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Bayesian Inference for Signal Detection Models of Recognition Memory Michael Lee Department of Cognitive Sciences University California Irvine

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Presentation on theme: "Bayesian Inference for Signal Detection Models of Recognition Memory Michael Lee Department of Cognitive Sciences University California Irvine"— Presentation transcript:

1 Bayesian Inference for Signal Detection Models of Recognition Memory Michael Lee Department of Cognitive Sciences University California Irvine Simon Dennis School of Psychology University of Adelaide

2 The Task Study a list of words Tested on another list of words, some of which appeared on the first list Subjects have to say whether each word is an old or new word Data take the form of counts –Hits –Misses –False Alarms –Correct Rejections

3 Signal Detection Model

4 Unofficial SDT Analysis Procedure Calculate a hit rate and false alarm rate per subject per condition Add a bit to false alarm rates of 0 and subtract a bit from hit rates of 1 to avoid infinite d' Throw out any subjects that are inconsistent with your hypothesis Run ANOVA While p > 0.05: collect more data Run ANOVA Publish

5 What we would like Sampling variability - variability estimates should depend on how many samples you have per cell Edge corrections – should follow from analysis assumptions Excluding subjects – should be done in a principled way Evidence in favour of null hypothesis Used iteratively – without violating likelihood principle Small sample sizes – not only applicable in the limit

6 A Proposal: The Individual Level Assume hits and false alarms are drawn from a binomial distribution (which allows us to generate a posterior distribution for the underlying rates that generated the data) Assume that both hits and false alarms are possible (given this the least informative prior about them is uniform) Assume d' and C are independent (which is true iff the hit rate and false alarm rates are independent) With these assumptions d' and C will be distributed as Gaussians

7 A Proposal: The Group Level Within subjects model Error Model: Error + Effect Model:

8 List Length Data (Dennis & Humphreys 2001) Is the list length effect in recognition memory a necessary consequence of interference between list items? List types Long | Study |Filler|--Test--| Short Start |-Study-| Filler |--Test--| Short End |----Filler-----|-Study-|Filler|--Test--|

9 Sampling Variability Rate Parameterization Posteriors

10 Sampling Variability Discriminability & Criterion Posteriors

11 Sampling Variability Discriminability & Log-Bias Posteriors

12 Edge Corrections Always assuming a beta posterior distribution of rates – never a single number Assumption of uniform priors provides principled method for determining “degree” of correction

13 Excluding Subjects Guess vs SDT Model in the Short-Start Condition

14 List Length Data (Kinnell & Dennis in prep) Does contextual reinstatement create a length effect? List types Long Filler | Study |Filler|--Test--| Short Filler |-Study-| Filler |--Test--| Long NoFiller | Study |--Test--| Short NoFiller |-Study-| Filler--|--Test--|

15 Evidence in Favour of Null Filler Error Model

16 Evidence in Favour of Null Filler Error + Effect Model

17 Evidence in Favour of Null No Filler Error Model

18 Evidence in Favour of Null No Filler Error + Effect Model

19 Evidence in Favour of Null Frequency Error Model

20 Evidence in Favour of Null Frequency Error + Effect Model

21 Evidence in Favour of Null Hypothesis: Summary

22 Used Iteratively Likelihood principle: –Inference should depend only on the outcome of the experiment not on the design of the experiment Conventional statistical inference procedures violate likelihood principle They cannot be used safely iteratively because as you increase sample size you change the design Bayesian methods (like ours) avoid this problem

23 Small Sample Sizes No asymptotic assumptions Applicable even with small samples Note: Still could be problems if there are strong violations of assumptions

24 Conclusions Sampling variability - variability estimates should depend on how many samples you have per cell Edge corrections – should follow from analysis assumptions Excluding subjects – should be done in a principled way Evidence in favour of null hypothesis Used iteratively – without violating likelihood principle Small sample sizes – not only applicable in the limit

25 Evidence in Favour of Null Hypothesis: Filler d'

26 Evidence in Favour of Null Hypothesis: No Filler d'

27 Evidence in Favour of Null Hypothesis: Frequency d'


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