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A Valid Self-Report is Never Late, Nor is it Early: On Considering the "Right" Temporal Distance for Assessing Emotional Experience

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arxiv 2302.02821 v1 pith:Q5BT2NLG submitted 2023-01-27 cs.HC cs.AI

classification cs.HCcs.AI
keywords distancetemporalself-reportsearlyemotionalexperienceinfluencestimuli
verification ladder T0 review T1 audit T2 compute T3 formal
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Developing computational models for automatic affect prediction requires valid self-reports about individuals' emotional interpretations of stimuli. In this article, we highlight the important influence of the temporal distance between a stimulus event and the moment when its experience is reported on the provided information's validity. This influence stems from the time-dependent and time-demanding nature of the involved cognitive processes. As such, reports can be collected too late: forgetting is a widely acknowledged challenge for accurate descriptions of past experience. For this reason, methods striving for assessment as early as possible have become increasingly popular. However, here we argue that collection may also occur too early: descriptions about very recent stimuli might be collected before emotional processing has fully converged. Based on these notions, we champion the existence of a temporal distance for each type of stimulus that maximizes the validity of self-reports -- a "right" time. Consequently, we recommend future research to (1) consciously consider the potential influence of temporal distance on affective self-reports when planning data collection, (2) document the temporal distance of affective self-reports wherever possible as part of corpora for computational modelling, and finally (3) and explore the effect of temporal distance on self-reports across different types of stimuli.

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Cited by 1 Pith paper

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  1. Indeterminacy in Affective Computing: Considering Meaning and Context in Data Collection Practices

    cs.AI 2025-02 conditional novelty 6.0 of 10

    Affective labels inherit four qualities of indeterminacy from human interpretation, and the paper argues that data collection must track the context of those interpretations to make emotion prediction reliable.

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