Browsing by Subject "Metacognition"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Building a theoretical model of metacognitive processes in complex modeling activities: a window into the development of students' metacognitive abilities
    (2013-07) Kim, Young Rae
    A theoretical model of metacognition in complex modeling activities has been developed based on existing frameworks, by synthesizing the re-conceptualization of metacognition at multiple levels by looking at the three sources that trigger metacognition. Using the theoretical model as a framework, this study was designed to explore how students' thinking becomes metacognitive while collaboratively solving a complex mathematical modeling task. This study used a series of Model-Eliciting Activities (MEAs), which are a type of problem-solving activity in which participants are required to verbalize their thoughts while working within a group, as an authentic method for analyzing verbal metacognitive actions, addressing several criticisms of self-report methods. Multiple cycles of data analysis, including a finer-grained analysis of conversational statements and a cross-case analysis, were conducted. Results from the data analysis provided empirical evidence supporting the soundness and appropriateness of the theoretical model of metacognition on multiple levels in identifying and interpreting students' metacognitive activities in complex mathematical modeling tasks. This study identified several patterns and tendencies of students' spontaneous metacognitive activities. This study provided empirical evidence supporting the potential similarity of students' developing metacognitive abilities to their developing cognitive abilities with respect to the dimensions of development. In addition, this study identified the circumstances facilitating or interfering with students' spontaneous metacognitive activities. This study furthers our understanding about how one develops metacognitive abilities within problem-solving processes, and ultimately informs how to effectively encourage students' metacognition and improve their problem-solving achievement.
  • Thumbnail Image
    Item
    A computational investigation of being in the world
    (2012-10) Srivastava, Nisheeth
    Is there a rational explanation for human behavior? Or is it fundamentally idiosyncratic and beyond the ability of science to accurately predict? In everyday life, we are able to predict the preferences of other people relatively well, and function in a society that is strongly predicated on our ability to do so. Theoretical efforts at predicting how people form preferences, however, have met with repeated failures, resulting in widespread pessimism regarding the possibility of a universal rational explanation for human behavior. In this thesis, we provide precisely such an explanation. We show that the errors plaguing existing systems of preference representations are a direct result of the mystery surrounding the actual act of {\em formation} of preferences, and that once this latter mechanism is clarified, a very large number of paradoxical and contradictory empirical results from the behavioral economics literature are theoretically reconciled. Our investigations lead us to believe that a combination of two simple natural principles is sufficient to both predict and explain why humans make the choices they do: one, that humans seek to always learn what to do in the most statistically efficient manner possible, and two, that this quest for understanding is constrained in remarkably systematic ways by a competing search for choices that can be made with minimal cognitive effort. We find, therefore, that the rational goal that best describes human choice behavior is attempting to minimize the cognitive effort required to make a decision. In other words, in this dissertation, we propose a theory that rational human action is governed by a universal explanatory principle, one that does not match traditional expectations of utility maximization - the principle of least cognitive effort. This redefinition of rationality has far-reaching implications. In order to better understand them, we constructed an information-theoretic description of a meta-cognitive agent engaging with its environment which allowed us to formulate computationally tractable intrinsically motivated agents. In this dissertation, we report simulation studies which confirm that the behavior of cognitively efficient agents provides a unified explanation for a large number of behavioral biases identified by behavioral and experimental economists in human subjects, as well as a number of variations in subjects' perception of risk observed in neuroeconomics studies.We further show using mathematical arguments, that this construction is in consonance with existing reinforcement learning literature and, in fact, subsumes multiple strands of current research in broadening the definitions of reward in reinforcement learning. Finally, we extend our analysis to studying social behavior among populations of agents and shed new light on paradoxes in game theory and theories of social interaction, resulting in a demonstration of an amoral basis for being good - the existence of an entirely self-interested (and non-evolutionary) basis for cooperative and altruistic behavior. In short, this thesis proposes a quantifiable description of agents {\em being in the world} - detailing universal principles that explain how and why beings develop preferences of the form they do given the structure of the world they inhabit. Our results provide a unification of explanations for several biological and behavioral phenomena spanning economics, psychology, neurobiology, cognitive science, artificial intelligence and metaphysics.
  • Thumbnail Image
    Item
    The measure of affective decision making: Modulatory circuitry as interface between emotion and decision
    (2019-12) Therior, Windy
    Decision making is influenced by modulatory processes that enable coordinated responses to environmental and emotional contexts. The measurement of modulatory processes is typically performed via biophysical metrics which carry only partial information on the unobserved processes. We provide an alternative, data-driven, methodology for the targeted measurement of the impact of modulatory processes on decisions. We apply directed dimensionality reduction to a large set of biometric measures including galvanic skin response, heart rate, pupilometry, facial emotion, and electroencephalography, to extract information predictive of human behavior in a standard two-alternative forced-choice decision making paradigm. Using a pre-existing model of decisions in this domain (i.e., the drift diffusion model) affords the ability to specify how the inferred modulatory process informs interpretable decision parameters. We validate this method with model comparisons together with cross validation. This method can be adapted to arbitrary decision domains to investigate how emotional state interacts with decision processes. We find an unexpected correlation between decision parameters, drift rate and decision threshold, when using this latent state extraction procedure not otherwise found when investigating behavioral responses alone. We interpret the correlation in parameters as evidence of their being both influenced by a common upstream modulatory process. We then systematically relax the constraints of the drift diffusion model and performed logistic regression to extract within trial weights on external information. We found that confidence acts as an internal representation of information reliability and adapts integration time to offset conditions of low information gain. Taken together, these findings support the interpretation that emotional state modulates decision making processes.
  • Thumbnail Image
    Item
    Remembering to Remember: Metamemory Judgments of Prospective Memory after Traumatic Brain Injury
    (2016-06) O'Brien, Kathryn
    Background: Impairments to prospective memory (PM) are ubiquitous after traumatic brain injury (TBI). PM is remembering to complete an intention at a future time – like picking up milk on the way home – and is critical for independent living. PM includes two primary components: recognizing the CUE when a task should occur, and recalling the TASK to be completed. Many adults use memory aids for PM, such as notes or phone alarms. Such strategy use is related to metamemory judgments, or self-assessments of future success. Purpose: The purpose of the current study was to examine how adults with and without TBI consider PM performance. Research questions compared predictions and recall performance at PM, as well as the relationship between PM metamemory predictions and standardized assessments of cognitive function. Methods: Eighteen adults with chronic moderate to severe TBI and 20 matched healthy controls played Tying the String, an online simulated workweek PM game. Participants studied PM items and made two judgments of learning about the likelihood of recognizing a PM CUE, and of recalling the PM TASK. Participants also completed a standard neuropsychological battery. Results: Participants with TBI were less confident in future recall than healthy controls and both groups were less confident about the TASK. For recall performance, healthy controls performed similarly across the CUE and TASK. In contrast, adults with TBI at times recognized a CUE, but were unlikely to remember the corresponding TASK. Absolute difference scores of metamemory accuracy showed that healthy adults were underconfident across PM, whereas adults with TBI were overconfident about the task. Adults with TBI adjusted judgments downward as the game progressed at a rate greater than healthy controls. During standardized testing, participants with TBI chose to use PM strategies, but those strategies were not effective at triggering PM recall. Discussion: Participants with TBI adjusted metamemory expectations downward, but not enough to account for poor recall performance. Individuals with TBI have metamemory awareness to use strategies, but deficient monitoring of memory performance results in incomplete metamemory knowledge. Future work should address linking PM metamemory monitoring with strategy use to direct intervention approaches.