Bayesian Cognitive Modeling

Lee, Michael; Wagenmakers, Eric–Jan

doi:10.1017/cbo9781139087759

Cited by 2,096 publications

(2,047 citation statements)

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“…A BF10 of 3, for example, states that the data is 3 times more likely in the alternative model than in the null model. Lee and Wagenmakers (2013) provide a guideline for interpretation: BF10 1-3 anecdotal evidence, 3-10 moderate evidence, BF10 > 10 strong evidence. A significant (p = 0.05) independent samples t test with t(40) = 2.021 corresponds to a BF10 = 1.49.…”

Section: Resultsmentioning

confidence: 99%

Introduction matters: Manipulating trust in automation and reliance in automated driving

2018

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Trust in automation is a key determinant for the adoption of automated systems and their appropriate use. Therefore, it constitutes an essential research area for the introduction of automated vehicles to road traffic. In this study, we investigated the influence of trust promoting (Trust promoted group) and trust lowering (Trust lowered group) introductory information on reported trust, reliance behavior and takeover performance. Forty participants encountered three situations in a 17-minute highway drive in a conditionally automated vehicle (SAE Level 3). Situation 1 and Situation 3 were non-critical situations where a take-over was optional. Situation 2 represented a critical situation where a take-over was necessary to avoid a collision. A non-driving-related task (NDRT) was presented between the situations to record the allocation of visual attention. Participants reporting a higher trust level spent less time looking at the road or instrument cluster and more time looking at the NDRT. The manipulation of introductory information resulted in medium differences in reported trust and influenced participants' reliance behavior. Participants of the Trust promoted group looked less at the road or instrument cluster and more at the NDRT. The odds of participants of the Trust promoted group to overrule the automated driving system in the non-critical situations were 3.65 times (Situation 1) to 5 times (Situation 3) higher. In Situation 2, the Trust promoted group's mean take-over time was extended by 1154 ms and the mean minimum time-to-collision was 933 ms shorter. Six participants from the Trust promoted group compared to no participant of the Trust lowered group collided with the obstacle. The results demonstrate that the individual trust level influences how much drivers monitor the environment while performing an NDRT. Introductory information influences this trust level, reliance on an automated driving system, and if a critical take-over situation can be successfully solved.

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Section: Resultsmentioning

confidence: 99%

Introduction matters: Manipulating trust in automation and reliance in automated driving

2018

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“…The transition in psychology has been aided by several factors. Introductory texts (Gelman & Hill, 2007;Kruschke, 2010a; M. D. Lee & Wagenmakers, 2012;Shiffrin et al, 2008) have been written; computers have increased in power; and algorithms required for estimating Bayesian models have become more accessible using various high level modeling languages such as WinBUGS, OpenBUGS, JAGS, and Stan. Bayesian hierarchical modeling is particularly suited to understanding repeated measures psychological data as demonstrated in recent studies on the forgetting curve (Averell & Heathcote, 2011) and reasoning (M. D. Lee, 2008).…”

Section: Bayesian Modelingmentioning

confidence: 99%

Hierarchical Bayesian models of subtask learning.

Anglim¹,

Wynton²

2015

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Abstract. The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking task, which logged participant actions, enabling measurement of strategy use and subtask performance. Model comparison was performed using DIC, posterior predictive checks, plots of model fits, and model recovery simulations. Results showed that while learning tended to be monotonically decreasing and decelerating, and approaching an asymptote for all subtasks, there was substantial inconsistency in learning curves both at the group-and individual-levels. This inconsistency was most apparent when constraining both the rate and the ratio of learning to asymptote to be equal across subtasks, thereby giving learning curves only one parameter for scaling. The inclusion of six strategy covariates provided improved prediction of subtask performance capturing different subtask learning processes and subtask trade-offs. In addition, strategy use partially explained the inconsistency in subtask learning. Overall, the model provided a more nuanced representation of how complex tasks can be decomposed in terms of simpler learning mechanisms.

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“…An alternative way of testing the findings is using Bayesian statistics, which is better equipped of capturing uncertainty in the data (e.g., due to small sample sizes, Lee & Wagenmakers, 2013) and is therefore able to provide more reliable estimates of, for example, mean differences between groups (Wagenmakers, Wetzels, Borsboom, & van der Maas, 2011; Wetzels et al, 2011). Moreover, Bayesian testing quantifies how likely the data are under two competing hypotheses and can, therefore, indicate evidence for the null hypothesis.…”

Section: Non‐preregistered Analysesmentioning

confidence: 99%

The first direct replication on using verbal credibility assessment for the detection of deceptive intentions

Kleinberg

Warmelink

Arntz

et al. 2018

Applied Cognitive Psychology

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Verbal deception detection has gained momentum as a technique to tell truth‐tellers from liars. At the same time, researchers' degrees of freedom make it hard to assess the robustness of effects. Replication research can help evaluate how reproducible an effect is. We present the first replication in verbal deception research whereby ferry passengers were instructed to tell the truth or lie about their travel plans. The original study found truth‐tellers to include more specific time references in their answers. The replication study that closely mimicked the setting, procedure, materials, coding, and analyses found no lie–truth difference for specific time references. Although the power of our replication study was suboptimal (0.77), Bayesian statistics showed evidence in favor of the null hypothesis. Given the great applied consequences of verbal credibility tests, we hope this first replication attempt ignites much needed preregistered, high‐powered, multilab replication efforts.

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Bayesian Cognitive Modeling

Cited by 2,096 publications

References 0 publications

Introduction matters: Manipulating trust in automation and reliance in automated driving

Introduction matters: Manipulating trust in automation and reliance in automated driving

Hierarchical Bayesian models of subtask learning.

The first direct replication on using verbal credibility assessment for the detection of deceptive intentions

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