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Introspective Discrimination: Probing the accuracy of memory, metacognition, and psychobiological prediction models under negative emotional contexts

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2025-05-14

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Castillo, Juan. 2025. Introspective Discrimination: Probing the Accuracy of Memory, Metacognition, and Psychobiological Prediction Models Under Negative Emotional Contexts. Doctoral Dissertation, Harvard University Graduate School of Arts and Sciences.

Abstract

Emotional experiences emerge from a complex amalgamation of objective and subjective information. However, due to the partially subjective nature of emotional experiences, objective and subjective measures of experience can sometimes dissociate. These dissociations can consequently affect the accuracy of memory, emotional well-being, and the conscious perception of emotional experiences. In the following body of work, I leverage a combination of statistical modeling, computational modeling, and machine learning alongside behavioral experiments and observational studies to provide insights into the alignment between objective and subjective measures of emotional experience. In study 1, I examined how objectively accurate autobiographical memory is for subjective emotional experiences. The results of this study conclusively demonstrate that autobiographical memories of subjective emotional experiences are objectively inaccurate in negatively valenced contexts, and that overestimating the subjective intensity of these remembered experiences is common and negatively associated with current emotional well-being. Study 2 examined how objectively accurate memory and associated subjective confidence judgements are under emotional contexts. Insights from this experiment suggest that negative valence influences how we think and respond – objectively biasing actions, and influences our self-monitoring capabilities – subjectively biasing self-confidence. Study 3 further develops this foundation of knowledge to examine the predictive validity of objective physiological information for predicting subjective reports about the intensity of emotional experiences. This study demonstrates that changes in electrodermal activity (a measure of physiological arousal) can effectively track the intensity of valenced experiences, but fail to capture nuanced variations of specific emotional states.
In other words, how we consciously feel and label our emotions is more complex than what can be measured by changes in physiological arousal alone. Altogether, this dissertation furthers our knowledge of the association between objective and subjective measures of emotional experience and begins to reveal how negative emotional contexts are linked to dissociations between these constructs – ultimately affecting emotional well-being, the accuracy of memory and metacognition, and the perception of emotional experiences.

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Affective Computing, Emotion, Machine Learning, Memory, Metacognition, Psychology

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