11,006 research outputs found
Cognitive Penetration and Attention
Zenon Pylyshyn argues that cognitively driven attentional effects do not amount to cognitive penetration of early vision because such effects occur either before or after early vision. Critics object that in fact such effects occur at all levels of perceptual processing. We argue that Pylyshyn’s claim is correct—but not for the reason he emphasizes. Even if his critics are correct that attentional effects are not external to early vision, these effects do not satisfy Pylyshyn’s requirements that the effects be direct and exhibit semantic coherence. In addition, we distinguish our defense from those found in recent work by Raftopoulos and by Firestone and Scholl, argue that attention should not be assimilated to expectation, and discuss alternative characterizations of cognitive penetrability, advocating a kind of pluralism
Supervised learning on graphs of spatio-temporal similarity in satellite image sequences
High resolution satellite image sequences are multidimensional signals
composed of spatio-temporal patterns associated to numerous and various
phenomena. Bayesian methods have been previously proposed in (Heas and Datcu,
2005) to code the information contained in satellite image sequences in a graph
representation using Bayesian methods. Based on such a representation, this
paper further presents a supervised learning methodology of semantics
associated to spatio-temporal patterns occurring in satellite image sequences.
It enables the recognition and the probabilistic retrieval of similar events.
Indeed, graphs are attached to statistical models for spatio-temporal
processes, which at their turn describe physical changes in the observed scene.
Therefore, we adjust a parametric model evaluating similarity types between
graph patterns in order to represent user-specific semantics attached to
spatio-temporal phenomena. The learning step is performed by the incremental
definition of similarity types via user-provided spatio-temporal pattern
examples attached to positive or/and negative semantics. From these examples,
probabilities are inferred using a Bayesian network and a Dirichlet model. This
enables to links user interest to a specific similarity model between graph
patterns. According to the current state of learning, semantic posterior
probabilities are updated for all possible graph patterns so that similar
spatio-temporal phenomena can be recognized and retrieved from the image
sequence. Few experiments performed on a multi-spectral SPOT image sequence
illustrate the proposed spatio-temporal recognition method
A Deflationary Account of Mental Representation
Among the cognitive capacities of evolved creatures is the capacity to represent. Theories in cognitive neuroscience typically explain our manifest representational capacities by positing internal representations, but there is little agreement about how these representations function, especially with the relatively recent proliferation of connectionist, dynamical, embodied, and enactive approaches to cognition. In this talk I sketch an account of the nature and function of representation in cognitive neuroscience that couples a realist construal of representational vehicles with a pragmatic account of mental content. I call the resulting package a deflationary account of mental representation and I argue that it avoids the problems that afflict competing accounts
Context Based Visual Content Verification
In this paper the intermediary visual content verification method based on
multi-level co-occurrences is studied. The co-occurrence statistics are in
general used to determine relational properties between objects based on
information collected from data. As such these measures are heavily subject to
relative number of occurrences and give only limited amount of accuracy when
predicting objects in real world. In order to improve the accuracy of this
method in the verification task, we include the context information such as
location, type of environment etc. In order to train our model we provide new
annotated dataset the Advanced Attribute VOC (AAVOC) that contains additional
properties of the image. We show that the usage of context greatly improve the
accuracy of verification with up to 16% improvement.Comment: 6 pages, 6 Figures, Published in Proceedings of the Information and
Digital Technology Conference, 201
Probabilistic Approach to Epistemic Modals in the Framework of Dynamic Semantics
In dynamic semantics meaning of a statement is not equated with its truth
conditions but with its context change potential. It has also been claimed
that dynamic framework can automatically account for certain paradoxes
that involve epistemic modals, such as the following one: it seems odd and
incoherent to claim: (1) “It is raining and it might not rain”, whereas
claiming (2) “It might not rain and it is raining” does not seem equally odd
(Yalcin, 2007). Nevertheless, it seems that it cannot capture the fact that
statement (2) seems odd as well, even though not as odd as the statement
(1) (Gauker, 2007). I will argue that certain probabilistic extensions to the
dynamic model can account for this subtlety of our linguistic intuitions and
represent if not an improved than at least an alternative framework for
capturing the way contexts are updated and beliefs revised with uncertain
information.Numer został przygotowany przy wsparciu Ministerstwa Nauki i Szkolnictwa Wyższego
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