2,495 research outputs found
Platonic model of mind as an approximation to neurodynamics
Hierarchy of approximations involved in simplification of microscopic theories, from sub-cellural to the whole brain level, is presented. A new approximation to neural dynamics is described, leading to a Platonic-like model of mind based on psychological spaces. Objects and events in these spaces correspond to quasi-stable states of brain dynamics and may be interpreted from psychological point of view. Platonic model bridges the gap between neurosciences and psychological sciences. Static and dynamic versions of this model are outlined and Feature Space Mapping, a neurofuzzy realization of the static version of Platonic model, described. Categorization experiments with human subjects are analyzed from the neurodynamical and Platonic model points of view
Literal Perceptual Inference
In this paper, I argue that theories of perception that appeal to Helmholtzâs idea of unconscious inference (âHelmholtzianâ theories) should be taken literally, i.e. that the inferences appealed to in such theories are inferences in the full sense of the term, as employed elsewhere in philosophy and in ordinary discourse.
In the course of the argument, I consider constraints on inference based on the idea that inference is a deliberate acton, and on the idea that inferences depend on the syntactic structure of representations. I argue that inference is a personal-level but sometimes unconscious process that cannot in general be distinguished from association on the basis of the structures of the representations over which itâs defined. I also critique arguments against representationalist interpretations of Helmholtzian theories, and argue against the view that perceptual inference is encapsulated in a module
Robust short-term memory without synaptic learning
Short-term memory in the brain cannot in general be explained the way
long-term memory can -- as a gradual modification of synaptic weights -- since
it takes place too quickly. Theories based on some form of cellular
bistability, however, do not seem able to account for the fact that noisy
neurons can collectively store information in a robust manner. We show how a
sufficiently clustered network of simple model neurons can be instantly induced
into metastable states capable of retaining information for a short time (a few
seconds). The mechanism is robust to different network topologies and kinds of
neural model. This could constitute a viable means available to the brain for
sensory and/or short-term memory with no need of synaptic learning. Relevant
phenomena described by neurobiology and psychology, such as local
synchronization of synaptic inputs and power-law statistics of forgetting
avalanches, emerge naturally from this mechanism, and we suggest possible
experiments to test its viability in more biological settings.Comment: 20 pages, 9 figures. Amended to include section on spiking neurons,
with general rewrit
Computational physics of the mind
In the XIX century and earlier such physicists as Newton, Mayer, Hooke, Helmholtz and Mach were actively engaged in the research on psychophysics, trying to relate psychological sensations to intensities of physical stimuli. Computational physics allows to simulate complex neural processes giving a chance to answer not only the original psychophysical questions but also to create models of mind. In this paper several approaches relevant to modeling of mind are outlined. Since direct modeling of the brain functions is rather limited due to the complexity of such models a number of approximations is introduced. The path from the brain, or computational neurosciences, to the mind, or cognitive sciences, is sketched, with emphasis on higher cognitive functions such as memory and consciousness. No fundamental problems in understanding of the mind seem to arise. From computational point of view realistic models require massively parallel architectures
The informational mind and the information integration theory of consciousness
According to Aleksander and Mortonâs informational mind hypothesis, conscious minds are state structures that are created through iconic learning. Distributed representations of colors, edges, objects, etc. are linked with proprioceptive and motor information to generate the awareness of an out-there world. The uniqueness and indivisibility of these iconically learnt states reflect the uniqueness and indivisibility of the world. This article summarizes the key claims of the informational mind hypothesis and considers them in relation to Tononiâs information integration theory of consciousness. Some suggestions are made about how the informational mind hypothesis could be experimentally tested, and its significance for work on machine consciousness is considered
The informational mind and the information integration theory of consciousness
According to Aleksander and Mortonâs informational mind hypothesis, conscious minds are state structures that are created through iconic learning. Distributed representations of colors, edges, objects, etc. are linked with proprioceptive and motor information to generate the awareness of an out-there world. The uniqueness and indivisibility of these iconically learnt states reflect the uniqueness and indivisibility of the world. This article summarizes the key claims of the informational mind hypothesis and considers them in relation to Tononiâs information integration theory of consciousness. Some suggestions are made about how the informational mind hypothesis could be experimentally tested, and its significance for work on machine consciousness is considered
Recommended from our members
From symbols to icons: the return of resemblance in the cognitive neuroscience revolution
We argue that one important aspect of the "cognitive neuroscience revolution" identified by Boone and Piccinini (2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both "from below" and "from above" - that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves ("from below"), and from a greater appreciation of the problems solved by bio-cognitive systems, chiefly regulation and prediction ("from above"). We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of (predictive) dynamical models as a central postulate in neurocognitive research
- âŚ