New Handbook of Mathematical Psychology

  title={New Handbook of Mathematical Psychology},
  author={William H. Batchelder and Hans Colonius and Ehtibar N. Dzhafarov and Jay I. Myung},

Mathematical Models of Human Learning

Modulation of Dopamine for Adaptive Learning: a Neurocomputational Model

A biologically detailed computational model is proposed that implements adaptive learning rates by modulating the gain on the dopamine response to reward prediction errors, and activity within this circuit is model activity at the level of spiking neurons.

Encoding Models in Neuroimaging

Computational Neuroscientific Models of Categorization

This chapter reviews a number of new theories that have been proposed that can account for the traditional cognitive results as well as for these newer neuroscience results.

A neurocomputational theory of how rule-guided behaviors become automatic.

A biologically detailed computational model of how rule-guided behaviors become automatic, implemented as a biologically detailed neural network constructed from spiking neurons and displaying a biologically plausible form of Hebbian learning.

True contextuality in a psychophysical experiment