Brain Mechanisms of Unconscious Inference

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Brain Mechanisms of Unconscious Inference

• J. McClelland
• Symsys 100
• April 14, 2009

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Last time

• We considered many examples of unconscious
  inference
• Size illusions
• Illusory contours
• Perception of objects from vague cues
• Unconsious associative priming
• Lexical effects on speech perception
• Effect of visual speech on speech perception.

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Today

• We ask about the mechanisms through which this
  occurs

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Three Problems for Unconscious Inference
TheoryGARY HATFIELD, Philosopher, Univ. of Penna.

• The cognitive machinery problem Are the
  unconscious inferences posited to explain size
  perception and other phenomena carried out by the
  same cognitive mechanisms that account for
  conscious and deliberate inferences, or does the
  visual system have its own inferential machinery?
  In either case, what is the structure of the
  posited mechanisms?
• The sophisticated content problem how shall we
  describe the content of the premises and
  conclusions? For instance, in size perception it
  might be that the premises include values for
  visual angle and perceived distance . But
  shall we literally attribute concepts of visual
  angle to the visual system?
• The phenomenal experience problem Third, to be
  fully explanatory, unconscious inference theories
  of perception must explain how the conclusion of
  an inference about size and distance leads to the
  experience of an object as having a certain size
  and being at a certain distance. In other words,
  the theories need to explain how the conclusion
  to an inference can be or can cause
  perceptual experience.

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Proposed answers to these questions

• The cognitive machinery problem. The machinery of
  unconscious inference is the propagation of
  activation among neurons. Neurons embedded in
  the perceptual system can carry out such
  inferences without engaging the mechanisms used
  in conscious and deliberative inference.
• The sophisticated content problem. Activation of
  particular neurons or groups of neurons codes for
  particular content. Connections among neurons
  code the conditional relationships between items
  of content.
• The phenomenal experience problem. Activity of
  certain populations of neurons is a necessary
  condition for conscious experience. Anything
  that affects the activation of they neurons will
  affect conscious experience. Is this activity
  the actual substrate of experience itself?

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Outline of Lecture

• Neurons Structure and Physiology
• Neurons and The Content of Experience
• How Neurons Make Inferences
• And how these capture features of Bayes Rule
• Integration of Information in Neurons and in
  Perception

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Neuronal Structure and Function

• Neurons combine excitatory and inhibitory signals
  obtained from other neurons.
• They signal to other neurons primarily via
  spikes or action potentials.

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Neurons and the Content of Experience

• The doctrine of specific nerve energies
• Activity of specific neurons corresponds to
  specific sensory experiences
• Touch at a certain point on the skin
• Light at a certain point in the visual field
• The brain contains many maps in which neurons
  correspond to specific points
• On the skin
• In the visual world
• In non-spatial dimensions such as auditory
  frequency
• If one stimulates these neurons in a conscious
  individual, an appropriate sensation is aroused.
• If these neurons are destroyed, a corresponding
  void in experience occurs.
• Visual scotomas arise from lesions to the maps
  in primary visual cortex.

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Feature Detectors in Visual Cortex

• Line and edge detectors in primary visual cortex
  (classic figure at left).
• Cells show a graded response depending on exact
  orientation of line.
• Representation of motion in area MT
• Destroy MT on one side of the brain, and
  perception of motion in the opposite side of
  space is greatly impaired.

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Neural Representations of Objects and their
Identify

• The Grandmother Cell hypothesis
• Is there a dedicated neuron, or set of neurons,
  for each cognized object, such as my Grandmother?
• Most argue no but some cells have surprizingly
  specific responses

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Stimuli used by Baylis, Rolls, and Leonard (1991)
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Responses of Four Neurons to Face andNon-Face
Stimuli in Previous Slide
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Responses to various stimuli by a neuron
responding to a Tabby Cat (Tanaka et al, 1991)
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The Infamous Jennifer Aniston Neuron
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A Halle Barry Neuron
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Outline of Lecture

• Neurons Structure and Physiology
• Neurons and The Content of Experience
• How Neurons Make Inferences
• And how these capture features of Bayes Rule
• Integration of Information in Neurons and in
  Perception

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The Key Idea

• We treat the firing rate of a neuron as
  corresponding to the posterior probability of the
  hypothesis for which the neuron stands.
• If the excitatory inputs to a neuron correspond
  to evidence that supports the hypothesis for
  which the neuron stands
• And the inhibitory inputs correspond to evidence
  that goes against the hypothesis for which the
  neuron stands
• And if the baseline firing rate of the neuron
  reflects the prior probability of the hypothesis
  for which the neuron stands
• And all elements of the evidence are
  conditionally independent given H.
• THEN the firing rate of the neuron can represent
  the posterior probability of the hypothesis given
  the evidence.

