Adaptive stimulus selection for multi-alternative psychometric functions with lapses
| Autor: | Jonathan W. Pillow, Ji Hyun Bak |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Psychometrics
Computer science Monte Carlo method Motion Perception Inference Sensory system Models Psychological Stimulus (physiology) Machine learning computer.software_genre adaptive stimulus selection 050105 experimental psychology Article 03 medical and health sciences symbols.namesake Psychometric function 0302 clinical medicine Psychophysics Animals 0501 psychology and cognitive sciences Multinomial logistic regression business.industry 05 social sciences Markov chain Monte Carlo Cognition Observer (special relativity) Mixture model Sensory Systems Markov Chains Bayesian adaptive design Ophthalmology Logistic Models Generalization Stimulus sequential optimal design symbols closed-loop experiments Macaca Artificial intelligence psychometric function business Monte Carlo Method computer Algorithms 030217 neurology & neurosurgery |
| Zdroj: | Journal of Vision |
| ISSN: | 1534-7362 |
| Popis: | Psychometric functions (PFs) quantify how external stimuli affect behavior and play an important role in building models of sensory and cognitive processes. Adaptive stimulus selection methods seek to select stimuli that are maximally informative about the PF given data observed so far in an experiment and thereby reduce the number of trials required to estimate the PF. Here we develop new adaptive stimulus selection methods for flexible PF models in tasks with two or more alternatives. We model the PF with a multinomial logistic regression mixture model that incorporates realistic aspects of psychophysical behavior, including lapses (trials where the observer ignores the stimulus) and omissions (trials where the observer "opts out" or fails to provide a valid response). We propose an information-theoretic criterion for stimulus selection and develop computationally efficient methods for inference and stimulus selection based on semi-adaptive Markov Chain Monte Carlo (MCMC) sampling. We apply these methods to data from macaque monkeys performing a multi-alternative motion discrimination task, and show in simulated experiments that our method can achieve a substantial speed-up over random designs. These advances will reduce the data needed to build accurate models of multi-alternative PFs and can be extended to high-dimensional PFs that would be infeasible to characterize with standard methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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