Academic journal article Psychonomic Bulletin & Review

Implicit Learning Mediates Base Rate Acquisition in Perceptual Categorization

Academic journal article Psychonomic Bulletin & Review

Implicit Learning Mediates Base Rate Acquisition in Perceptual Categorization

Article excerpt

Published online: 19 July 2014

© Psychonomic Society, Inc. 2014

Abstract We explored the possibility, suggested by Koehler (Behavioral and Brain Sciences, 19,1-53,1996;also Spellman Behavioral and Brain Sciences, 19,38,1996), that implicit learning mediates the influence of base-rates on category knowledge acquired through direct experience. In two experiments, participants learned simple perceptual categories with unequal base-rates (i.e., presentation frequency). In Experiment 1, participants received either response training or observational training. In Experiment 2, participants received response training with either immediate or delayed feedback. In previous studies, observational training and delayed feedback training have been shown to disrupt implicit learning. We found that base-rate influence was weaker in these conditions when category discriminability was low (i.e., when category membership was difficult to determine). This conclusion was based on signal detection β values as well as decision-bound modeling results. Because these disruptions to implicit learning attenuate the base-rate effect, we conclude that implicit learning does indeed underlie the influence of base-rates learned through direct experience. This suggests that the implicit learning system postulated by the COVIS theory of categorization (Ashby, Alfonso-Reese, Turken, & Waldron Psychological Review, 105, 442-481, 1998)maybe involved in developing sensitivity to category base-rates.

Keywords Base rate · Categorization · Implicit learning · COVIS


Classification judgments are commonly based on observable stimulus features. Medical diagnosis, for example, relies on symptoms exhibited by a patient. But observable features are sometimes insufficient for accurate judgment. For instance, a set of symptoms (e.g., fever, aches, fatigue) may stem from a variety of causes. In such cases, choice may be guided by knowledge that is separate from outwardly apparent stimulus information. Base-rates-the relative prevalence of category alternatives-are a well-known example. Fever and fatigue may suggest allergies in July but imply the flu in January.

Several variables determine the influence of base-rate information, such as presentation format (e.g., probability vs. frequency summary statistics) and the diagnosticity of stimulus features (to name just a few; see Koehler, 1996 for a review). A useful task for studying these variables is perceptual classification learning. Many variables can be manipulated within this context, and decision-criterion location provides a basis for comparing results across studies. A widely-held conclusion has been that base-rates exert a strong influence over performance when directly experienced over a long series of classification trials (Bohil & Maddox, 2001;Estes, 1989; Gluck & Bower, 1988). Maddox and Bohil demonstrated that different levels of category discriminability and baserate ratio moderate the influence that base-rates have on criterion placement when learned via direct experience (for a review see Maddox, 2002).

Although the effect of direct experience on base-rate learning has received much attention from researchers, one proposed explanation for this result has not. Koehler speculated that, while gaining direct experience with a set of categories, base-rate sensitivity results from implicit learning of relative prevalence over time (Koehler, 1996; see also Holyoak & Spellman, 1993;Spellman,1996). To our knowledge, however, this possibility has not been directly tested. We hypothesize that manipulations that disrupt implicit learning should diminish the effect of base-rates during category learning, and this should be reflected in decision criterion placement.

Implicit learning in classif ication

Outside the base-rate literature, the role of implicit learning in classification has become a prominent research topic. Stemming largely from the work of Ashby and colleagues (e. …

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