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.

Executive control processes and flexible behaviors rely on the integrity of, and dynamic interactions between, large-scale functional brain networks. The right insular cortex is a critical component of a salience/midcingulo-insular network that is thought to mediate interactions between brain networks involved in externally oriented (central executive/lateral frontoparietal network) and internally oriented (default mode/medial frontoparietal network) processes. How these brain systems reconfigure with development is a critical question for cognitive neuroscience, with implications for neurodevelopmental pathologies affecting brain connectivity. I will describe studies examining how brain network dynamics support flexible behaviors in typical and atypical development, presenting evidence suggesting a unique role for the dorsal anterior insular from studies of meta-analytic connectivity modeling, dynamic functional connectivity, and structural connectivity. These findings from adults, typically developing children, and children with autism suggest that structural and functional maturation of insular pathways is a critical component of the process by which human brain networks mature to support complex, flexible cognitive processes throughout the lifespan.

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.

Did we eat spaghetti for lunch because we saw our colleague eat spaghetti? What drives a risk decision? How can our breakfast impact our decisions throughout the day? Research from different disciplines such as economics, psychology and neuroscience have attempted to investigate the motives and modulators of human decision making. Human decisions can be flexibly modulated by the different experiences we have in our daily lives, at the same time, bodily processes, such as metabolism can also impact economic behavior. These modulations can occur through our social networks, through the impact of our own behavior on the social environment, but also simply by the food we have eaten. Here, I will present a series of recent studies from my lab in which we shed light on the psychological, neural and metabolic motives and modulators of human decision making.

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.

The brain is a prediction machine. Yet the world is never entirely predictable, for any animal. Unexpected events are surprising and this typically evokes prediction error signatures in animal brains. In humans such mismatched expectations are often associated with an emotional response as well. Appropriate emotional responses are understood to be important for memory consolidation, suggesting that valence cues more generally constitute an ancient mechanism designed to potently refine and generalize internal models of the world and thereby minimize prediction errors. On the other hand, abolishing error detection and surprise entirely is probably also maladaptive, as this might undermine the very mechanism that brains use to become better prediction machines. This paradoxical view of brain functions as an ongoing tug-of-war between prediction and surprise suggests a compelling new way to study and understand the evolution of consciousness in animals. I will present approaches to studying attention and prediction in the tiny brain of the fruit fly, Drosophila melanogaster. I will discuss how an ‘active’ sleep stage (termed rapid eye movement – REM – sleep in mammals) may have evolved in the first animal brains as a mechanism for optimizing prediction in motile creatures confronted with constantly changing environments. A role for REM sleep in emotional regulation could thus be better understood as an ancient sleep function that evolved alongside selective attention to maintain an adaptive balance between prediction and surprise. This view of active sleep has some interesting implications for the evolution of subjective awareness and consciousness.

I will begin by describing recent research taking a new, model-based approach to perceptual development. This approach uncovers fundamental changes in information processing underlying the protracted development of perception, action, and decision-making in childhood. For example, integration of multiple sensory estimates via reliability-weighted averaging – widely used by adults to improve perception – is often not seen until surprisingly late into childhood, as assessed by both behaviour and neural representations. This approach forms the basis for a newer question: the scope for the nervous system to deploy useful computations (e.g. reliability-weighted averaging) to optimise perception and action using newly-learned sensory signals provided by technology. Our initial model system is augmenting visual depth perception with devices translating distance into auditory or vibro-tactile signals. This problem has immediate applications to people with partial vision loss, but the broader question concerns our scope to use technology to tune in to any signal not available to our native biological receptors. I will describe initial progress on this problem, and our approach to operationalising what it might mean to adopt a new signal comparably to a native sense. This will include testing for its integration (weighted averaging) alongside the native senses, assessing the level at which this integration happens in the brain, and measuring the degree of ‘automaticity’ with which new signals are used, compared with native perception.

Our senses are constantly bombarded with a myriad of diverse signals. Transforming this sensory cacophony into a coherent percept of our environment relies on solving two computational challenges: First, we need to solve the causal inference problem - deciding whether signals come from a common cause and thus should be integrated, or come from different sources and be treated independently. Second, when there is a common cause, we should integrate signals across the senses weighted in proportion to their sensory reliabilities. I discuss recent research at the behavioural, computational and neural systems level that investigates how the brain addresses these two computational challenges in multisensory perception.

From robots to humans, the ability to learn from experience turns a rigid response system into a flexible, adaptive one. In this talk, I will discuss emerging findings regarding the neural and cognitive mechanisms by which learning shapes decisions. The lecture will focus on how multiple brain regions interact to support learning, what this means for how memories are built, and the consequences for how decisions are made. Results emerging from this work challenge the traditional view of separate learning systems and advance understanding of how memory biases decisions in both adaptive and maladaptive ways.