Increased variability but intact integration during visual navigation in Autism Spectrum Disorder

Abstract

Autism Spectrum Disorder (ASD) is a common neurodevelopmental disturbance afflicting a variety of functions. The recent computational focus suggesting aberrant Bayesian inference in ASD has yielded promising but conflicting results in attempting to explain a wide variety of phenotypes by canonical computations. Here, we used a naturalistic visual path integration task that combines continuous action with active sensing and allows tracking of subjects' dynamic belief states. Both groups showed a previously documented bias pattern by overshooting the radial distance and angular eccentricity of targets. For both control and ASD groups, these errors were driven by misestimated velocity signals due to a nonuniform speed prior rather than imperfect integration. We tracked participants' beliefs and found no difference in the speed prior, but there was heightened variability in the ASD group. Both end point variance and trajectory irregularities correlated with ASD symptom severity. With feedback, variance was reduced, and ASD performance approached that of controls. These findings highlight the need for both more naturalistic tasks and a broader computational perspective to understand the ASD phenotype and pathology.

Keywords: autism; multisensory; navigation; optic flow; path integration.

Fig. 1.

Experimental protocol and normal performance. (A) Participants use a joystick to navigate to a flashed target (yellow disk or firefly) using optic flow generated by ground-plane triangles. (B) Example trajectory of a participant approaching the unseen target. In the feedback block, after participants have made their response, concentric circles and either a green (if rewarded) or red (if unrewarded) arrow appears indicating the true location of the target. (C) Distribution of targets (C, Left) and example trajectories from one experimental block (C, Right). (D, Left) Target and end points expressed in polar coordinates (angular distance: θ; radial distance: r). (D, Right) Errors of the example trajectories. (E–H) Scatter plots of radial and angular distance responses (y axis) as a function of the respective target distance (x axis) for a representative subject (control subject #3) shown separately without and with feedback. Individual dots are single trials. Solid lines: linear regression; dashed lines: identity lines. (I–L) Scatter plots of regression slopes (1 = no bias; <1 = undershooting; >1 = overshooting) for all participants individually (gray dots) and population average (error bars: ±SEM).

Fig. 2.

Speed prior dynamic Bayesian Observer Model of path integration. (A) Biases in path integration originate from an underestimation of velocity modeled as a posterior (in green) based on a prior for speeds (black solid lines) and a likelihood distribution (black dashed lines). The likelihood width is taken to scale with velocity as shown by the superimposed gray likelihoods increasing with width. This velocity posterior is then integrated into an estimate of position. Improvement in performance may be due to either the prior relaxing (Lower Left) or the scaling of likelihood width becoming shallower. (B) Extraction of the parameters best accounting for participant trajectories suggests that the prior does not change with feedback (FB) vs. without feedback (woFB), but instead, the scaling of the likelihood width with velocity becomes shallower. *P ≤ 0.001.

Fig. 3.

Increased uncertainty in autism. (A and B) Goodness of fit (R²) of linear regression between response vs. target distance (from plots as in Fig. 1 E–H) for ASD (red) and control (black) subjects without and with feedback. Data are shown for individual subjects and group averages (±SEM). (C and D) SD of the end point responses within specific target distance bins (x axis) in the radial (C) and angular (D) dimension. (E and F) Variance of the likelihood function (computed from the speed prior model fit). (G and H) Radial R² correlates inversely with ASD symptomatology prior to feedback (solid colors and dashed lines): the larger the end point variability, the higher participants scored on the AQ and the SCQ. Single dots are individual participants. **P ≤ 0.01; ***P ≤ 0.001.

Fig. 4.

Trajectory smoothness correlates inversely with ASD symptomatology. (A) Radial distance from the origin as a function of time (solid lines) was fit with a sigmoidal function (dashed lines); shown for a handful of example trajectories from control (black) and ASD (red) individuals. Blue arrows mark examples of jerkiness in ASD trajectories. (B) Scatter plot of R² of the sigmoidal fit with and without feedback for ASD (red) and control (black) individuals (also shown are means ± SEM). (C and D) The R² values of sigmoidal fits correlated with both the AQ and SCQ, suggesting that the smoother participants’ trajectory, the lower they scored on ASD-related symptomatology. With feedback, FB; without feedback, (woFB).