ANC Workshop - Filippo Ferrari, Henrique Reis Aguiar
Tuesday, 6th June 2023
Risk and loss aversion and attitude to COVID and vaccines in anxious individuals - Filippo Ferrari
Abstract: Anxious individuals are known to show impaired decision-making in economic gambling task and in everyday life decisions. This impairment can be due to aversion to uncertainty about outcomes (risk aversion) and/or aversion to negative outcomes (loss aversion). We investigate how non-clinical individuals with high levels of Generalised Anxiety Disorder (GAD) (N = 54) behave compared to less anxious subjects (N = 61) in a gambling decision-making task delivered online and designed to separate the distinct influences of risk and loss aversion on decision-making. By modelling subjects' choices using computational models derived from Prospect Theory and fitted using Hierarchical Bayesian methods, we estimate individual levels of risk and loss aversion. We also link estimates of these parameters to individual propensity to risk averse behaviours during the COVID pandemic, like wearing safer types of face masks, or completing a COVID vaccination course. We report increased loss aversion in individuals with increased level of GAD compared to less anxious individuals and no differences in risk aversion. We also report no links between risk and loss aversion and attitudes towards COVID and vaccines. These results shed new light on the interplay of anxiety and risk and loss aversion and they can provide useful directions for clinical intervention.
Representation learning in biological neural networks - Henrique Reis Aguiar
Abstract: During brain development, most sensory areas are in a highly plastic state known as the critical period. During this period neural circuitry is assembled by local plasticity mechanisms which are mainly driven by a combination of spontaneous activity and incoming sensory stimuli. Predictive coding theory suggests that such circuitry allows the brain to extract the underlying factors generating incoming sensory stimuli forming a sensory representation that allows higher-level modules to do few-shot learning of generalizable concepts. For instance, a child only needs to be told what a chair is 2 or 3 times and then quickly generalizes the word across all possible viewpoints of all possible instances of chairs. The actual plasticity mechanisms behind this feat remain poorly understood. Here we investigate models of biological networks with different local plasticity rules and find that some learn good generalizable representations from continuous non-i.i.d. stimuli. In addition, we find that certain models learn circuitry that can be used for sequence completion and prediction. To further test this ability for spatiotemporal learning, we show simple visual stimuli with heavy temporal dependencies and observe the emergence of direction-selective cells, previously reported to exist in the primary visual cortex and shown here to be crucial for stable predictive circuitry. Furthermore, we find that a certain kind of plasticity rule leads the model to learn a metabolically efficient code (akin to a factorized representation) by using the fewest possible number of neurons to represent any given stimuli.
Event type: Workshop
Date: Tuesday, 6th June 2023
Speaker(s): Filippo Ferrari, Henrique Reis Aguiar
Chair/Host: Nina Kudryashova