PSYCH 322 1st Edition Lecture 22Outline of Last Lecture I.KnowledgeII.DemoIII.ExampleOutline of Current LectureIV.The prototype approachV.The exemplar approachVI.Semantic networksVII.The connectionist approachCurrent LectureI.The prototype approacha.Prototype = “typical”b.An abstract representation, not an actual example.c.Characteristic features that describe what members of that conceptare like. d.Best for detailing what happens EARLY in development and for larger categories.e.Strong positive relationship between prototypicality and family resemblance.i.When items have large amount of overlap with characteristics of other items in the category, the family resemblance of these items is highii.Low overlap = low family resemblancef.Most applicable when the categories are really large and rich. Example: plants, fishg.Less applicable when categories are small.h.Rosch ran an experiment in 1975 in which participant’s judge’s objects on a scale of 1-7. The most prototypical examples get betterratings.i.High-prototypicality: category member closely resembles category prototype1.Ex: for category “bird” = robinThese notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.ii.Low-prototypicality: category member does not closely resemble category prototype1.For category “bird” = penguin i.Typicality effect: prototypical objects are processed preferentially and are named more rapidly and members are more affected by a priming stimulusi.Example: a blue jay is a bird? True yes/no. how long did it take you to verify that notification? j.Rosch (1975)priming experiment: [know about this**]II.The exemplar approach-concept is represented by multiple examples (rather than a single prototype) and the examples are actually category members.a.To categorize, compare the new item to stored examples.b.Best for detailing what happens LATER in development and for smaller categories. Example: types of dogsc.The more similar a specific exemplar is to a known member, the faster it will be categorized.III.Levels of categories examplesa.Global- furnitureb.Basic- tablec.Specific- kitchen tablei.when you are an expert in an areaIV.Semantic networks- concepts are arranged in networks that represent the way concepts are organized.a.Collins and Quillian (1969)- fig 9.12- develop computer models to test how concepts and properties are associated in the mind.i.Node = category/conceptii.concepts are linkediii.fig 9.13 and 9.14- how we might test this model in peopleb.Heirarchial model (fig 9.12)- concepts are in color; general at top, specific at bottom, properties are blacki.The distance between each concept matters. We have to metaphorically travel this distance. This distance between concepts predicts how long it takes to retrieve information about concepts as measured by the sentence verification technique.ii.Exceptions are stored at lower level nodes.c.Spreading activation- (fig 9.14 – priming) brings other nodes to a highlighted readiness.i.Activation is the arousal level of a nodeii.When a node is activated, activity spread out along all connected linksiii.Concepts that receive activation are primes and more easily accessed from memoryd.Myer and Schvaneveldy (1971)- Lexical decision task: participants read stimuli and are asked to say as quickly as possible whether theitem is a word or not. “Yes” if both strings are words, “no” if not. Some pairs are closely associated, and their reaction time was faster for those pairs (spreading Activation). i.The time to respond when one string isn’t a word doesn’t change.ii.Time slows down when string of words are not related e.Criticism of Collins and Quillian: (1969 sentence verification task fig-13) cannot explain typicality effects- why is it faster for a canary as a bird vs an ostrich as a bird?i.Cognitive economy?ii.Some sentence-verification results are problematic for the model. (why is it faster for a pig as an animal vs a pig as a mammal) f.Collins and Loftus (1975)- modifications: shorter links to connect closely related concepts, longer linkers for less closely related concepts, and no hierarchical structure; based on a person’s experience.g.Assessment of Semantic Networks (Not Great)i.It is predictive and explanatory of some results but not allii.It is generated multiple experimentsiii.It has a lack of falsifiability:1.No rules for determining link length or how long activation will spread. Therefore, there is no experiment that would “prove it wrong”, circular reasoning.V.The connectionist approach-a.How do neurons that con only either signal of not signal (on-off) represent knowledge?b.Model on digital circuitry: 1 bit can only be on or off, group them together and get many more patterns (1byte: 00110011)c.Our neurons group together to form a network to represent concepts.d.A network of nodes and links but operated very differently from semantic networkse.“network-like units”- like neurons, some units are activated by stimuli from the environments and some are activated by signals received from other units:i.Input units: activated by stimulation from environmentii.Hidden units: receive input from input unitsiii.Output units: receive input from hidden unitsf.Connection weight- some synapses can cause a decrease in firing rate, some can increaseg.Concepts and properties are represented in network by the pattern of activation in these units;i.A stimulus presented to the input units is represented by the pattern of activity that is distributed across the other