* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Cognition and Perception as Interactive Activation
Biological neuron model wikipedia , lookup
Stimulus (physiology) wikipedia , lookup
Holonomic brain theory wikipedia , lookup
Metastability in the brain wikipedia , lookup
Central pattern generator wikipedia , lookup
Cortical cooling wikipedia , lookup
Embodied cognitive science wikipedia , lookup
Optogenetics wikipedia , lookup
Brain Rules wikipedia , lookup
Feature detection (nervous system) wikipedia , lookup
Neural modeling fields wikipedia , lookup
Channelrhodopsin wikipedia , lookup
Synaptic gating wikipedia , lookup
Convolutional neural network wikipedia , lookup
Nervous system network models wikipedia , lookup
Time perception wikipedia , lookup
Types of artificial neural networks wikipedia , lookup
Catastrophic interference wikipedia , lookup
Neural correlates of consciousness wikipedia , lookup
Recurrent neural network wikipedia , lookup
Cognition and Perception as Interactive Activation Jay McClelland Symsys 100 April 16, 2009 What does it mean ‘to think’? • Is it… – To follow a set sequence of rules that algorithmically produces an answer? • Or is it… – To explore alternative possibilities until you find something that works? ‘Find something that works’ • The two string problem – You are in an office. There is a desk with a printer, some paper, stapler, binder clips, pens and pencils. – Two strings are hanging from a ceiling, about 12 feet apart in a room with a workbench. – You can’t reach the second one while holding the first. – What can you do to bring them both together? ‘Find something that works (2)’ • You know how much gold weighs per cubic centimeter, and you want to test whether the king’s golden crown is pure gold. But you don’t know how many cc’s of gold are in the crown. How can you find out? • Find a word that you can combine with each of the next three words to make a compound word: Pine, crab, tree • One more to work on later S. + H. of R. = U. S. C. Perception as ‘Finding Something that Works’ • For the first figure, one sees it a nothing until the ‘solution’ emerges. • For the second, there are two alternative solutions, each involving a reinterpretation of every part of the larger whole. Finding Perceptual Solutions • It appears that our brains can search for alternative solutions until one pops out. • How are such solutions found? – One answer is that the process occurs through a gradual, noisy, interactive activation process. The interactive Activation Model: a Gradual Mutual Constraint Satisfaction Process • Units represent hypotheses about the visual input at several levels and positions. – Features – Letters – Words • Connections code contingent relations: – Excitatory connections for consistent relations – Inhibitory connections for inconsistent relations – Lateral inhibition for competition among mutually inconsistent possibilities within levels. • Connections run in both directions – So that the network tends to evolve toward a state of activation in which everything is consistent. Interactive Activation Simultaneously Identifies Words and Letters • Stimulus input comes first to letter level, but as it builds up, it starts to influence the word level. • Letter input from all four positions makes work the most active word unit (there is no word worr). • Although the bottom up input to the letter level supports K and R equally in the fourth letter position, feedback from the word level supports K, causing it to become more active, and lateral inhibition then suppresses activation of R. Goodness and Constraint Satisfaction • Consider a network with symmetric connections, i.e. for all pairs of units i, j: wij = wji • Provide external input ei to some of the units. • Define the Goodness of a state of the network as: G = Si,jwijaiaj + eiai • Then as the network settles it tends toward states of higher goodness • Examples: – – – Three-unit network Necker Cube Network Interactive activation network • A little randomness allows networks to ‘break symmetry’ and jump out of local Goodness maxima. • If we gradually reduce the randomness, we can guarantee finding the best solution Interactivity in the Brain • Bidirectional Connectivity • Interactions between MT and ‘lower’ visual areas • Subjective Contours in V1 • Distributed Constraint Satisfaction in Binocular Rivalry Effect of Cooling MT on neural activation in lower visual areas • Investigated effects of cooling MT on neuronal responses in V1, V2, and V3 to a bar on a background grid of lower contrast. • MT cooling typically produces a reversible reduction in firing rate to V1/V2/V3 cells’ preferred stimulus (figure). • Top down effect is greatest for stimuli of low contrast. If the stimulus is easy to see, topdown influence from MT has little effect. Response decrease due to cooling in MT Lee & Nguyen (PNAS, 2001, 98, 1907-1911) • They asked the question: Do V1 neurons participate in the formation of a representation of the illusory contour seen in the upper panel (but not in the lower panel)? • They recorded from neurons in V1 tuned to the illusory line segment, and varied the position of the illusory segment with respect to the most responsive position of the neuron. Response to the illusory contour is found at precisely the expected location. Temporal Response to Real and Illusory Contours Neuron’s receptive field falls right over the middle of the real or illusory line defining the bottom edge of the square • The patterns seen in the physiology are comparable to those seen in the interactive activation model in that the effect of direct input is manifest first, followed somewhat later by contextual influences, presumably mediated in the physiology by neurons sensitive to the overall configuration of display elements. direct context Distributed Alternation of Brain Activity in Binocular Rivalry Discussion: Conscious Vs. Unconscious Cognition • We’ve considered perception predominantly but we can ask: How much of thought is like perception? • We’ve seen that our unconscious inferences are sometimes quite rational, though many of our conscious inferences are not. – What is the difference, and why are conscious inferences so errorful? • What have we learned, overall about whether human cognition is rational? Section Business • Please attend the section you've been assigned to -your attendance will be taken there. • Sunday, Apr 19th is the add-deadline on Axess. • Before the 19th, ensure that you are enrolled in the section corresponding to your section leader's name. • Sections times and places are as follows – – – – Thurs 3:45-4:35, 50-51B - Anubha Thurs 5:15-6:05, 160-318 - Rob Thurs 6:15-7:05, 160-325 - Jason Fri 2:15-3:05, 90-92Q - Anubha