Minds and Machines

1. Minds and Machines Summer 2011 Wednesday, 8/3

2. 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.

3. 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.

4. 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.

5. 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.

6. 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).

7. 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.

8. 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)

9. 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”.

10. 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.

11. 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).