29.07.2014

Tom Wallis: New abstract accepted as a poster at European Mathematical Psychology Group Meeting (EMPG) from 30.07.-01.08.2014

Title: "A Bayesian multilevel modelling approach to characterising contrast sensitivity in naturalistic movies" by Thomas Wallis, Michael Dorr, Peter Bex.

Abstract:

A Bayesian multilevel modelling approach to characterising contrast sensitivity in naturalistic movies

Sensitivity to luminance contrast is a fundamental property of the visual system.

We presented contrast increments gaze-contingently within naturalistic video, freely viewed by observers, to examine contrast increment detection performance in a way that approximates the natural environmental input of the visual system.

On each trial, one spatial scale of the video sequence was incremented in contrast in a local region at one of four locations relative to the observer's current gaze point.

The target was centred 2 degrees from the fovea and smoothly blended with the surrounding unmodified video in space (Gaussian, SD = 0.5

deg) and time (modified raised cosine with 120~ms at maximum amplitude).

Five observers made forced-choice responses to the location of the target (4AFC), resulting in approximately 25,000 Bernoulli trials.

Contrast discrimination performance is typically modelled by assuming the underlying contrast response follows a nonlinear transducer function, which is used to determine the expected proportion correct via signal detection theory.

We implemented this model in a Bayesian multilevel framework (with a population level across subjects) and estimated the posterior over model parameters via MCMC.

Our data poorly constrain the parameters of this model to interpretable values, and constraining the model using strong priors taken from previous research provides a poor fit to the data.

In contrast, logistic regression models were better constrained by the data, more interpretable, and provide equivalent prediction performance to the best-performing nonlinear transducer model.

We explore the properties of an extended logistic regression that incorporates both eye movement and image content features to predict performance.

Using this varying-intercept model, we demonstrate the characteristic contrast sensitivity function with a peak in the range of 0.75--3 cycles per degree.