"Explaining-Away" Effects in Rule-Learning: Evidence for Generative Probabilistic Inference in Infants and Adults

Persistent Link:
http://hdl.handle.net/10150/145270
Title:
"Explaining-Away" Effects in Rule-Learning: Evidence for Generative Probabilistic Inference in Infants and Adults
Author:
Dawson, Colin Reimer
Issue Date:
2011
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
The human desire to explain the world is the driving force behind our species' rich history of scientific and technological advancement. The ability of successive generations to build cumulatively on the scientific progress made by their ancestors rests on the ability of individual minds to rapidly assimilate the explanatory models developed by those who came before. But is this explanatory, model-based way of thinking limited to deliberate, conscious cognition, with the larger, unconscious portion of the workings of the mind dependent on simpler mechanisms of association and prediction, or is explanation a more fundamental drive? In this dissertation I explore theoretical, empirical and computational attempts to shed some light on this question. I first present a number of theoretical advantages that model-based learning has over its associative counterparts. I focus particularly on the inferential phenomenon of \emph{explaining away}, which is difficult to account for in a model-free system of learning. Next I review some recent empirical literature which helps to establish just what mechanisms of learning are available to human infants and adults, including a number of findings that suggest that there is more to learning than mere prediction. Among these are a number of experiments suggesting that explaining away occurs in a variety of cognitive domains. Having set the stage, I report a new set of experiments, one with infants and two with adults, along with a related computational model, which provide further evidence for unconscious explaining away, and hence for some for of model-based inference, in the domain of abstract, relational pattern-learning. In particular, I find that when learners are presented with a novel environment of tone sequences, the structure of their initial experience with that environment, and implicitly the model of the environment which best accounts for that experience, influences what kinds of abstract structure can easily be learned later. If indeed learners are able to construct explanatory models of particular domains of experience which are then used to learn the details of each domain, it may undermine claims by some philosophers and cognitive scientists that asymmetries in learning across domains constitutes evidence for an innately modular organization of the mind.
Type:
Electronic Dissertation; text
Keywords:
Bayesian Modeling; Infant Cognition; Language Learning; Music Cognition; Statistical Learning
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Psychology
Degree Grantor:
University of Arizona
Advisor:
Gerken, LouAnn

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.title"Explaining-Away" Effects in Rule-Learning: Evidence for Generative Probabilistic Inference in Infants and Adultsen_US
dc.creatorDawson, Colin Reimeren_US
dc.contributor.authorDawson, Colin Reimeren_US
dc.date.issued2011-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThe human desire to explain the world is the driving force behind our species' rich history of scientific and technological advancement. The ability of successive generations to build cumulatively on the scientific progress made by their ancestors rests on the ability of individual minds to rapidly assimilate the explanatory models developed by those who came before. But is this explanatory, model-based way of thinking limited to deliberate, conscious cognition, with the larger, unconscious portion of the workings of the mind dependent on simpler mechanisms of association and prediction, or is explanation a more fundamental drive? In this dissertation I explore theoretical, empirical and computational attempts to shed some light on this question. I first present a number of theoretical advantages that model-based learning has over its associative counterparts. I focus particularly on the inferential phenomenon of \emph{explaining away}, which is difficult to account for in a model-free system of learning. Next I review some recent empirical literature which helps to establish just what mechanisms of learning are available to human infants and adults, including a number of findings that suggest that there is more to learning than mere prediction. Among these are a number of experiments suggesting that explaining away occurs in a variety of cognitive domains. Having set the stage, I report a new set of experiments, one with infants and two with adults, along with a related computational model, which provide further evidence for unconscious explaining away, and hence for some for of model-based inference, in the domain of abstract, relational pattern-learning. In particular, I find that when learners are presented with a novel environment of tone sequences, the structure of their initial experience with that environment, and implicitly the model of the environment which best accounts for that experience, influences what kinds of abstract structure can easily be learned later. If indeed learners are able to construct explanatory models of particular domains of experience which are then used to learn the details of each domain, it may undermine claims by some philosophers and cognitive scientists that asymmetries in learning across domains constitutes evidence for an innately modular organization of the mind.en_US
dc.typeElectronic Dissertationen_US
dc.typetexten_US
dc.subjectBayesian Modelingen_US
dc.subjectInfant Cognitionen_US
dc.subjectLanguage Learningen_US
dc.subjectMusic Cognitionen_US
dc.subjectStatistical Learningen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplinePsychologyen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorGerken, LouAnnen_US
dc.contributor.committeememberGómez, Rebeccaen_US
dc.contributor.committeememberPeterson, Maryen_US
dc.contributor.committeememberTemperley, Daviden_US
dc.identifier.proquest11509-
dc.identifier.oclc752261373-
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