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

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 500ms. 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: http://dx.doi.org/10.1101/36461

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

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

Binaural summation of amplitude modulation involves weak interaural suppression

The brain combines sounds from the two ears, but what is the algorithm used to achieve this summation of signals? Here we combine psychophysical amplitude modulation discrimination and steady-state electroencephalography (EEG) data to investigate the architecture of binaural combination for amplitude-modulated tones. Discrimination thresholds followed a ‘dipper’ shaped function of pedestal modulation depth, and were consistently lower for binaural than monaural presentation of modulated tones. The EEG responses were greater for binaural than monaural presentation of modulated tones, and when a masker was presented to one ear, it produced only weak suppression of the response to a signal presented to the other ear. Both data sets were well-fit by a computational model originally derived for visual signal combination, but with suppression between the two channels (ears) being much weaker than in binocular vision. We suggest that the distinct ecological constraints on vision and hearing can explain this difference, if it is assumed that the brain avoids over-representing sensory signals originating from a single object. These findings position our understanding of binaural summation in a broader context of work on sensory signal combination in the brain, and delineate the similarities and differences between vision and hearing

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 focussed on sample size, and assumed sufficient trials. Here we explore the influence of both factors on statistical power, represented as a two-dimensional plot on which iso-power contours can be visualised. We demonstrate the conditions under which the number of tri- als is particularly important, i.e. when the within-participant variance is large relative to the between-participants variance. We then derive power contour plots using existing data sets for eight 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-participant 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 pro- vided (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

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

Neural noise and suppression in visual processing

Signal transduction in sensory systems is affected by two major neural mechanisms: neural noise and suppression. Both of these factors present limits on the perceptual abilities of the observer. For example, in contrast discrimination both elevate thresholds. Suppression and neural noise have been implicated in normal sensory development, ageing and several neurological disorders. Of particular interest are autism spectrum conditions (ASCs), in which both neural noise and suppressive mechanisms seem to be atypical. This thesis addresses several issues surrounding the measurement and neural implications of neural noise and suppression. Firstly, it investigates where in the brain neural noise affects sensory processing. Using machine learning algorithms to analyse electro- and magneto-encephalography data, it was found that the main source of neural noise is early sensory cortex. Secondly, it compares psychophysical paradigms used to dissociate the effects of noise and suppression, and suggests refined methods, in particular, using double-pass consistency. Thirdly, it investigates the neural effects of modulating neural noise and suppression selectively using transcranial magnetic stimulation (TMS). It reveals that two existing TMS protocols are suitable for this: single pulses suppress neural signals, whereas triple-pulse TMS increases neural noise. Lastly, the thesis investigates neural noise and gain control (a suppressive mechanism) in ASC. The findings show a relationship between sensory noise and autistic traits in the neurotypical population. Furthermore, electrophysiology data from ASC children and adults as well as a genetic Drosophila model of autism revealed a deficit in the transient dynamics of ASC visual systems, which changes over the course of development. Striking similarities between the fruit fly (Nhe3) model and humans suggests that the genetic model is suitable for further research on ASC sensory symptoms. Taken together, this thesis expands the understanding of neural noise and suppression as well as the situations in which these mechanisms are implicated

Data summary for paper "Individual differences in internal noise are consistent across two measurement techniques"

The data used in the 'Individual differences in internal noise are consistent across two measurement techniques' paper by Vilidaite & Baker (Vision Research)