Cognitive Psychology seminars

The predictive map hypothesis provides a promising framework to model representations in the hippocampal formation. I will introduce a tractable implementation of a predictive map called the successor representation (SR), before presenting data showing that rats and humans display SR-like navigational choices on a novel open-field maze. Next, I will show how such a predictive map could be implemented using spatial representations found in the hippocampal formation, before finally presenting how such learning might be well approximated by phenomena that exist in the spatial memory system - namely spike-timing dependent plasticity and theta phase precession.

The gradual accumulation of hyperphosphorylated forms of the Tau protein (pTau) in the human brain correlate with cognitive dysfunction and neurodegeneration. I will present our recent findings on the consequences of human pTau aggregation in the hippocampal formation of a mouse tauopathy model. We show that pTau preferentially accumulates in deep-layer pyramidal neurons, leading to their neurodegeneration. In aged but not younger mice, pTau spreads to oligodendrocytes. During ‘goal-directed’ navigation, we detect fewer high-firing pyramidal cells, but coupling to network oscillations is maintained in the remaining cells. The firing patterns of individually recorded and labelled pyramidal and GABAergic neurons are similar in transgenic and non-transgenic mice, as are network oscillations, suggesting intact neuronal coordination. This is consistent with a lack of pTau in subcortical brain areas that provide rhythmic input to the cortex. Spatial memory tests reveal a reduction in short-term familiarity of spatial cues but unimpaired spatial working and reference memory. These results suggest that preserved subcortical network mechanisms compensate for the widespread pTau aggregation in the hippocampal formation. I will also briefly discuss ideas on the subcortical origins of spatial memory and the concept of the cortex as a monitoring device.

Not all individuals are equally competent at recognizing the faces they interact with. Revealing how the brains of different individuals support variations in this ability is a crucial step to develop an understanding of real-world human visual behaviour. In this talk, I will present findings from a large high-density EEG dataset (>100k trials of participants processing various stimulus categories) and computational approaches which aimed to characterise the brain representations behind real-world proficiency of “super-recognizers”—individuals at the top of face recognition ability spectrum. Using decoding analysis of time-resolved EEG patterns, we predicted with high precision the trial-by-trial activity of super-recognizers participants, and showed that evidence for face recognition ability variations is disseminated along early, intermediate and late brain processing steps. Computational modeling of the underlying brain activity uncovered two representational signatures supporting higher face recognition ability—i) mid-level visual & ii) semantic computations. Both components were dissociable in brain processing-time (the first around the N170, the last around the P600) and levels of computations (the first emerging from mid-level layers of visual Convolutional Neural Networks, the last from a semantic model characterising sentence descriptions of images). I will conclude by presenting ongoing analyses from a well-known case of acquired prosopagnosia (PS) using similar computational modeling of high-density EEG activity.