Online, offline and transfer learning for decision-making

Menghi, Nicholas (2021) Online, offline and transfer learning for decision-making. Doctoral thesis, University of East Anglia.

[img]
Preview
PDF
Download (3MB) | Preview

Abstract

This thesis investigates online, offline and transfer learning for decision making tasks using a combination of behavioural experiments, computational modelling and Electroencephalography (EEG). Our experiments used a new set of decision-making tasks, in which the appropriate response depended on the linear or nonlinear combination of multiple stimulus features, and were developed to have better ecological validity than many previous tasks in the literature. The first study, in chapter 2, outlines the contextual settings in which representations of the environment can be learnt online. We manipulated the temporal structure of trials, and nature of stimulus-response mappings, and showed their effects on performance and declarative learning. We fitted a Latent Cause Model (LCM) of participants behaviour and derived measures that we used to gain insight into the representations formed. In chapter 3 we used EEG to identify the multiple successive stages of representation learning preceding decisions and following feedback. We used a Computational-EEG approach in which subject-specific LCM variables were used to predict a subject’s EEG data, and found evidence of feature representation in sensory regions and more complex representations in frontal regions. In chapter 4 we shifted the focus to offline learning, by examining the effect of a period of quiet wakefulness on performance in the same task. We found that quiet wakefulness significantly improved the generalization of previously learnt associations. Finally, in the last study, in chapter 5, we investigated how knowledge acquired in one task can be transferred to another. We borrowed the concept of shared subspaces from the multitask learning literature and showed that this provides a useful framework for the study of human transfer learning. Taken as a whole, the thesis shows how humans form representations online and offline, and how extracted knowledge can be transferred to new tasks.

Item Type: Thesis (Doctoral)
Faculty \ School: Faculty of Social Sciences > School of Psychology
Depositing User: Chris White
Date Deposited: 02 Mar 2022 11:39
Last Modified: 02 Mar 2022 11:39
URI: https://ueaeprints.uea.ac.uk/id/eprint/83841
DOI:

Actions (login required)

View Item View Item