External Noise Distinguishes Mechanisms of Attention

Lü, Zhong-Lin; Dosher, Barbara Anne

doi:10.1016/b978-012375731-9/50078-1

Cited by 103 publications

(169 citation statements)

References 14 publications

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“…By what mechanism does attention improve spatial resolution? An improved signal-to-noise ratio (SNR) at the attended location contributes [2], due to retinotopically-specific signal gain [3–10]. Additionally, attention could sharpen position tuning at the neural population level, so that adjacent objects activate more distinct regions of visual cortex.…”

Section: Discussionmentioning

confidence: 99%

Attention Narrows Position Tuning of Population Responses in V1

Fischer

Whitney

2009

Current Biology

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When attention is directed to a region of space, visual resolution at that location flexibly adapts, becoming sharper to resolve fine-scale details or coarser to reflect large-scale texture and surface properties. By what mechanism does attention improve spatial resolution? An improved signal-to-noise ratio (SNR) at the attended location contributes, because of retinotopically specific signal gain. Additionally, attention could sharpen position tuning at the neural population level, so that adjacent objects activate more distinct regions of the visual cortex. A dual mechanism involving both signal gain and sharpened position tuning would be highly efficient at improving visual resolution, but there is no direct evidence that attention can narrow the position tuning of population responses. Here, we compared the spatial spread of the fMRI BOLD response for attended versus ignored stimuli. The activity produced by adjacent stimuli overlapped less when subjects were attending at their locations versus attending elsewhere, despite a stronger peak response with attention. Our results show that even as early as primary visual cortex (V1), spatially directed attention narrows the tuning of population-coded position representations.

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Section: Discussionmentioning

confidence: 99%

Attention Narrows Position Tuning of Population Responses in V1

Fischer

Whitney

2009

Current Biology

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“…Error bars show 95% confidence intervals. (Dosher & Lu, 2000;Lu & Dosher, 1998). Inefficient perceptual categories could impair the recognition of letters and letter sequences and thus hamper reading efficiency.…”

Section: Discussionmentioning

confidence: 99%

Coloured filters improve exclusion of perceptual noise in visually symptomatic dyslexics

Northway

Manahilov

Simpson

2009

Journal of Research in Reading

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Previous studies of visually symptomatic dyslexics have found that their contrast thresholds for pattern discrimination are the same as non‐dyslexics. However, when noise is added to the stimuli, contrast thresholds rise markedly in dyslexics compared with non‐dyslexics. This result could be due to impaired noise exclusion in dyslexics. Some dyslexics report a reduction in discomfort experienced while reading and show increased reading speed when using coloured lenses. We hypothesised that coloured lenses work by aiding noise exclusion. Thus the coloured lenses should help performance best under conditions of high visual noise. We measured contrast thresholds for discriminating symbols in the absence and presence of visual noise. The results showed that visually symptomatic dyslexics, who otherwise had elevated contrast thresholds for discriminating symbols in visual noise, had thresholds similar to non‐dyslexics when wearing coloured filters. These findings provide evidence that in visually symptomatic dyslexics coloured filters can improve noise exclusion to normal levels.

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“…(17)). Lu and Dosher (1998) have shown that these two processes are mathematically equivalent in the PTM framework, and are empirically indistinguishable. Signal enhancement and additive noise reduction will lower thresholds primarily in conditions where external noise is low.…”

Section: Mechanisms Of Learningmentioning

confidence: 99%

Characterizing perceptual learning with external noise

2004

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Performance in perceptual tasks often improves with practice. This effect is known as 'perceptual learning, ' and it has been the source of a great deal of interest and debate over the course of the last century. Here, we consider the effects of perceptual learning within the context of signal detection theory. According to signal detection theory, the improvements that take place with perceptual learning can be due to increases in internal signal strength or decreases in internal noise. We used a combination of psychophysical techniques (external noise masking and double-pass response consistency) that involve corrupting stimuli with externally added noise to discriminate between the effects of changes in signal and noise as observers learned to identify sets of unfamiliar visual patterns. Although practice reduced thresholds by as much as a factor of 14, internal noise remained virtually fixed throughout training, indicating learning served to predominantly increase the strength of the internal signal. We further examined the specific nature of the changes that took place in signal strength by correlating the externally added noise with observer's decisions across trials (response classification). This technique allowed us to visualize some of the changes that took place in the linear templates used by the observers as learning occurred, as well as test the predictions of a linear template-matching model. Taken together, the results of our experiments offer important new theoretical constraints on models of perceptual learning.

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External Noise Distinguishes Mechanisms of Attention

Cited by 103 publications

References 14 publications

Attention Narrows Position Tuning of Population Responses in V1

Attention Narrows Position Tuning of Population Responses in V1

Coloured filters improve exclusion of perceptual noise in visually symptomatic dyslexics

Characterizing perceptual learning with external noise

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