(BBSRC DTP) Perceptual stability and optic flow parsing in Autism Spectrum Condition

Autism Spectrum Condition (ASC) is a neurodevelopmental disorder diagnosed by deficits in social communication and interaction and restricted patterns of behaviour. However, it is also accompanied by a range of other less well-known symptoms including changes in sensory and sensorimotor performance with at least 80% of autistic individuals showing coordination difficulties such as poor eye – hand coordination, unstable balance and general clumsiness. This is interesting in the context of a neural mechanism that was identified relatively recently that acts to help us maintain a stable perception of the environment when we are moving. The optic flow parsing mechanism acts to directly suppress the large patterns of retinal motion that arise when we move so that scene-stability is preserved. We hypothesise that given the well-established changes in local and global motion processing observed in ASC, it is possible that the symptoms identified above result from altered optic flow parsing, i.e. clumsiness and poor coordination might arise because optic flow is inappropriately supressed in ASC.

In the present project we will examine optic flow parsing and ability to maintain perceptual stability in participants with ASC using both lab-based visual psycophysics together with motion capture and virtual reality technology. More specifically we will measure characteristics of optic flow parsing in control participants and in ASC and look to relate these to performance in: i) simple visual tasks requiring suppression of optic flow to make judgements about scene stability; ii) virtual real-world tasks that rely upon optic flow suppression, such as driving and or moving through a cluttered environment without colliding with obstacles. We will also investigate the relative contributions of efference copy, vestibular and visual (ie optic flow) information about self-movement to the recovery of scene stability. This project will therefore shed light on important mechanisms that underpin our ability to assess external movement in the environment during self-movement and the changes in this mechanism in ASC. Moreover, by using VR technology we will be able to bridge the gap between simplistic lab-based changes observed in a clinical population and the real-world consequences of such changes.

PI Warren’s PADLab: https://personalpages.manchester.ac.uk/staff/paul.warren/PADLab.html
Gowen’s BEAM Lab: http://beamlab.lab.manchester.ac.uk/

Entry Requirements:
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.