Technical Program

Paper Detail

Paper: PS-1B.1
Session: Poster Session 1B
Location: H Fläche 1.OG
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Generalized Unrestricted Models (GUMs), a flexible and interpretable tool for behavioral and neural analysis
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Alexandre Hyafil, Centre de Recerca Matemàtica, Spain; Vincent Adam,, Spain
Abstract: Generalized Linear Model (GLMs) analysis is a popular tool in psychophysics and neuroscience for inferring the relative influence of various experimental factors onto choices, reaction times, neural activity and other observables. However, GLMs are intrinsically limited by their linearity assumption, and can lead to severe misattribution errors when (correlated) regressors contribute nonlinearly to the observed response. We show how this framework can be expanded to capture nonlinear functions. First, Generalized Additive Models (GAMs) allow to capture a nonlinear contribution for each regressor. A Gaussian Processes (GP) treatment of GAMs allows to recover the posterior distribution for each nonlinear mapping. Second, as neuroscience is often interested in the interaction of cognitive factors, we present a Bayesian treatment of Generalized Multilinear Models (GMMs) that capture multilinear interactions between different sets of regressors. GMMs can be applied e.g. when inferring the modulation of sensory processing by additional factors such as attention factors. Merging the frameworks of GAMs and GMMs yield Generalized Unrestricted Models (GUMs), a highly versatile and interpretable environment to capture cognitive determinants of behavior and neural activity. Crucially, these models can be efficiently estimated, even with limited dataset; Bayesian techniques can be applied to test which model is best supported by data.