Motion detection and neuropsychology (Version 1.0)
Research type
Research Study
Full title
Visual statistical learning and Bayesian inference in psychiatric disorders
IRAS ID
204249
Contact name
Stephen Lawrie
Contact email
Sponsor organisation
University of Edinburgh
Duration of Study in the UK
5 years, 0 months, 0 days
Research summary
A growing idea in neuroscience is that perception and decision-making can be well described using statistical (Bayesian) models. According to these models, visual perception is shaped by both incoming sensory information and learned expectations about the world. Interestingly, these learned expectations can have a large influence on 'what' we see, particularly in times of uncertainty. Research indicates that internal representations of likelihood (i.e. probabilities) are used to make these visual expectations.
It has been suggested that the unusual perceptual experience in some psychiatric diagnoses could result from differences in the development and use of visual expectations and statistical learning. The current study therefore aims to look at how individuals with certain diagnoses, more specifically schizophrenia, autism, and attention deficit hyperactivity disorder, learn implicit (without conscious awareness) visual expectations, and how these go on to influence what they see. Compared to controls we anticipate that these groups will rely less upon learned expectations or will do so in a sporadic manner.
To evaluate this, participants will complete a one-hour visual motion detection task, and demographic, clinical, and cognitive measures (e.g. problem solving tests). We will be recruiting 120 participants (30 schizophrenia; 30 autism spectrum disorder; 30 attention deficit hyperactivity disorder; 30 healthy controls) from mental health services, research register databases, universities, and third sector organizations, either through referrals from direct care teams or responses to advertisements. The research project will take approximately 60 months.The main benefit of the study will be the novel application of Bayesian modelling in psychiatry, which will hopefully deepen our understanding of how different people make sense of the world and consequently what might be the best way to support them.
REC name
South East Scotland REC 01
REC reference
16/SS/0117
Date of REC Opinion
22 Aug 2016
REC opinion
Further Information Favourable Opinion