The truth revisited: Bayesian analysis of individual differences in the truth effect
- PDF / 766,294 Bytes
- 16 Pages / 595.224 x 790.955 pts Page_size
- 115 Downloads / 210 Views
THEORETICAL REVIEW
The truth revisited: Bayesian analysis of individual differences in the truth effect Martin Schnuerch1
· Lena Nadarevic1
· Jeffrey N. Rouder2
Accepted: 5 September 2020 © The Author(s) 2020
Abstract The repetition-induced truth effect refers to a phenomenon where people rate repeated statements as more likely true than novel statements. In this paper, we document qualitative individual differences in the effect. While the overwhelming majority of participants display the usual positive truth effect, a minority are the opposite—they reliably discount the validity of repeated statements, what we refer to as negative truth effect. We examine eight truth-effect data sets where individuallevel data are curated. These sets are composed of 1105 individuals performing 38,904 judgments. Through Bayes factor model comparison, we show that reliable negative truth effects occur in five of the eight data sets. The negative truth effect is informative because it seems unreasonable that the mechanisms mediating the positive truth effect are the same that lead to a discounting of repeated statements’ validity. Moreover, the presence of qualitative differences motivates a different type of analysis of individual differences based on ordinal (i.e., Which sign does the effect have?) rather than metric measures. To our knowledge, this paper reports the first such reliable qualitative differences in a cognitive task. Keywords Individual differences · Qualitative differences · Truth effect · Hierarchical models · Bayesian model comparison In the usual course of experimental psychology, we often understand phenomena by computing the mean effect. This mean effect may be used to compute effect sizes or statistical tests, and the resulting inferences are about the mean level in the population. In our view, this focus on the mean makes sense when all people experience a phenomenon in a qualitatively similar way. For example, suppose we ask people to identify a briefly presented and subsequently masked letter. In this case, increasing the stimulus duration of the letter should affect every individual in the same direction, namely that longer durations correspond to better performance. It seems implausible in fact for any person’s true performance to decrease with increasing stimulus duration, and it is in this sense where we can be almost sure that a phenomenon affects people in a qualitatively similar manner, that recourse to the mean seems judicious.
Martin Schnuerch
[email protected] 1
Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
2
University of California, Irvine, CA, USA
What happens if a treatment affects different people differently? A good example might be the effect of aspirin. For most people, the drug aspirin safely relieves pain. Yet, a minority of the population are allergic to aspirin, and for these people the allergic reaction may be serious. In this case, questions about the mean response seem unimportant. Instead, t
Data Loading...
