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Minds and Machines. Summer 2011 Wednesday, 8/3. Biological Approaches to Understanding the Mind. Connectionism is not the only approach to understanding the mind that draws on Biology, or the actual organization of the brain.

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minds and machines

Minds and Machines

Summer 2011

Wednesday, 8/3

biological approaches to understanding the mind
Biological Approaches to Understanding the Mind
  • Connectionism is not the only approach to understanding the mind that draws on Biology, or the actual organization of the brain.
  • In his chapter, Clark shows how our knowledge of the brain has led us to move away from an influential, computational paradigm for the study of vision.
  • This is significant because: if studying the brain can help us understand one mental process, then it is reasonable to expect it to illuminate many others.
marr s levels of analysis
Marr’s Levels of Analysis

Level 1 (task): Characterize the task being performed (e.g. identifying 3-d objects via 2-d inputs)

Level 2 (algorithm): Describe a scheme for representing the inputs and outputs and a sequence of mechanical steps that would carry out the task.

Level 3 (implementation): Determine how to build a device that would run through the sequence of steps.

marr s levels of analysis1
Marr’s Levels of Analysis
  • Until the 80s, many cognitive scientists took this framework to warrant ignoring or downplaying the importance of understanding the brain.
  • Nowdays, most cognitive scientists agree that discovering good computational models of cognition should be informed by neuroscience.
  • Still, there is agreement that there was something deeply right in Marr’s approach. We won’t understand the mind just by looking at what goes on in the brain. We need a general understanding of what the brain does, or what computational strategies it implements.
e volution vs h uman e ngineering
Evolution vs. Human Engineering
  • The computational processes that we are tempted to posit are likely to diverge from the evolved computational processes our brains actually carry out.
  • Evolution is both constrained and liberated in ways human engineers are not.
  • It is constrained because it builds its solution incrementally by a process of “tinkering”.
  • It is liberated because it is able to discover “messy” and unobvious solutions that would baffle human engineers.
e volution vs h uman e ngineering1
Evolution vs. Human Engineering
  • For example, consider the problem of controlling finger motion in monkeys.
  • A human engineer may come up with a neat solution where dedicated groups of neurons individually control each finger. So you would expect more neurons to fire when a whole-hand movement (e.g. a grasping movement) takes place.
  • It turns out that exactly the opposite happens: moving individual fingers requires more activity (to inhibit aspects of whole-hand movement). When you think about it from an evolutionary perspective, it makes a lot of sense!
  • This is also a good illustration of how evolution solves problems by tinkering (in this case with coordinated whole-hand commands).
vision the traditional view
Vision: the traditional view
  • The function of vision is to produce detailed inner representations of the 3-d visual scene on the basis of (impoverished) 2-d retinal images.
  • Such representations are then given as inputs to reasoning and planning centers, whose job is to determine a course of action and then send commands to the motor areas to carry it out.
chipping away at the traditional view
Chipping away at the traditional view
  • Psychology and neuroscience gives us reasons to think that the visual-system retrieves information as and when the information is needed for some specific problem solving purpose.
  • Our sense that we enjoy rich and detailed visual experiences, that every part of the visual scene enters our awareness, is a grand illusion! (Compare: the illusion that everything is there in a store that uses a computer ordering system)
chipping away at the traditional view1
Chipping away at the traditional view
  • We can design machines that can perform complex tasks that require sophisticated environmental sensitivity, without requiring rich representations of the environment or advance planning.
  • E.g. Herbert: walks around randomly, can avoid basic obstacles, detects outlines of cans using laser, can put himself in a standard position in front of a can and picks it up.
  • This requires no complex inner model of the environment. Sometimes: “the world is its own best model”.
chipping away at the traditional view2
Chipping away at the traditional view
  • Actions may play a role in the computation process that leads to visual outputs. Sometimes action guides vision rather the other way around!
  • For example, the process of distinguishing figure from ground uses information obtained from head movement during eye fixation.
  • The process involved in depth perception uses cues obtained by the observer’s motion towards the object.
chipping away at the traditional view3
Chipping away at the traditional view
  • Neural representations of events in the world may themselves already be recipes for action rather than passive data-structures that are given as inputs to reasoning processes.
  • For example, there are neurons in the monkey’s ventral pre-motor cortex (called mirror neurons) that are active both when the monkey observes a specific action and when the monkey performs the same kind of action. The perceived action is stored in terms of an action code, not a perceptual code.
  • Such representations may describe the world by depicting it in terms of possible actions (e.g. visual experience of artifacts, Phenomenology).