When people use rule-based integration of abstracted cues to make multiple-cue judgments they tend to default to linear additive integration of the cues, which may interfere with efficient learning in non-additive tasks. We hypothesize that this effect becomes especially pronounced when cues are presented numerically rather than verbally, because numbers elicit expectations about a task with a simple numerical solution that can be appropriately addressed by linear and additive integration. This predicts that, relative to a verbal format, a numerical format should be advantageous for learning in additive tasks, but detrimental for learning in non-additive tasks. In two experiments, we find support for the hypothesis that a verbal format can improve learning in non-additive tasks. The division-of-labor between cognitive processes observed in previous research (Juslin et al., 2008), with cue abstraction in additive tasks and exemplar memory in non-additive tasks, was only present in conditions with numeric information and may therefore in part be driven by the use of numeric formats. This illustrates how surface characteristic of stimuli can elicit different priors about the nature of the variables and the generative model that produced the cues and the criterion. We fitted cue-abstraction and exemplar algorithms by PNP-modeling (Sundh et al., 2021). At the end of training both cue abstraction and exemplar memory processes primarily involved exact analytic processes marred by occasional error, rather than the noisy and approximate intuitive processes typically assumed in previous studies - specifically, cue abstraction was primarily implemented by number crunching and exemplar memory by rote memorization.
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