Algorithmic Orientalism and Its Neurocognitive Correlates: Affective Bias and Stereotype Processing Revealed by Affective Computing and EEG

This study aims to empirically examine Algorithmic Orientalism (AO), a critical communication theory concept, through objective neurophysiological measurements. The Algorithmic Orientalism framework describes how stereotypes rooted in historical biases can become embedded and amplified within social media recommendation and moderation algorithms, leading to systematic distortions in information dissemination, such as selective content removal or suppression of certain accounts. To address the current lack of physiological evidence, this research integrates Affective Computing (AC) with Electroencephalography/Event-Related Potentials (EEG/ERP) to objectively assess the cognitive conflict and affective stress induced by stimuli related to Algorithmic Orientalism. A cross-cultural experimental design was adopted, comparing a group sensitive to Algorithmic Orientalism (Targeted Group) with a control group. Both groups were exposed to simulated stereotype-laden content and algorithmic moderation scenarios, including simulated content removal. Core predictive indicators include: Affective Computing results, which are expected to reveal measurable negative valence and heightened arousal in response to Algorithmic Orientalism stimuli; and ERP analyses, focusing on key waveforms, where Algorithmic Orientalism content is predicted to elicit a significantly larger N400 amplitude, reflecting cognitive conflict arising from semantic or contextual incongruence, along with a sustained Late Positive Potential (LPP), indicating ongoing emotional engagement. Overall, this study provides objective neural evidence of the real-time cognitive and affective impacts of algorithmic bias on the human brain, offering an empirical foundation for the development of AI systems that are sensitive to fairness and bias.