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Individual Differences in Cognitive Biases: Some Evidence for a Rationality Factor and Its Convergent/Discriminant and Ecological Validity

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Individual Differences in Cognitive Biases: Some Evidence for a Rationality Factor and Its Convergent/Discriminant and Ecological Validity Individual differences in cognitive biases: Some evidence for a rationality factor and its convergent/discriminant and ecological validity. Nikola Erceg1, Zvonimir Galić1, Andreja Bubić2 1 Faculty of humanities and social sciences, University of Zagreb 2 Faculty of humanities and social sciences, University of Split This work is a part of the project “Implicit personality, decision making and organizational leadership” funded by the Croatian science foundation (Grant no. 9354). Data can be found here: https://osf.io/yt52k/ This article is a preprint and it has not yet been published in a peer-reviewed journal. Comments, feedback, ideas, critiques, suggestions are very welcome. Feel free to address them to Nikola Erceg, Email: [email protected], Twitter handle: @nertzeg. Abstract The goal of this study was twofold. First, we investigated the dimensionality of several prominent cognitive bias tasks across two studies to see whether a single robust rationality factor can explain a performance on these tasks. Second, we validated this factor by correlating it with a number of constructs from its nomological network (fluid intelligence, numeracy, actively open-minded thinking, conspiracy and superstitious thinking, personality traits) and several real-life outcomes (decision- outcome inventory, job and career satisfaction, peer-rated decision-making quality). We found that, although a two-factor structure of biases was the best one in Study 1, in both studies one factor (called Rationality factor) was able to explain a substantial portion of variance in the tasks. Furthermore, across both studies, the two strongest correlations of Rationality factor were with numeracy and actively open-minded thinking measures. We conclude that there seems to exist a robust Rationality factor and that abilities and dispositions assessed with numeracy and actively open-minded thinking measures largely underpin it. We discuss how our findings relate to dual-process theories and offer our view on the place of rationality in a broader model of human intelligence. Keywords: Rationality; Cognitive bias; Numeracy; Actively open-minded thinking; Intelligence; Introduction Recently, Stankov (2017) argued that the research on intelligence has been stifled because of the continuing emphasis on the “g” factor and advocated for broadening the study of cognitive abilities by including additional, understudied constructs and measures. In particular, he believes that research on individual differences in cognitive abilities, among the other things, could be enriched by studying concepts and constructs from the domain of decision-making. There have been some indications that tasks that measure different cognitive biases (CBs) capture something other than intelligence, a construct that is labelled as rationality (e.g. Stanovich & West, 2008; Stanovich, West & Toplak, 2016) or decision-making competence (DMC, e.g., Bruine de Bruin, Parker & Fischoff, 2007; Parker & Fischoff, 2005), and as such could enrich our understanding of individual differences in cognitive processing. However, before rationality can be deemed a proper aspect of cognitive functioning, Stankov believes that it has to meet three criteria: a) measures of CBs must be reliable; b) correlations between different CBs measures should be high enough to allow for defining a single rationality factor; and c) rationality ought to show a moderate relationship with intelligence but not completely overlap with it. In line with this, the present study has three goals that correspond with the described requirements. Our first goal was to investigate whether different CBs can be measured with short, yet reliable measures. Previous studies (e.g. Bruine de Bruin et al., 2007; Finucane & Gullion, 2010; Teovanović, Knežević & Stankov, 2015) showed that it is possible to measure most of CBs sufficiently reliably, although this perhaps depends on the type of bias and the measurement method (Aczel, Bago, Szollosi, Foldes & Lukacs, 2015). Our second goal was to investigate a factorial structure of a set of CBs tasks. The main question here is whether one factor of rationality would account for substantial variance in CBs tasks or if the latent structure of CBs tasks would be more complex. On the one hand, some previous studies mostly point to a complex factorial structure of CBs tasks with a minimum of two to three factors needed to sufficiently account for the common variance among the tasks (e.g. Aczel et al., 2015; Ceschi, Constantini, Sartoti, Weller & Di Fabio, 2019; Slugoski, Shields & Dawson, 1993; Teovanović et al., 2015; Weaver & Stewart, 