Download Chapter 7 part two

yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

Neuropsychopharmacology wikipedia, lookup

Synaptic gating wikipedia, lookup

Channelrhodopsin wikipedia, lookup

Nervous system network models wikipedia, lookup

Feature detection (nervous system) wikipedia, lookup

Optogenetics wikipedia, lookup

Stimulus (physiology) wikipedia, lookup

Premovement neuronal activity wikipedia, lookup

Neural coding wikipedia, lookup

Neural oscillation wikipedia, lookup

Clinical neurochemistry wikipedia, lookup

Development of the nervous system wikipedia, lookup

Metastability in the brain wikipedia, lookup

Time perception wikipedia, lookup

Convolutional neural network wikipedia, lookup

Environmental enrichment wikipedia, lookup

Neural engineering wikipedia, lookup

Neuroeconomics wikipedia, lookup

Neural correlates of consciousness wikipedia, lookup

Aging brain wikipedia, lookup

Human brain wikipedia, lookup

Neuroesthetics wikipedia, lookup

Cognitive neuroscience of music wikipedia, lookup

Neuroanatomy of memory wikipedia, lookup

Emotion and memory wikipedia, lookup

Psychophysics wikipedia, lookup

Eyeblink conditioning wikipedia, lookup

Binding problem wikipedia, lookup

Allochiria wikipedia, lookup

Visual selective attention in dementia wikipedia, lookup

Cortical cooling wikipedia, lookup

Affective neuroscience wikipedia, lookup

Emotional lateralization wikipedia, lookup

Executive functions wikipedia, lookup

Inferior temporal gyrus wikipedia, lookup

C1 and P1 (neuroscience) wikipedia, lookup

Transsaccadic memory wikipedia, lookup

Sensory cue wikipedia, lookup

Visual N1 wikipedia, lookup

Visual search wikipedia, lookup

P200 wikipedia, lookup

Visual extinction wikipedia, lookup

Visual spatial attention wikipedia, lookup

Attention wikipedia, lookup

Human (ERP and imaging) and monkey
(cell recording) data together
1. Modality specific extrastriate cortex is modulated
by attention (V4, IT, MT).
2. V1 is modulated when task conditions are
demanding in cell studies, but disagreement
between ERP and fMRI for V1 may reflect both initial
(ERP) effects and later (fMRI) effects. Have to see it
before you attend to it?
3. Attention Mechanisms include both enhancement
and inhibition. Increased ‘neuronal gain’
Modeling Selective Visual Attention
Robert Desimone and John Duncan(1995)
Biased Competition Model
Multiple stimuli in the visual field activate populations of
neurons that automatically engage in competitive
interaction, which are assumed to be through intracortical
When attention is directed to a stimulus, this is thought to
be accompanied by feedback signals generated within
areas outside the classical visual system.
These signals bias the competition. As a result, neurons
responding to attended stimulus remain active while
suppressing neurons responding to unattended stimulus.
Duncan and Desimone proposal for
attention selection
Competition results in few or one stimulus actively
represented at a time in distributed representations
(lateral inhibition, winner take all effect). This
prevents cross talk or interference problems.
Pattern completion highlights commonalities.
Attending to a color will bring up all stimuli that have
that color.
Winner determined both by bottom up effects
(intensity and novelty) and top down activation from
higher areas.
More about biased competition
One theory that brings together all of the reviewed attention effects (top-down biases,
gain modulation, enhancement and suppression) is Desimone and Duncan’s ‘biased
competition’model of attention. The theory rests on three assumptions.
First, given the limits on our ability to process several stimuli at once, visual objects compete for
representational resources, and only one or a small number of stimuli can be represented
at one time. As the neural representations of visual stimuli are highly distributed,
competitive processing occurs in many of the brain areas sensitive to visual input.
Second, the competition is integrated across several areas, such that the neural
populations that represent different aspects of a single object interact in a mutually
facilitatory fashion. The gain in response to the selected object is accompanied by
suppressed processing in the neural populations representing features of different
objects. Therefore, as a ‘winner’ emerges in one system, the same object becomes
dominant across the distributed network.
Last, the competition can be biased not only by bottom-up factors (for example, stimulus intensity),
but also by top-down influences that are based on current task demands. Top-down bias is reflected in neural
priming (enhanced processing) of populations representing the relevant object attributes,
resulting in a competitive advantage for the relevant stimulus.
An important challenge for this theory (and other theories of attention) is to explain precisely how the
distributed neural populations responding to a single object ‘know’ that they are representing the
