Base-rate sensitivity through implicit learning.

Two experiments assessed the contributions of implicit and explicit learning to base-rate sensitivity. Using a factorial design that included both implicit and explicit learning disruptions, we tested the hypothesis that implicit learning underlies base-rate sensitivity from experience (and that explicit learning contributes comparatively little). Participants learned to classify two categories of simple stimuli (bar graph heights) presented in a 3:1 base-rate ratio. Participants learned either from "observational" training to disrupt implicit learning or "response" training which supports implicit learning. Category label feedback on each trial was followed either immediately or after a 2.5 second delay by onset of a working memory task intended to disrupt explicit reasoning about category membership feedback. Decision criterion values were significantly larger following response training, suggesting that implicit learning underlies base-rate sensitivity. Disrupting explicit processing had no effect on base-rate learning as long as implicit learning was supported. These results suggest base-rate sensitivity develops from experience primarily through implicit learning, consistent with separate learning systems accounts of categorization

Base-Rate Sensitivity Through Implicit Learning

Two experiments assessed the contributions of implicit and explicit learning to base-rate sensitivity. Using a factorial design that included both implicit and explicit learning disruptions, we tested the hypothesis that implicit learning underlies base-rate sensitivity from experience (and that explicit learning contributes comparatively little). Participants learned to classify two categories of simple stimuli (bar graph heights) presented in a 3: 1 base-rate ratio. Participants learned either from “observational” training to disrupt implicit learning or “response” training which supports implicit learning. Category label feedback on each trial was followed either immediately or after a 2.5 second delay by onset of a working memory task intended to disrupt explicit reasoning about category membership feedback. Decision criterion values were significantly larger following response training, suggesting that implicit learning underlies base-rate sensitivity. Disrupting explicit processing had no effect on base-rate learning as long as implicit learning was supported. These results suggest base-rate sensitivity develops from experience primarily through implicit learning, consistent with separate learning systems accounts of categorization

Implicit Learning Mediates Base Rate Acquisition In Perceptual Categorization

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) may be involved in developing sensitivity to category base-rates

Proportion of participants (and FRB model β values) in Experiment 1 whose data were most parsimoniously accounted for by each model.

<p>Proportion of participants (and FRB model β values) in Experiment 1 whose data were most parsimoniously accounted for by each model.</p

Participant instructions describing each phase of the experiment.

<p>Underlined text was seen only in response conditions, italicized text in observational conditions. All other text was seen by all participants.</p

Average signal detection β values by condition.

<p>Resp = response training; Obs = observational training; Short = short feedback processing time; Long = long feedback processing time. Optimal β value = 3. Error bars show standard error.</p

Layout of trials in the training phase for long and short feedback processing time response conditions.

<p>Layout was identical for response and observational conditions, with the exception that observational conditions were shown the category label without making a response.</p

Experimental design.

<p>Participants completed the baseline phase followed by 5 cycles of alternating training and test blocks with unequal base-rates. (WM = Working memory task, Resp = response training condition, Obs = observational training condition).</p

Average proportion correct (& signal-detection β values) in each condition and block in Experiment 2.

<p>Average proportion correct (& signal-detection β values) in each condition and block in Experiment 2.</p

Average signal detection β values in Experiment 2 by condition.

<p>Resp = response training; Obs = observational training; Short = short feedback processing time; Long = long feedback processing time. Optimal β value = 3. Error bars represent standard error.</p