Relevance and uncertainty jointly influence reward anticipation at the level of the SPN ERP component

The stimulus-preceding negativity (SPN) component reflects the anticipatory phase of reward processing. Its amplitude is usually larger for informative compared to uninformative upcoming stimuli, as well as for uncertain relative to predictable ones. In this study, we sought to assess whether these two effects, when combined together, produced a synergistic effect or rather independent ones on the SPN during performance monitoring. Participants performed a speeded Go/NoGo task while 64-channel EEG was recorded concurrently. We focused on the SPN activity generated in anticipation of feedback, which was either positive (for correct and fast reactions) or negative (for correct but slow responses). Further, the feedback's informativeness about the satisfaction status of goals was alternated across blocks. When uncertainty about the action outcome was low (in conditions where positive feedback was either less or more frequent than negative feedback), the SPN amplitude (measured at fronto-central electrodes) did not vary as a function of feedback's relevance or valence. By comparison, when positive and negative feedback were equiprobable (uncertainty was high), the SPN was more pronounced for relevant compared to irrelevant feedback. Interestingly, in this condition, it was also larger at right fronto-central sites for positive than negative feedback. These ERP results suggest that both factors-relevance and uncertainty- combine and influence reward anticipation at the SPN level

Precursors and downstream consequences of prediction in language comprehension

During language comprehension, the brain rapidly integrates incoming linguistic stimuli to not only incrementally build a contextual representation, but also predict upcoming information. This predictive mechanism leads to behavioral facilitation of processing of expected words, as well as a reduction in amplitude of the N400, a neural response reflecting access of semantic memory. However, little research has identified a behavioral or neurophysiological cost of errors in prediction. Additionally, only recent work has begun to investigate neural activity related to prediction prior to encountering a predicted stimulus. Most work has focused on what happens immediately after a predicted or unpredicted stimulus is encountered. Here, I explore new avenues of research by examining downstream consequences of prediction during language comprehension on future recognition memory. Additionally, I test whether these consequences occur following any violation of predictions, or whether the semantic fit of the violation to the established context plays a role. Finally, I adapt a classic paradigm, word stem completion, to investigate electrophysiological activity following a cue that is modulated by how predictive the outcome is. With this work, I not only have discovered costs of failed and successful predictions and identified neural signals potentially related to generation of predictions, but also have researched prediction in novel ways that can continue to expand and further elucidate how this mechanism affects cognition and changes across populations

Alexithymia as impairment in constructing the internal representation of emotional stimuli

Alexithymia is a personality trait characterized by difficulties in processing emotional stimuli. Here, Experiment1 shows that alexithymia has a significant impact on everyday life, being related to lower emotional intelligence, empathy and wellbeing. Experiment2 shows that alexithymia is related to the need for more emotional intensity to identify fear in facial expressions. Although these experiments contribute to the literature describing the difficulties of alexithymia, the basic mechanisms underlying such difficulties remain poorly understood. For this reason, the remaining of this thesis focuses on investigating whether differences in emotional learning may characterize alexithymia. Indeed, through emotional learning the internal representation of stimuli is shaped, so that neutral stimuli acquire emotional value. Impairment in this process has been reported in clinical conditions marked by difficulties in emotion processing; nevertheless, this has never been investigated in alexithymia. Given this, Experiment3 shows that alexithymia is related to impairment in learning the aversive value of stimuli, evidenced by reduced physiological markers of emotional prediction in Pavlovian threat conditioning. On the contrary, Experiment4 did not find such evidence, when learning appetitive value. Nevertheless, evidence for impairment in learning the appetitive value of stimuli was found in Experiment5, where electrophysiological markers of prediction and prediction error were assessed during Pavlovian reward conditioning. Finally, Experiment 6 examined the ability to learn the emotional value of actions during instrumental learning, and to use this learned value for adaptive behavior in a new context. Here, alexithymia was related to a difficulty in learning from punishment, marked by longer response time when having to avoid stimuli, which had previously acquired aversive value, encountered in a new context. Together, these results indicate impairment in emotional learning in alexithymia, which may be more severe for aversive than appetitive stimuli. The new insight provided by these results for the understanding of alexithymia is discussed

The computational neurology of active vision

In this thesis, we appeal to recent developments in theoretical neurobiology – namely, active inference – to understand the active visual system and its disorders. Chapter 1 reviews the neurobiology of active vision. This introduces some of the key conceptual themes around attention and inference that recur through subsequent chapters. Chapter 2 provides a technical overview of active inference, and its interpretation in terms of message passing between populations of neurons. Chapter 3 applies the material in Chapter 2 to provide a computational characterisation of the oculomotor system. This deals with two key challenges in active vision: deciding where to look, and working out how to look there. The homology between this message passing and the brain networks solving these inference problems provide a basis for in silico lesion experiments, and an account of the aberrant neural computations that give rise to clinical oculomotor signs (including internuclear ophthalmoplegia). Chapter 4 picks up on the role of uncertainty resolution in deciding where to look, and examines the role of beliefs about the quality (or precision) of data in perceptual inference. We illustrate how abnormal prior beliefs influence inferences about uncertainty and give rise to neuromodulatory changes and visual hallucinatory phenomena (of the sort associated with synucleinopathies). We then demonstrate how synthetic pharmacological perturbations that alter these neuromodulatory systems give rise to the oculomotor changes associated with drugs acting upon these systems. Chapter 5 develops a model of visual neglect, using an oculomotor version of a line cancellation task. We then test a prediction of this model using magnetoencephalography and dynamic causal modelling. Chapter 6 concludes by situating the work in this thesis in the context of computational neurology. This illustrates how the variational principles used here to characterise the active visual system may be generalised to other sensorimotor systems and their disorders