Visual crowding refers to the impairment of recognizing peripherally presented objects flanked by distractors. Crowding effects, exhibiting a certain spatial extent between target and flankers, can be reduced by perceptual learning. In this experiment, we investigated the learning-induced reduction of crowding in normally sighted participants and tested if learning on one optotype (Landolt-C) transfers to another (Tumbling-E) or vice versa. Twenty-three normally sighted participants (18-42 years) trained on a crowding task in the right-upper quadrant (target at 6.5 degrees eccentricity) over four sessions. Half of the participants had the four-alternative forced-choice task to discriminate the orientation of a Landolt-C, the other half of participants had the task to discriminate the orientation of a Tumbling-E, each flanked by distractors. In the fifth session, all participants switched to the other untrained optotype, respectively. Learning success was measured as reduction of the spatial extent of crowding. We found an overall significant and comparable learning-induced reduction of crowding in both conditions (Landolt-C and Tumbling-E). However, only in the group who trained on the Landolt-C task did learning effects transfer to the other optotype. The specific target-flanker-constellations may modulate the transfer effects found here. Perceptual learning of a crowding task with optotypes could be a promising tool in rehabilitation programs to help improve peripheral vision (e.g. in patients with central vision loss), but the dependence of possible transfer effects on the optotype and distractors used requires further clarification.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8543403 | PMC |
http://dx.doi.org/10.1167/jov.21.11.13 | DOI Listing |
J Neurosci
January 2025
Department of Physiology, Anatomy and Genetics, University of Oxford.
Limits on information processing capacity impose limits on task performance. We show that male and female mice achieve performance on a perceptual decision task that is near-optimal given their capacity limits, as measured by policy complexity (the mutual information between states and actions). This behavioral profile could be achieved by reinforcement learning with a penalty on high complexity policies, realized through modulation of dopaminergic learning signals.
View Article and Find Full Text PDFJ Cogn Neurosci
December 2024
Brown University, Providence, RI.
Each day, humans must parse visual stimuli with varying amounts of perceptual experience, ranging from incredibly familiar to entirely new. Even when choosing a novel to buy at a bookstore, one sees covers they have repeatedly experienced intermixed with recently released titles. Visual exposure to stimuli has distinct neural correlates in the lateral prefrontal cortex (LPFC) of nonhuman primates.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
Second High School Attached to Beijing Normal University, Beijing 100088, China.
This study investigates the acoustic cues for listeners to differentiate checked syllables and tones from unchecked ones. In Xiapu Min, checked and unchecked syllables and tones differ in f0, glottalization, and duration, whereas these differences are reduced in their sandhi forms. In citation forms, listeners utilize all three cues while relying on duration the most.
View Article and Find Full Text PDFSci Rep
January 2025
NeMO Lab, ASST GOM Niguarda Cà Granda Hospital, Milan, Italy.
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that can result in a progressive loss of speech due to bulbar dysfunction, which can have significant negative impact on the patient's mental well-being. Alternative Augmentative Communication (AAC) strategies based on synthetic voices have been shown to assist patients in maintaining communication and improving their Quality of Life (QoL). However, such synthetic voices are often perceived as impersonal and fail to capture the unique voice and identity of the patient.
View Article and Find Full Text PDFBiol Imaging
November 2024
Institut de Recherche en Informatique de Toulouse (IRIT), CNRS & Université de Toulouse, Toulouse, France.
We propose a neural network architecture and a training procedure to estimate blurring operators and deblur images from a single degraded image. Our key assumption is that the forward operators can be parameterized by a low-dimensional vector. The models we consider include a description of the point spread function with Zernike polynomials in the pupil plane or product-convolution expansions, which incorporate space-varying operators.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!