Parameter recovery for the Leaky Competing Accumulator model
Publication date: February 2017 Source:Journal of Mathematical Psychology, Volume 76, Part A Author(s): Steven Miletić, Brandon M. Turner, Birte U. Forstmann, Leendert van Maanen The Leaky Competitive Accumulator (LCA) model for perceptual discrimination is rapidly growing in popularity due to its neural plausibility. The model assumes that perceptual choices and associated response times are the consequence of the accrual of evidence for the various response alternatives up to a certain predetermined threshold. In addition, accrual of evidence is influenced by temporal leakage of information and mutual inhibition b...
Source: Journal of Mathematical Psychology - January 17, 2017 Category: Psychiatry & Psychology Source Type: research
Identification of probabilities
Publication date: February 2017 Source:Journal of Mathematical Psychology, Volume 76, Part A Author(s): Paul M.B. Vitányi, Nick Chater Within psychology, neuroscience and artificial intelligence, there has been increasing interest in the proposal that the brain builds probabilistic models of sensory and linguistic input: that is, to infer a probabilistic model from a sample. The practical problems of such inference are substantial: the brain has limited data and restricted computational resources. But there is a more fundamental question: is the problem of inferring a probabilistic model from a sample possible even in...
Source: Journal of Mathematical Psychology - December 25, 2016 Category: Psychiatry & Psychology Source Type: research
Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach
Publication date: Available online 21 December 2016 Source:Journal of Mathematical Psychology Author(s): Sobanawartiny Wijeakumar, Joseph P. Ambrose, John P. Spencer, Rodica Curtu A fundamental challenge in cognitive neuroscience is to develop theoretical frameworks that effectively span the gap between brain and behavior, between neuroscience and psychology. Here, we attempt to bridge this divide by formalizing an integrative cognitive neuroscience approach using dynamic field theory (DFT). We begin by providing an overview of how DFT seeks to understand the neural population dynamics that underlie cognitive process...
Source: Journal of Mathematical Psychology - December 21, 2016 Category: Psychiatry & Psychology Source Type: research
The first-passage time distribution for the diffusion model with variable drift
Publication date: February 2017 Source:Journal of Mathematical Psychology, Volume 76, Part A Author(s): Steven P. Blurton, Miriam Kesselmeier, Matthias Gondan The Ratcliff diffusion model is now arguably the most widely applied model for response time data. Its major advantage is its description of both response times and the probabilities for correct as well as incorrect responses. The model assumes a Wiener process with drift between two constant absorbing barriers. The first-passage times at the upper and lower boundary describe the responses in simple two-choice decision tasks, for example, in experiments with per...
Source: Journal of Mathematical Psychology - December 14, 2016 Category: Psychiatry & Psychology Source Type: research
The entangled nature of interdependence. Bistability, irreproducibility and uncertainty
Publication date: Available online 10 December 2016 Source:Journal of Mathematical Psychology Author(s): W.F. Lawless With models focused on individuals in research that often fails to be reproduced, social science has been unable to generalize theory into a mathematical physics of social reality to advance the science of teams. For example, Shannon’s information theory and the social sciences, including economics, assume that individual observation of behavior records the actual behaviors that have occurred, including self-reports of behavior. In the social sciences this phenomenon allows social scientists to assume ...
Source: Journal of Mathematical Psychology - December 10, 2016 Category: Psychiatry & Psychology Source Type: research
