1,975 research outputs found
Quantum Cognition based on an Ambiguous Representation Derived from a Rough Set Approximation
Over the last years, in a series papers by Arrechi and others, a model for
the cognitive processes involved in decision making has been proposed and
investigated. The key element of this model is the expression of apprehension
and judgement, basic cognitive process of decision making, as an inverse Bayes
inference classifying the information content of neuron spike trains. For
successive plural stimuli, it has been shown that this inference, equipped with
basic non-algorithmic jumps, is affected by quantum-like characteristics. We
show here that such a decision making process is related consistently with
ambiguous representation by an observer within a universe of discourse. In our
work ambiguous representation of an object or a stimuli is defined by a pair of
maps from objects of a set to their representations, where these two maps are
interrelated in a particular structure. The a priori and a posteriori
hypotheses in Bayes inference are replaced by the upper and lower
approximation, correspondingly, for the initial data sets each derived with
respect to a map. We show further that due to the particular structural
relation between the two maps, the logical structure of such combined
approximations can only be expressed as an orthomodular lattice and therefore
can be represented by a quantum rather than a Boolean logic. To our knowledge,
this is the first investigation aiming to reveal the concrete logic structure
of inverse Bayes inference in cognitive processes.Comment: 23 pages, 8 figures, original research pape
Factory of realities: on the emergence of virtual spatiotemporal structures
The ubiquitous nature of modern Information Retrieval and Virtual World give
rise to new realities. To what extent are these "realities" real? Which
"physics" should be applied to quantitatively describe them? In this essay I
dwell on few examples. The first is Adaptive neural networks, which are not
networks and not neural, but still provide service similar to classical ANNs in
extended fashion. The second is the emergence of objects looking like
Einsteinian spacetime, which describe the behavior of an Internet surfer like
geodesic motion. The third is the demonstration of nonclassical and even
stronger-than-quantum probabilities in Information Retrieval, their use.
Immense operable datasets provide new operationalistic environments, which
become to greater and greater extent "realities". In this essay, I consider the
overall Information Retrieval process as an objective physical process,
representing it according to Melucci metaphor in terms of physical-like
experiments. Various semantic environments are treated as analogs of various
realities. The readers' attention is drawn to topos approach to physical
theories, which provides a natural conceptual and technical framework to cope
with the new emerging realities.Comment: 21 p
Neurocognitive Informatics Manifesto.
Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given
Infinte Idealization and Contextual Realism
The paper discusses the recent literature on abstraction/idealization in connection with
the \paradox of infinite idealization." We use the case of taking thermodynamics limit
in dealing with the phenomena of phase transition and critical phenomena to broach
the subject. We then argue that the method of infinite idealization is widely used
in the practice of science, and not all uses of the method are the same (or evoke the
same philosophical problems). We then confront the compatibility problem of infinite
idealization with scientific realism. We propose and defend a contextualist position
for (local) realism and argue that the cases for infinite idealization appear to be fully
compatible with contextual realism
Infinte Idealization and Contextual Realism
The paper discusses the recent literature on abstraction/idealization in connection with
the \paradox of infinite idealization." We use the case of taking thermodynamics limit
in dealing with the phenomena of phase transition and critical phenomena to broach
the subject. We then argue that the method of infinite idealization is widely used
in the practice of science, and not all uses of the method are the same (or evoke the
same philosophical problems). We then confront the compatibility problem of infinite
idealization with scientific realism. We propose and defend a contextualist position
for (local) realism and argue that the cases for infinite idealization appear to be fully
compatible with contextual realism
If physics is an information science, what is an observer?
Interpretations of quantum theory have traditionally assumed a "Galilean"
observer, a bare "point of view" implemented physically by a quantum system.
This paper investigates the consequences of replacing such an
informationally-impoverished observer with an observer that satisfies the
requirements of classical automata theory, i.e. an observer that encodes
sufficient prior information to identify the system being observed and
recognize its acceptable states. It shows that with reasonable assumptions
about the physical dynamics of information channels, the observations recorded
by such an observer will display the typical characteristics predicted by
quantum theory, without requiring any specific assumptions about the observer's
physical implementation.Comment: 30 pages, comments welcome; v2 significant revisions - results
unchange
Bayesian Cognitive Science, Monopoly, and Neglected Frameworks
A widely shared view in the cognitive sciences is that discovering and assessing explanations of cognitive phenomena whose production involves uncertainty should be done in a Bayesian framework. One assumption supporting this modelling choice is that Bayes provides the best approach for representing uncertainty. However, it is unclear that Bayes possesses special epistemic virtues over alternative modelling frameworks, since a systematic comparison has yet to be attempted. Currently, it is then premature to assert that cognitive phenomena involving uncertainty are best explained within the Bayesian framework. As a forewarning, progress in cognitive science may be hindered if too many scientists continue to focus their efforts on Bayesian modelling, which risks to monopolize scientific resources that may be better allocated to alternative approaches
- …