance, and helps us to understand the nature of the social norms we live by. (shrink)

A substantial body of experimental evidence has demonstrated that labels have an impact on infant categorization processes. Yet little is known regarding the nature of the mechanisms by which this effect is achieved. We distinguish between two competing accounts: supervised name‐based categorization and unsupervised feature‐based categorization. We describe a neurocomputational model of infant visual categorization, based on self‐organizing maps, that implements the unsupervised feature‐based approach. The model successfully reproduces experiments demonstrating the impact of labeling on infant visual categorization reported in (...) Plunkett, Hu, and Cohen (2008). It mimics infant behavior in both the familiarization and testing phases of the procedure, using a training regime that involves only single presentations of each stimulus and using just 24 participant networks per experiment. The model predicts that the observed behavior in infants is due to a transient form of learning that might lead to the emergence of hierarchically organized categorical structure and that the impact of labels on categorization is influenced by the perceived similarity and the sequence in which the objects are presented. The results suggest that early in development, say before 12 months old, labels need not act as invitations to form categories nor highlight the commonalities between objects, but they may play a more mundane but nevertheless powerful role as additional features that are processed in the same fashion as other features that characterize objects and object categories. (shrink)

Expectation-based theories of language comprehension, in particular Surprisal Theory, go a long way in accounting for the behavioral correlates of word-by-word processing difficulty, such as reading times. An open question, however, is in which component of the Event-Related brain Potential signal Surprisal is reflected, and how these electrophysiological correlates relate to behavioral processing indices. Here, we address this question by instantiating an explicit neurocomputational model of incremental, word-by-word language comprehension that produces estimates of the N400 and the P600—the two most (...) salient ERP components for language processing—as well as estimates of “comprehension-centric” Surprisal for each word in a sentence. We derive model predictions for a recent experimental design that directly investigates “world-knowledge”-induced Surprisal. By relating these predictions to both empirical electrophysiological and behavioral results, we establish a close link between Surprisal, as indexed by reading times, and the P600 component of the ERP signal. The resultant model thus offers an integrated neurobehavioral account of processing difficulty in language comprehension. (shrink)

Although great progress in neuroanatomy and physiology has occurred lately, we still cannot go directly to those levels to discover the neural mechanisms of higher cognition and consciousness. But we can use neurocomputational methods based on these details to push this project forward. Here we describe vector subtraction as an operation that computes sequential paths through high-dimensional vector spaces. Vector-space interpretations of network activity patterns are a fruitful resource in recent computational neuroscience. Vector subtraction also appears to be implemented neurally (...) in primate frontal eye field activity, which computes dimensions of saccadic eye movements. We use this apparent neural implementation as a model and construct from it a general neurocomputational account of an important type of sequential cognitive and conscious process. We defend the biological plausibility of all components of the general model and show that it yields testable anatomical and physiological predictions. We close by suggesting some interesting consequences for consciousness if our model characterizes correctly the neural mechanisms producing a common type of episode in our conscious streams. (shrink)

In order to reach a better understanding of brain function, conceptual synergies linking empirical neurobiological studies and neurocomputational studies should be pursued. I describe an example of a potential synergy based on studies of neural network pruning. Simulations demonstrate that selective elimination of connections enhances the computational capacity of networks capable of temporal processing. These findings may shed light on the functional significance of postnatal neuro-developmental pruning of cortical connections that occurs in mammals.

A distinction is made between superpositional and non-superpositional quantum computers. The notion of quantum learning systems - quantum computers that modify themselves in order to improve their performance - is introduced. A particular non-superpositional quantum learning system, a quantum neurocomputer, is described: a conventional neural network implemented in a system which is a variation on the familiar two-slit apparatus from quantum physics. This is followed by a discussion of the advantages that quantum computers in general, and quantum neurocomputers in particular, (...) might bring, not only to our search for more powerful computational systems, but also to our search for greater understanding of the brain, the mind, and quantum physics itself. (shrink)

Taking inspiration from developments in neurocomputational modeling, Paul Church-land develops his positions in the philosophy of mind and the philosophy of science. Concerning the former, Churchland relaxes his eliminativism at various points and seems to endorse a traditional identity account of sensory qualia. Although he remains unsympathetic to folk psychology, he no longer seeks the elimination of normative epistemology, but rather its transformation to a philosophical enterprise informed by current developments in the relevant sciences. Churchland supplies suggestive discussions of the (...) character of knowledge, simplicity, explanation, theory, and conceptual change. Many of his treatments turn on his prototype activation model of neural representation, which looks to the notion of a 'prototype' as it is employed in the psychological literature on concept representation, however, this and other features of Churchland's neurocomputational program do not square well with some of his views about cross-scientific relations. (shrink)

According to John Haugeland, the capacity for “authentic intentionality” depends on a commitment to constitutive standards of objectivity. One of the consequences of Haugeland’s view is that a neurocomputational explanation cannot be adequate to understand “authentic intentionality”. This paper gives grounds to resist such a consequence. It provides the beginning of an account of authentic intentionality in terms of neurocomputational enabling conditions. It argues that the standards, which constitute the domain of objects that can be represented, reflect the statistical structure (...) of the environments where brain sensory systems evolved and develop. The objection that I equivocate on what Haugeland means by “commitment to standards” is rebutted by introducing the notion of “florid, self-conscious representing”. Were the hypothesis presented plausible, computational neuroscience would offer a promising framework for a better understanding of the conditions for meaningful representation. (shrink)

The importance of the Stability Problem in neurocomputing is discussed, as well as the need for the study of infinite networks. Stability must be the key ingredient in the solution of a problem by a neural network without external intervention. Infinite discrete networks seem to be the proper objects of study for a theory of neural computability which aims at characterizing problems solvable, in principle, by a neural network. Precise definitions of such problems and their solutions are given. Some (...) consequences are explored, in particular, the neural unsolvability of the Stability Problem for neural networks. (shrink)