Mind wandering: A window into human introspection and meta-awareness

Polychroni, Panagiota. 2022. Mind wandering: A window into human introspection and meta-awareness. Doctoral thesis, Goldsmiths, University of London [Thesis]

No full text available
[img] Text (Mind wandering: A window into human introspection and meta-awareness)
PSY_thesis_PolychroniP_2022.pdf - Accepted Version
Permissions: Administrator Access Only until 30 September 2025.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (7MB)

Abstract or Description

Mind wandering (MW), an experiential state characterized by engagement in thoughts that are not related to the external environment, is an omnipresent feature of human experience. MW is intrinsically a private experience and thus research on this elusive phenomenon is fundamentally based on individuals’ self-reports. Despite substantial advances in our understanding of MW, two salient issues remain. First, the identification of behavioural and neural markers that can be employed in detecting MW could circumvent methodological challenges that arise from reliance on self-reports. Secondly, although numerous lines of evidence highlight the deleterious impact of MW on behaviour, the extent to which MW reports are confounded by sensory information (e.g., performance indicators) is unknown. The research in this thesis focuses on these two main objectives. To address these, the predictive power of behavioural markers of MW and meta-awareness (i.e., higher-order awareness that one is MW) was examined (Chapter 2). The evidence from this study suggests that dominant response time patterns may not have sufficient predictive utility to detect MW. The results led to an investigation of the impact of performance indicators on self-reports of MW. Across two studies (Chapters 3 and 4), I demonstrate that self-reports are affected by two different performance indicators. These findings suggest that self-reports of experiential states represent a form of inference based on the combination of available information, including both the contents of experience but also contextual cues. Additionally, oscillatory substrates were investigated and were used as features in the decoding of experiential states using machine learning techniques (Chapter 5). This in turn allowed to explore the relationship between confidence in self-reports and classification of experiential states. The results highlight the potential of using EEG machine learning classifiers to capture MW and suggest that confidence gauges variability in access to, or differential phenomenological characteristics, of experiential states.

Item Type:

Thesis (Doctoral)

Identification Number (DOI):



mind wandering, meta-awareness, confidence, introspection

Departments, Centres and Research Units:



30 September 2022

Item ID:


Date Deposited:

13 Oct 2022 15:45

Last Modified:

18 Oct 2022 12:33



View statistics for this item...

Edit Record Edit Record (login required)