Cognitive abilities, monitoring, and control explain individual differences in heuristics and biases

ORIGINAL RESEARCH published: 13 October 2016 doi: 10.3389/fpsyg.2016.01559 Cognitive Abilities, Monitoring Confidence, and Control Thresholds Explain Individual Differences in Heuristics and Biases Simon A. Jackson *, Sabina Kleitman, Pauline Howie and Lazar Stankov School of Psychology, The University of Sydney, Sydney, NSW, Australia Edited by: Ulrich Hoffrage, University of Lausanne, Switzerland Reviewed by: Stephan Dickert, Vienna University of Economics and Business, Austria Sabine Greta Scholl, University of Mannheim, Germany Maggie E. Toplak, York University, Canada *Correspondence: Simon A. Jackson Specialty section: This article was submitted to Cognition, a section of the journal Frontiers in Psychology Received: 25 May 2016 Accepted: 23 September 2016 Published: 13 October 2016 Citation: Jackson SA, Kleitman S, Howie P and Stankov L (2016) Cognitive Abilities, Monitoring Confidence, and Control Thresholds Explain Individual Differences in Heuristics and Biases. Front. Psychol. 7:1559. doi: 10.3389/fpsyg.2016.01559 In this paper, we investigate whether individual differences in performance on heuristic and biases tasks can be explained by cognitive abilities, monitoring confidence, and control thresholds. Current theories explain individual differences in these tasks by the ability to detect errors and override automatic but biased judgments, and deliberative cognitive abilities that help to construct the correct response. Here we retain cognitive abilities but disentangle error detection, proposing that lower monitoring confidence and higher control thresholds promote error checking. Participants (N = 250) completed tasks assessing their fluid reasoning abilities, stable monitoring confidence levels, and the control threshold they impose on their decisions. They also completed seven typical heuristic and biases tasks such as the cognitive reflection test and Resistance to Framing. Using structural equation modeling, we found that individuals with higher reasoning abilities, lower monitoring confidence, and higher control threshold performed significantly and, at times, substantially better on the heuristic and biases tasks. Individuals with higher control thresholds also showed lower preferences for risky alternatives in a gambling task. Furthermore, residual correlations among the heuristic and biases tasks were reduced to null, indicating that cognitive abilities, monitoring confidence, and control thresholds accounted for their shared variance. Implications include the proposal that the capacity to detect errors does not differ between individuals. Rather, individuals might adopt varied strategies that promote error checking to different degrees, regardless of whether they have made a mistake or not. The results support growing evidence that decision-making involves cognitive abilities that construct actions and monitoring and control processes that manage their initiation. Keywords: decision-making, cognitive abilities, confidence, control, heuristics, biases, metacognition INTRODUCTION Decision-making often depends on the use of mental shortcuts (heuristics), which avoid the need for overwhelming mental computation but can also bias our judgments under certain conditions (Tversky and Kahneman, 1974; Gilovich et al., 2002; Kahneman and Klein, 2009). Yet there are pervasive individual differences in the degree to which people exhibit these sorts of biases Frontiers in Psychology | www.frontiersin.org 1 October 2016 | Volume 7 | Article 1559 Jackson et al. Individual Differences in Heuristics and Biases H&B tasks are not collectively and consistently tapping a single, general, and desirable construct. The second approach has involved seeking correlates/ predictors of individual’s H&B task performance on the basis of dual-process theories. According to these theories, two broad categories of cognitive processes construct judgments and actions (Evans and Stanovich, 2013 for a review). Type 1 processes are automatic and tend to rely on knowledge structures acquired via learning. They include processes like associative and constructive intuition (Glöckner and Witteman, 2010; Evans and Stanovich, 2013). Type 1 processes are therefore the typical source of heuristic responses that lead to errors on H&B tasks. Type 2 processes are deliberative and effortful mental operations. A classic example is fluid reasoning ability (Gf; Carroll, 1993; Stankov, 2000; McGrew, 2005, 2009). Gf is defined as “deliberate and controlled mental operations to solve novel problems that cannot be performed automatically” (McGrew, 2009, p. 5). Their reliance on working memory and controlled attention impose limits of their processing capacity (Evans and Stanovich, 2013; Shipstead et al., 2014). Such Type 2 abilities are the source of accurate responses on many H&B tasks. Investigated predictors of H&B tasks, therefore, tend to relate to Type 2 abilities or constructs that help shift decision makers from erroneous Type 1 to more accurate Type 2 thinking. Stanovich and West (2008) proposed that Type 1 heuristic errors on tasks like the Cognitive Reflection Test must be detected so that Type 2 processes like Gf can become engaged. By this account, individuals with stronger Type 2 cognitive abilities and who are better able to detect errors of judgment will perform better on H&B tasks. Researchers have therefore tested models such as that shown in Figure 1A. Adopting this approach, researchers measure individuals’ performance on a H&B task and predict the scores with cognitive abilities (e.g., Gf) and variables related to error detection (e.g., West et al., 2008; Toplak et al., 2011; Del Missier et al., 2012; Thompson and Johnson, 2014). In these studies, individuals’ ability to detect errors tends to be measured via executive control tasks in which they must suppress proponent and inaccurate responses in response time tasks. Alternatively, individuals are asked to self-report their tendency to engage in or enjoy Type 2 cognitive processing via self-report measures. Such studies typically find that Gf and error detection-like constructs are significant positive predictors of performance on H&B tasks in the manner proposed above. Given these findings, we will assess here two ways to obtain greater information about individual differences in H&B tasks like the Cognitive Reflection Test. The first will be to model changes in covariance among H&B tasks. Rather than factor analyzing all tasks together or regressing each task independently on predictors, we will adopt a new approach. Specifically, we will regress individuals’ H&B task scores on a set of predictors in a single Structural Equation Model, and allow their residuals to correlate freely. If the predictors are generally underlying performance on the H&B tasks, then we should observe more than just the significant regression coefficients that are found when each H&B task is used in separate regression models. More specifical (...truncated)