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Attention
is a tool to adapt what we see to our current needs. It can be focused
narrowly on a single object or spread over several or distributed over
the scene as a whole. In addition to increasing or decreasing the number
of objects, these different deployments may have different effects on
what we see. The talk will describe some research contrasting focused
attention and its use in binding features with distributed attention and
the kinds of information we gain and lose with the attention window opened
wide. One kind of processing that we suggest occurs automatically with
distributed attention is a statistical summary of sets of similar objects.
Another is the gist of the scene, which may be inferred from sets of features
registered in parallel. Flexible use of these different modes of attention
allows us to reconcile sharp capacity limits with a richer understanding
of the scene.
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At
any moment of time the scene around us is filled with objects differing
along many dimensions, which we see from particular angles, distances,
illuminations, and which may themselves move and transform. We must both
represent their current state in order to interact with them, recognize
their identities in order to retrieve semantic information relevant to
our behavior, and store an episodic memory of the particular events in
which they play a role. We developed the object file metaphor to help
capture results of experiments exploring these phenomena. They include
negative and positive priming, feature binding, perceptual deficits in
Balint's patients, change and repetition blindness, and the limits of
visual working memory. I will outline the framework and describe some
recent findings that help fill out the theory.
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Attention
limits preclude our perceiving every detail of a complex scene. We must
quickly summarize the perceptual layout, and use the results to guide
more detailed processing. Our research shows that statistical properties
can be rapidly and automatically extracted for different sets of items.
In tasks requiring participants to estimate the mean size of sets of circles,
we found surprisingly little effect on accuracy of either exposure duration,
display size, delay time, or heterogeneity of distributions. The mean
size seems to be registered automatically, since it will prime its subsequent
perception, or generate illusory targets in a search task. In dual task
conditions performance was better when the competing task required global
rather than local attention. We suggest that global attention enables
a statistical mode of processing in which the average size of a set of
elements, and perhaps other statistical properties like their average
color, direction of motion and orientation, are automatically extracted.
Other statistics like the range and variance may also be available. Feature
binding, however, may be severely limited in this statistical processing
mode.
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Whereas
there were long-lasting interests in both psychology and neurophysiology,
it is only the last several decades that crossmodal integration has become
one of the hottest topics. Other than the front-end differences at the
level of sensory receptors, how fundamentally are these sensory modalities
different or similar in neural processing? And, how fundamentally are
crossmodal integrations different or similar depending on the specific
modalities involved? In this lecture, I will inch towards answers to these
questions.
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Visual
surface representation is what is missing between the level of neuron's
receptive field and human perception. It may be formed by feature binding
with a special role of depth information, and seems to be the level where
attention operates. I will summarize evidence from psychophysics and TMS
(Transcranial Magnetic Stimulation).
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We
as humans typically do not have a conscious access to implicit process
underlying and preceding conscious emotional decision, nor awareness on
the causal relationship between them. We found a gaze bias which progressively
becomes stronger towards the final conscious decision of preference ("gaze
cascade effect"). I will review psychophysical evidence for intrinsic
involvement of such gaze bias as a somatic precursor of preference decision,
and EEG/fMRI evidence for the underlying neural mechanisms.
| - | Overview
of the organization of the human visual system |
| - | Neural
representation of visual features and objects across the visual
pathway |
| - | Basic
relationships between neural activity and conscious perception |
| - | Current
theories of visual awareness |
| - | Overview
of recent studies of binocular rivalry and perceptual filling-in
to identify the relationship between conscious perception and early
visual activity. |
| - | Overview
of new methods of analyzing brain imaging data to "read out"
a person's visual or mental state |
| - | How
to extract high-resolution information from low-resolution fMRI
signals |
| - | Applications
of fMRI pattern analysis to studying the neural representation of
visual features, complex objects, and conscious |