🤖 AI Summary
This study investigates whether machines can accurately perceive human emotional responses they elicit, thereby testing the affective closed-loop hypothesis. In a virtual reality experiment, participants were exposed to six emotionally charged stimuli derived from critical incidents experienced by emergency responders, while subjective ratings of valence and arousal, along with physiological signals—including electrodermal activity and heart rate—were simultaneously recorded. The findings reveal, for the first time, an “expression–perception asymmetry”: although the machine effectively induced anger, fear, and sadness (dz = 1.1–1.7), self-reported arousal showed no significant change, and physiological measures responded only to the most salient events. Moreover, emotional valence became decoupled from physiological signals, suggesting that current peripheral physiological channels are insufficiently reliable for decoding machine-induced affective states.
📝 Abstract
Affect-adaptive systems increasingly act as communicators that sense a user's emotion and respond with events meant to change it, closing an affective loop. This vision assumes both that a machine's affective messages are received and that the bodily channel it monitors carries an intelligible reply-assumptions rarely tested together. In a within-subjects virtual-reality study (N = 20), an autonomous system delivered six empirically derived affective patterns-scripted emotional events distilled from 104 practitioners' (first responders') critical incidents-while we recorded the human reply across felt emotion, felt arousal, and autonomic (electrodermal and cardiac) activity. Acting only as an author of designed messages, the machine reliably evoked strong, differentiated emotions: valence fell sharply for every pattern (|dz| = 1.1-1.7), and the patterns produced distinguishable, individually classifiable signatures of anger, fear, and sadness, functioning as a vocabulary of machine-to-human affective messages. Yet the channel the machine would read stayed largely silent. Self-reported arousal did not change, with Bayesian and equivalence evidence for no effect; sympathetic and cardiac arousal moved only for the most perceptually salient events, not for the messages experienced as most powerful; and within individuals, felt valence was decoupled from both bodily registers. This valence-arousal dissociation reveals an expressive-sensing asymmetry: machine agency extends to expression but not perception, and closed-loop designs regulate on a variable their messages neither reliably move nor are legible in. We draw out implications for theories of machine agency and for affect-adaptive design.