Steady-state measures of visual suppression.

In the early visual system, suppression occurs between neurons representing different stimulus properties. This includes features such as orientation (cross-orientation suppression), eye-of-origin (interocular suppression) and spatial location (surround suppression), which are thought to involve distinct anatomical pathways. We asked if these separate routes to suppression can be differentiated by their pattern of gain control on the contrast response function measured in human participants using steady-state electroencephalography. Changes in contrast gain shift the contrast response function laterally, whereas changes in response gain scale the function vertically. We used a Bayesian hierarchical model to summarise the evidence for each type of gain control. A computational meta-analysis of 16 previous studies found the most evidence for contrast gain effects with overlaid masks, but no clear evidence favouring either response gain or contrast gain for other mask types. We then conducted two new experiments, comparing suppression from four mask types (monocular and dichoptic overlay masks, and aligned and orthogonal surround masks) on responses to sine wave grating patches flickering at 5Hz. At the occipital pole, there was strong evidence for contrast gain effects in all four mask types at the first harmonic frequency (5Hz). Suppression generally became stronger at more lateral electrode sites, but there was little evidence of response gain effects. At the second harmonic frequency (10Hz) suppression was stronger overall, and involved both contrast and response gain effects. Although suppression from different mask types involves distinct anatomical pathways, gain control processes appear to serve a common purpose, which we suggest might be to suppress less reliable inputs.

Power contours: Optimising sample size and precision in experimental psychology and human neuroscience.

When designing experimental studies with human participants, experimenters must decide how many trials each participant will complete, as well as how many participants to test. Most discussion of statistical power (the ability of a study design to detect an effect) has focused on sample size, and assumed sufficient trials. Here we explore the influence of both factors on statistical power, represented as a 2-dimensional plot on which iso-power contours can be visualized. We demonstrate the conditions under which the number of trials is particularly important, that is, when the within-participant variance is large relative to the between-participants variance. We then derive power contour plots using existing data sets for 8 experimental paradigms and methodologies (including reaction times, sensory thresholds, fMRI, MEG, and EEG), and provide example code to calculate estimates of the within- and between-participants variance for each method. In all cases, the within-participant variance was larger than the between-participants variance, meaning that the number of trials has a meaningful influence on statistical power in commonly used paradigms. An online tool is provided (https://shiny.york.ac.uk/powercontours/) for generating power contours, from which the optimal combination of trials and participants can be calculated when designing future studies. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Internal noise in contrast discrimination propagates forwards from early visual cortex.

Human contrast discrimination performance is limited by transduction nonlinearities and variability of the neural representation (noise). Whereas the nonlinearities have been well-characterised, there is less agreement about the specifics of internal noise. Psychophysical models assume that it impacts late in sensory processing, whereas neuroimaging and intracranial electrophysiology studies suggest that the noise is much earlier. We investigated whether perceptually-relevant internal noise arises in early visual areas or later decision making areas. We recorded EEG and MEG during a two-interval-forced-choice contrast discrimination task and used multivariate pattern analysis to decode target/non-target and selected/non-selected intervals from evoked responses. We found that perceptual decisions could be decoded from both EEG and MEG signals, even when the stimuli in both intervals were physically identical. Above-chance decision classification started <100 ms after stimulus onset, suggesting that neural noise affects sensory signals early in the visual pathway. Classification accuracy increased over time, peaking at >500 ms. Applying multivariate analysis to separate anatomically-defined brain regions in MEG source space, we found that occipital regions were informative early on but then information spreads forwards across parietal and frontal regions. This is consistent with neural noise affecting sensory processing at multiple stages of perceptual decision making. We suggest how early sensory noise might be resolved with Birdsall's linearisation, in which a dominant noise source obscures subsequent nonlinearities, to allow the visual system to preserve the wide dynamic range of early areas whilst still benefitting from contrast-invariance at later stages. A preprint of this work is available at: https://doi.org/10.1101/364612.

Autism sensory dysfunction in an evolutionarily conserved system.

There is increasing evidence for a strong genetic basis for autism, with many genetic models being developed in an attempt to replicate autistic symptoms in animals. However, current animal behaviour paradigms rarely match the social and cognitive behaviours exhibited by autistic individuals. Here, we instead assay another functional domain-sensory processing-known to be affected in autism to test a novel genetic autism model in Drosophila melanogaster. We show similar visual response alterations and a similar development trajectory in Nhe3 mutant flies (total n = 72) and in autistic human participants (total n = 154). We report a dissociation between first- and second-order electrophysiological visual responses to steady-state stimulation in adult mutant fruit flies that is strikingly similar to the response pattern in human adults with ASD as well as that of a large sample of neurotypical individuals with high numbers of autistic traits. We explain this as a genetically driven, selective signalling alteration in transient visual dynamics. In contrast to adults, autistic children show a decrease in the first-order response that is matched by the fruit fly model, suggesting that a compensatory change in processing occurs during development. Our results provide the first animal model of autism comprising a differential developmental phenotype in visual processing.

