Affective Experience Evaluation in Human-Computer Interaction

Evaluating affective experience in human–computer interaction (HCI) encompasses the measurement and interpretation of users’ emotional responses during and after interaction with digital systems. This domain integrates psychophysiological signals, behavioural markers and subjective reports to capture real‐time valence and arousal, as well as retrospective assessments shaped by memory biases. Advances in wearable sensors and machine learning have enabled continuous affective mapping, allowing systems to adapt dynamically to users’ emotional states in applications from virtual reality to adaptive educational platforms. Affective experience evaluation informs the design of empathetic interfaces, supports accessibility goals and drives the development of personalised user journeys across cultural and socio‐economic contexts. By reconciling instantaneous affective fluctuations with longer‐term memory biases, researchers have begun to formulate comprehensive models that underpin emotionally intelligent computing.

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One line of inquiry has assessed the generalisability of the peak–end rule in complex, heterogeneous virtual environments. Investigators used immersive virtual reality to record continuous valence and arousal, finding that averaged in‐the‐moment ratings often predict remembered experience more accurately than peak and end measures. These findings suggest that affective computing frameworks should incorporate full‐episode aggregation rather than relying solely on extremal moments.

In the realm of online retail interfaces, studies have demonstrated that the presentation mode of products induces serial position effects in user decision-making. Separate evaluation prompts a recency bias, leading users to favour items encountered last, whereas joint evaluation evokes a primacy bias, with earlier items weighted more heavily. Such insights underscore the necessity of interface design that mitigates order effects to support rational, emotionally informed purchasing decisions.

Investigations into retrospective evaluation strategies have linked memory recall patterns to systematic judgement errors in global assessments. Experiments reveal that individuals disproportionately retrieve first and last elements of a sequence, producing predictable biases in overall affective ratings. This work highlights the potential for adaptive interfaces to prompt supplementary context retrieval, thereby enhancing the accuracy of self‐reported emotional states.

Affective Experience Evaluation in Human-Computer Interaction publication trend

The graph below shows the total number of articles in affective experience evaluation in human-computer interaction across all publications each year (not limited to Nature Index journals).

Technical terms

Affective computing: The interdisciplinary field focused on recognising, interpreting and responding to human emotions via computational methods.

Electrodermal activity (EDA): A psychophysiological measure of skin conductance used to index sympathetic nervous system arousal during interaction.

Peak–end rule: A memory bias whereby retrospective evaluations of an experience are disproportionately influenced by the most intense moment and by the final moment.

Serial position effect: A cognitive phenomenon in which the order of information presentation leads to primacy or recency biases in judgement and memory.

Ecological momentary assessment (EMA): A method of gathering real‐time self-reports of experience, typically via mobile devices, to reduce recall bias and capture in-situ affective data.

References

1. From Experience to Memory: On the Robustness of the Peak-and-End-Rule for Complex, Heterogeneous Experiences. Frontiers in Psychology (2019).
2. The Influence of Commodity Presentation Mode on Online Shopping Decision Preference Induced by the Serial Position Effect. Applied Sciences (2021).
3. Predicting serial position effects and judgment errors in retrospective evaluations from memory recall. Journal of Economic Psychology (2023).