Sequential Analysis of Automatic Target Detection with Classification Algorithms and Optimality of Dynamic Decision Making Under Uncertainty
Abstract
Applied novel mathematical techniques to a published data-set (Roitman and Shadlen, 2002) on LIP neuronal activities during a random-dot motion discrimination paradigm to identify/classify individual neurons in terms of their sensori-motor locus and the putative decisional process that translates a perceptual to a motor representation. Developed a Signal-Detection-Theory based analysis providing a quantitative measure of sensorimotor locus of the neuron at each time point, and a Poisson regression model incorporating orthogonal decomposition of neuronal activity in term of how the stimulus, response, and stimulus-response mapping components contribute to the spike activity. Developed techniques to extract S-, R-, or SR mapping components in neural recordings of time series, e.g. event-related potentials (ERPs) where trial-by-trial variations in response-time have contaminated their contributions in averaged waveforms. Modeled motivational impact ( incentive salience ) in reinforcement learning, a standard paradigm for sequential decision making.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 2013
- Accession Number
- ADA578207
Entities
People
- Jun Zhang
Organizations
- University of Michigan