Contrast sensitivity as a function of position on the retina

Rijsdijk, JP; Kroon, JN Johannes; Wildt, GJ van der

doi:10.1016/0042-6989(80)90108-x

Cited by 173 publications

(107 citation statements)

References 13 publications

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“…The estimated parameters of g(x, y) are listed in Table 3 and shown in Figure 7. We note that the summation field is, on average, displaced slightly to the lower left visual field, consistent with previous results showing higher contrast sensitivity for low to moderate spatial frequencies in the lower visual field (Rijsdijk et al, 1980;Thomas and Elias, 2011), and the general finding that tasks with a lower visual field advan-tage also tend to be biased to the left visual field (Christman and Niebauer, 1997;Thomas and Elias, 2011). We find that incorporating this global attenuation function to capture the falloff in sensitivity seen in our classification images also leads to an improvement in the trial-by-trial consistency of human and model responses (see below, Evaluation and comparison with competing models).…”

Section: Estimating the Local Energy Modelsupporting

confidence: 80%

Local Visual Energy Mechanisms Revealed by Detection of Global Patterns

Morgenstern

Elder

2012

J. Neurosci.

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A central goal of visual neuroscience is to relate the selectivity of individual neurons to perceptual judgments, such as detection of a visual pattern at low contrast or in noise. Since neurons in early areas of visual cortex carry information only about a local patch of the image, detection of global patterns must entail spatial pooling over many such neurons. Physiological methods provide access to local detection mechanisms at the single-neuron level but do not reveal how neural responses are combined to determine the perceptual decision. Behavioral methods provide access to perceptual judgments of a global stimulus but typically do not reveal the selectivity of the individual neurons underlying detection. Here we show how the existence of a nonlinearity in spatial pooling does allow properties of these early mechanisms to be estimated from behavioral responses to global stimuli. As an example, we consider detection of large-field sinusoidal gratings in noise. Based on human behavioral data, we estimate the length and width tuning of the local detection mechanisms and show that it is roughly consistent with the tuning of individual neurons in primary visual cortex of primate. We also show that a local energy model of pooling based on these estimated receptive fields is much more predictive of human judgments than competing models, such as probability summation. In addition to revealing underlying properties of early detection and spatial integration mechanisms in human cortex, our findings open a window on new methods for relating system-level perceptual judgments to neuron-level processing.

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Section: Estimating the Local Energy Modelsupporting

confidence: 80%

Local Visual Energy Mechanisms Revealed by Detection of Global Patterns

Morgenstern

Elder

2012

J. Neurosci.

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“…In correspondence with this theory of an attentional advantage when crossing the vertical versus the horizontal meridian, other research has shown that visual performance is superior across the vertical meridian because discriminability is comparatively enhanced and temporal dynamics are comparatively rapid (although the rate of information accrual may be faster along the disadvantaged meridian; Carrasco et al, 2002). Indeed various studies have shown that visual acuity (Beirne, Zlatkova, & Anderson, 2005;Rovamo, Virsu, Laurinen, & Hyvarinen, 1982) and contrast sensitivity (Carrasco, Evert, Chang, & Katz, 1995;Carrasco, Talgar, & Cameron, 2001;Rijsdijk, Kroon, & van der Wildt, 1980) are better when crossing the vertical versus the horizontal meridian. Importantly, the presence of the meridian effect in object-based attention underscores the importance of including Orientation as a factor when analyzing data, which is surprisingly atypical across studies in this field (e.g., Goldsmith & Yeari, 2003;He, Fan, Zhou, & Chen, 2004;Lamy & Egeth, 2002;Marino & Scholl, 2005;Moore et al, 1998).…”

Section: Discussionmentioning

confidence: 91%

Target–object integration, attention distribution, and object orientation interactively modulate object-based selection

Al-Janabi

Greenberg

2016

Atten Percept Psychophys

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“…did show significant effects (see the error bars) at 0* eccentricity when the luminance of 11 The image sampfing density falls off with increasing eccentricity because of the drop-off in the retinal photoreceptor cell density and the ganglion cell density and the consequent drop-off in the cortical magnification (e.g., Rijsdijk, Kroon, & van der Wildt, 1980;Wassle, Grunert, Rohrenbeck, & Boycott, 1990).…”

Section: Resultsmentioning

confidence: 99%