The Opposite of Attention is
Epilepsy:
Six ways of thinking about attention and why you
should
Everyone Knows What Attention Is

“Everyone knows what attention is. It
is the taking possession by the mind in
clear and vivid form, of one out of
what seem several simultaneously
possible objects or trains of
thought...It implies withdrawal from
some things in order to deal effectively
with others…”

William James
Everyone Knows What Attention Is

James’ definition is a good start toward
operationalizing attention

What we would call “selective attention”
 As distinct from “arousal” “alerting” “cognitive effort”
Everyone Knows What Attention Is

The goal of this talk:

Give you some conceptual handholds to
start thinking about attention

Tell you my (and my lab’s) line of thinking
about attention
Everyone Knows What Attention Is

Preview:
1. Attention solves the philosophical problem of a
unitary consciousness
2. Attention solves a metabolic and thermal
engineering problem
3. Attention solves a signal-to-noise problem by
filtering noise
4. Attention solves a signal-to-noise problem by
boosting signal gain
5. Attention solves a problem of ambiguity
6. Attention solves a network complexity problem
Everyone Knows What Attention Is

Notice that none of these ideas are
exclusive of any others

They are all compatible ways to conceptualize the
same phenomenon
Attention Solves a Philosophical
Problem

Unitary Consciousness
◦ We are each one conscious self
◦ Attention is the phenomenological
manifestation of this constraint
◦ You only get one “train of thought”
◦ Maybe attention is “epiphenomenal”
If you are a “neurophilosopher”, this is why you should think about attention.
Attention Solves an Engineering
Problem
Waste heat is both an applied and
theoretical characteristic of computation
 The human brain is metabolically
demanding

The cortex is a
thermal engineering
nightmare

If you design microprocessors, this is why you should think about attention.
Attention Solves a Signal-to-Noise
Problem by Filtering “Noise”

“capacity limit” or “sensory bottleneck” notion first
proposed by Donald Broadbent in the 60’s

“leaky filter” notion proposed by Anne Treismann in the
70’s
Attention Solves a Signal-to-Noise
Problem by Filtering “Noise”

Evidence: Selective attention acts as a gate to awareness
Simons & Levin
If you do cognitive psychology (or neurophilosophy), this is why
you should think about attention.
Attention Solves a Signal-to-Noise
Problem by Filtering “Noise”

What are the neural correlates of such
“gating”?
Attention Solves a Signal-to-Noise Problem
by Filtering Noise
Chelazzi et al.
 Evidence: Selective attention suppresses neurons representing taskirrelevant features or objects

◦ Note that search array always contains a “good” stimulus for the recorded cell – but that
might not be the target
Intracranial Recordings of Attentional
Selection

Initial response of
cells is “classical”
Intracranial Recordings of Attentional
Selection

Initial response of
cells is “classical”

Response during
delay maintains a
representation of
the target feature
Intracranial Recordings of Attentional
Selection

Initial response of
cells is “classical”

Response during
delay represents the
target feature

Initial response to
search array is
“classical”
Intracranial Recordings of Attentional
Selection

About 200 ms after
array onset, response
of cell begins to
depend on attention
◦ Response becomes
more vigorous if cell is
tuned to features of the
target (i.e. the selected
stimulus)
◦ Response becomes
suppressed if cell is
tuned to a non-target
distractor
If you do electrophysiology: This is why you should think about attention!
(note this effect is absent in anesthetized animals)
Attention Solves a Signal-to-Noise
Problem by Boosting Signal Gain

Evidence: responses are faster and more accurate
(memory !) for attended relative to unattended events
If you’re a cognitive psychologist, this is why you should think about
attention (and probably you already do).
Attention Solves a Signal-to-Noise
Problem by Boosting Signal Gain

Evidence: Event-Related Potentials are enhanced for
attended relative to unattended stimuli
If you do electrophysiology, this is why you should think about attention.
Attention Solves an Ambiguity
Problem

Sensory Input Ambiguity
Cell “tuned” to red.
Should it fire?
Area V4 Receptive field = ~4 deg visual angle
Attention Solves an Ambiguity
Problem

Sensory Input Ambiguity
Cell “tuned” to red.
Should it fire?
If you do computational neuroscience,
This is why you should think about attention.
Area V4 Receptive field = ~4 deg visual angle
Attention Solves an Ambiguity
Problem

Response Mapping Ambiguity (e.g. Stroop
Task)
Cell “tuned” to line
orientation. Should it
affect your response?
BLUE
If you do computational neuroscience,
This is why you should think about attention.
Area V4 Receptive field = ~4 deg visual angle
Attention Solves an Ambiguity
Problem

