PhD Student: Veronika Wendler; Partner: The James Hutton Institute; Supervisors: Dr. Lei Zhang and Prof. Caroline Moraes; School of Psychology
Many people express strong motivation to act in environmentally sustainable ways yet often fail to do so in everyday choices. My PhD project investigates when and why this gap between motivation and action occurs by combining research on attention, learning, and agent-based modelling (ABM). How do attention and motivation shape pro-environmental decision-making at the individual level, and how do such micro-level processes scale up to collective behavioural patterns?
People frequently attend to pro-environmental information (such as sustainability labels or long-term benefits) but still choose less sustainable options. This gap can reflect many factors, including uncertainty, cognitive load, delayed gratification, and socio-economic constraints that shape how feasible sustainable action is in the first place. Even when people are motivated, they may lack the resources, time, or information needed to accurately anticipate environmental consequences.
ABMs can be used to test which of these drivers best explains behaviour in context. Further, we aim to test learning theories alongside measures of attention to identify which learning strategies, such as learning from direct experience or through social influence, are most likely to produce sustained pro-environmental behaviour. We will investigate these mechanisms using online studies, as well as laboratory experiments that combine joint eye-tracking with EEG.
The project begins with a systematic review of how ABMs are used in psychology, marketing, and the wider social sciences, with the goal of identifying dominant approaches and methodological gaps. Building on Thoma et al. (2025), I will apply a bibliometric framework that combines computational semantics, large language models (LLMs), and clustering to map the ABM literature at scale. A central focus is the limited use of multimodal methods and the lack of empirical validation of ABM assumptions.