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

arXiv: https://arxiv.org/abs/2507.14084 · HTML: https://mlsanity.com/paper/2507.14084/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 60 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability](https://mlsanity.com/paper/1812.01973/) (2018)
- [Multimodal Memorability: Modeling Effects of Semantics and Decay on Video Memorability](https://mlsanity.com/paper/2009.02568/) (2020)
- [Emotion in Reinforcement Learning Agents and Robots: A Survey](https://mlsanity.com/paper/1705.05172/) (2017)
- [PATE: Proximity-Aware Time series anomaly Evaluation](https://mlsanity.com/paper/2405.12096/) (2024)
- [Experiences from the MediaEval Predicting Media Memorability Task](https://mlsanity.com/paper/2212.03955/) (2022)
- [A Valid Self-Report is Never Late, Nor is it Early: On Considering the "Right" Temporal Distance for Assessing Emotional Experience](https://mlsanity.com/paper/2302.02821/) (2023)
- [Dynamics of Collective Group Affect: Group-level Annotations and the Multimodal Modeling of Convergence and Divergence](https://mlsanity.com/paper/2409.08578/) (2024)
- [Introducing MeMo: A Multimodal Dataset for Memory Modelling in Multiparty Conversations](https://mlsanity.com/paper/2409.13715/) (2024)

## Abstract (arXiv)

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.