title: Spatiotemporal neural characterization of social decision making
Creators: name: Arabadzhiyska, Desislava Hristova
Publisher: name: University of Glasgow
Year: 2022

description: Some of the most important decisions we make over the course of our lives are social in origin. Whether it is to decide to start a business with someone or choosing a retiring home for a family member, these social decisions often have uncertain outcomes. Recent research has begun to elucidate the neurocomputational principles underlying social choices, however many questions about how we process and navigate our uncertain social environments remain. In this thesis, we examine the spatiotemporal neural characteristics of decisions based on social information on an algorithmic (i.e. what mechanistic processes are involved) and an implementational (i.e. which brain structures are involved) level and we assess whether social choices are a part of the same decision‐making framework developed to describe non‐social decisions. We present three experiments ‐ a behavioural pilot study, a simultaneous electroencephalography and functional magnetic resonance imaging (EEG‐fMRI) experiment and a transcranial direct current stimulation (tDCS) study. We also outline an experimental paradigm based on an economic game, which attempts to ensure the fair comparison between social and non‐social choices by manipulating the likelihood of a favourable outcome depending on the decision domain ‐ in‐ direct facial trustworthiness for social decisions and explicit reward probability ranges for non‐social choices. As a result, we observed that social decisions display similar behavioural trends to the ones typically seen across the non‐social decision‐making literature. More importantly, however, we found that a drift diffusion model (DDM), which assumes the accumulation of relevant evidence to an internal boundary (i.e. evidence accumulation process), was able to account for these behavioural patterns and made comparable predictions across both domains, suggesting that social choices may employ similar algorithmic considerations to the ones at the basis of non‐social decisions. To study the implementational specificities of social and non‐social choices, we identified neural signatures of evidence accumulation (EA) in our EEG data and used them to discern the neural site that gives rise to these dynamics. This al‐ lowed us to implicate the posterior medial‐frontal cortex (pMFC) as the potential site for EA for both social and non‐social decisions. We also found that the social and non‐social information were initially encoded in distinct regions and that the pMFC clusters co‐varied in a task‐dependent way with areas of the human valuation system. Taken together, these results suggest that early representations of the two types of uncertainty are encoded in domain‐specific areas and then compared in a common human valuation system. Afterwards, the comparison information is accumulated for the decision in the pMFC, thus showing the embodied nature of the choice as this region is adjacent to the relevant motor cortex. We also attempted to examine the mechanistic role of the pMFC in social choices even further in a pre‐registered tDCS experiment. Although we were not able to collect our intended sample size due to slow recruitment caused by the COVID‐19 pandemic, we present preliminary results to illustrate the types of findings that could be produced by this investigation. Our hierarchical DDM comparison suggested that the pMFC might modulate the rate of EA in addition to determining the amount of evidence necessary for a decision, however this notion was not supported by our formal statistical analysis and we thus conclude that more evidence is needed to establish the details of how social choices are implemented in the pMFC. Overall, this thesis offers detailed insight into the spatiotemporal neural pro‐ cesses involved in social decision making. It further provides support in favour of a universal decision‐making architecture and presents an example for the systematic comparison across decision‐making domains