Perception & Decision Lab

Overview

The Perception & Decision Lab combines psychophysics, neuroimaging, and normative modelling to understand how human and animal observers solve the fundamental problem of acting under uncertainty. The group treats perception and decision as two phases of a single inference process: the brain forms probabilistic beliefs about the world from noisy sensory data, then commits to an action according to an (approximately) optimal policy that trades speed against accuracy.

Experimental work in the lab uses carefully controlled psychophysical paradigms — including multi-cue integration, time-pressure tasks, and perceptual learning protocols — combined with EEG, high-density MEG, and fMRI to track neural correlates of evidence accumulation and confidence. Modelling work employs drift-diffusion models, Bayesian observer models, and more recently, recurrent neural networks as normative benchmarks.

The lab collaborates with the Computational Neuroscience Group on mechanistic network models and with the Neural Engineering Group on brain-computer interface protocols that exploit perceptual decision signatures. External partnerships include clinical neuropsychology groups studying decision impairments in psychiatric disorders.

Research themes

• Bayesian and signal-detection models of multi-sensory cue integration
• Neural correlates of evidence accumulation and decision commitment in EEG/MEG
• Metacognition: representations of confidence and uncertainty in prefrontal cortex
• Speed-accuracy tradeoffs and urgency signals in time-pressured decisions
• Perceptual learning: how practice reshapes neural representations and decision criteria
• Cross-modal attention and its modulation of perceptual sensitivity