2,533 research outputs found
A Connectionist Theory of Phenomenal Experience
When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as
many of them have been doing recently, there are two fundamentally distinct approaches available. Either
consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or
it is to be explained in terms of the computational processes defined over these vehicles. We call versions of
these two approaches vehicle and process theories of consciousness, respectively. However, while there may
be space for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because
of the influence exerted, on the one hand, by a large body of research which purports to show that the
explicit representation of information in the brain and conscious experience are dissociable, and on the
other, by the classical computational theory of mind – the theory that takes human cognition to be a species
of symbol manipulation. But two recent developments in cognitive science combine to suggest that a
reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the
experimental methodologies employed in the dissociation studies – so critical, in fact, it’s no longer
reasonable to assume that the dissociability of conscious experience and explicit representation has been
adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in
cognitive science as it once was. It now has a lively competitor in the form of connectionism; and
connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory
of consciousness. In this paper we develop and defend this connectionist vehicle theory of consciousness. It
takes the form of the following simple empirical hypothesis: phenomenal experience consists in the explicit
representation of information in neurally realized PDP networks. This hypothesis leads us to re-assess some
common wisdom about consciousness, but, we will argue, in fruitful and ultimately plausible ways
Indoor wireless communications and applications
Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter
Training neural networks to encode symbols enables combinatorial generalization
Combinatorial generalization - the ability to understand and produce novel
combinations of already familiar elements - is considered to be a core capacity
of the human mind and a major challenge to neural network models. A significant
body of research suggests that conventional neural networks can't solve this
problem unless they are endowed with mechanisms specifically engineered for the
purpose of representing symbols. In this paper we introduce a novel way of
representing symbolic structures in connectionist terms - the vectors approach
to representing symbols (VARS), which allows training standard neural
architectures to encode symbolic knowledge explicitly at their output layers.
In two simulations, we show that neural networks not only can learn to produce
VARS representations, but in doing so they achieve combinatorial generalization
in their symbolic and non-symbolic output. This adds to other recent work that
has shown improved combinatorial generalization under specific training
conditions, and raises the question of whether specific mechanisms or training
routines are needed to support symbolic processing
Research and applications: Artificial intelligence
The program is reported for developing techniques in artificial intelligence and their application to the control of mobile automatons for carrying out tasks autonomously. Visual scene analysis, short-term problem solving, and long-term problem solving are discussed along with the PDP-15 simulator, LISP-FORTRAN-MACRO interface, resolution strategies, and cost effectiveness
Communications Biophysics
COntains reports on six research projects.National Institutes of Health (Grant 2 P01 MH-04737-06)National Institutes of Health (Grant 5 ROl NB-05462-02)Joint Services Electronics Programs (U. S. Army, U. S. Navy, and U. S. Air Force) under Contract DA 36-039-AMC-03200(E)National Science Foundation (Grant GK-835)National Aeronautics and Space Administration (Grant NsG-496
An efficient coding approach to the debate on grounded cognition
The debate between the amodal and the grounded views of cognition seems to be stuck. Their only substantial disagreement is about the vehicle or format of concepts. Amodal theorists reject the grounded claim that concepts are couched in the same modality-specific format as representations in sensory systems. The problem is that there is no clear characterization of (modal or amodal) format or its neural correlate. In order to make the disagreement empirically meaningful and move forward in the discussion we need a neurocognitive criterion for representational format. I argue that efficient coding models in computational neuroscience can be used to characterize modal codes: These are codes which satisfy special informational demands imposed by sensory tasks. Additionally, I examine recent studies on neural coding and argue that although they do not provide conclusive evidence for either the grounded or the amodal views, they can be used to determine what predictions these approaches can make and what experimental and theoretical developments would be required to settle the debate
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Attention and awareness in human learning and decision making
This dissertation presents an investigation of the modifying role of attention and awareness in human learning and decision making. A series of experiments showed that performance in a range of tests of unconscious cognition can be better explained as resulting from conscious attention rather than from implicit processes.
The first three experiments utilised a modification of the Serial Reaction Time task in order to measure the interaction of implicit and explicit learning processes. The results did not show evidence for an interaction, but did exhibit an effect of explicit knowledge of the underlying rules of the task.
Subsequent studies examined the role of selective attention in learning. The investigation failed to provide evidence that learning inevitably results from the simple presentation of contingent stimuli over repeated trials. Instead, the learning effects appeared to be modulated by explicit attention to the association between stimuli. The following study with a novel test designed to measure the role of selective attention in prediction learning demonstrated that learning is not an obligatory consequence of simultaneous activation of representations of the associated stimuli. Rather, learning occurred only when attention was drawn explicitly to the association between the stimuli.
Finally, the Deliberation without Attention Paradigm was tested in a replication study along with two novel versions of the task. Additional assessment of the conscious status of participants’ judgments indicated that explicit deliberation and memory could best explain the effect and that the original test may not be a reliable measure of intuition.
In summary, the data in these studies did not require explanation in terms of unconscious cognition. These results do not preclude the possibility that unconscious processes could occur in these or other designs. However, the present work emphasises the role conscious attention plays in human learning and decision making
An efficient coding approach to the debate on grounded cognition
The debate between the amodal and the grounded views of cognition seems to be stuck. Their only substantial disagreement is about the vehicle or format of concepts. Amodal theorists reject the grounded claim that concepts are couched in the same modality-specific format as representations in sensory systems. The problem is that there is no clear characterization of (modal or amodal) format or its neural correlate. In order to make the disagreement empirically meaningful and move forward in the discussion we need a neurocognitive criterion for representational format. I argue that efficient coding models in computational neuroscience can be used to characterize modal codes: These are codes which satisfy special informational demands imposed by sensory tasks. Additionally, I examine recent studies on neural coding and argue that although they do not provide conclusive evidence for either the grounded or the amodal views, they can be used to determine what predictions these approaches can make and what experimental and theoretical developments would be required to settle the debate
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