The Role of Categorization and Scale Endpoint Comparisons in Numerical Information Processing: A Two-Process Model
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AbstractWe propose a two-process conceptualization of numerical information processing to describe how people form impressions of a score that is described along a bounded scale. According to the model, people spontaneously categorize a score as high or low. Furthermore, they compare the numerical discrepancy between the score and the endpoint of the scale to which it is closer, if they are not confident of their categorization, and use implications of this comparison as a basis for judgment. As a result, their evaluation of the score is less extreme when the range of numbers along the scale is large (e.g., from 0 to 100) than when it is small (from 0 to 10). Six experiments support this two-process model and demonstrate its generalizability. Specifically, the magnitude of numbers composing the scale has less impact on judgments (a) when the score being evaluated is extreme, (b) when individuals are unmotivated to engage in endpoint comparison processes (i.e., they are low in need for cognition), and (c) when they are unable to do so (i.e., they are under cognitive load). Moreover, the endpoint to which individuals compare the score can depend on their regulatory focus.
Acceptance Date29/11/2016
All Author(s) ListTao Tao, Wyer Jr. Robert S., Zheng Yuhuang
Journal nameJournal of Experimental Psychology: General
Year2017
Month3
Volume Number146
Issue Number3
PublisherAmerican Psychological Association
Place of PublicationAmerica
Pages409 - 427
ISSN0096-3445
eISSN1939-2222
LanguagesEnglish-United States
Keywordsnumerical information processing, numerosity, scale range, two-process model

Last updated on 2020-11-07 at 02:31