Evolution,%20Thought%20and%20Cognition

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Chapter 9

• Evolution, Thought and Cognition

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Some Points to Remember

• Costs and benefits
• Evolution doesnt optimize systems design to the
  level of good enough
• Inclusive fitness

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Costs of our Large Brain

• Energetically expensive (20 energy budget)
• Risk of CNS damage
• Birthing complications
• From evolutionary perspective, whats the benefit
  that justifies the costs?

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Whats the Brain Do?

• Biological computer
• Computational mechanisms to deal with
  environmental challenges
• Computational theory of mind
• Develop computational models of brain function
• Test
• Substrate neutrality - hardware (mostly) doesnt
  matter

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Levels of Explanation

• Computational Theory
• What problems was brain evolved to solve
• Representation and Algorithm
• What abstract mental computations is the brain
  evolved to execute to meet its goals
• Hardware Implementation
• How does the physical brain actually work to
  carry out computations

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Evolution Applied to Cognitive Science

• Visual perception
• Memory
• Categorization and reasoning

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Visual Perception

• What is the visual system for?
• Gives a representation of the external world
• Question is one of representational accuracy
• Many cases where visual system does not represent
  the external world as is
• Is this a design flaw, or an adaptation?

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Optical Illusions

• Show that the internal representation is not the
  same as the external features

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Hermann Grid
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Hering Illusion
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Julian Beevers Pavement Art
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Intentional (Mis)representation

• Visual system doesnt represent the world as it
  actually is
• Marr (1982) argues that this is not an error, but
  an adaptation
• Brain processes visual input and turns it into
  something useful

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• Brain evolved to function in the real world
• Visual illusions play with this
• Visual representation by brain interprets the
  input into a something that is more beneficial to
  viewer
• Fills in missing pieces, maintains colour
  consistency, adds scale and perspective
• Value of visual processing lies in keeping the
  individual alive long enough to reproduce (and
  maybe longer)

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Memory

• Value use past experience to predict future
  events.
• Preparedness
• Episodic and Semantic
• Specific experiences vs. general facts
• Inceptive and derived
• All information stored at time of experience vs.
  processed summaries of experience

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CostBenefit in Memory

• Recovery of complete encoded information
• Speed and ease of recall
• Depending on situation, different a balance is
  required

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Categorization

• A technique to parse information space
• Prototypes (stereotypes)
• Succinct, but non-inclusive
• Majority rule
• Increases retrieval speed and ease, but
  inaccuracies may occur as a byproduct

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Faulty Memory

• Why isnt memory perfect?
• Schacters seven sins of memory
• Transience, absent-mindedness, blocking,
  misattribution, suggestibility, bias, persistence

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Reasoning and Problem-Solving

• Variability exists in environment
• Heuristics
• Short-cuts for problem solving
• Not always correct
• Algorithms
• Computationally expensive
• Guarantee a correct answer

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Representational Fallacies

• Conjunction fallacy
• For event 1 and event 2 to be true, event 1 has
  to occur first, and is therefore more likely
• E.g. Linda is 31 years old, single, outspoken,
  and very bright. She majored in philosophy. As a
  student, she was deeply concerned with issues of
  discrimination and social justice, and also
  participated in anti-nuclear demonstrations.
  Which of the following statements about Linda is
  more probable? 1. She is a bank teller. 2. She is
  a bank teller who is active in the feminist
  movement.
• What is more representative of the real world?
• Brain mechanisms evolved to solve real world
  problems

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• Gamblers fallacy
• A run of bad luck must eventually be replaced
  with good luck
• E.g. Coin toss. Which is more likely HHHTTT or
  HTTHHT?
• An algorithm interpretation would say neither is
  more likely
• A representational heuristic, though, results in
  the second option, because it appears more
  random, i.e., more like the real world

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• The probability of something occurring often
  depends on something else happening first, for
  which there is also some ambiguity
• Bayes Theorem is a statistical principle that
  calculates the probability of an event being true
  given the probability of earlier events occurring
• People generally dont problem solve according to
  Bayes Theorem
• Demonstrates Base-rate Neglect (failure to take
  prior probabilities into account)
• But, restructure problem into one of frequencies
  rather than probabilities, and people do much
  better

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Frequency vs. Single-Case Probabilities

• Representational problems may be like visual
  illusions not actually flaws in the evolved
  system, but adaptations to operating in a
  particular (real) environment
• Cosimides Toobey (1996) argue that the human
  brain is good at dealing with frequencies (i.e.,
  repeatedly occurring events), but not single-case
  probabilities (one-off events)

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Frequency Based Decisions

• Optimal foraging theory
• How should animals partition limited time to
  maximize gain of required resources?
• Basically, an issue of choice
• Choice behaviour learned by making repeated
  choices and preferentially shifting towards those
  that give more benefits
• In essence, based upon frequency of reward

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Difficulty with Single-Case Probabilities

• Require particular reference classes to be useful
• Non-generalized
• E.g., Odds of winning lottery less than the odds
  of being struck by lightening.
• Butis this for someone who works outdoors? Lives
  on a high hill in the open prairie? Has metal
  golf clubs?

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Conditional and Logical Reasoning

• Not really that good at using rules of logic
• E.g., In science, a theory can only be disproven,
  never proven
• Much better at conditional reasoning

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Johnson-Laird Wason (1970)

• If p, then q logical rule
• Card with vowel has even number on back.
• Which card(s) do you turn over to test the rule?

Cards chosen Expressed logically E 3 p and not-q E 4 p and q E p only E, 4 3 p, q and not-q
Percentage of participants choosing this response 4 46 33 7
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Griggs Cox (1982)

• If a person is drinking alcohol, they must be
  over 19 years of age
• Imagine you are police checking for underage
  drinkers

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Cheat Detection Theory

• Cosimides (1989)
• Important for social exchange, reciprocity
• Due to social nature of humans, evolved modules
  for detecting freeloading are expected

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Domain Specific Algorithm

• Difficult to do abstract logic task
• Underage drinking task triggers mental modules
  for cheat detection
• Social contract infringement
• Omit police cover story and performance much
  closer to abstract logic task (Pollard Evans,
  1987)

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Information Gain Theory

• Oaksford Chater (1994)
• Two tasks dealing with entirely different domains
• Abstract task determine truth or falsehood of a
  rule (an indicative task)
• Underage drinking task not concerned with truth,
  but with obligations (deontic task)

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Testing for Rules

• Indicative tasks
• Reject rule based on finding contradictory
  evidence
• E.g., all swans are white now test
• Deontic tasks
• Cant prove rules true or false
• E.g., Under 19 not allowed to drink. But
  finding someone breaking the rule doesnt make it
  false

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Presented with Indicative Task

• Act to reduce level of uncertainty about world
• Rarity assumption in most cases, finding out
  something that is true is more informative than
  finding out something not true
• So, in WST, more likely to choose q card than
  not-q card
• Usually, positive information more useful than
  negative information

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Presented with Deontic Task

• Task requires you to take some perspective
  towards the rule, such as enforcing it
• Rarity assumption does not apply here
• High value placed on catching violators
• Rational choice is to select p and not-q

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Which Theory?

• Information gain theory explains wider range of
  logical reasoning tasks than cheat detection
  theory
• Humans as informavores
• Humans consume information in an analogous way to
  other animals consume food