We've updated our Privacy Policy to make it clearer how we use your personal data. We use cookies to provide you with a better experience. You can read our Cookie Policy here.

Advertisement

How Do We Recognize Objects Even When They Become Hard To See?

How Do We Recognize Objects Even When They Become Hard To See? content piece image
Credit: Chase Clark/ Unsplash
Listen with
Speechify
0:00
Register for free to listen to this article
Thank you. Listen to this article using the player above.

Want to listen to this article for FREE?

Complete the form below to unlock access to ALL audio articles.

Read time: 1 minute

The appearance of objects can often change. For example, in dim evenings or fog, the contrast of the objects decreases, making it difficult to distinguish them. However, after repeatedly encountering specific objects, the brain can identify them even if they become indistinct. The exact mechanism contributing to the perception of low-contrast familiar objects remains unknown.

In the primary visual cortex (V1), the area of the cerebral cortex dedicated to processing basic visual information, the visual responses have been considered to reflect directly the strength of external inputs. Thus, high-contrast visual stimuli elicit strong responses and vice versa.

In this study, Rie Kimura and Yumiko Yoshimura found that in rats, the number of V1 neurons preferentially responding to low-contrast stimuli increases after repeated experiences. In these neurons, low-contrast visual stimuli elicit stronger responses, and high-contrast stimuli elicit weaker responses. These low contrast–preferring neurons show a more evident activity when rats correctly perceive a low-contrast familiar object. It was first reported in Science Advances that low-contrast preference in V1 is strengthened in an experience-dependent manner to represent low-contrast visual information well. This mechanism may contribute to the perception of familiar objects, even when they are indistinct.

“This flexible information representation may enable a consistent perception of familiar objects with any contrast,” Kimura says. “The flexibility of our brain makes our sensation effective, although you may not be aware of it. An artificial neural network model may reproduce the human sensation by incorporating not only high contrast–preferring neurons, generally considered until now, but also low contrast–preferring neurons, the main focus of this research.”

Reference: Kimura R, Yoshimura Y. The contribution of low contrast–preferring neurons to information representation in the primary visual cortex after learning. Sci Adv. 7(48):eabj9976. doi: 10.1126/sciadv.abj9976

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.