A critical engagement with Clark's account of the role of motivation in a Bayesian information processing model of the brain.
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Date
2014
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Abstract
The action selection problem can be approached with two goals in mind: to
account for why actions are selected, i.e. what factors play a role in making the
decision, and how actions are selected, i.e. what mechanisms are involved in
action selection. These two goals form the main focus of this dissertation and
can be referred to as the efficiency problem and the architecture problem. This
dissertation examines the Environmental Complexity Hypothesis as an approach
to the former goal (why actions are selected). The Environmental Complexity
Hypothesis adequately explains why actions are selected but lacks consideration
of the architecture and mechanisms involved in complex cognitive systems. In
response to this shortcoming, the predictive processing account is offered as a
solution to the second problem of action selection, the architecture problem.
The predictive processing account will be critically examined and one objection
will be raised against its claim about the function of cognition. According to the
predictive processing account, the function of cognition is to minimize free
energy. One objection to this claim takes the form of a reductio ad absurdum
argument and suggests that if the predictive processing account is correct about
the function of cognition, then biological agents will find themselves in the state
of seeking environments with no sensory stimuli. This is biologically nonsensical
because environments with no sensory stimuli will result in no surprise and no
prediction error which, the account claims, needs to be reduced for successful
adaptation. However, reducing prediction error alone does not result in adaptive
fitness and is not the sole function of the cognitive system; agents explore and
participate in the environment to reproduce and to satisfy desires and
preferences as well. This view is reflected in the field of neuroeconomics which
provides a fairly novel solution to the problem of action selection and decision
making. Neuroeconomic models of action selection can provide insight about
why agents select certain actions when alternatives are available and it also gives
an account of the architecture involved in action selection. It is proposed that
prediction error is coded in relation to rewards. By recommending
neuroeconomics as an alternative account of action selection, nothing is lost in
either the Environmental Complexity Hypothesis or the predictive processing
account. Instead, neuroeconomics represents the best features of the two
theories in a unified account.
Description
Master of Arts in Cognitive Science. University of KwaZulu-Natal, Howard College 2014.
Keywords
Bayesian statistical decision theory., Motivation (Psychology), Decision making -- Mathematical models., Theses -- Cognitive science.