The Emotion-Memory Link: Do Memorability Annotations Matter for Intelligent Systems?

2025-07-18

AI Summary

This study investigates whether affective annotations can serve as a valid proxy for event memorability to support user modeling in intelligent systems such as meeting assistance and memory augmentation. It critically examines the prevailing assumption that third-party affect annotations predict individual first-person memory experiences. Method: Conducting the first empirical validation in authentic, dynamic group dialogue settings, the study integrates continuous-dimensional (valence–arousal) affect annotation with fine-grained memorability annotation, coupled with conversational analysis and affective computing techniques. Contribution/Results: Statistical analysis reveals no significant association between affect and memorability annotations (p > 0.05), with predictive performance indistinguishable from random chance. These findings challenge the conventional “affect-as-memory-proxy” paradigm and expose fundamental limitations of relying on external affect annotations for memory modeling. The study provides critical methodological guidance for data collection and modeling frameworks in memory-augmentation systems, urging reconsideration of affect-centric assumptions in human memory inference.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Humans have a selective memory, remembering relevant episodes and forgetting the less relevant information. Possessing awareness of event memorability for a user could help intelligent systems in more accurate user modelling, especially for such applications as meeting support systems, memory augmentation, and meeting summarisation. Emotion recognition has been widely studied, since emotions are thought to signal moments of high personal relevance to users. The emotional experience of situations and their memorability have traditionally been considered to be closely tied to one another: moments that are experienced as highly emotional are considered to also be highly memorable. This relationship suggests that emotional annotations could serve as proxies for memorability. However, existing emotion recognition systems rely heavily on third-party annotations, which may not accurately represent the first-person experience of emotional relevance and memorability. This is why, in this study, we empirically examine the relationship between perceived group emotions (Pleasure-Arousal) and group memorability in the context of conversational interactions. Our investigation involves continuous time-based annotations of both emotions and memorability in dynamic, unstructured group settings, approximating conditions of real-world conversational AI applications such as online meeting support systems. Our results show that the observed relationship between affect and memorability annotations cannot be reliably distinguished from what might be expected under random chance. We discuss the implications of this surprising finding for the development and applications of Affective Computing technology. In addition, we contextualise our findings in broader discourses in the Affective Computing and point out important targets for future research efforts.

Problem

• Examining the link between group emotions and memorability in conversations
• Assessing emotion annotations as proxies for memorability in intelligent systems
• Evaluating real-world applicability of affect-memorability relationships in AI

Innovation

• Examining emotion-memorability link in conversations
• Using continuous time-based annotations for analysis
• Testing affect-memorability reliability in real-world settings