How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria

Lerche, Veronika; Voss, Andreas; Nagler, Markus

doi:10.3758/s13428-016-0740-2

Cited by 147 publications

(182 citation statements)

References 58 publications

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“…Our results are in line with findings from simulation studies Lerche et al, 2015): In these studies, for small and medium trial numbers as employed in our LDT and RMT, a higher accuracy of parameter recovery for ML, compared to the widely used CS approach, was found. This more precise parameter estimation is assumingly also the reason for higher reliabilities for ML compared to CS in the present study.…”

Section: Discussionsupporting

confidence: 91%

“…As other studies (e.g., Lerche et al, 2015;Wiecki, Sofer, & Frank, 2013) demonstrate, the accuracy of parameter estimation depends on the number of trials. While in Study 1 and Study 2 only one trial number (200 for the RMT, 400 for the LDT and for the APT) was analyzed, in Study 3 we compared several different trial numbers.…”

Section: Discussionmentioning

confidence: 75%

“…In two further conditions, we made the data generation more realistic by adding contaminants, that is, trials in which the diffusion model is not the data generating process. For this purpose, we substituted 4 % of simulated trials by either fast or slow contaminants following the procedure described in Lerche et al (2015).…”

Section: Methodsmentioning

confidence: 99%

“…In the latest version of fast-dm , the user can choose between three different optimization criteria for parameter estimation: Kolmogorov-Smirnov (KS), a Maximum Likelihood (ML) and a Chi-square based criterion (CS). Lerche, Voss, and Nagler (2015) compared the accuracy of parameter recovery between these three criteria for different trial numbers and different levels of model complexity. In addition, in their simulation study, data were either uncontaminated (i.e., all trials emanated from a diffusion process), or contaminated with 4 % of either fast or slow contaminants.…”

Section: Estimation Of the Diffusion Model Parametersmentioning

confidence: 99%

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Retest reliability of the parameters of the Ratcliff diffusion model

Lerche

Voss

2016

Psychological Research

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In the recent years, there is a growing interest to use the Ratcliff Diffusion Model (1978) for diagnostic purposes as the parameters of the model capture interindividual differences in specific cognitive processes. The parameters are estimated using reaction time data from binary classification tasks. For a potential diagnostic application of parameter values sufficient reliability is a necessary precondition. In two studies, each with two sessions separated by 1 week, the retest reliability of the diffusion model parameters was assessed. In Study 1, 105 participants completed a lexical decision task and a recognition memory task. In Study 2, 128 participants worked on an associative priming task. Results show that the reliability of the main parameters of the Ratcliff Diffusion Model (in particular of the speed of information accumulation and the threshold separation with rs > 0.70 for all three tasks) is satisfying. Besides, we analyzed the influence of the number of trials on the retest reliability using different estimation methods (Kolmogorov-Smirnov, Maximum Likelihood, Chi-square and EZ) and both empirical and simulated datasets.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 75%

Section: Methodsmentioning

confidence: 99%

Section: Estimation Of the Diffusion Model Parametersmentioning

confidence: 99%

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Retest reliability of the parameters of the Ratcliff diffusion model

Lerche

Voss

2016

Psychological Research

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“…Further, it is rare to see any strong motivation or justification for the particular choices made by different researchers. The extent to which the various factors influence the conclusions drawn from an evidence-accumulation model analysis is under-explored (but see Lerche, Voss, & Nagler, 2016;van Ravenzwaaij & Oberauer, 2009). …”

Section: Threats To the Validity Of Cognitive Modelsmentioning

confidence: 99%

The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models

Dutilh¹,

Annis²,

Brown³

et al. 2016

Preprint

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Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) nondecision time. Inferences about these psychological factors, hinge upon the validity of the models' parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants' behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler's degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models.

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Neurocomputational models of altruistic decision‐making and social motives: Advances, pitfalls, and future directions

Tusche

Bas

2021

WIRES Cognitive Science

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This article discusses insights from computational models and social neuroscience into motivations, precursors, and mechanisms of altruistic decisionmaking and other-regard. We introduce theoretical and methodological tools for researchers who wish to adopt a multilevel, computational approach to study behaviors that promote others' welfare. Using examples from recent studies, we outline multiple mental and neural processes relevant to altruism.To this end, we integrate evidence from neuroimaging, psychology, economics, and formalized mathematical models. We introduce basic mechanismspertinent to a broad range of value-based decisions-and social emotions and cognitions commonly recruited when our decisions involve other people.Regarding the latter, we discuss how decomposing distinct facets of social processes can advance altruistic models and the development of novel, targeted interventions. We propose that an accelerated synthesis of computational approaches and social neuroscience represents a critical step towards a more comprehensive understanding of altruistic decision-making. We discuss the utility of this approach to study lifespan differences in social preference in late adulthood, a crucial future direction in aging global populations. Finally, we review potential pitfalls and recommendations for researchers interested in applying a computational approach to their research.

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How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria

Cited by 147 publications

References 58 publications

Retest reliability of the parameters of the Ratcliff diffusion model

Retest reliability of the parameters of the Ratcliff diffusion model

The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models

Neurocomputational models of altruistic decision‐making and social motives: Advances, pitfalls, and future directions

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