Application of Quantum-Markov Open System Models to Human Cognition and Decision.

Jerome Busemeyer,S N Balakrishnan,Qizi Zhang,Zheng Wang

doi:10.3390/e22090990

Abstract

Markov processes, such as random walk models, have been successfully used by cognitive and neural scientists to model human choice behavior and decision time for over 50 years. Recently, quantum walk models have been introduced as an alternative way to model the dynamics of human choice and confidence across time. Empirical evidence points to the need for both types of processes, and open system models provide a way to incorporate them both into a single process. However, some of the constraints required by open system models present challenges for achieving this goal. The purpose of this article is to address these challenges and formulate open system models that have good potential to make important advancements in cognitive science.

Highlights

One of the most fundamental topics in the cognitive and neural sciences concerns the dynamic and stochastic processes that humans use to make choices and decisions.Consider the following example of what is called a signal detection task: Suppose a radiologist is examining an image of a breast and trying to decide whether or not a cancerous node is present.The process requires accumulating evidence across time until a sufficient amount of evidence has been accumulated to make a decision
How can this work in cognitive science? The purpose of this article is to provide some guidelines for applying open system models to cognitive science in a compelling and effective manner
Before jumping into the general open system model, it may be helpful to first review versions of Markov and quantum walk processes in isolation

Summary

Introduction

One of the most fundamental topics in the cognitive and neural sciences concerns the dynamic and stochastic processes that humans (as well as other animals) use to make choices and decisions.Consider the following example of what is called a signal detection task: Suppose a radiologist is examining an image of a breast and trying to decide whether or not a cancerous node is present.The process requires accumulating evidence across time (by looking at various parts of the image) until a sufficient amount of evidence has been accumulated to make a decision. Consider the following example of what is called a signal detection task: Suppose a radiologist is examining an image of a breast and trying to decide whether or not a cancerous node is present. The decision process is fundamentally probabilistic in the sense that if the same radiologist is presented the same image on two different occasions (separated in time with other images in between), she might make different decisions. After a decision is made, the radiologist could be asked to report how confident she is about her decision. The basic measures that are collected in a signal detection task are the probability of making each choice and confidence rating, and the distribution of decision times for each choice

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Conclusion