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Revealing the influence of bias in a letter acuity identification task: A noisy template model by Mark A. Georgeson; Hatem Barhoom; Mahesh R. Joshi; Paul H. Artes; Gunnar Schmidtmann is a Neuroscience article available to read on EtoBox.

What is Revealing the influence of bias in a letter acuity identification task: A noisy template model about?

In clinical testing of visual acuity, it is often assumed that performance reflects sensory abilities and observers do not exhibit strong biases for or against specific letters, but this assumption has not been extensively tested. We re-analyzed single-letter identification data as a function of letter size, spanning the resolution threshold, for 10 Sloan letters at central and paracentral visual field locations. Individual observers showed consistent letter biases across letter sizes. Preferred letters were named much more often and others less often than expected (group averages ranged from 4% to 20% across letters, where the unbiased rate was 10%). In the framework of signal detection theory, we devised a noisy template model to distinguish biases from differences in sensitivity. When bias varied across letter templates the model fitted very well - much better than when sensitivity varied without bias. The best model combined both, having substantial biases and small variations in sensitivity across letters. The over- and under-calling decreased at larger letter sizes, but this was well-predicted by template responses that had the same additive bias for all letter sizes: with st

Who reads Revealing the influence of bias in a letter acuity identification task: A noisy template model?

Author

Mark A. Georgeson; Hatem Barhoom; Mahesh R. Joshi; Paul H. Artes; Gunnar Schmidtmann

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Neuroscience (Life Sciences)