Optimality of Human Contour Integration

Ernst, Udo; Mandon, Sunita; Schinkel-Bielefeld, Nadja; Neitzel, Simon D; Kreiter, Andreas K.; Pawelzik, Klaus

doi:10.1371/journal.pcbi.1002520

Cited by 36 publications

(29 citation statements)

References 50 publications

(86 reference statements)

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“…This contour integration serves important visual functions, such as boundary identification and figure-ground segregation [1]. For static visual stimuli, contour integration is a well-established research topic in visual psychophysics and neuroscience.…”

Section: Introductionmentioning

confidence: 99%

Low Level Constraints on Dynamic Contour Path Integration

2014

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Contour integration is a fundamental visual process. The constraints on integrating discrete contour elements and the associated neural mechanisms have typically been investigated using static contour paths. However, in our dynamic natural environment objects and scenes vary over space and time. With the aim of investigating the parameters affecting spatiotemporal contour path integration, we measured human contrast detection performance of a briefly presented foveal target embedded in dynamic collinear stimulus sequences (comprising five short ‘predictor’ bars appearing consecutively towards the fovea, followed by the ‘target’ bar) in four experiments. The data showed that participants' target detection performance was relatively unchanged when individual contour elements were separated by up to 2° spatial gap or 200 ms temporal gap. Randomising the luminance contrast or colour of the predictors, on the other hand, had similar detrimental effect on grouping dynamic contour path and subsequent target detection performance. Randomising the orientation of the predictors reduced target detection performance greater than introducing misalignment relative to the contour path. The results suggest that the visual system integrates dynamic path elements to bias target detection even when the continuity of path is disrupted in terms of spatial (2°), temporal (200 ms), colour (over 10 colours) and luminance (−25% to 25%) information. We discuss how the findings can be largely reconciled within the functioning of V1 horizontal connections.

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Section: Introductionmentioning

confidence: 99%

Low Level Constraints on Dynamic Contour Path Integration

2014

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“…We have verified that doing so may explain the observed decrease in oscillation frequency with increasing stimulus size. In addition, such recurrent spatial summation might explain various empirical measurements of contour integration (164)(165)(166)(167)(168)(169).…”

Section: Failures and Extensionsmentioning

confidence: 99%

Dynamic Normalization

Heeger

Zemlianova

2020

Preprint

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Classification. Biological Sciences: Neuroscience; Social Sciences: Psychological and Cognitive Sciences. AbstractThe normalization model has been applied to explain neural activity in diverse neural systems including primary visual cortex (V1). The model's defining characteristic is that the response of each neuron is divided by a factor that includes a weighted sum of activity of a pool of neurons. In spite of the success of the normalization model, there are 3 unresolved issues. 1) Experimental evidence suggests that normalization in V1 operates via recurrent amplification, i.e., amplifying weak inputs more than strong inputs. It is unknown how normalization arises from recurrent amplification. 2) Experiments have demonstrated that normalization is weighted such each weight specifies how one neuron contributes to another's normalization pool. It is unknown how weighted normalization arises from a recurrent circuit.3) Neural activity in V1 exhibits complex dynamics, including gamma oscillations, linked to normalization. It is unknown how these dynamics emerge from normalization. Here, a new family of recurrent circuit models is reported, each of which comprises coupled neural integrators to implement normalization via recurrent amplification with arbitrary normalization weights, some of which can recapitulate key experimental observations of the dynamics of neural activity in V1. Significance StatementA family of recurrent circuit models is proposed to explain the dynamics of neural activity in primary visual cortex (V1). Each of the models in this family exhibits steady state output responses that are already known to fit a wide range of experimental data from diverse neural systems. These models can recapitulate the complex dynamics of V1 activity, including oscillations (so-called gamma oscillations, ~30-80 Hz). This theoretical framework also explains key aspects of working memory and motor control. Consequently, the same circuit architecture is applicable to a variety of neural systems, and V1 can be used as a model system for understanding the neural computations in many brain areas.

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“…For salience, we calculated a theoretical predictor called Association Strength. Its value represents how likely it is for an element of the display to belong to a contour (Ernst et al, 2012;Field, Hayes, & Hess, 1993;Watt, Ledgeway, & Dakin, 2008). Association Strength takes a higher value to the extent that adjacent elements are cocircular (i.e., when they are tangent to a common circle) or coaligned along a smooth contour.…”

Section: Association Field Strengthmentioning

confidence: 99%

“…It depends on three parameters, namely, the sensitivity to curvature σ α , the sensitivity to cocircularity σ β , and the scaling constant λ (for details on the computation of these parameters, see Van Humbeeck et al [2013]). We used parameter values that were found suitable for matching or even exceeding human performance (i.e., σ α = 0.15, σ β = 0.24, λ = 0.3; Ernst et al, 2012).…”

Section: Association Field Strengthmentioning

confidence: 99%

Presaccadic EEG activity predicts visual saliency in free‐viewing contour integration

et al. 2018

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While viewing a scene, the eyes are attracted to salient stimuli. We set out to identify the brain signals controlling this process. In a contour integration task, in which participants searched for a collinear contour in a field of randomly oriented Gabor elements, a previously established model was applied to calculate a visual saliency value for each fixation location. We studied brain activity related to the modeled saliency values, using coregistered eye tracking and EEG. To disentangle EEG signals reflecting salience in free viewing from overlapping EEG responses to sequential eye movements, we adopted generalized additive mixed modeling (GAMM) to single epochs of saccade-related EEG. We found that, when saliency at the next fixation location was high, amplitude of the presaccadic EEG activity was low. Since presaccadic activity reflects covert attention to the saccade target, our results indicate that larger attentional effort is needed for selecting less salient saccade targets than more salient ones. This effect was prominent in contour-present conditions (half of the trials), but ambiguous in the contour-absent condition. Presaccadic EEG activity may thus be indicative of bottom-up factors in saccade guidance. The results underscore the utility of GAMM for EEG-eye movement coregistration research.

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Optimality of Human Contour Integration

Cited by 36 publications

References 50 publications

Low Level Constraints on Dynamic Contour Path Integration

Low Level Constraints on Dynamic Contour Path Integration

Dynamic Normalization

Presaccadic EEG activity predicts visual saliency in free‐viewing contour integration

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