2012). On the other hand, some other researchers pointed out that different CB tasks are generally in positive intercorrelations and that, although multifactorial solutions are generally better, a single factor can often explain a substantial amount of variance in CBs tasks suggesting evidence for a rationality or DMC factor (e.g. Bruine de Bruin et al., 2007; Parker & Fischoff, 2005). Finally, our third goal was to investigate the relationship(s) between rationality factor(s) and different cognitive abilities such as fluid intelligence and numerical ability. Although Stanovich and West (2008) showed that some CB tasks are independent of cognitive abilities, the majority of studies demonstrated that the correlations between CBs and cognitive abilities are low to moderate (e.g. Bruine de Bruin et al., 2007; Erceg, Galić & Bubić, 2019; Parker & Fischoff, 2005; Teovanović et al., 2015; Toplak, West & Stanovich, 2011). A recent study by Blacksmith, Behrend, Dalal & Hayes (2019) even found a correlation between CBs tasks composite and general mental ability as high as to declare them to be empirically redundant. In addition to these three, we formulated a fourth goal – to validate our rationality measure by correlating it with variables from its nomological network, such as superstitious and conspiracy beliefs, thinking dispositions and personality traits (convergent validity), as well as with potential real life consequences of decisions (ecological validity). Previous studies showed that better performances on CB tasks are related to lower susceptibility towards epistemically suspect beliefs (superstitious/ paranormal/conspiracy beliefs; Čavojova, Šrol & Jurkovič, 2020; Erceg et al., 2019; Pennycook, Cheyne, Barr, Koehler & Fugelsang, 2015; Šrol, 2020, Toplak et al., 2017) but greater orientation towards actively open-minded thinking (Stanovich & West, 1997, 1998; Sá, West, & Stanovich, 1999; Toplak, West & Stanovich, 2014a). A few studies looking at the relationship between personality traits and CB performance found that the ability to resist framing errors was positively related with emotional stability, agreeableness and conscientiousness (Soane & Chmiel, 2005) and that a composite score on different CB tasks was positively correlated with conscientiousness, openness and honesty/humility (Weller, Ceschi, Hirsch, Sartori, & Costantini, 2018). Finally, performance on CBs tasks seems to be predictive of more positive real-life outcomes (Bruine de Bruin et al., 2007; Toplak et al., 2017). Stanovich, Toplak and West (2008) developed a taxonomy of CBs based on the dual-process theory of reasoning. According to this view, Type 1 processes (reflexive, deliberate) always generate initial response and the task of Type 2 processes (autonomous, automatic) is either to accept it or to intervene and correct it. Most of the CB tasks are deliberately designed in such a way to trigger an incorrect intuitive response that needs to be corrected in order to give a correct response. Thus, according to Stanovich et al. (2008), people can fail at different stages of detecting the incorrectness of intuitive response and coming up with a correct response, and different CBs typically result in failures at different steps of this process. Specifically, cognitive failures can be due to cognitive miserliness (defaulting to Type 1 processing without even noticing the need to engage in Type 2 processing, or engaging in Type 2 processes to insufficient extent such as in the case of availability bias or framing effects), override failures (Type 2 processes are engaged and try to override Type 1 responses, but ultimately fail; e.g. belief bias, denominator neglect), mindware gaps (when relevant knowledge needed to come up with correct response is lacking; e.g. conjunction fallacy, base-rate neglect) or contaminated mindware (when present knowledge and mindware is faulty and a thus a direct cause of irrationality; e.g. confirmation and myside bias). Another approach to developing a CB taxonomy is one by Oreg and Bayazit (2009). They propose that biases result from people trying to satisfy three motivational end states: achievement of consistency and coherence, comprehension of reality and approach to pleasure/avoidance of pain. Thus, there are three different types of biases: verification biases, simplification biases and regulation biases. Verification biases distort people’s perceptions of themselves and the world around them in order to achieve consistency and coherence (e.g. overconfidence bias, self-serving bias, unrealistically positive self- views). Simplification biases occur in the process of comprehending the reality and result in distortion and inaccuracies in the way people process information and analyze problems (e.g. base-rate neglect, belief bias, denominator neglect, conjunction fallacy, four-card selection errors). Finally, regulation biases occur when trying to approach pleasure and avoid
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