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The Multi-Particle Multi-Well (Mpmw) Framework: A Quantum Framework Incorporating Attentional Capture, Representational Generality, and Arousal to Perceptual Choice Problems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	The Multi-Particle Multi-Well (Mpmw) Framework: A Quantum Framework Incorporating Attentional Capture, Representational Generality, and Arousal to Perceptual Choice Problems./
Author:	Rosendahl, Morgan L.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	202 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
Subject:	Neurosciences. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29068555
ISBN:	9798802749890

The Multi-Particle Multi-Well (Mpmw) Framework: A Quantum Framework Incorporating Attentional Capture, Representational Generality, and Arousal to Perceptual Choice Problems.
Rosendahl, Morgan L.

The Multi-Particle Multi-Well (Mpmw) Framework: A Quantum Framework Incorporating Attentional Capture, Representational Generality, and Arousal to Perceptual Choice Problems. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 202 p.

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--Princeton University, 2022.

This item must not be sold to any third party vendors.

This dissertation presents a new quantum cognitive framework, the Multi-Particle Multi-Well (MPMW) Framework, that models forced choice decisions among any number of alternatives. This framework maps the cognitive parameters of control(such as selective attention), automaticity (arising from task stimulus salience and prior practice), representational generality, and arousal to parameters of square at-tractors in a quantum landscape. The stimulus is modeled as an emitter of quantum particles, and information accumulation toward a decision is represented by measurements thereof. Particles are treated as sequentially emitted and independent, acted upon by the attractor landscape, and, by a positional measurement, cleared there-from. These modeling choices provide several desirable features, the combination of which are unique to the MPMW framework.Mapping cognitive parameters to quantum landscapes allows stochasticity (noise), leak (decay), and lateral inhibition (competition) to arise directly from the parameters of the attractor landscape. Modeling evidence accumulation by particle measurements imposes two biologically plausible constraints on that process. First, the rate on information accumulation is finite where, in standard continuous classical models (such as the Drift Diffusion Model (DDM)), it is not. Second, information is accumulated in discrete time steps, an effect that few models incorporate despite rowing empirical evidence at both the neural and psychological levels of analysis.These constraints increase the model's analytical tractability, allowing the evidence accumulation process to be modeled by a Markov chain or random walk.Furthermore, the MPMW improves upon other quantum cognitive models of decision making by incorporating cognitive control, arousal, and leak through inclusion of a wider spectrum of quantum variables, such as access to the "classically forbidden region" and the full eigen energy spectrum. Additionally, where other quantum cognitive models require classical extension to better account for empirical phenomena, the MPMW inherently includes quantum and classical components. Finally, unlike many models of decision making, the MPMW is easily extensible to greater than two alternatives. This thesis presents theoretical work illustrating these features alongside theoretical and modeling work that validates the MPMW by demonstrating its capacity to recreate many established cognitive phenomena. Additionally, the thesis addresses a variety of new predictions made by the framework.

ISBN: 9798802749890Subjects--Topical Terms:

588700
Neurosciences.
Subjects--Index Terms:

Quantum cognition

The Multi-Particle Multi-Well (Mpmw) Framework: A Quantum Framework Incorporating Attentional Capture, Representational Generality, and Arousal to Perceptual Choice Problems.
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520 $a This dissertation presents a new quantum cognitive framework, the Multi-Particle Multi-Well (MPMW) Framework, that models forced choice decisions among any number of alternatives. This framework maps the cognitive parameters of control(such as selective attention), automaticity (arising from task stimulus salience and prior practice), representational generality, and arousal to parameters of square at-tractors in a quantum landscape. The stimulus is modeled as an emitter of quantum particles, and information accumulation toward a decision is represented by measurements thereof. Particles are treated as sequentially emitted and independent, acted upon by the attractor landscape, and, by a positional measurement, cleared there-from. These modeling choices provide several desirable features, the combination of which are unique to the MPMW framework.Mapping cognitive parameters to quantum landscapes allows stochasticity (noise), leak (decay), and lateral inhibition (competition) to arise directly from the parameters of the attractor landscape. Modeling evidence accumulation by particle measurements imposes two biologically plausible constraints on that process. First, the rate on information accumulation is finite where, in standard continuous classical models (such as the Drift Diffusion Model (DDM)), it is not. Second, information is accumulated in discrete time steps, an effect that few models incorporate despite rowing empirical evidence at both the neural and psychological levels of analysis.These constraints increase the model's analytical tractability, allowing the evidence accumulation process to be modeled by a Markov chain or random walk.Furthermore, the MPMW improves upon other quantum cognitive models of decision making by incorporating cognitive control, arousal, and leak through inclusion of a wider spectrum of quantum variables, such as access to the "classically forbidden region" and the full eigen energy spectrum. Additionally, where other quantum cognitive models require classical extension to better account for empirical phenomena, the MPMW inherently includes quantum and classical components. Finally, unlike many models of decision making, the MPMW is easily extensible to greater than two alternatives. This thesis presents theoretical work illustrating these features alongside theoretical and modeling work that validates the MPMW by demonstrating its capacity to recreate many established cognitive phenomena. Additionally, the thesis addresses a variety of new predictions made by the framework.
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