<b>bridgesampling</b>: An <i>R</i> Package for Estimating Normalizing Constants

Gronau, Quentin Frederik; Singmann, Henrik; Wagenmakers, Eric‐Jan

doi:10.18637/jss.v092.i10

Cited by 154 publications

(121 citation statements)

References 32 publications

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“…We assumed the three models had the same prior probability (0.33). Models were compared via a bridge sampling estimate of the marginal likelihood (Gronau et al 2017a), using the “bridgesampling” package in R (Gronau et al 2017b). Bridge sampling directly estimates the marginal likelihood, and therefore the posterior probability of each model given the data (and prior model probabilities), as well as the assumption that the models represent the entire group of those to be considered.…”

Section: Methodsmentioning

confidence: 99%

Effect of lysergic acid diethylamide (LSD) on reinforcement learning in humans

Kanen

Luo

Kandroodi

et al. 2020

Preprint

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The non-selective serotonin 2A (5-HT2A) receptor agonist lysergic acid diethylamide (LSD) holds promise as a treatment for some psychiatric disorders. Psychedelic drugs such as LSD have been suggested to have therapeutic actions through their effects on learning. The behavioural effects of LSD in humans, however, remain largely unexplored. Here we examined how LSD affects probabilistic reversal learning in healthy humans. Healthy volunteers received intravenous LSD (75μg in 10 mL saline) or placebo (10mL saline) in a within-subjects design and completed a probabilistic reversal learning task. Participants had to learn through trial and error which of three stimuli was rewarded most of the time, and these contingencies switched in a reversal phase. Computational models of reinforcement learning were fitted to the behavioural data to assess how LSD affected the updating (“learning rates”) and deployment (“reinforcement sensitivity”) of value representations during choice, as well as “stimulus stickiness”, which assays choice repetition irrespective of reinforcement history. Conventional measures assessing sensitivity to immediate feedback (“win-stay” and “lose-shift” probabilities) were unaffected, whereas LSD increased the impact of the strength of initial learning on perseveration. Computational modelling revealed that the most pronounced effect of LSD was enhancement of the reward learning rate. The punishment learning rate was also elevated. Stimulus stickiness was decreased by LSD, reflecting heightened exploratory behaviour, while reinforcement sensitivity was unaffected. Increased reinforcement learning rates suggest LSD induced a state of heightened plasticity. These results indicate a potential mechanism through which revision of maladaptive associations could occur in the clinical application of LSD.Significance statementThe psychedelic (“mind-manifesting”) drug LSD holds promise for the treatment of some psychiatric disorders. Theories have postulated its therapeutic potential centres on enhancing learning and flexible thinking. Here we provide substantiating empirical evidence by examining the computations underlying behaviour as healthy volunteers learned through trial and error under LSD. Viewing choice as based on representations of an action’s value, LSD increased the speed at which value was updated following feedback, which was more pronounced following reward than punishment. Behaviour was also more exploratory under LSD, irrespective of the outcome of actions. These results indicate that LSD impacted a fundamental belief-updating process inherent in the brain which can be leveraged to revise maladaptive associations characteristic of a range of mental disorders.

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Section: Methodsmentioning

confidence: 99%

Effect of lysergic acid diethylamide (LSD) on reinforcement learning in humans

Kanen

Luo

Kandroodi

et al. 2020

Preprint

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“…In each analysis, we fitted a maximal model with main effects of prediction type (Default, Domain, Fact, Learner, Fact & Learner) and population (Lab, MTurk) plus the interaction of these terms. Both this maximal model and models with a simpler fixed effects structure were compared to a baseline intercept-only model (see the rows in Table 1) using the bridgesampling package (version 0.7-2; Gronau et al, 2020). Following Rouder and Morey (2012), fixed effects had weakly informative Cauchy(0,1) priors.…”

Section: Resultsmentioning

confidence: 99%

Alleviating the Cold Start Problem in Adaptive Learning using Data-Driven Difficulty Estimates

et al. 2021

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An adaptive learning system offers a digital learning environment that adjusts itself to the individual learner and learning material. By refining its internal model of the learner and material over time, such a system continually improves its ability to present appropriate exercises that maximise learning gains. In many cases, there is an initial mismatch between the internal model and the learner’s actual performance on the presented items, causing a “cold start” during which the system is poorly adjusted to the situation. In this study, we implemented several strategies for mitigating this cold start problem in an adaptive fact learning system and experimentally tested their effect on learning performance. The strategies included predicting difficulty for individual learner-fact pairs, individual learners, individual facts, and the set of facts as a whole. We found that cold start mitigation improved learning outcomes, provided that there was sufficient variability in the difficulty of the study material. Informed individualised predictions allowed the system to schedule learners’ study time more effectively, leading to an increase in response accuracy during the learning session as well as improved retention of the studied items afterwards. Our findings show that addressing the cold start problem in adaptive learning systems can have a real impact on learning outcomes. We expect this to be particularly valuable in real-world educational settings with large individual differences between learners and highly diverse materials.

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“…Gronau et al (2017) provide a detailed and accessible tutorial on computing Bayes factors with bridge sampling. The approach has been implemented in an R package by Gronau, Singmann, and Wagenmakers (2020), which we use in our work as well.…”

Section: Bayes Factorsmentioning

confidence: 99%

Meaningful comparisons with ordinal-scale items

Schnuerch¹,

Jm²,

Sarafoglou³

et al. 2021

Preprint

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Ordinal rating scales---say that from *strongly agree* to *strongly disagree* with a proposition---are exceedingly popular in psychological research. Yet, there is often a lingering question of whether metric-level descriptions of the results and parametric tests are appropriate. We consider a different problem, perhaps one that supersedes the ordinal-vs-metric issue: When is it appropriate to use single relations---say, a difference in central tendencies---to compare two ratings distributions? In this paper, we propose a set of four models of increasing flexibility to guide researchers in drawing meaningful inferences from ordinal scales. We develop four statistical models that represent possible relations across two ratings distributions: a null model, a parametric single-relation model, a nonparametric single-relation model, and a multiple-relations model that accounts for complex effects. We show how these models can be compared in light of data with Bayes factors and illustrate their usefulness with two real-world examples. We also provide a freely available web applet for researchers who wish to adopt the approach.

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bridgesampling: An R Package for Estimating Normalizing Constants

Cited by 154 publications

References 32 publications

Effect of lysergic acid diethylamide (LSD) on reinforcement learning in humans

Effect of lysergic acid diethylamide (LSD) on reinforcement learning in humans

Alleviating the Cold Start Problem in Adaptive Learning using Data-Driven Difficulty Estimates

Meaningful comparisons with ordinal-scale items

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