Read this article for Friday
[1]Chelazzi L, Miller EK, Duncan J, Desimone
R. A neural basis for visual search in inferior
temporal cortex. Nature 1993; 363: 345-347.
“My theory is that …”
• Be able to complete this sentence by Nov 1
– This means you’ve completed some background reading
including some primary literature
– You’ve put lots of thought into crafting a testable, focused
theory and predictions that follow from that theory
Midterm 1 Grade Distribution
Visual Neuron Responses
• LGN cells converge on “simple” cells in V1 imparting orientation
(and location) specificity
The Feed-Forward Sweep
• Hierarchy can be defined more functionaly
• The feed-forward sweep is the initial response of each visual
area “in turn” as information is passed to it from a “lower” area
• Consider the latencies of the first responses in various areas
After the Forward Sweep
• By 150 ms, virtually every visual brain area has
responded to the onset of a visual stimulus
• But visual cortex neurons continue to fire for
hundreds of milliseconds!
• What are they doing?
After the Forward Sweep
• By 150 ms, virtually every visual brain area has
responded to the onset of a visual stimulus
• But visual cortex neurons continue to fire for
hundreds of milliseconds!
• What are they doing?
• with sufficient time (a few tens of ms) neurons begin
to reflect aspects of cognition other than “detection”
Extra-RF Influences
• One thing they seem to be doing is helping each
other figure out what aspects of the entire scene
each RF contains
– That is, the responses of visual neurons begin to change to
reflect global rather than local features of the scene
– recurrent signals sent via feedback projections are thought
to mediate these later properties
Extra-RF Influences
• consider texture-defined
boundaries
– classical RF tuning
properties do not allow
neuron to know if RF
contains figure or
background
– At progressively later
latencies, the neuron
responds differently
depending on whether it is
encoding boundaries,
surfaces, the background,
etc.
Extra-RF Influences
• How do these data contradict the notion of a
“classical” receptive field?
Extra-RF Influences
• How do these data contradict the notion of a
“classical” receptive field?
• Remember that for a classical receptive field (i.e.
feature detector):
– If the neuron’s preferred stimulus is present in the receptive
field, the neuron should fire a stereotypical burst of APs
– If the neuron is firing a burst of APs, its preferred stimulus
must be present in the receptive field
Extra-RF Influences
• How do these data contradict the notion of a
“classical” receptive field?
• Remember that for a classical receptive field (i.e.
feature detector):
– If the neuron’s preferred stimulus is present in the receptive
field, the neuron should fire a stereotypical burst of APs
– If the neuron is firing a burst of APs, its preferred stimulus
must be present in the receptive field
Recurrent Signals in Object
Perception
• Can a neuron represent whether or not its receptive
field is on part of an attended object?
• What if attention is initially directed to a different part
of the object?
Recurrent Signals in Object
Perception
• Can a neuron represent whether or not its receptive
field is on part of an attended object?
• What if attention is initially directed to a different part
of the object?
Yes, but not during the feed-forward sweep
Recurrent Signals in Object
Perception
• curve tracing
– monkey indicates whether a
particular segment is on a
particular curve
– requires attention to scan
the curve and “select” all
segments that belong
together
– that is: make a
representation of the entire
curve
– takes time
Recurrent Signals in Object
Perception
• curve tracing
– neuron begins to respond
differently at about 200 ms
– enhanced firing rate if
neuron is on the attended
curve
Feedback Signals and the binding
problem
• What is the binding problem?
Feedback Signals and the binding
problem
• What is the binding problem?
• curve tracing and the binding problem:
– if all neurons with RFs over the attended curve spike
faster/at a specific frequency/in synchrony, this might be the
binding signal
Feedback Signals and the binding
problem
• So what’s the connection between Attention and
Recurrent Signals?
Feedback Signals and Attention
• One theory is that attention (attentive processing)
entails the establishing of recurrent “loops”
• This explains why attentive processing takes time feed-forward sweep is insufficient
Feedback Signals and Attention
• Instruction cues (for example in the Posner CueTarget paradigm) may cause feedback signal prior to
stimulus onset (thus prior to feed-forward sweep)
• think of this as pre-setting the system for the
upcoming stimulus
• What does this accomplish?
Feedback Signals and Attention
• What does this accomplish?
• Preface to attention: Two ways to think about
attention
– Attention improves perception, acts as a gateway to memory
and consciousness
– Attention is a mechanism that routes information through the
brain
• It is the brain actively reconfiguring itself by changing the way
signals propagate through networks
• It is a form of very fast, very transient plasticity
Feedback Signals and Attention
• Put another way:
– It may strike you as remarkable that a single visual stimulus should
“activate” so many brain areas so rapidly
– In fact it should be puzzling that a visual input doesn’t create a
runaway “chain reaction”
• The brain is massively interconnected
• Why shouldn’t every neuron respond to a visual stimulus
Feedback Signals and Attention
• We’ll consider the role of feedback signals in
attention in more detail as we discuss the
neuroscience of attention
Attention as Information Selection
– consider a simple visual scene:
Attention as Information Selection
– What if the scene and task gets more complex: “Point to the red vertical
line”?
– What has to happen in order for this task to be accomplished?
Point to Waldo
Attention as Information Selection
•
One conceptualization of attention is that it is the process by which
irrelevant neural representations are disregarded (deemphasized?
suppressed?)
•
Another subtly different conceptualization is that attention is a
process by which the neural representations of relevant stimuli are
enhanced (emphasized? biased?)
Attention as Information Selection
• These ideas apply to other
modalities
– auditory “Cocktail Party”
problem
– somatosensory “I don’t feel
my socks” problem
Early Selection
• Early Selection
model postulated
that attention acted
as a strict gate at
the lowest levels of
sensory processing
• Based on concept of
a limited capacity
bottleneck
Late Selection
• Late Selection
models postulated
that attention acted
on later processing
stages (not sensory)
Early Selection
• Early Selection model
was intuitive and
explained most data
but failed to explain
some findings
• Shadowing studies
found that certain
information could
“intrude” into the
attended stream
– Subject’s name, loud
noises, etc.
Late vs. Early
• Various hybrid models have been proposed
– Early attenuation of non-attended input
– Late enhancement of attended input
Electrophysiological Investigations of
Attention
Modulation of Auditory Pathways
• Hillyard et al. (1960s)
showed attention effects
in human auditory
pathway using ERP
• Selective listening task
using headphones
– Every few minutes the
attended side was reversed
– Thus they could measure
the brain response to
identical stimuli when
attended or unattended
attending LEFT
Ignoring RIGHT
beep beep
beep beep
boop beep
beep beep
beep boop
beep beep
Modulation of Auditory Pathways
• Result: ERP elicited by attended and unattended
stimuli diverges by about 90ms post stimulus
– Long before response is made
– Probably in primary or nearby auditory cortex
Modulation of Auditory Pathways
•
Other groups have found ERP modulation even earlier – as early as Brainstem Auditory
Response
•
Probably no robust modulation as low as cochlea
•
by ~40 ms, feed forward sweep is already well into auditory and associated cortex
–
Thus ERP effects may reflect recurrent rather than feed forward processes