same object and so should enhance each other while suppressing the neural representations of other objects
(the binding problem).
Modeling Selective Visual Attention
Figure from Neural Mechanisms of
Selective Visual Attention by Robert
Desimone and John Duncan
Controlling attention: The top down
Prefrontal cortex
Prefrontal cortex is called the executive system of brain and has major role
in working memory (maintaining representations while we need to keep
thinking about them) consistent with a control of attention role.
Dorsolateral prefrontal cells maintain activity during active attention to a
location in absence of stimulus, and level of activity is related to level of
attention so it could bias earlier areas. Ventromedial areas maintain activity
during active attention to objects.
Bloodflow studies provide best evidence, but need good designs to be
convincing. Best control is exogenous (nonpredictive) cue like at bright box
at some location versus endogenous pointing cue at fixation. Right
dorsolateral prefrontal bloodflow is unique to endogenous (top down)
More top down: The posterior parietal
Neurons in posterior parietal cortex increase
firing for attended stimuli and locations.
Attention to spatial locations increases
bloodflow in posterior parietal cortex.
Visual neglect or extinction occurs with right
posterior parietal damage.
Why do we need posterior parietal
Need to mediate between ‘high level representations of objects
in space that guide our topdown allocation’ and retinotopically
mapped modality specific representations of visual stimuli that
are subject to competition effects’
We don’t want attention to jump when we move our eyes, we
also want to attend to other aspects besides visual. Unlike
attention experiments discussed so far we don’t keep our eyes
on a constant fixation point. We move around so
correspondence between retinal location and location in the
world is constantly shifting.
We need to translate between world and retinal coordinates.
Posterior parietal cortex neurons
encode for intention to move
Egocentric space is represented. Eye
movements, head movements, limb
movements that will get you to what you are
interested in.
Salient (important, attended to stimuli) are
So interface between what we want and how
we get to it.L6Action.swf
Parietal cortex translates between
world and retinotopic co-ordinates
Parietal neurons modulate
firing to receptive field
stimuli depending on fixation
or eye position. This isn’t all
you need to figure out where
the stimulus is in headcentered space but if you
have a population of these
(distributed representation)
then you can pinpoint one
Parietal cortex also has nonretinotopic
fields (LIP)
Cells encode the
memory of the location
of the field and shift
with eye movement
(before the eyes get
A planned shift
in the visual
world is seen
by parietal
neurons before
it happens
Supramodel attentional control
Need to co-index attention
for visual, auditory and
tactile qualities.Need
integration of multiple
sensory and motor
representations. See this in
VIP. Hemispatial neglect is
all about loss of supramodal
attentional control (p 207208.)
Desimone 2005: Parallel searches then serial searches
so not a single spotlight model and not a simple binding by location model.
New parallel and serial model
Throughout the period of searching, neurons gave enhanced
responses and synchronized their activity in the gamma range
whenever a preferred stimulus in their receptive field matched a
feature of the target, as predicted by parallel models.
Neurons also gave enhanced responses to candidate targets
that were selected for saccades, or foveation, reflecting a serial
component of visual search.
Thus, serial and parallel mechanisms of response
enhancement and neural synchrony work together to identify
objects in a scene.
Features (parallel)
The feature-related enhancement we observed is likely
the result of a combination of feature-selective
responses in the visual cortex, including V4, and
top-down feedback from structures involved in
working memory and executive control, such as the
prefrontal cortex and possibly the parietal cortex.
Such feedback must be capable of targeting
neurons with the appropriate feature preferences
throughout the visual field map.
Location (serial)
The saccade-related enhancement, on the other hand,
likely originates from feedback to V4 neurons with
RFs at particular locations, originating from
structures with spatial attention and oculomotor
functions such as the frontal eye field and the
lateral intraparietal area. These areas are thought
to represent a salience map in which stimuli are
represented according to their behavioral relevance
independent of their features
More top down: The posterior parietal
Neurons in posterior parietal cortex increase
firing for attended stimuli and locations.
Attention to spatial locations increases
bloodflow in posterior parietal cortex.
Visual neglect or extinction occurs with right
posterior parietal damage.
Hemispatial neglect
Left Visual Extinction