Bayesian hypothesis testing for psychologists: A tutorial on the Savage–Dickey method

Wagenmakers, Eric‐Jan; Lodewyckx, Tom; Kuriyal, Himanshu; Grasman, Raoul P. P. P.

doi:10.1016/j.cogpsych.2009.12.001

Cited by 638 publications

(673 citation statements)

References 111 publications

(117 reference statements)

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“…Another recommended method, the Savage-Dickey estimator (Dickey, 1971;Wagenmakers, Lodewyckx, Kuriyal, & Grasman, 2010), only applies to nested models. 8.…”

Section: Acknowledgmentsmentioning

confidence: 99%

Weighing Outcomes by Time or Against Time? Evaluation Rules in Intertemporal Choice

2014

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Models of intertemporal choice draw on three evaluation rules, which we compare in the restricted domain of choices between smaller sooner and larger later monetary outcomes. The hyperbolic discounting model proposes an alternative-based rule, in which options are evaluated separately. The interval discounting model proposes a hybrid rule, in which the outcomes are evaluated separately, but the delays to those outcomes are evaluated in comparison with one another. The tradeoff model proposes an attribute-based rule, in which both outcomes and delays are evaluated in comparison with one another: People consider both the intervals between the outcomes and the compensations received or paid over those intervals. We compare highly general parametric functional forms of these models by means of a Bayesian analysis, a method of analysis not previously used in intertemporal choice. We find that the hyperbolic discounting model is outperformed by the interval discounting model, which, in turn, is outperformed by the tradeoff model. Our cognitive modeling is among the first to offer quantitative evidence against the conventional view that people make intertemporal choices by discounting the value of future outcomes, and in favor of the view that they directly compare options along the time and outcome attributes.

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“…Another recommended method, the Savage-Dickey estimator (Dickey, 1971;Wagenmakers, Lodewyckx, Kuriyal, & Grasman, 2010), only applies to nested models. 8.…”

Section: Acknowledgmentsmentioning

confidence: 99%

Weighing Outcomes by Time or Against Time? Evaluation Rules in Intertemporal Choice

2014

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“…To determine how well a model predicted the observed data, we need to take into account all predictions that the model made and weight these by their prior probability. Statistically, this is accomplished by averaging the likelihood of the observed data D across all possible parameter values k of a model M k , weighted by the prior probability of k (e.g., Myung & Pitt, 1997;Wagenmakers, Lodewyckx, Kuriyal, & Grasman, 2010):…”

Section: Part I: a Bayesian Approach To Testing Toolboxesmentioning

confidence: 99%

Testing adaptive toolbox models: A Bayesian hierarchical approach.

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Rieskamp

Wagenmakers

2013

Psychological Review

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103

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Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra-and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively specified? How can the number of toolbox strategies be limited to prevent uncontrolled strategy sprawl? How can a toolbox model be formally tested against alternative theories? The authors show how these challenges can be met by using Bayesian inference techniques. By means of parameter recovery simulations and the analysis of empirical data across a variety of domains (i.e., judgment and decision making, children's cognitive development, function learning, and perceptual categorization), the authors illustrate how Bayesian inference techniques allow toolbox models to be quantitatively specified, strategy sprawl to be contained, and toolbox models to be rigorously tested against competing theories. The authors demonstrate that their approach applies at the individual level but can also be generalized to the group level with hierarchical Bayesian procedures. The suggested Bayesian inference techniques represent a theoretical and methodological advancement for toolbox theories of cognition and behavior.

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“…We also evaluated the level of evidence for the hypothesis that the slope was nonzero by estimating the Bayes Factor for the slope estimates. The Bayes Factor is a measure of the weight of evidence derived from the observed data (Jeffreys, 1961) and was estimated using a Savage-Dickey analysis (see Wagenmakers, Lodewyckx, Kuriyal, & Grasman, 2010;Lee & Wagenmakers, 2014, for helpful tutorials).…”

Section: Methodsmentioning

confidence: 99%

Dynamic memory searches: Selective output interference for the memory of facts

2015

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The benefits of testing on later memory performance are well documented; however, the manner in which testing harms memory performance is less well understood. This research is concerned with the finding that accuracy decreases over the course of testing, a phenomena termed Boutput interference^(OI). OI has primarily been investigated with episodic memory, but there is limited research investigating OI in measures of semantic memory (i.e., knowledge). In the current study, participants were twice tested for their knowledge of factual questions; they received corrective feedback during the first test. No OI was observed during the first test, when participants presumably searched semantic memory to answer the general-knowledge questions. During the second test, OI was observed. Conditional analyses of Test 2 performance revealed that OI was largely isolated to questions answered incorrectly during Test 1. These were questions for which participants needed to rely on recent experience (i.e., the feedback in episodic memory) to respond correctly. One possible explanation is that episodic memory is more susceptible to the sort of interference generated during testing (e.g., gradual changes in context, encoding/updating of items) relative to semantic memory. Alternative explanations are considered.

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Bayesian hypothesis testing for psychologists: A tutorial on the Savage–Dickey method

Cited by 638 publications

References 111 publications

Weighing Outcomes by Time or Against Time? Evaluation Rules in Intertemporal Choice

Weighing Outcomes by Time or Against Time? Evaluation Rules in Intertemporal Choice

Testing adaptive toolbox models: A Bayesian hierarchical approach.

Dynamic memory searches: Selective output interference for the memory of facts

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