Selection through rejection: Reconsidering the invariant learning paradigm

Quarterly Journal of Experimental Psychology, 51A(1), 1-17, 1998
Selection through rejection: Reconsidering the invariant learning paradigm
Elizabeth Churchill, David Gilmore
Abstract

Two experiments are reported that investigate the nature of selections in the McGeorge and Burton (1990) invariant learning paradigm. McGeorge and Burton suggest that subjects implicitly acquire abstract knowledge of an invariant feature (usually the presence of the digit “3”) in a set of 30 stimuli.

McGeorge and Burton's analysis has recently been challenged by Cock, Berry, and Gaffan (1994) and by Wright and Burton (1995). In this paper, we demonstrate that performance is based on knowledge of other aspects of the learning set besides the invariant digit, but that this knowledge is still implicit.

Altering the nature of the learning stimuli to highlight these co-varying features enhances the effects and increases the reporting of explicit knowledge. Our results indicate that performance within this paradigm is more easily characterized as rejection of salient negatives than selection of positive instances, but that salience is not based simply on similarity.

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Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.

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