There has been increasing interest in the neurocomputational mechanisms underlying psychotic disorders in recent years. One promising approach is based on the theoretical framework of predictive processing, which proposes that inferences regarding the state of the world are made by combining prior beliefs with sensory signals. Delusions and hallucinations are the core symptoms of psychosis and often co-occur. Yet, different predictive-processing alterations have been proposed for these two symptom dimensions, according to which the relative weighting of prior beliefs in perceptual inference is decreased or increased, respectively. I will present recent behavioural, neuroimaging, and computational work that investigated perceptual decision-making under uncertainty and ambiguity to elucidate the changes in predictive processing that may give rise to psychotic experiences. Based on the empirical findings presented, I will provide a more nuanced predictive-processing account that suggests a common mechanism for delusions and hallucinations at low levels of the predictive-processing hierarchy, but still has the potential to reconcile apparently contradictory findings in the literature. This account may help to understand the heterogeneity of psychotic phenomenology and explain changes in symptomatology over time.

3D Morphable Models are my favorite class of generative models and are commonly used to model faces. They are typically applied to ill-posed problems such as 3D reconstruction from 2D data. I'll start my presentation with an introduction into 3D Morphable Models and show what they are capable of doing. I'll then focus on our recent finding, the Identity-Expression Ambiguity: We demonstrate that non-orthogonality of the variation in identity and expression can cause identity-expression ambiguity in 3D Morphable Models, and that in practice expression and identity are far from orthogonal and can explain each other surprisingly well. Whilst previously reported ambiguities only arise in an inverse rendering setting, identity-expression ambiguity emerges in the 3D shape generation process itself. The goal of this presentation is to demonstrate the ambiguity and discuss its potential consequences in a computer vision setting as well as for understanding face perception mechanisms in the human brain.

Knowledge discovery with computers requires a huge amount of search. Analogical reasoning is effective for efficient knowledge discovery. Therefore, we proposed analogical reasoning systems based on first-order predicate logic using Neuro-Symbolic AI. Neuro-Symbolic AI is a combination of Symbolic AI and artificial neural networks and has features that are easy for human interpretation and robust against data ambiguity and errors. We have implemented analogical reasoning systems by Neuro-symbolic AI models with word embedding which can represent similarity between words. Using the proposed systems, we efficiently extracted unknown rules from knowledge bases described in Prolog. The proposed method is the first case of analogical reasoning based on the first-order predicate logic using deep learning.

In daily life, we consistently make decisions in pursuit of some goal. Many decisions are informed by multiple sources of information. Unfortunately, these sources often provide ambiguous information about what course of action to take. Therefore, determining how the brain integrates information to resolve this ambiguity is key to understanding the neural mechanisms of decision-making. In the domain of time, this topic can be studied by training subjects to predict when a future event will occur based on distinct cues (e.g., tone, light, etc.). If multiple cues are presented simultaneously and their cue-to-event intervals differ (e.g., tone-10s + light-30s), subjects will often expect the event to occur at the average of their intervals. This ‘temporal averaging’ effect is presumably how the timing system resolves ambiguous time-information. The neural mechanisms of temporal averaging are currently unclear. Here, we will propose how temporal averaging could emerge in cortical circuits using a simple modification of a ‘drift-diffusion’ model of timing.

Reliable and validated measures of emotion in animals are of great import; they are crucial to better understanding and developing treatments for human mood disorders, and they are necessary for ensuring good animal welfare. We have developed a novel measure of emotion in animals that is grounded in theory and psychological research – decision-making under ambiguity. Specifically, we consider that more ‘optimistic’ decisions about ambiguous stimuli reflect more positive emotional states, while the opposite is true for more ‘pessimistic’ decisions. In this talk, we will outline the background behind and implementation of this measure, meta-analyses that have been conducted to validate the measure, and discuss how computational modelling has been used to further understand the cognitive processes underlying ‘optimistic’ and ‘pessimistic’ decision-making as an indicator of animal emotion and welfare.