High internal noise and poor external noise filtering characterize perception in autism spectrum disorder

Abstract

An emerging hypothesis postulates that internal noise is a key factor influencing perceptual abilities in autism spectrum disorder (ASD). Given fundamental and inescapable effects of noise on nearly all aspects of neural processing, this could be a critical abnormality with broad implications for perception, behavior, and cognition. However, this proposal has been challenged by both theoretical and empirical studies. A crucial question is whether and how internal noise limits perception in ASD, independently from other sources of perceptual inefficiency, such as the ability to filter out external noise. Here, we separately estimated internal noise and external noise filtering in ASD. In children and adolescents with and without ASD, we computationally modeled individuals’ visual orientation discrimination in the presence of varying levels of external noise. The results revealed increased internal noise and worse external noise filtering in individuals with ASD. For both factors, we also observed high inter-individual variability in ASD, with only the internal noise estimates significantly correlating with severity of ASD symptoms. We provide evidence for reduced perceptual efficiency in ASD that is due to both increased internal noise and worse external noise filtering, while highlighting internal noise as a possible contributing factor to variability in ASD symptoms.

Online Transcranial Random Noise stimulation improves perception at high levels of visual white noise

“…Gabor stimuli were temporally sandwiched between white noise (Figure 3) sampled from a Gaussian distribution that varied in root 183 mean squared contrast in 8 steps, evenly log-distributed between 0 and 33%. This contrast range 184 ensured that we successfully capture the entire threshold versus noise (TvN) curve, as confirmed by 185 our pilot work and prior studies in the lab (Park et al, 2017). Rapid sandwiching of the Gabor 186 stimulus between white noise frames allowed for the full, potential, dynamic range of contrast to be 187 used (0 to 100%) while still maintaining the percept of a single Gabor, with varying quantities of 188 noise in the image subject's head was measured from inion to occiput and measured for circumference at this height as 201 established in the 10 -20 EEG system.…”

Internal noise measures in coarse and fine motion direction discrimination tasks, and the correlation with autism traits

“…Similar differences in neural variability were found using resting state measurements in magnetoencephalography (MEG; Domínguez et al, 2013), suggesting that high internal noise is a widespread cortical characteristic of ASD and may represent a fundamental physiological alteration of neural processing (but see, Butler, Molholm, Andrade, & Foxe, 2017;Coskun et al, 2009). At a behavioural level, the impact of internal noise on visual perception in ASD has mostly been investigated in the motion and orientation domain (Manning, Tibber, Charman, Dakin, & Pellicano, 2015;Manning, Tibber, & Dakin, 2017;Park, Schauder, Zhang, Bennetto, & Tadin, 2017;Zaidel, Goin-Kochel, & Angelaki, 2015).…”

Functional reorganization of sensory processing following long-term neural adaptation to optical defects

“…Changes in perceptual thresholds with external noise result in a characteristic threshold-versus-noise (TvN) function, which can be fitted with the Perceptual Template Model (PTM) (21,22) to quantify the effects of noise on perception ( Fig.4 ). The PTM is a computational model of visual processing that had been successfully used to identify the mechanisms underlying perceptual differences for a wide range of brain functions, such as attention (30,31) and perceptual learning (21), as well and between specific populations, such as in amblyopia (32), autism (24), or dyslexia (33). The PTM considers that differences in perceptual performance between groups can result from changes in three possible sources of noise ( Fig.…”

“…For each participant, we estimated three parameters N mul , N add , and W ext . We assumed a fixed β (1.25) and γ (2) for all participants to simplify the model, which was within a reasonable range reported in previous studies (21,22,24). An MCMC technique was used to sample from the posterior distributions and estimate the free parameters.…”

“…Five SFs (0.5, 1, 3, 9 and 16 cpd) were tested across different experimental sessions. Similar to Park and colleagues (24), data were pooled from the FAST structures to estimate psychophysical thresholds using a Bayesian model fitting method implementing a Markov Chain Monte Carlo (MCMC). Further details are provided in SI Methods .…”