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Sequential Sampling Models of the Flanker Task: Model Comparison and Parameter Validation Sequential sampling models of the flanker task: Model comparison and parameter validation Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Corey Nathan White, M. A. Graduate Program in Psychology The Ohio State University 2010 Dissertation Committee: Roger Ratcliff, Advisor Gail McKoon Simon Dennis Alex Petrov Copyright Corey Nathan White 2010 Abstract The present study tests sequential sampling models of processing in the flanker task. In a standard flanker task, participants must identify a central target that is flanked by items that indicate the same response (congruent) or the opposite response (incongruent). The standard finding is that incongruent flankers produce interference that primarily affects the early component of the decision process. The goal was to contrast different mechanisms of visual attention and to identify a simple processing model that can be used to augment analyses of this task. Several models were contrasted in their ability to account for flanker data from experiments that manipulated response bias, speed/accuracy tradeoffs, attentional focus, and stimulus configuration. Models that assume dynamic focusing of attention provided the best overall account of the behavioral data. Among the dynamic models, a spotlight model that assumes gradual narrowing of attention provided the best balance of fit and parsimony, though dual process models that assume discrete selection of the target also captured the main trends in the data when only standard congruent and incongruent conditions were used. Importantly, the ii experimental manipulations were reflected by the appropriate model parameters, supporting future use of these models to decompose behavioral data from the flanker task into meaningful psychological constructs. The results of this study also indicate that standard flanker experiments do not provide strong evidence for contrasting gradual versus discrete target selection, and consequently they do not provide strong evidence to support or refute theories of the underlying mechanisms of dynamic attention models. iii Dedication To Heather and our animals, for keeping me happy. iv Acknowledgments I would like to thank Roger and Gail for their support and guidance, my labmates for their assistance and their empathy, and Scott Brown for assistance with the Bayesian models. v Vita May 1982..................................................................................................Born in Missouri 2001.............................................................................................Winnetonka High School 2005..................................................................B.S. Psychology, Truman State University 2006..............................................................M.A. Psychology, The Ohio State University 2006-2008..........................................................Research Assistant, Ratcliff and McKoon Cognition and Language Lab 2007-2009.................................................Teaching Assistant, Department of Psychology, The Ohio State University Publications Vittengl, J. R., White, C. N., Mcgovern, R. J., Morton, B. J. (2006). Comparative validity of seven scoring systems for the Instrumental Activities of Daily Living scale in rural elders. Aging & Mental Health, 10, 33-39. Leite, F. P., Ratcliff, R., White, C. N. (2007). Individual differences on speeded cognitive tasks: Comment on Chen, Hale, and Myerson (2008). Psychonomic Bulletin and Review, 14, 1007-1009. White, C. N., Ratcliff, R., Vasey, M. W., McKoon, G. (2009). Dysphoria and memory for emotional material: A diffusion model analysis. Cognition & Emotion, 23, 181-205. White, C. N., Ratcliff, R., Vasey, M. W., McKoon, G. (2010). Anxiety enhances threat processing without competition among multiple inputs: A Diffusion model analysis. Emotion. in press. White, C. N., Ratcliff, R., Vasey, M. W., McKoon, G. (2010). Using diffusion models to understand clinical disorders. Journal of Mathematical Psychology, 54, 39-53. Field of Study Major Field: Psychology Area of Emphasis: Cognitive Modeling vi Table of Contents Abstract..............................................................................................................................ii Dedication.........................................................................................................................iv Acknowledgments..............................................................................................................v Vita....................................................................................................................................vi List of Tables..................................................................................................................viii List of Figures...................................................................................................................ix 1. Introduction....................................................................................................................1 2. Models of processing in the flanker task.......................................................................6 3. Model details................................................................................................................18 4. Experiments..................................................................................................................33 5. Discussion.....................................................................................................................70 References.........................................................................................................................83 Appendix A: Mathematical Details of the Diffusion Model ............................................88 Appendix B: Validation of Simulation Method.................................................................89 vii List of Tables Table 1: Behavioral data from Experiments 1 and 2........................................................36 Table 2: Best-fitting model parameters from Experiment 1.............................................40 Table 3: Best-fitting model parameters from Experiment 2.............................................47 Table 4: Behavioral data from Experiments 3 and 4........................................................53 Table 5: Best-fitting model parameters from Experiment 3.............................................54 Table 6: Best-fitting model parameters from Experiment 4.............................................58 Table 7: Behavioral data from Experiment 5....................................................................65 Table 8: Best-fitting model parameters from Experiment 5.............................................66 viii List of Figures Figure 1: Theoretical conditional accuracy function..........................................................5 Figure 2: Schematic of the standard diffusion model.......................................................14 Figure 3: Dynamic attention models.................................................................................24 Figure 4: Bayesian perceptual models..............................................................................29 Figure 5: Observed and predicted data from dynamic models in Experiment 1..............38 Figure 6: Observed and predicted data from perceptual models in Experiment 1...........41 Figure 7: Observed and predicted QPFs from Experiment 2...........................................46 Figure 8: Observed and predicted CAFs from Experiment 2...........................................48 Figure 9: Schematic of model behavior across response bias..........................................50 Figure 10: Observed and predicted QPFs from Experiment 3.........................................55 Figure 11: Observed and predicted CAFs from Experiment 3.........................................56 Figure 12: Observed and predicted QPFs from Experiment 4.........................................59 Figure 13: Observed and predicted CAFs from Experiment 4.........................................60 Figure 14: Conditions in Experiment 5.............................................................................63 ix Figure 15: Observed and predicted QPFs from Experiment 5..........................................67 Figure 16: Observed and predicted CAFs from Experiment 5.........................................68 Figure B1: Generated and recovered parameter values from the diffusion model...........89 x 1. Introduction The Eriksen flanker task has been extensively used to investigate the mechanisms underlying visual attention (Eriksen & Eriksen, 1974). In the standard task, participants must discriminate a single item (e.g., letter or arrow) that is surrounded, or flanked, by items that indicate the same or opposite response. For example, if participants had to decide whether the central arrow in a display faced right or left, a congruent trial would include flankers that faced the same direction as the central target (e.g., > > > > >), whereas an incongruent trial would include flankers that faced the opposite direction (e.g., < < > < <). The standard finding is that the incongruent flankers produce interference that leads to slower and less accurate responses compared to the congruent condition, known as the flanker congruency effect (FCE). The present study compares the ability of several response time (RT) models to account
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