Modeling one-choice and two-choice driving tasks

An experiment is presented in which subjects were tested on both one-choice and two-choice driving tasks and on non-driving versions of them. Diffusion models for one- and two-choice tasks were successful in extracting model-based measures from the response time and accuracy data. These include measures of the quality of the information from the stimuli that drove the decision process (drift rate in the model), the time taken up by processes outside the decision process and, for the two-choice model, the speed/accuracy decision criteria that subjects set. Drift rates were only marginally different between the driving and non-driving tasks, indicating that nearly the same information was used in the two kinds of tasks. The tasks differed in the time taken up by other processes, reflecting the difference between them in response processing demands. Drift rates were significantly correlated across the two two-choice tasks showing that subjects that performed well on one task also performed well on the other task. Nondecision times were correlated across the two driving tasks, showing common abilities on motor processes across the two tasks. These results show the feasibility of using diffusion modeling to examine decision making in driving and so provide for a theoretical examination of factors that might impair driving, such as extreme aging, distraction, sleep deprivation, and so on.

[1]  Xiao-Jing Wang,et al.  A Recurrent Network Mechanism of Time Integration in Perceptual Decisions , 2006, The Journal of Neuroscience.

[2]  Roger Ratcliff,et al.  Measuring psychometric functions with the diffusion model. , 2014, Journal of experimental psychology. Human perception and performance.

[3]  H. Engeland,et al.  Basic Impairments in Regulating the Speed-Accuracy Tradeoff Predict Symptoms of Attention-Deficit/Hyperactivity Disorder , 2010, Biological Psychiatry.

[4]  R. Ratcliff,et al.  Multialternative decision field theory: a dynamic connectionist model of decision making. , 2001, Psychological review.

[5]  Philip L. Smith,et al.  Psychology and neurobiology of simple decisions , 2004, Trends in Neurosciences.

[6]  Manuel Perea,et al.  A diffusion model account of normal and impaired readers , 2004, Brain and Cognition.

[7]  Eric-Jan Wagenmakers,et al.  An EZ-diffusion model for response time and accuracy , 2007, Psychonomic bulletin & review.

[8]  R Ratcliff,et al.  The effects of aging on reaction time in a signal detection task. , 2001, Psychology and aging.

[9]  R. Ratcliff,et al.  Diffusion model for one-choice reaction-time tasks and the cognitive effects of sleep deprivation , 2011, Proceedings of the National Academy of Sciences.

[10]  Klaus Oberauer,et al.  Individual differences in components of reaction time distributions and their relations to working memory and intelligence. , 2007, Journal of experimental psychology. General.

[11]  Thomas V. Wiecki,et al.  HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python , 2013, Front. Neuroinform..

[12]  A. Rangel,et al.  Visual fixations and the computation and comparison of value in simple choice. , 2010, Nature neuroscience.

[13]  Roger Ratcliff,et al.  Individual differences, aging, and IQ in two-choice tasks , 2010, Cognitive Psychology.

[14]  Clarissa A. Thompson,et al.  Children are not like older adults: a diffusion model analysis of developmental changes in speeded responses. , 2012, Child development.

[15]  Philip L. Smith,et al.  A comparison of sequential sampling models for two-choice reaction time. , 2004, Psychological review.

[16]  R. Ratcliff,et al.  The effects of aging on reaction time in a signal detection task. , 2001, Psychology and aging.

[17]  J. Schall,et al.  Neural Control of Voluntary Movement Initiation , 1996, Science.

[18]  R. Ratcliff,et al.  Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability , 2002, Psychonomic bulletin & review.

[19]  J. Townsend,et al.  Multialternative Decision Field Theory: A Dynamic Connectionist Model of Decision Making , 2001 .

[20]  Roger Ratcliff,et al.  Effects of aging and IQ on item and associative memory. , 2011, Journal of experimental psychology. General.

[21]  R. Ratcliff,et al.  A Diffusion Model Analysis of the Effects of Aging on Recognition Memory Journal of Memory and Language , 2003 .

[22]  Roger Ratcliff,et al.  Individual Differences and Fitting Methods for the Two-Choice Diffusion Model of Decision Making. , 2015, Decision.

[23]  Roger Ratcliff,et al.  The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks , 2008, Neural Computation.

[24]  R. Ratcliff A diffusion model account of response time and accuracy in a brightness discrimination task: Fitting real data and failing to fit fake but plausible data , 2002, Psychonomic bulletin & review.

[25]  R. Ratcliff,et al.  Modeling simple driving tasks with a one-boundary diffusion model , 2013, Psychonomic Bulletin & Review.

[26]  David L. Strayer,et al.  Effects of Simulator Practice and Real-World Experience on Cell-Phone—Related Driver Distraction , 2008, Hum. Factors.

[27]  R. Ratcliff,et al.  Using diffusion models to understand clinical disorders. , 2010, Journal of mathematical psychology.

[28]  Richard P. Heitz,et al.  Neurally constrained modeling of perceptual decision making. , 2010, Psychological review.

[29]  H. Huizenga,et al.  Specifying theories of developmental dyslexia: a diffusion model analysis of word recognition. , 2011, Developmental science.

[30]  R. Ratcliff,et al.  Connectionist and diffusion models of reaction time. , 1999, Psychological review.

[31]  R. Ratcliff,et al.  Neural Representation of Task Difficulty and Decision Making during Perceptual Categorization: A Timing Diagram , 2006, The Journal of Neuroscience.

[32]  Eric-Jan Wagenmakers,et al.  Methodological and empirical developments for the Ratcliff diffusion model of response times and accuracy , 2009 .

[33]  J. Gold,et al.  Neural computations that underlie decisions about sensory stimuli , 2001, Trends in Cognitive Sciences.

[34]  Roger Ratcliff,et al.  A Theory of Memory Retrieval. , 1978 .

[35]  R. Ratcliff,et al.  A comparison of macaque behavior and superior colliculus neuronal activity to predictions from models of two-choice decisions. , 2003, Journal of neurophysiology.

[36]  James Kozloski,et al.  Self-referential forces are sufficient to explain different dendritic morphologies , 2013, Front. Neuroinform..

[37]  Andreas Voss,et al.  A diffusion model analysis of adult age differences in episodic and semantic long-term memory retrieval. , 2006, Journal of experimental psychology. Learning, memory, and cognition.

[38]  Andreas Voss,et al.  Fast-dm: A free program for efficient diffusion model analysis , 2007, Behavior research methods.

[39]  R. Ratcliff,et al.  Sleep deprivation affects multiple distinct cognitive processes , 2009, Psychonomic bulletin & review.

[40]  Francis Tuerlinckx,et al.  Diffusion model analysis with MATLAB: A DMAT primer , 2008, Behavior research methods.

[41]  R. Ratcliff,et al.  A diffusion model analysis of the effects of aging on brightness discrimination , 2003, Perception & psychophysics.

[42]  R. Ratcliff,et al.  A comparison of four methods for simulating the diffusion process , 2001, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.