Investigating the origin and consequences of endogenous default options in repeated economic choices

Couto, Joaquina; Maanen, Leendert van; Lebreton, Maël

doi:10.1371/journal.pone.0232385

Cited by 9 publications

(14 citation statements)

References 81 publications

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“…Examples include perceptual decision-making (Forstmann et al, 2016;Mulder et al, 2014;Ratcliff et al, 2016), economic decisionmaking (e.g. Couto et al, 2020;Kocher & Sutter, 2006;Reutskaja et al, 2011;Rubinstein, 2007), judgement under uncertainty (Edland & Svenson, 1993;Maule et al, 2000;Ordóñez & Benson, 1997;Young et al, 2012), probabilistic reasoning (e.g. Furlan et al, 2016;Gershman & Goodman, 2014), and syllogistic reasoning (e.g.…”

Section: Time Pressurementioning

confidence: 99%

Probabilistic causal reasoning under time pressure

Kolvoort¹,

Fisher²,

Rooij³

et al. 2022

Preprint

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Causal reasoning is a core facet of our cognitive abilities. However, the time-course of causal reasoning is not studied to its fullest. The duration of reasoning, including those that yield a reasoning error, might prove crucial in understanding the cognitive processes underlying causal reasoning. In two experiments we asked participants to make probabilistic causal inferences while manipulating external time pressure and measuring response times. We found that participants are less accurate under time pressure, which can be interpreted as a speed-accuracy-tradeoff. In addition, we found that conservative responding increased under time pressure. Surprisingly, we found that two other persistent reasoning errors - Markov violations and failures to explain away - appear insensitive to the time pressure we employed. These observations seemed related to the confidence that participants expressed in their causal judgment: Conservatism was associated with low confidence, whereas Markov violations and failures to explain were not. These findings shed doubt on existing explanations of causal reasoning that predict an association between time pressure and all reasoning errors. Our findings suggest that these errors should not be attributed to a single cognitive mechanism and emphasize the need for an understanding of causal judgements as the result of multiple processes.

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Section: Time Pressurementioning

confidence: 99%

Probabilistic causal reasoning under time pressure

Kolvoort¹,

Fisher²,

Rooij³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Cognitive dissonance and premature closure also tended to associate with default (or status quo) bias , i.e. when people stick to previously made decisions and/or prefer things to stay the same by doing nothing (inertia) although this occurs in spite of changing circumstances [ 55 ] Time pressure can foster this bias, as recently suggested by Couto et al [ 56 ] Moreover, preferring status quo options can serve to reduce the negative emotions connected to choice making (anticipatory emotions) [ 57 , 58 ] Adhering to a status quo can be seen as a coping strategy against the stressful and “cognitive expensive” situation of decision making in the “liquid times” of pandemic, dominated by unpredictability and uncertainty and involving conflicting attitudes, beliefs or behaviors (i.e. cognitive dissonance).…”

Section: Discussionmentioning

confidence: 99%

Clinical reasoning in dire times. Analysis of cognitive biases in clinical cases during the COVID-19 pandemic

et al. 2022

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Cognitive biases are systematic cognitive distortions, which can affect clinical reasoning. The aim of this study was to unravel the most common cognitive biases encountered in in the peculiar context of the COVID-19 pandemic. Case study research design. Primary care. Single centre (Division of General Internal Medicine, University Hospitals of Geneva, Geneva, Switzerland). A short survey was sent to all primary care providers ( N = 169) taking care of hospitalised adult patients with COVID-19. Participants were asked to describe cases in which they felt that their clinical reasoning was “disrupted” because of the pandemic context. Seven case were sufficiently complete to be analysed. A qualitative analysis of the clinical cases was performed and a bias grid encompassing 17 well-known biases created. The clinical cases were analyzed to assess for the likelihood (highly likely, plausible, not likely) of the different biases for each case. The most common biases were: “anchoring bias”, “confirmation bias”, “availability bias”, and “cognitive dissonance”. The pandemic context is a breeding ground for the emergence of cognitive biases, which can influence clinical reasoning and lead to errors. Awareness of these cognitive mechanisms could potentially reduce biases and improve clinical reasoning. Moreover, the analysis of cognitive biases can offer an insight on the functioning of the clinical reasoning process in the midst of the pandemic crisis.

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“…People can employ a variety of strategies to perform a particular task and so it is often assumed that observed behavior is the result of a mixture of such strategies (Campbell & Xue, 2001;Coltheart et al, 2001;Couto et al, 2020;Donkin et al, 2013;Dunlosky & Hertzog, 2001;Dutilh et al, 2011;Evans, 2008;Kahneman, 2011;Smith et al, 1998;Van Maanen et al, 2014;Van Maanen & Van Rijn, 2010;Van Rijn et al, 2003). For example, the dual-process framework of decision-making is built on the idea we can solve problems in either a more intuitive/heuristic manner or in a more deliberative manner (Evans, 2008;Kahneman, 2011;Stanovich, 1999).…”

Section: Modeling Mixturesmentioning

confidence: 99%

The Bayesian Mutation Sampler explains distributions of causal judgments

Kolvoort¹,

Temme²,

Maanen³

2023

Preprint

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One consistent finding in the causal reasoning literature is that causal judgments are rather variable. In particular, distributions of probabilistic causal judgments tend not to be normal and are often not centered on the normative response. As an explanation for these response distributions, we propose that people engage in ‘mutation sampling’ when confronted with a causal query and integrate this information with prior information about that query. The Mutation Sampler model (Davis & Rehder, 2020) posits that we approximate probabilities using a sampling process, explaining the average responses of participants on a wide variety of tasks. Careful analysis, however, shows that its predicted response distributions do not match empirical distributions. We develop the Bayesian Mutation Sampler (BMS) which extends the original model by incorporating the use of generic prior distributions. We fit the BMS to experimental data and find that, in addition to average responses, the BMS explains multiple distributional phenomena including the moderate conservatism of the bulk of responses, the lack of extreme responses, and spikes of responses at 50%.

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Investigating the origin and consequences of endogenous default options in repeated economic choices

Cited by 9 publications

References 81 publications

Probabilistic causal reasoning under time pressure

Probabilistic causal reasoning under time pressure

Clinical reasoning in dire times. Analysis of cognitive biases in clinical cases during the COVID-19 pandemic

The Bayesian Mutation Sampler explains distributions of causal judgments

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