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Remember/Know 100 Synonym Rhyme 90 Lure 80 Percent Correct 70 60 50 40 30 20 10 0 Remember Know Not on list False Memory • Concepts and Knowledge Percent of Reports 120 100 80 60 40 20 0 In original list Normal Distractor Special Distractor Knowledge • Knowledge is - presumably - what memories create • It can be gained and lost • Where is it? – We don’t know • How is it organized? – We have some guesses Categories and Terms • Category: Movie Stars – Exemplar – Individual members of a category (Harrison Ford, Halle Berry) – Rule – A precise definition of the criteria for a category (Must appear in a Movie) • Prototype – Specifies the properties that are most likely to be true of a category Stereotype or Prototype • Prototype - A template against which new examples are compared – Flexibility is acceptable • Stereotype - A fixed set of traits that a member of a category is assumed to possess – No flexibility PROTOTYPICALITY: For each category, list five traits associated with each category VEHICLES CLOTHING FRUIT PROTOTYPICALITY: For each category, assign the number “1” to the best exemplar, “2” to the second best, “3” to the third best, “4” to the fourth best, and “5” to the worst member of the category. VEHICLES car elevator sled tractor train CLOTHING jacket mittens necklace pajamas pants FRUIT olive grapefruit orange pear honeydew Prototypicality • Low values are assigned to those items with most of the features assigned to the prototype – Greatest family resemblance – These are also most likely to be listed if asked to list members of each group • Consider your results from Part 1 – Items with low scores probably had most of the traits you listed • Culture and personal experience play a role (olive) Reaction Times and Prototypes Task 1. Primed “Green” 2. Asked “Same?” 3. Respond “Yes/No” Rosch’s (1975b) Priming Experiment Reaction Times and Spreading Activation • We know that neurons are connected and that electrical activity passes through the network of neurons • Reaction times are assumed to be a reflection of how information travels from point-to-point through the network/brain • Fast reaction times are associated with short distances between the prime and target Typicality Gradients • Sentence verification experiments Each approach leads to the same results. Do they all support the existence of cognitive prototypes? Prototypes vs. Exemplars • Prototype theory – We have one “ideal” member of a category and we make judgments by comparing a stimulus to the ideal (which may not be an exemplar!). • Exemplar theory – We have lots of exemplars stored and we make judgments by comparing a stimulus to all exemplars and adding up the result. Support for Exemplars Medin et al.’s (1982) “burlosis” experiment. Results of the burlosis experiment. Is it settled? Exemplars vs. Prototypes • Classification is probably based on both strategies • One approach is probably favored when there is insufficient data for the other Semantic Networks • Might reflect the organization of the brain, but much can be learned without the direct neural link How does Classification Emerge? Where do you start when… Teaching a language to children/new learners? Which terms do you use when discussing basic elements of stories? How does Classification Emerge? Rosch provided evidence for the idea that the basic level is “psychologically privileged.” We start at the middle. - Categories at the middle level are most consistent across cultures, easiest to process, and members are more clearly grouped (What else do you notice?) Colins and Quillian (1969) Semantic Networks The objective was to develop a model of memory/knowledge that could be tested with computers! Cognitive Economy Common properties can be associated with categories, rather than individuals. The time required to retrieve information can be compared to the time it takes to “travel” through the semantic network. RT and Network Spreading Activation could explain “Priming Effects.” CogLab: Lexical Decision Task Meyer and Schvaneveldt (1971) Problem: Rips et al. (1973) A pig is a mammal. RT = 1476 ms A pig is an animal. RT = 1268 ms Challenges to Spreading Activation • The trouble with models – Don’t assume the network is fixed from moment to moment – Don’t assume that the network is the same from person to person The Solution! Collins and Loftus (1975). The hierarchy is abandoned in favor of a network that is based on experience! The Connectionist Approach: Remember the Neuron! Knowledge doesn’t “live at a node,” but is, instead represented by a combined firing of neurons. Weights Weights A parallel-distributed-processing (PDP) network. All seven units carry the message for both animals. The pattern of activity determines what you are thinking about. Learning in a PDP network. (a) Initially presenting canary causes a pattern of activation in the output units that is different than the pattern that stands for canary. (b) An error signal is transmitted back through the network to indicate how weights need to be changed to achieve the correct output response. continued on next slide Figure 8.30c (p. 300) (c) After the processes in (a) and (b) are repeated many times, the network has learned to respond correctly to canary. Pros and Cons of PDP • Uses the rules of the nervous system • Uses rules of learning – Try, get feedback, adjust, try again • Exhibits graceful degradation • Can’t explain fast learning • New learning will compromise old knowledge • May explain some kind of learning, but not all. Neuro-physiological Evidence • We have “category” neurons (i.e. neurons that respond to the image of any dog) • Specific forms of agnosia CogLab • Lexical Decision (done) • Prototypes Parallel Distributed Processing (PDP) as a Model of Cognition • • • • Immune to minor damage Work when input is noisy or incomplete Allow retrieval by content Retrieve typical instances of categories (prototypes) PDP Models • McClelland and Rummelhart(1986) • Knowledge is distributed • Computations take place in parallel Conclusions are based on consensus! PDP vs. Traditional Computer • Processing is parallel, not serial – In a serial system, one broken link stops everything • Information is distributed, not local Distributed Information Storage • The “benign” qualities of a tumor live in the same set of weights and connections as the malignant qualities Fuzzy Nature of Neurons • The response of neuron to any particular input is probabalistic, not fixed Light ON Fuzzy Nature of Neurons • Q: How can perceptions be consistent if neurons aren’t? • A: Perceptions arise from many active neurons and the responses (opinions) are averaged. – No neuron has the final say – No neuron is indespensable Access by Content • Connectionist networks allow access by content, rather than address. – Phone book: Access by address • You can find a number if you know name • You can’t find a name if you know a number • You cant find a number if you know an address – Access by content allows you to work either way. Any bit of info can lead to all other bits Access by Content • Each piece of information is linked to the other pieces and, therefore, can activate them (bring them into awareness). • Access is fault-tolerant, so small errors in input can still lead to a correct solution. A distributed system will look for a “best-fit.” – An erroneous spelling will not lead to a correct phone number Jets and Sharks Access by Address Conventional Database Works well if you start with a name. • Is Fred a pusher? • Do you know a pusher? • Are the Sharks educated? • Are Jets likely to be divorced? • What are Jets like? Access by Content Content Addressable Database You can start from anywhere! • Is Fred a pusher? • Do you know a pusher? • Are the Sharks educated? • Are Jets likely to be divorced? • What are Jets like? A Closer Look Excitatory Connections Nodes Knowledge Areas Inhibitory Connections Instance Units A Closer Look • All nodes start at the same level of activity. • Input (question) activates a node and “information” starts flowing through the network. • Eventually, the network stabilizes, and the solution is represented by the most active nodes