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Physiological arousal in processing recognition information: Ignoring or integrating cognitive cues?

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  • Guy Hochman
  • Shahar Ayal
  • Andreas Gloeckner
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    Abstract

    The recognition heuristic (RH; Goldstein \& Gigerenzer, 2002) suggests that, when applicable, probabilistic inferences are based on a noncompensatory examination of whether an object is recognized or not. The overall findings on the processes that underlie this fast and frugal heuristic are somewhat mixed, and many studies have expressed the need for considering a more compensatory integration of recognition information. Regardless of the mechanism involved, it is clear that recognition has a strong influence on choices, and this finding might be explained by the fact that recognition cues arouse affect and thus receive more attention than cognitive cues. To test this assumption, we investigated whether recognition results in a direct affective signal by measuring physiological arousal (i.e., peripheral arterial tone) in the established city-size task. We found that recognition of cities does not directly result in increased physiological arousal. Moreover, the results show that physiological arousal increased with increasing inconsistency between recognition information and additional cue information. These findings support predictions derived by a compensatory Parallel Constraint Satisfaction model rather than predictions of noncompensatory models. Additional results concerning confidence ratings, response times, and choice proportions further demonstrated that recognition information and other cognitive cues are integrated in a compensatory manner.

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    Bibliographic Info

    Article provided by Society for Judgment and Decision Making in its journal Judgment and Decision Making.

    Volume (Year): 5 (2010)
    Issue (Month): 4 (July)
    Pages: 285-299

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    Handle: RePEc:jdm:journl:v:5:y:2010:i:4:p:285-299

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    Related research

    Keywords: affect; recognition heuristic; physiological arousal; parallel constraint satisfaction; noncompensatory models.;

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