Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01k643b432k
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dc.contributor.authorRosendahl, Morgan L
dc.contributor.otherMechanical and Aerospace Engineering Department
dc.date.accessioned2022-06-13T17:49:33Z-
dc.date.available2022-06-13T17:49:33Z-
dc.date.created2022-01-01
dc.date.issued2022
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01k643b432k-
dc.description.abstractThis dissertation presents a new quantum cognitive framework, the Multi-ParticleMulti-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 measure- ments 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 param- eters of the attractor landscape. Modeling evidence accumulation by particle mea- surements 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 growing 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 de- cision 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 cog- nitive models require classical extension to better account for empirical phenomena, iii 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 theoret- ical 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.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherPrinceton, NJ : Princeton University
dc.relation.isformatofThe Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: <a href=http://catalog.princeton.edu>catalog.princeton.edu</a>
dc.subjectQuantum Cognition
dc.subject.classificationNeurosciences
dc.subject.classificationMechanical engineering
dc.subject.classificationQuantum physics
dc.titleTHE MULTI-PARTICLE MULTI-WELL (MPMW) FRAMEWORK: A QUANTUM FRAMEWORK INCORPORATING ATTENTIONAL CAPTURE, REPRESENTATIONAL GENERALITY, AND AROUSAL TO PERCEPTUAL CHOICE PROBLEMS