context model, bayesian exemplar models, neural networks
DESCRIPTION
Context Model, Bayesian Exemplar Models, Neural Networks. Medin and Shaffer’s ‘Context Model’. No category information -- only specific items or exemplars. Evidence for category A given probe p: E A,p = S i in a S ( p,i )/( S i in a S ( p,i ) + S i in b S ( p,i )) Where - PowerPoint PPT PresentationTRANSCRIPT
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Context Model, Bayesian Exemplar Models, Neural Networks
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Medin and Shaffer’s ‘Context Model’
• No category information -- only specific items or exemplars.
• Evidence for category A given probe p:
EA,p = Si in aS(p,i)/(Si in aS(p,i) + Si in bS(p,i))
• Where
S(p,i) = Pj (Pj = Iij ? 1:aj) ; aj = c,f,s,p
• Prob. of choosing category A given probe p:
PA,p = EA,p
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Medin and Shaffer’s ‘Context Model’
• No category information -- only specific items or exemplars.
• Evidence for category A given probe p: •
EA,p = Si in aS(p,i)/(Si in aS(p,i) + Si in bS(p,i))
• Where•
S(p,i) = Pj (Pj = Iij ? 1:aj) ; aj = c,f,s,p
• Probability of choosing category A given probe p:
• PA,p = EA,p
![Page 4: Context Model, Bayesian Exemplar Models, Neural Networks](https://reader036.vdocument.in/reader036/viewer/2022081513/56816741550346895ddbf414/html5/thumbnails/4.jpg)
Some things about the model• Good matches count more than weak matches• An exact match counts a lot• But many weak matches can work together to make a (non-presented)
prototype come out better than any exemplar• Dimension weights like ‘effective distance’ (or maybe ‘log of effective
distance?’• If weight = 0, we get a categorical effect• Dimension weights are important – how are they determined?
– Best fit to data?– Best choice to categorize examples correctly?
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Independent cue models
For items 1, 2, 3 and 7:
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Neural Network Model Similar to Context Model
Choice rule:
)()1( restydnetyy iiii
)(min)( restydnetyy iiii
if neti(t) > 0
else
Within each pool, units inhibit each other; between pools, they are mutually exictatory
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What REMERGE Adds to Exemplar Models
Recurrence allows similarity between stored items to influence performance, independent of direct activation by the probe.
X
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Bayes/Exemplar-like Version of the Remerge Model
inpi
inpi
Choice rule:
Hedged softmax function:
Logistic function:
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Acquired Equivalence(Shohamy & Wagner, 2008)
• Study:– F1-S1; – F3-S3;– F2-S1; – F2-S2;– F4-S3; – F4-S4
• Test:– Premise: F1: S1 or S3?– Inference: F1: S2 or S4?
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F1 S1 F2 S2 F3 S3 F4 S4
Acquired Equivalence(Shohamy & Wagner, 2008)
• Study:– F1-S1; – F3-S3;– F2-S1; – F2-S2;– F4-S3; – F4-S4
• Test:– Premise: F1: S1 or S3?– Inference: F1: S2 or S4?
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F1 S1 F2 S2 F3 S3 F4 S4
Acquired Equivalence(Shohamy & Wagner, 2008) S1 S2 S3 S4
• Study:– F1-S1; – F3-S3;– F2-S1; – F2-S2;– F4-S3; – F4-S4
• Test:– Premise: F1: S1 or S3?– Inference: F1: S2 or S4?
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F1 S1 F2 S2 F3 S3 F4 S4
Acquired Equivalence(Shohamy & Wagner, 2008) S1 S2 S3 S4
• Study:– F1-S1; – F3-S3;– F2-S1; – F2-S2;– F4-S3; – F4-S4
• Test:– Premise: F1: S1 or S3?– Inference: F1: S2 or S4?
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Acquired Equivalence(Shohamy & Wagner, 2008)
• Study:– F1-S1; – F3-S3;– F2-S1; – F2-S2;– F4-S3; – F4-S4
• Test:– Premise: F1: S1 or S3?– Inference: F1: S2 or S4?