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Lecture 5

PSY260H1F Lecture 5 (Summer)

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Daniela Bellicoso

PSY260H1F L5; May 30, 13  Generalization Gradient: graph showing how physical Generalization & Discrimination Learning: Ch. 6 changes in stimuli (x-axis) correspond to changes in bhvr’al response (y-axis)  Generalization: transfer of past learning to novel events & o Key feature: rapid exponential decline on either side problems of the peak (…target stimulus response point) o Application of past knowledge to new info o Stimulus similarity determines how quickly the o Core issues of generalization: need to find an graph tapers off appropriate balance btwn specificity & generality o Physical traits influence degree of perceived similarity  Discrimination: recognizing that 2 stimuli or situations are different, and knowing which to prefer o Easy to judge what animal saw as similar or dissimilar o Depends on criteria, rules  Experience (learning) determines our ability to generalize & or discriminate btwn stimuli o Learning predisposes us to look at stimuli in dif ways Bhvr’al Processes Same Outcome Different Outcome Similar Similar stimuli  Similar stimuli  Dif Stimuli Same outcome outcomes Broccoli & Broccoli  nasty Cauliflower  nasty Cauliflower  tasty -same cruciferous -might dislike green veggie grp veggies & like white  If flat line at top: lots of generalization; many response to all veggies like potatoes stimuli since expect outcome from each Dissimilar Dissimilar stimuli  Dissimilar stimuli  Dif  If flat line at bottom: lots of generalization; no responses to all stimuli since expect no outcome from any of them Stimuli Same outcome outcomes Broccoli & Red Broccoli  nasty pepper  nasty Red pepper  tasty Generalization as a Search for Similar Conseqs -maybe you just  Generalized responding suggests that the responder dislike veggies overall (person, animal, etc) is a good estimator of future event  All vegetables probability  Key issue for generalization: identifying an inclusive set or Same Outcome Different Outcome range of stimuli w the same consequences as the training or Similar Similar stimuli  Same Similar stimuli  Dif target stimulus o The consequential region: how far we generalize from Stimuli outcome outcomes Malibu & Impala (4-door Malibu & Impala the target (to have the same outcome) Chevrolet sedans)  bad (mid-size vs full-size)  Previous ex. Yellow-green & yellow-orange  Not over-including, not over-discriminating since not 2-doors Dissimilar Dissimilar stimuli  Same Dissimilar stimuli   Balancing generality & specificity Stimuli outcome Dif outcomes  Generalization gradients represent an organisms’ attempt to predict the likelihood of a connection existing btwn a target Malibu & Corvet Malibu & Corvet (Utility vehicle & (more functional vs stimulus & similar stimuli based on experience Sportscar, but both are GM more sporty)  The Challenge of Incorporating Similarity into Learning Models cars) trusts GM so thinks wants the Corvet both cars are equally good since doesn’t need  Rescorla-Wagner model – used to show simple associations in fn’ality classical conditioning paradigms  Rescorla-Wagner model predicts a discrete-component  Cars for a bachelor & his decisions representation model for representing stimulus-outcome  All from General Motors Chevrolet associations o Discrete-Component Representation: representation When Similar Stimuli Predict Similar Conseqs where each indiv stimulus (or stimulus feature) Guttman & Kalish (1956)  Phase 1: Trained pigeons to peck at a yellow light for food corresponds to 1 element (node) of the model  This model contains 1 output node for the reinforcement response  Phase 2: Tested pigeons on dif trials showing single coloured lights & measured pecking  Good for specific types of targets that need high level of discrimination to pick out from a o Up to the bird what colour they could peck at crowd  Result: Most pecking occurred for trained (yellow) colour;  No generalization – don’t account for / learn 2nd best pecking to similar colours o Pigeons generalized due to seeing similarity btwn about similarities  Rescorla-Wagner rule indicates that weights from input colours nodes are modifiable by learning  Considering the pigeon paradigm… o Good for showing associations btwn highly  Training for various light pecking–outcome associations distinguished/dissimilar stimuli produces dif input node values  Looking for details o Don’t generalize well  Not good for showing associations btwn different but closely related stimuli  Discrete-component representations only work for understanding certain learning paradigms where stimuli have little shared similarity  Discrete component representations – each stimulus corresponds to 1 outcome  After Training: R-W says... o Thru experience  strong weight of Yellow o Strong association btwn yellow & food; all others weak by default  R-W generalization gradient – doesn’t have the usually o The other inputs can be perceived, but produce a “No Response” type of bhvr exponential curve  To get into the model, Input Nodes must be coded The Limitations of Discrete-Component Representations of Stimuli o Dif input patterns produce dif kinds of activity in the model  Discrete-component representations are best for predicting response rates to very different stimuli o They are not effective or accurate models for describing highly related sets of stimuli  Keep these important points in mind: o Representations of stimuli are chosen to suit a particular need  Ex. Call person’s house & ask for “Name”, but at school you’re a # (since within a large group of ppl) o Representations are context-specific  Representations might vary in their appropriateness depending on the situation or context  Ex. Students call teacher “Mrs. Smith”, friends call her “Slugger”, husband calls her “Hunny”  Dif representations in dif contexts yield dif patterns of similarity  Ex. 2 employees – Katherine & Catherine  Yellow node to output node: weight 1.0 o Association not active because yellow stimulus light w many similarities, similar hobbies & employment #s  better to have a not present Discrete Component Model to say that  Yellow-orange node is active: weight 0.0 o No activity will occur in the output node they are different ppl even tho they are similar  According to the R-W model: 100% responding to yellow  So sometimes good to have a high level of light, 0% responding to both similar yellow-based stimuli & completely dissimilar stimuli discrimination ability  But the original results of the pecking study prove this is Shared Elements & Distributed Representations actually not the case  Law of Effect – conseqs of action will affect if you repeat action o Thorndike proposed that stimulus generalization  Resulting generalization gradient from this overly simplified is due to the shared elements of stimuli model does not reflect Guttman & Kalish’s actual findings  Suggests simple models based on the R-W model have limited o Ppl might still produce the same response to dif stimuli if they think the same outcome will occur scope  Distributed Representations: representation where info is coded as a pattern of activation distributed across many dif nodes o What is learned from 1 stimulus will likely be generalized to other stimuli activating the same nodes  Suggests there is overlap in how the nodes are activated o Will respond to stimuli similar to the target stimulus  The model requires a representational transformation of a stimulus btwn input node & internal representation nodes o 0: not activated o 1: activated  Yellow light representation at input node level: 0 0 1 0 0  Model has 3 node layers, 2 weight layers o At this level: Discrete component representation  Yellow light representation at internal representation  Each stimulus activates 1 input node  connects to an node level: 0 0 1 1 1 0 0 internal representation layer via fixed nonmodifiable weights o At this level: Distributed representation o Now aware that similar things can signal we should o Internal representation nodes are connected to the respond to get the same outcome single output node via modifiable weights o A single input node can activate up to 3 internal o Things similar to yellow are also activated  If a particular stimulus is always given a reward, the representation nodes network learns to generate an appropriate response  Once processed, overlapping nodes can be activated  The layout of these nodes represents a topographic o Strengthening in the network o We realize this stimulus leads to the strongest representation outcome (is most likely to produce a food response) o Topographic Representation: stimulus representation where nodes that respond to physically similar stimuli in the world are placed in close proximity to each other in the model o Physical info is coded  Possibly less responding, but still a res
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