Decision-making beyond “left or right”. A computational study on the neurophysiology behind multiple-choice decision-making and choice reevaluation.
Neurophysiological brain processes during perceptual decision-making have mainly been investigated under the simplified conditions of two-alternative forced-choice (2AFC) tasks. How do established principles of decision-making, obtained from these simple binary tasks, extend to more complex aspects...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2011 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/52754 |
| Acceso en línea: | http://hdl.handle.net/10803/52754 |
| Access Level: | acceso abierto |
| Palabra clave: | Presa de decisió Neurofisiologia Neurociència computacional 612 |
| Sumario: | Neurophysiological brain processes during perceptual decision-making have mainly been investigated under the simplified conditions of two-alternative forced-choice (2AFC) tasks. How do established principles of decision-making, obtained from these simple binary tasks, extend to more complex aspects like multiple choice-alternatives and changes of mind? Here, we first address this question theoretically: based on recent experimental findings, we extend a biophysically realistic attractor model of decision-making to account for multiple choice-alternatives and choice reevaluation. Moreover, we complement our computational approach by a psychophysical experiment, exploring how changes of mind depend on the number of choice-alternatives. Our results affirm the general conformance of attractor networks with higher-level neural processes. In particular, we found evidence for the physiological relevance of a so far unregarded bifurcation. Furthermore, our findings suggest an advantage of a pooled multi-neuron representation of choice-alternatives, and a negative correlation between reaction time and changes of mind, possibly regulated by the decision threshold. Finally, we gained testable predictions on neural firing rates during changes of mind and propose future experiments to distinguish nonlinear attractor from linear diffusion models. |
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