Reward mapping ambiguity
Cell “tuned” to red.
Should it be associated
with reward?
If you do computational neuroscience,
This is why you should think about attention.
Area V4 Receptive field = ~4 deg visual angle
Attention Solves a Network
Complexity Problem

The brain is a massively interconnected
network - each neuron makes ~ 1000
connections
Gordon Kindlmann &
Andrew Alexander
University of Wisconsin
Van Essen, Andersen & Felleman (1992)
Attention Solves a Network
Complexity Problem
On the time scale of behaviour, the
network is anatomically hard-wired
 Fast functional reconfiguration

Attention Solves a Network
Complexity Problem

Point to the red horizontal line
Attention Solves a Network
Complexity Problem

Point to the red horizontal line
Visual stimulus
drives visual
neurons
Black Brain Box
Motor plan
is executed
Attention Solves a Network
Complexity Problem

Point to the red horizontal line
Visual stimulus
drives visual
neurons
Black Brain Box
Motor plan
is executed
Attention Solves a Network
Complexity Problem
Point to the red horizontal line
 Notice the mapping is selective:

Attention Solves a Network
Complexity Problem
Point to the red horizontal line
 Notice the mapping is selective:

Attention Solves a Network
Complexity Problem
Now point to the green vertical line
 Notice the mapping is easily reconfigured

Attention Solves a Network
Complexity Problem
Attention Solves a Network
Complexity Problem

Thus sensory neurons are in
some sense omnipotent

each one’s contribution to
cognitive and motor
networks is not determined
by anatomical connectivity

it is determined dynamically
by some control system
Attention Solves a Network
Complexity Problem

Notice this is an extension of
the “binding problem”

Cells representing features of
the same objects must
contribute to a
“reconstituted” whole object
representation

These cells must be “bound”
to all the other cells
mediating the current
cognitive or motor behaviour
If you study the “connectome”, this is why you should think about attention.
Attention Solves a Network
Complexity Problem

The brain is a massively interconnected
network - each neuron makes ~ 1000
connections
Attention Solves a Network
Complexity Problem

The brain is a massively interconnected
network - each neuron makes ~ 1000
connections
X 1000
Attention Solves a Network
Complexity Problem

The brain is a massively interconnected
network - each neuron makes ~ 1000
connections
X 1000
X 1000
Attention Solves a Network
Complexity Problem

The brain is a massively interconnected
network - each neuron makes ~ 1000
connections
X 1000
X 1000
X 1000
X 1000
X 1000
Attention Solves a Network
Complexity Problem
Crude Analogy
 By 4 synapses the tree comprises more
than 10 Billion cells!


Attention prevents runaway connectivity:
◦ Clearly the brain must have a system by which
information is routed appropriately through
the network
Attention Solves a Network
Complexity Problem
What does runaway connectivity look like?
 Here’s a hint: the “feed forward” sweep of signal
following a visual event is relatively
unconstrained by attention

Red = earliest response at this latency
Yellow = has already responded
Lamme (2000)
By ~115 ms post-stimulus, much of the cortex has responded to the visual event
Attention Solves a Network
Complexity Problem

What would be the consequence if
attention did not select cell assemblies?
Neural Gridlock? Maybe not the right
concept.
Attention Solves a Network
Complexity Problem
The brain is a system of coupled
oscillators
 Driving such systems can trigger
unexpected synchronization

Attention Solves a Network
Complexity Problem

Classic Example of spontaneous
synchronization
Attention Solves a Network
Complexity Problem

See a fabulous TED talk about
synchronization by Steven Strogatz at:
www.ted.com/talks/steven_strogatz_on_sync.html
Attention Solves a Network
Complexity Problem

Do brains exhibit runaway
global synchronization?

Yes, this is characteristic of
certain kinds of epileptic
seizures.
3 Hz “Spike and Wave” EEG pattern
during absence seizure
Attention Solves a Network
Complexity Problem

OK so how might a brain solve this problem? How might the
attention system facilitate a dominant cell assembly and suppress
others?

“Neuronal communication through neuronal coherence”
- Pascal Fries, TINS (2005)
Attention Solves a Network
Complexity Problem

Individual oscillators coupled to a central
oscillator
Attention Solves a Network
Complexity Problem

Role of the “central oscillator” has been
called the “dominant network”

Communication-through-coherence
suggests that oscillations within cell
assemblies become phase locked

One set of such assemblies achieves
global dominance by having their
individual phases nudged into coherence
Thanks

To my lab past, present and future:
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Greg Christie
Andrew Butcher
Jarrod Dowdall
Karla Ponjavic
Scott Oberg
Dillon Hambrook
Amanda McMullen
Sheena McInnes
Aja Mason