Cognitive Reflection, Decision Biases, and Response Times

ORIGINAL RESEARCH published: 22 September 2016 doi: 10.3389/fpsyg.2016.01402 Cognitive Reflection, Decision Biases, and Response Times Carlos Alós-Ferrer *, Michele Garagnani and Sabine Hügelschäfer Department of Economics, University of Cologne, Cologne, Germany Edited by: Nikolaos Georgantzis, University of Reading, UK Reviewed by: Michael Roy, Elizabethtown College, USA Conny Ernst-Peter Wollbrant, University of Gothenburg, Sweden *Correspondence: Carlos Alós-Ferrer Specialty section: This article was submitted to Personality and Social Psychology, a section of the journal Frontiers in Psychology Received: 15 July 2016 Accepted: 01 September 2016 Published: 22 September 2016 Citation: Alós-Ferrer C, Garagnani M and Hügelschäfer S (2016) Cognitive Reflection, Decision Biases, and Response Times. Front. Psychol. 7:1402. doi: 10.3389/fpsyg.2016.01402 Frontiers in Psychology | www.frontiersin.org We present novel evidence on response times and personality traits in standard questions from the decision-making literature where responses are relatively slow (medians around half a minute or above). To this end, we measured response times in a number of incentivized, framed items (decisions from description) including the Cognitive Reflection Test, two additional questions following the same logic, and a number of classic questions used to study decision biases in probability judgments (base-rate neglect, the conjunction fallacy, and the ratio bias). All questions create a conflict between an intuitive process and more deliberative thinking. For each item, we then created a non-conflict version by either making the intuitive impulse correct (resulting in an alignment question), shutting it down (creating a neutral question), or making it dominant (creating a heuristic question). For CRT questions, the differences in response times are as predicted by dual-process theories, with alignment and heuristic variants leading to faster responses and neutral questions to slower responses than the original, conflict questions. For decision biases (where responses are slower), evidence is mixed. To explore the possible influence of personality factors on both choices and response times, we used standard personality scales including the Rational-Experiential Inventory and the Big Five, and used them as controls in regression analysis. Keywords: cognitive reflection, decision biases, response times, decision making, Bayesian updating, multiple processes 1. INTRODUCTION Human beings attempt to behave rationally, but they often struggle as intuitive impulses get in the way. Sometimes the latter are useful, sometimes they invite disaster. Modern economic thinking is shaping the view that decisions are often the result of the interaction between fast intuitive thinking and the attempt (often unsuccessful) to behave in a rational way. While neoclassic economics concentrated on rationalistic behavior, other branches as, e.g., the literature on learning in games (following Kandori et al., 1993; Young, 1993) focused on the study of behavioral rules of thumb. More recently, dual-process models from psychology (Epstein, 1994; Sloman, 1996; Strack and Deutsch, 2004; Evans, 2008; Alós-Ferrer and Strack, 2014) have received increasing attention in economics. These models postulate decision-process heterogeneity at the intra-individual level, that is, the interaction of more intuitive and more deliberative processes within a decision maker’s mind. Individual heterogeneity, however, remains an important topic. Across individuals, heterogeneity concerns whether each particular decision maker relies more or less on one or the other kind of process. To measure this dimension, a number of scales and questionnaires have been developed. Among them are the Rational-Experiential Inventory of Epstein et al. (1996), including its two subscales Faith in Intuition (FI) and Need for Cognition (NFC), and the three-item 1 September 2016 | Volume 7 | Article 1402 Alós-Ferrer et al. Cognitive Reflection and Decision Biases Cognitive Reflection Test (CRT) of Frederick (2005), recently expanded by Toplak et al. (2014) and Primi et al. (2015). A recent branch of the literature has investigated interindividual differences regarding faulty probability judgments (heuristics and biases) using these scales. Oechssler et al. (2009) and Hoppe and Kusterer (2011) find that higher test scores in the CRT are correlated with lower incidences of certain biases, e.g., the conjunction fallacy (Tversky and Kahneman, 1983). As argued by Toplak et al. (2011), low CRT scores might indicate a tendency to act on impulse and give an intuitive response. Alós-Ferrer and Hügelschäfer (2012, 2016) showed that higher scores in Faith in Intuition are associated with higher error rates aligned with certain heuristics, e.g., based on representativeness or reinforcement, but found no systematic relation between the CRT and FI. This work continues the exploration of individual differences in faulty probability judgments and extends previous works by considering process data. The dual-process literature naturally relies on process data for the analysis of multi-process decisions, an approach which allows inferences which would be impossible with choice data only. The simplest kind of process data arises from response times. However, the heuristics-and-biases literature typically relies on decisions made on the basis of verbal descriptions, that is, on relatively complex, non-repeatable questions related to a more or less artificial situation (as for instance, the LINDA problem from Tversky and Kahneman, 1983). The use of response times in such a setting faces two main difficulties. The first difficulty is that within-subject comparisons for a single question are not possible. However, precisely those are the standard for response-times studies. In many behavioral studies, decisions are made in paradigms which allow for repetition, sometimes even for a large number of trials for each individual participant. In these cases, one can compare the response times of different responses for the same individual, which allows predictions linked to the very nature of processes. For instance, if (in an extreme case) it is assumed that a certain response overwhelmingly follows from a certain intuitive process, while another response overwhelmingly follows from a more deliberative one, one would predict the first response to be on average faster, simply because intuitive processes are faster. In a typical description-based decision, however, a paragraph-long decision situation is presented, the participant makes a decision, and moves on to a different question. Hence there is a unique observation per participant, which is either correct or not. It is not possible to test hypotheses on the relative speed of different responses, because such comparisons would be confounded with personal characteristics. For instance, if a process-based model predicted erro (...truncated)