Decoding moral judgement from text: a pilot study

TL;DR

This pilot study investigates decoding moral judgments from text stimuli using passive brain-computer interfaces, aiming to enhance neuroadaptive human-computer interaction and improve large language models.

Contribution

It explores the feasibility of detecting moral judgments from text with brain signals, introducing a novel approach combining affective priming and neural decoding.

Findings

Good accuracy in classifying neutral vs. morally-charged trials

Difficulty in reliably distinguishing moral congruency vs. incongruency

Preliminary evidence supporting potential for moral judgment decoding

Abstract

Moral judgement is a complex human reaction that engages cognitive and emotional dimensions. While some of the morality neural correlates are known, it is currently unclear if we can detect moral violation at a single-trial level. In a pilot study, here we explore the feasibility of moral judgement decoding from text stimuli with passive brain-computer interfaces. For effective moral judgement elicitation, we use video-audio affective priming prior to text stimuli presentation and attribute the text to moral agents. Our results show that further efforts are necessary to achieve reliable classification between moral congruency vs. incongruency states. We obtain good accuracy results for neutral vs. morally-charged trials. With this research, we try to pave the way towards neuroadaptive human-computer interaction and more human-compatible large language models (LLMs)