Estimation of internal noise using double passes: Does it matter how the second pass is delivered?

Hasan, Bashar; Joosten, Eva R. M.; Neri, Peter

doi:10.1016/j.visres.2012.06.014

Cited by 17 publications

(8 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More specifically, we have verified that other plausible distributions (e.g. Laplacian [31]) lead to estimates that only differ by amounts that cannot be resolved empirically under realistic laboratory conditions [28]. Furthermore, our focus here is not on absolute values but on trends , i.e.…”

Section: Methodsmentioning

confidence: 68%

“…We estimated internal noise using established double-pass techniques [25, 26], arguably the most effective psychophysical tool for tackling this problem [27]. Blocks consisted of 50 trials for which the second 25 trials (trial #26 to #50) contained the same stimuli that were presented during the first 25 trials (trial #1 to #25) in randomly permuted trial order [28]. p is the fraction of paired trials on which observers generated the same response; p is the overall fraction of correct responses.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The empirical characteristics of human pattern vision defy theoretically-driven expectations

Neri

2018

PLoS Comput Biol

Self Cite

View full text Add to dashboard Cite

Contrast is the most fundamental property of images. Consequently, any comprehensive model of biological vision must incorporate this attribute and provide a veritable description of its impact on visual perception. Current theoretical and computational models predict that vision should modify its characteristics at low contrast: for example, it should become broader (more lowpass) to protect from noise, as often demonstrated by individual neurons. We find that the opposite is true for human discrimination of elementary image elements: vision becomes sharper, not broader, as contrast approaches threshold levels. Furthermore, it suffers from increased internal variability at low contrast and it transitions from a surprisingly linear regime at high contrast to a markedly nonlinear processing mode in the low-contrast range. These characteristics are hard-wired in that they happen on a single trial without memory or expectation. Overall, the empirical results urge caution when attempting to interpret human vision from the standpoint of optimality and related theoretical constructs. Direct measurements of this phenomenon indicate that the actual constraints derive from intrinsic architectural features, such as the co-existence of complex-cell-like and simple-cell-like components. Small circuits built around these elements can indeed account for the empirical results, but do not appear to operate in a manner that conforms to optimality even approximately. More generally, our results provide a compelling demonstration of how far we still are from securing an adequate computational account of the most basic operations carried out by human vision.

show abstract

Section: Methodsmentioning

confidence: 68%

Section: Methodsmentioning

confidence: 99%

The empirical characteristics of human pattern vision defy theoretically-driven expectations

Neri

2018

PLoS Comput Biol

Self Cite

View full text Add to dashboard Cite

show abstract

“…To obtain an upper bound on the performance of the model, we carried out a “double-pass” analysis [4, 58, 59] by testing observers twice on the same set of stimuli, and comparing model-observer agreement (proportion of trials with consistent responses) on the double-pass set to observer-observer agreement between the two passes. For 5 observers in Experiment 1-VAR, a fold of 1000 stimuli was used as the double-pass set, and the model was trained on the remaining 3000 stimuli.…”

Section: Resultsmentioning

confidence: 99%

Modeling second-order boundary perception: A machine learning approach

DiMattina

Baker

2019

PLoS Comput Biol

View full text Add to dashboard Cite

Visual pattern detection and discrimination are essential first steps for scene analysis. Numerous human psychophysical studies have modeled visual pattern detection and discrimination by estimating linear templates for classifying noisy stimuli defined by spatial variations in pixel intensities. However, such methods are poorly suited to understanding sensory processing mechanisms for complex visual stimuli such as second-order boundaries defined by spatial differences in contrast or texture. We introduce a novel machine learning framework for modeling human perception of second-order visual stimuli, using image-computable hierarchical neural network models fit directly to psychophysical trial data. This framework is applied to modeling visual processing of boundaries defined by differences in the contrast of a carrier texture pattern, in two different psychophysical tasks: (1) boundary orientation identification, and (2) fine orientation discrimination. Cross-validation analysis is employed to optimize model hyper-parameters, and demonstrate that these models are able to accurately predict human performance on novel stimulus sets not used for fitting model parameters. We find that, like the ideal observer, human observers take a region-based approach to the orientation identification task, while taking an edge-based approach to the fine orientation discrimination task. How observers integrate contrast modulation across orientation channels is investigated by fitting psychophysical data with two models representing competing hypotheses, revealing a preference for a model which combines multiple orientations at the earliest possible stage. Our results suggest that this machine learning approach has much potential to advance the study of second-order visual processing, and we outline future steps towards generalizing the method to modeling visual segmentation of natural texture boundaries. This study demonstrates how machine learning methodology can be fruitfully applied to psychophysical studies of second-order visual processing.

show abstract

“…We performed additional experiments specifically designed to enable internal noise estimates using double-pass protocols [ 36 , 37 ]. Observers collected 100-trial blocks during which the second 50 trials (51–100) were identical to the first 50 trials (1–50) except for random permutation of their order [ 38 ]. The degree to which observers reproduce their own responses to the first pass of 50 trials when those trials are re-presented during the second pass is controlled by their intrinsic variability [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

Object segmentation controls image reconstruction from natural scenes

Neri

2017

PLoS Biol

Self Cite

View full text Add to dashboard Cite

The structure of the physical world projects images onto our eyes. However, those images are often poorly representative of environmental structure: well-defined boundaries within the eye may correspond to irrelevant features of the physical world, while critical features of the physical world may be nearly invisible at the retinal projection. The challenge for the visual cortex is to sort these two types of features according to their utility in ultimately reconstructing percepts and interpreting the constituents of the scene. We describe a novel paradigm that enabled us to selectively evaluate the relative role played by these two feature classes in signal reconstruction from corrupted images. Our measurements demonstrate that this process is quickly dominated by the inferred structure of the environment, and only minimally controlled by variations of raw image content. The inferential mechanism is spatially global and its impact on early visual cortex is fast. Furthermore, it retunes local visual processing for more efficient feature extraction without altering the intrinsic transduction noise. The basic properties of this process can be partially captured by a combination of small-scale circuit models and large-scale network architectures. Taken together, our results challenge compartmentalized notions of bottom-up/top-down perception and suggest instead that these two modes are best viewed as an integrated perceptual mechanism.

show abstract

Estimation of internal noise using double passes: Does it matter how the second pass is delivered?

Cited by 17 publications

References 11 publications

The empirical characteristics of human pattern vision defy theoretically-driven expectations

The empirical characteristics of human pattern vision defy theoretically-driven expectations

Modeling second-order boundary perception: A machine learning approach

Object segmentation controls image reconstruction from natural scenes

Contact Info

Product

Resources

About