Sequential sampling models with variable boundaries and non-normal noise

• Verfasst von: Voß, Andreas [VerfasserIn]
• Verfasst von: Lerche, Veronika [VerfasserIn]
• Verfasst von: Mertens, Ulf K. [VerfasserIn]
• Verfasst von: Voß, Jochen [VerfasserIn]
• Titel: Sequential sampling models with variable boundaries and non-normal noise
• Titelzusatz: A comparison of six models
• Verf.angabe: Andreas Voss, Veronika Lerche, Ulf Mertens, Jochen Voss
• E-Jahr: 2019
• Jahr: 16 January 2019
• Umfang: 20 S.
• Fussnoten: Published online: 16 January 2019 ; Gesehen am 13.02.2020
• Titel Quelle: Enthalten in: Psychonomic bulletin & review
• Ort Quelle: New York, NY : Springer, 1994
• Jahr Quelle: 2019
• Band/Heft Quelle: 26(2019), 3, Seite 813-832
• ISSN Quelle: 1531-5320
• DOI: doi:10.3758/s13423-018-1560-4
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Collapsing boundaries
• Sach-SW: Decision making
• Sach-SW: Diffusion model
• Sach-SW: Lévy flight model
• K10plus-PPN: 1689980575

Abstract

One of the most prominent response-time models in cognitive psychology is the diffusion model, which assumes that decision-making is based on a continuous evidence accumulation described by a Wiener diffusion process. In the present paper, we examine two basic assumptions of standard diffusion model analyses. Firstly, we address the question of whether participants adjust their decision thresholds during the decision process. Secondly, we investigate whether so-called Lévy-flights that allow for random jumps in the decision process account better for experimental data than do diffusion models. Specifically, we compare the fit of six different versions of accumulator models to data from four conditions of a number-letter classification task. The experiment comprised a simple single-stimulus task and a more difficult multiple-stimulus task that were both administered under speed versus accuracy conditions. Across the four experimental conditions, we found little evidence for a collapsing of decision boundaries. However, our results suggest that the Lévy-flight model with heavy-tailed noise distributions (i.e., allowing for jumps in the accumulation process) fits data better than the Wiener diffusion model.