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Unpacking this idea

• It is common to consider a neuron to have an
  activation value corresponding to its
  instantaneous firing rate or p(spike) per unit
  time.
• The baseline firing rate of the neuron is thought
  to depend on a constant background input called
  its bias.
• When other neurons are active, their influences
  are combined with the bias to yield a quantity
  called the net input.
• The influence of a neuron j on another neuron i
  depends on the activation of j and the weight or
  strength of the connection to i from j.
• Note that connection weights can be positive
  (excitatory) or negative (inhibitory).
• These influences are summed to determine the net
  input to neuron i
• neti biasi Sjajwij where aj is the
  activation of neuron j, and wij is the strength
  of the connection to unit i from unit j. Note
  that j ranges over all of the units that have
  connections to neuron i.

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How a Neurons Activation can Reflect P(HE)

• The activation of neuron i given its net input
  neti is assumed to be given by
• ai exp(neti) 1 exp(neti)
• This function is called the logistic function
  (graphed at right)
• Under this activation function
• ai P(HiE) iff aj 1 when Ej is present,
  0 when Ej is absent wij
  log(P(EjH)/P(EjH) biasi log(P(H)/P(H))
• In short, idealized neurons using the logistic
  activation function can compute the probability
  of the hypothesis they stand for, given the
  evidence represented in their inputs, if their
  weights and biases have the appropriate values,
  andthe elements of the evidence are
  conditionally independent given H.

ai
neti
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Math Supporting Above Statements
Bayes Rule with two conditionally independent
sources of information
Divide through by And let We obtain This is
equivalent to And more generally, when E
consists of multiple conditionally independent
elements Ej
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Choosing between N alternatives

• Often we are interested in cases where there are
  several alternative hypotheses (e.g., different
  directions of motion of a field of dots). Here
  we have a situation in which the alternatives to
  a given H, say H1, are the other hypotheses, H2,
  H3, etc.
• In this case, the probability of a particular
  hypothesis given the evidence becomes
• P(HiE) p(EHi)p(Hi)
  
  Sip(EHi)p(Hi)
• The normalization implied here can be performed
  by computing net inputs as before but now setting
  each units activation according to ai
  exp(neti) Siexp(neti)
• This normalization effect is approximated by
  lateral inhibition mediated by inhibitory
  interneurons (shaded unit in illustration).

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Outline of Lecture

• Neurons Structure and Physiology
• Neurons and The Content of Experience
• How Neurons Make Inferences
• And how these capture features of Bayes Rule
• Integration of Information in Neurons and in
  Perception

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• Cue Integrationin Monkeys
• Saltzman and Newsome (1994) combined two cues to
  theperception of motion
• Partially coherent motion in a specific direction
• Direct electrical stimulation
• They measured the probability of choosing each
  direction with and without stimulation at
  different levels of coherence (next slide).

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• Model used by SN
• SN applied the model we have been discussing
• Pi exp(neti)/Siexp(neti)
• Where Pi represents probability of responding in
  direction i
• neti biasi wiee wijvj
• wie effect of microstimulation on neurons
  representing percept of motion in direction i
• e 1 if stimulation was applied, 0 otherwise
• Wij effect of visual stimulation in direction j
• vj strength of motion in direction j

Electrical Input
Visual Input
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Evidence for the Model

• Effect of electrical stimulation is absent if
  visual motion is very strong, but is considerable
  if visual motion is weak (below).
• Responses arent just averages, but correctly
  reflect how different sources of evidence should
  combine, as per the model equation (right)

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Summary The Mechanism of Unconscious Perceptual
Inference

• Neurons (or populations of neurons) can represent
  perceptual hypotheses at different levels of
  abstraction and specificity
• Connections among neurons can code conditional
  relations among hypotheses.
• Excitation and Inhibition code p(EH)/p(EH)
• Lateral inhibition codes mutual exclusivity
• Propagation of activation produces results
  corresponding approximately to Bayesian
  inference.
• The resulting activity incorporates inferential
  processes that may alter our phenomenal
  experience.

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My Final Lecture on Thursdayand Next Homework

• Next time I hope to return to a format involving
  more discussion.
• For that, your reading of the Rumelhart
  McClelland paper listed under the readings will
  prepare you to discuss.
• We will consider the idea that the brain carries
  out a massively distributed collective
  inferential process during perception, using
  processes like those discussed in the RM paper,
  and applying these ideas to experimental evidence
  from perception and from physiology.
• Also look at the Assignment 2 handout, to be
  posted later this evening.
• If you run out of time, save the Logothetis paper
  for later (I will present the high points in
  class).