News Release

It’s not you that makes vision mistakes, it’s your brain

A study conducted by David Heeger of NYU and David Ress of Stanford shows brain activity responds to what we think we see, even when it’s wrong

Peer-Reviewed Publication

New York University

It's common knowledge that things aren't always as they appear, but a new study shows our brains are complicit in our vision errors even at the earliest point in the brain's visual processing system. In an article to be featured in an upcoming issue of Nature Neuroscience, David Ress of Stanford University and David Heeger of NYU report that activity in the brain's visual cortex corresponds to what the subjects perceive, rather than what they actually see.

The scientists based their findings on experiments using functional magnetic resonance imaging (fMRI) to measure activity in carefully circumscribed regions of the visual cortex of the brain while human subjects performed a challenging visual discrimination task. Subjects attempted to detect the presence of slight contrast increments added to a background pattern. Behavioral responses were recorded so that the corresponding cortical activity could be grouped into four categories: "hits", when subjects correctly identified the image shown as the higher contrast image; "false alarms", when subjects misidentified the lower-contrast image as the higher contrast image; "misses", when subjects presented with the higher contrast image misidentified it as the lower contrast image; and "correct rejects", when subjects correctly identified the lower contrast image.

Hits and false alarms produced significantly more cortical activity than misses, indicating that activity in the visual cortex corresponded to the subjects' precepts, rather than to the physically presented stimulus.

"In plain terms, whether we see something or just think we see something, the magnitude of activity in the early visual cortex is almost identical, implying that our misperceptions or 'hallucinations' are physically manifest by neuronal activity in the brain," said Heeger. "This is a particularly significant finding in the early visual cortex, because it shows that distortions occur at the first stages of the brain's cortical vision processing."

The study's findings could help to shed light on conditions such as amblyopia or other visual disabilities that aren't attributable to disease or problems in optics of the eye, but related to processing deficits in the visual cortex of the brain. The resulting enhanced understanding of visual processing may also help reduce the error rate of activities that require subtle discrimination, such as reading. More generally, the research could also provide additional insight into the understanding of awareness and consciousness and how it manifests itself in particular brain states.

Heeger and Ress's experiment could potentially open up more areas of study to ascertain why the brain makes errors in judgment and why human perception and behavior is non-deterministic. The research also raises the question of how stored memory could be biasing subjects' responses, implying that other areas of the brain are affecting visual processing even at this early stage.

To help facilitate brain research, New York University opened its Brain Imaging Center in October 2002, making it one of a handful of universities to have research-only MRI facilities on campus. David Heeger, professor of psychology and neural science at NYU since fall 2002, is one of the nation's leading researchers using MRI technology. He holds a BA in Mathematics and a Ph.D. in computer science, both from the University of Pennsylvania.

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New York University, which was established in 1831, is one of the largest and most prestigious private research universities in the U.S. Through its 13 schools and colleges, NYU conducts research and provides education in the arts and sciences, law, medicine, dentistry, education, nursing, business, social work, the cinematic and performing arts, public administration and policy, and continuing studies, among other areas.


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