Diffusion process models are widely used in psychology and neuroscience to understand the processes involved in making rapid decisions, both in the laboratory and in clinical and applied settings. This book is an essential resource for students and specialists who seek a detailed understanding of the models and their applications.
Diffusion process models are widely used in psychology and neuroscience to understand the processes involved in making rapid decisions, both in the laboratory and in clinical and applied settings. This book is an essential resource for students and specialists who seek a detailed understanding of the models and their applications.
Philip L. Smith has published numerous articles on mathematical models of decision processes and is the developer of the circular diffusion model of continuous-outcome decisions. He has served as a former Editor-in-Chief of the Journal of Mathematical Psychology and was awarded the Society for Mathematical Psychology's 2002 Outstanding Paper Award for his research on diffusion process models.
Inhaltsangabe
Preface 1. Overview 2. Basic concepts and data 3. Sequential-sampling models of decision making 4. Obtaining predictions for diffusion models 5. Empirical assessment of sequential-sampling models 6. Time-varying diffusion models, I. Time pressure, urgency, collapsing boundaries, and optimality 7. Diffusion models for time-controlled processing tasks 8. Time-varying diffusion models, II. Detection and simple RT 9. Diffusion processes driven by time-varying stimulus representations in visual working memory 10. Neural diffusion models, I. Network and dynamical system models 11. Neural diffusion models, II. Poisson shot noise and related models 12. Diffusion models for continuous-outcome decision tasks 13. Response confidence 14. EZ and moment models, multialternative decisions, and expanded judgment tasks References Index.
Preface 1. Overview 2. Basic concepts and data 3. Sequential-sampling models of decision making 4. Obtaining predictions for diffusion models 5. Empirical assessment of sequential-sampling models 6. Time-varying diffusion models, I. Time pressure, urgency, collapsing boundaries, and optimality 7. Diffusion models for time-controlled processing tasks 8. Time-varying diffusion models, II. Detection and simple RT 9. Diffusion processes driven by time-varying stimulus representations in visual working memory 10. Neural diffusion models, I. Network and dynamical system models 11. Neural diffusion models, II. Poisson shot noise and related models 12. Diffusion models for continuous-outcome decision tasks 13. Response confidence 14. EZ and moment models, multialternative decisions, and expanded judgment tasks References Index.
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