Individual differences in use of the recognition heuristic are stable across time, choice objects, domains, and presentation formats

Abstract

The recognition heuristic (RH) is a simple decision strategy that performs surprisingly well in many domains. According to the RH, people decide on the basis of recognition alone and ignore further knowledge when faced with a recognized and an unrecognized choice object. Previous research has revealed noteworthy individual differences in RH use, suggesting that people have preferences for using versus avoiding this strategy that might be causally linked to cognitive or personality traits. However, trying to explain differences in RH use in terms of traits presupposes temporal and cross-situational stability in use of the RH, an important prerequisite that has not been scrutinized so far. In a series of four experiments, we therefore assessed the stability in RH use across (1) time, (2) choice objects, (3) domains, and (4) presentation formats of the choice objects. In Experiment 1, participants worked on the same inference task and choice objects twice, separated by a delay of either one day or one week. Experiment 2 replicated Experiment 1 using two different object sets from the same domain, whereas Experiment 3 assessed the stability of RH use across two different domains. Finally, in Experiment 4 we investigated stability across verbal and pictorial presentation formats of the choice objects. For all measures of RH use proposed so far, we found strong evidence for both temporal and cross-situational stability in use of the RH. Thus, RH use at least partly reflects a personspecific style of decision making whose determinants await further research.

Use of the recognition heuristic depends on the domain’s recognition validity, not on the recognition validity of selected sets of objects

“…Just as in Experiment 1, two additional analyses, namely an individual-participant analysis based on the means of the r-model parameter estimates for each participant (excluding non-fitting data sets) and a hierarchical r-model analysis (Michalkiewicz & Erdfelder, 2016), yielded mainly the same pattern of results (see Tables 7 and 8 in Appendix C), thus confirming that the aggregate analysis reported above did not produce artificial results based on pooling across individuals. There was only one deviation: The small difference in recognition validity that was significant in some of the aggregate analyses was not significant in either of the two additional analyses.…”

“…First, we applied the rmodel to the data of each participant separately and then computed the means of the parameter estimates across participants (excluding non-fitting data sets). Second, we applied a hierarchical variant of the r-model to the data that estimates individual and group parameters and takes c o r r e l a t i o n s b e t w e e n p a r a m e t e r s i n t o a c c o u n t (Michalkiewicz & Erdfelder, 2016). This model is based on Klauer's (2010) hierarchical latent-trait approach to MPT models.…”

“…Significant differences (i.e., 95% Bayesian credible intervals of the difference between parameter estimates of the two conditions do not include zero; cf. Horn et al, 2015;Michalkiewicz & Erdfelder, 2016) are given in bold type…”

Do Smarter People Employ Better Decision Strategies? The Influence of Intelligence on Adaptive Use of the Recognition Heuristic

“…Illustration of the hierarchical r‐model including intelligence as a predictor of RH‐use (adapted from Michalkiewicz & Erdfelder, ). The shaded and unshaded nodes represent observable and unobservable variables, respectively; the square and circular nodes represent discrete and continuous variables; the single‐bordered and double‐bordered nodes represent to‐be‐estimated and derived variables.…”

“…To close the methodological gap between individual estimates of RH‐use distorted by sampling error and the individual true scores of RH‐use as influenced by intelligence measures, we extended the hierarchical latent‐trait r‐model (Hilbig et al, ; Michalkiewicz & Erdfelder, ) based on Klauer's () latent‐trait approach to include either general intelligence (for the analysis of Hilbig's, data) or fluid and crystallized intelligence (for the analysis of the new experiment) as predictors of RH‐use. This model allows assessment of individual RH‐use along with the influence of intelligence measures on RH‐use in a single joint step.…”