Using diffusion models to understand clinical disorders

Using diffusion models to understand clinical disorders.

Sequential sampling models provide an alternative to traditional analyses of reaction times and accuracy in two-choice tasks. These models are reviewed with particular focus on the diffusion model (Ratcliff, 1978) and how its application can aide research on clinical disorders. The advantages of a diffusion model analysis over traditional comparisons are shown through simulations and a simple l...

Using developmental trajectories to understand developmental disorders.

PURPOSE In this article, the authors present a tutorial on the use of developmental trajectories for studying language and cognitive impairments in developmental disorders and compare this method with the use of matching. METHOD The authors assess the strengths, limitations, and practical implications of each method. The contrast between the methodologies is highlighted using the example of d...

Using developmental trajectories to understand genetic disorders

Using Multiple Models to Understand Data

A human’s ability to diagnose errors, gather data, and generate features in order to build better models is largely untapped. We hypothesize that analyzing results from multiple models can help people diagnose errors by understanding relationships among data, features, and algorithms. These relationships might otherwise be masked by the bias inherent to any individual model. We demonstrate this...

Using Measurement Models to Understand Eyewitness Identification

Much research effort has been expended improving police lineup procedures used in collecting eyewitness identification evidence. Sequential presentation of lineup members, in contrast to simultaneous presentation, has been posited to increase witness accuracy, though analyses based in Signal Detection Theory (SDT) have challenged these claims. A possible way to clarify the effect of presentatio...