How many trials are required for parameter estimation in diffusion modeling?

• Verfasst von: Lerche, Veronika [VerfasserIn]
• Verfasst von: Voß, Andreas [VerfasserIn]
• Verfasst von: Nagler, Markus [VerfasserIn]
• Titel: How many trials are required for parameter estimation in diffusion modeling?
• Titelzusatz: A comparison of different optimization criteria
• Verf.angabe: Veronika Lerche, Andreas Voss, Markus Nagler
• Umfang: 25 S.
• Fussnoten: Gesehen am 30.11.2017 ; Article was first published online on 10 June 2016.
• Titel Quelle: Enthalten in: Behavior research methods
• Jahr Quelle: 2017
• Band/Heft Quelle: 49(2017), 2, S. 513-537
• ISSN Quelle: 1554-3528
• DOI: doi:10.3758/s13428-016-0740-2
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1565895150

Abstract

Diffusion models (Ratcliff, 1978) make it possible to identify and separate different cognitive processes underlying responses in binary decision tasks (e.g., the speed of information accumulation vs. the degree of response conservatism). This becomes possible because of the high degree of information utilization involved. Not only mean response times or error rates are used for the parameter estimation, but also the response time distributions of both correct and error responses. In a series of simulation studies, the efficiency and robustness of parameter recovery were compared for models differing in complexity (i.e., in numbers of free parameters) and trial numbers (ranging from 24 to 5,000) using three different optimization criteria (maximum likelihood, Kolmogorov-Smirnov, and chi-square) that are all implemented in the latest version of fast-dm (Voss, Voss, & Lerche, 2015). The results revealed that maximum likelihood is superior for uncontaminated data, but in the presence of fast contaminants, Kolmogorov-Smirnov outperforms the other two methods. For most conditions, chi-square-based parameter estimations lead to less precise results than the other optimization criteria. The performance of the fast-dm methods was compared to the EZ approach (Wagenmakers, van der Maas, & Grasman, 2007) and to a Bayesian implementation (Wiecki, Sofer, & Frank, 2013). Recommendations for trial numbers are derived from the results for models of different complexities. Interestingly, under certain conditions even small numbers of trials (N < 100) are sufficient for robust parameter estimation.