The construction of categorization judgments: using subjective confidence and response latency to test a distributed model.

The classification of objects to natural categories exhibits cross-person consensus and within-person consistency, but also some degree of between-person variability and within-person instability. What is more, the variability in categorization is also not entirely random but discloses systematic patterns. In this study, we applied the Self-Consistency Model (SCM, Koriat, 2012) to category membership decisions, examining the possibility that confidence judgments and decision latency track the stable and variable components of categorization responses. The model assumes that category membership decisions are constructed on the fly depending on a small set of clues that are sampled from a commonly shared population of pertinent clues. The decision and confidence are based on the balance of evidence in favor of a positive or a negative response. The results confirmed several predictions derived from SCM. For each participant, consensual responses to items were more confident than non-consensual responses, and for each item, participants who made the consensual response tended to be more confident than those who made the nonconsensual response. The difference in confidence between consensual and nonconsensual responses increased with the proportion of participants who made the majority response for the item. A similar pattern was observed for response speed. The pattern of results obtained for cross-person consensus was replicated by the results for response consistency when the responses were classified in terms of within-person agreement across repeated presentations. These results accord with the sampling assumption of SCM, that confidence and response speed should be higher when the decision is consistent with what follows from the entire population of clues than when it deviates from it. Results also suggested that the context for classification can bias the sample of clues underlying the decision, and that confidence judgments mirror the effects of context on categorization decisions. The model and results offer a principled account of the stable and variable contributions to categorization behavior within a decision-making framework.