Internal noise estimates correlate with autistic traits.

Previous neuroimaging research has reported increased internal (neural) noise in sensory systems of autistic individuals. However, it is unclear if this difference has behavioural or perceptual consequences, as previous attempts at measuring internal noise in ASD psychophysically have been indirect. Here, we use a "gold standard" psychophysical double-pass paradigm to investigate the relationship between internal noise and autistic traits in the neurotypical population (n = 43). We measured internal noise in three tasks (contrast perception, facial expression intensity perception, and number summation) to estimate a global internal noise factor using principal components analysis. This global internal noise was positively correlated with autistic traits (rs = 0.32, P = 0.035). This suggests that increased internal noise is associated with the ASD phenotype even in subclinical populations. The finding is discussed in relation to the neural and genetic basis of internal noise in ASD. Autism Res 2017, 10: 1384-1391. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

Individual differences in internal noise are consistent across two measurement techniques.

Internal noise is a fundamental limiting property on visual processing. Internal noise has previously been estimated with the equivalent noise paradigm using broadband white noise masks and assuming a linear model. However, in addition to introducing noise into the detecting channel, white noise masks can suppress neural signals, and the linear model does not satisfactorily explain data from other paradigms. Here we propose estimating internal noise from a nonlinear gain control model fitted to contrast discrimination data. This method, and noise estimates from the equivalent noise paradigm, are compared to a direct psychophysical measure of noise (double-pass consistency) using a detailed dataset with seven observers. Additionally, contrast discrimination and double-pass paradigms were further examined with a refined set of conditions in 40 observers. We demonstrate that the gain control model produces more accurate double-pass consistency predictions than a linear model. We also show that the noise parameter is strongly related to consistency scores whereas the gain control parameter is not; a differentiation of which the equivalent noise paradigm is not capable. Lastly, we argue that both the contrast discrimination and the double-pass paradigms are sensitive measures of internal noise that can be used in the study of individual differences.

Unbiased measures of interocular transfer of motion adaptation.

Numerous studies have measured the extent to which motion aftereffects transfer interocularly. However, many have done so using bias-prone methods, and studies rarely compare different types of motion directly. Here, we use a technique designed to reduce bias (Morgan, 2013, Journal of Vision, 13(8):26, 1-11) to estimate interocular transfer (IOT) for five types of motion: simple translational motion, expansion/contraction, rotation, spiral, and complex translational motion. We used both static and dynamic targets with subjects making binary judgments of perceived speed. Overall, the average IOT was 65%, consistent with previous studies (mean over 17 studies of 67% transfer). There was a main effect of motion type, with translational motion producing stronger IOT (mean: 86%) overall than any of the more complex varieties of motion (mean: 51%). This is inconsistent with the notion that IOT should be strongest for motion processed in extrastriate regions that are fully binocular. We conclude that adaptation is a complex phenomenon too poorly understood to make firm inferences about the binocular structure of motion systems.

Broadband noise masks suppress neural responses to narrowband stimuli.

White pixel noise is widely used to estimate the level of internal noise in a system by injecting external variance into the detecting mechanism. Recent work (Baker and Meese, 2012) has provided psychophysical evidence that such noise masks might also cause suppression that could invalidate estimates of internal noise. Here we measure neural population responses directly, using steady-state visual evoked potentials, elicited by target stimuli embedded in different mask types. Sinusoidal target gratings of 1 c/deg flickered at 5 Hz, and were shown in isolation, or with superimposed orthogonal grating masks or 2D white noise masks, flickering at 7 Hz. Compared with responses to a blank screen, the Fourier amplitude at the target frequency increased monotonically as a function of target contrast when no mask was present. Both orthogonal and white noise masks caused rightward shifts of the contrast response function, providing evidence of contrast gain control suppression. We also calculated within-observer amplitude variance across trials. This increased in proportion to the target response, implying signal-dependent (i.e., multiplicative) noise at the system level, the implications of which we discuss for behavioral tasks. This measure of variance was reduced by both mask types, consistent with the changes in mean target response. An alternative variety of noise, which we term zero-dimensional noise, involves trial-by-trial jittering of the target contrast. This type of noise produced no gain control suppression, and increased the amplitude variance across trials.