The Opposite of Attention is Epilepsy:

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: • Attention solves the philosophical problem of a unitary consciousness • Attention solves a metabolic and thermal engineering problem • Attention solves a signal-to-noise problem by filtering noise • Attention solves a signal-to-noise problem by boosting signal gain • Attention solves a problem of ambiguity • 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 task-irrelevant 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) B L U E Cell “tuned” to line orientation. Should it affect your response? 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 Motor plan is executed Black Brain Box

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: • Greg Christie • Andrew Butcher • Jarrod Dowdall • Karla Ponjavic • Scott Oberg • Dillon Hambrook • Amanda McMullen • Sheena McInnes • Aja Mason

The Opposite of Attention is Epilepsy: