{
  "id": 4691132,
  "title": "Predicting Conscious Perception from Pupil's Aperture Size Using Machine Learning Techniques",
  "url": "https://urgent.news/2026/08/31/predicting-conscious-perception-from-pupils-aperture-size-using",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-31T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.08.26.747446v1?rss=1"
  },
  "original_language": "en",
  "account": "Researchers have discovered a potential method to predict conscious perception of stimuli using machine learning techniques and the size of a person's pupil. In a study examining the phenomenon of attentional blink (AB), where observers often fail to detect a second target (T2) presented shortly after the first target (T1), scientists found that variations in pupil size could reveal the underlying mechanisms of AB and accurately predict conscious perception on a trial-by-trial basis.\n\nThe study recorded pupil diameter and gaze locations using an infrared eye tracker while participants completed an AB task. Machine learning algorithms were then employed to classify trials where T2 was detected versus when it was not, after correctly identifying T1. The researchers also deconstructed the attentional episodes (AEs) associated with each visual stimulus in the stream to identify patterns linked to AB.\n\nTheir findings revealed that cross-validating classifiers achieved near-perfect accuracy not only in distinguishing whether T2 was detected or missed but also in predicting conscious perception of the second stimulus. Moreover, the researchers discovered that AEs exhibited greater power when T2 was detected compared to when it was missed. This differential power in AEs on a logarithmic scale was found to be highly synchronized with the differential pupil size, suggesting a strong correlation between pupil dynamics and conscious perception.\n\nIn conclusion, these results establish a promising framework for predicting attention-driven perceptual outcomes based on pupil dynamics at a finer time-scale. This discovery may have significant implications for understanding the neural processes involved in perception and attention, as well as potential applications in developing more effective attention restoration techniques and enhancing human-computer interaction.",
  "summary": "Introduction: Decision making for selecting an object or a course of action from possible alternatives largely depends on our perceptual ability modulated by attention. When multiple stimuli appear close together in time, processing one stimulus can temporarily impair the processing of another due to temporal limitations of attention. Observers frequently fail to detect the second target (T2)…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}