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PSY270 - Mar. 4th, 2014.pdf

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University of Toronto Mississauga
Elizabeth Johnson

Experiment 3:▯ Overall we have that false memory effect, however the manipulation (random number generation task) did not influence false memory recognition; it did influence correct memory recognition ▯ * pay attention to the actual mean values ▯ * can talk a lot about the trends ▯ * mean value more that 0.05 it is insignificant ▯ ▯ How is knowledge organized? (actually talking about semantic memory, what is its structure) ▯ - if you are presented with small information, you can connect it to other information ▯ - knowledge is divided into categories (group of objects that belong together and have something in common) ▯ - Exemplar is an item in the category▯ - Concept is a mental representation knowledge ▯ ▯ Definitional approach:▯ - categories are defined by a list of necessary (must all have defining features included for that category) and sufficient (other attributes are not required for category membership - e.g. when you think of a grandmother: spoil grandchildren, white hair, etc.) features ▯ - its really hard to come up with any list of defining features (problem) ▯ - not all categories have a list of defining features ▯ - or maybe, we just can’t come up with lists of feature ▯ ▯ - typicality ratings: poses a problem for definitional approach ▯ - this is impossible for the definitional approach ▯ - the fact that people find that the bat could be “bird-like” is a problem ▯ ▯ - we categorize typical exemplars faster than atypical ones ▯ - we generate typical exemplars more frequently than atypical ones ▯ ▯ Prototype Theory: ▯ - categories are fuzzy (they have a graded structure) ▯ - typical exemplars have more characteristic features than atypical ones (rather than having a set defining feature) ▯ - to determine category membership by matching item with prototype stored in memory (the more similar the exemplar is to the prototype the more likely it will be in that category) ▯ ▯ Posner & Keele Demo: do we actually easily come up with these prototypes? ▯ - participants were shown the random, high or low distortion NOT the prototype ▯ - training phase: categorize dots into group A or B ▯ - test phase: ID whether the patterns of dots were old or new ▯ - false memory: more likely to report that they saw the prototype even though it was not seen ▯ - this shows that we easily make prototypes [what it looks like] - you created it ▯ ▯ - there are 3 levels of prototype theory: basic (not too high not too low, just right), superordinate (one level up - more broad), subordinate (one level down - more specific)▯ - the specificity of these levels are not set in stone, the more knowledge you have the more specific or broad your levels get ▯ - basic level categories have a special psychological status: use to identify objects, produce priming effects (dog -> cat, faster reaction because the word “cat” is primed with the word dog) ▯ - basic level categories are not the same things as a prototype ▯ ▯ Problems with theories based on similarity ▯ - people can give typicality ratings to clearly defined categories [problem for defining, support prototype]: the fact that that we give typicality ratings means that we don’t have defined categories, even though this might not be the case▯ - how do we decide which characteristics are important and which aren't ▯ - category members may not be typical; typical cases may not be category members ▯ - we use experience to draw conclusions and create connections ▯ ▯ Semantic Network Models▯ ▯ Hierarchical model▯ - information is stored in nodes and connected to each other by links ▯ - property inheritance: information in a node is represented once, not at each of the nodes -> can go down the model; this allows for cognitive economy ▯ - ▯ problem: hierarchy effects only work for typical objects not atypical objects (reaction times)▯ Spreading activation model ▯ - once activated, it
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