A Novel Quantum Approach to the Dynamics of Decision Making

We present a new quantum-markovian model of two-alternative forced choice (2AFC) decision-making. We treat the decision-making process as an accumulation of evidence between two competing alternatives, analogous to the drift diffusion model (DDM), in which the stimulus acts as a generative process, emitting bits of information that are treated as quantum particles. The particles are acted on by a landscape determined by the agent's experience with the task or stimulus, signal strength, and allocated cognitive control. We derive closed form expressions for success rates under both the interrogation and free response paradigms. Under the free response paradigm, we show that this model reduces to a Markov process with closed form response time (RT) distributions that take the form of inverse gaussians (IGs) with periodic noise characteristic to the task set. In the limit of long RT, the RT distributions become smooth, recovering true IG distributions analogous to the standard DDM.