Exploring patterns of ongoing thought under naturalistic and conventional task-based conditions
Introduction
Patterns of ongoing experience are hypothesised to be influenced by both the environment and intrinsic features of individuals such as their cognitive expertise or affective style. For example, studies show that complex task environments reduce the self-generation of personally relevant information and increase patterns of cognition with detailed task focus (Turnbull, Wang, Murphy, et al., 2019a). In addition, reading interesting texts helps individuals to maintain attention on the narrative while more complex texts show the opposite pattern (Giambra and Grodsky, 1989, Smallwood et al., 2009, Unsworth and McMillan, 2013). Most notably, recent work has demonstrated that patterns of ongoing thought in the context of the real-world have both similarities and differences with patterns observed in the laboratory (Ho et al., 2020, Linz et al., 2019). The disparity between patterns of thought in the lab and in the real-world suggests that the types of tasks that individuals often engage with in daily life may not correspond to those that are often used in experimental contexts. This may be particularly true for tasks like the Sustained Attention to Response Task (SART) which engenders situations that maximise the need to maintain attention on task-relevant material with little or no support from the external environment (Robertson, Ridgeway, Greenfield, & Parr, 1997). Paradigms such as the SART may provide a useful tool with which to study sustained attention but may not relate well to many of the everyday situations in which people generally spend their time. One specific aim of our study was to understand whether patterns of experience vary across tasks with different requirements, a possibility that has yet to be formally explored by research.
Studies examining the role of intrinsic influences on patterns of ongoing thought highlight the relevance of individual differences in affective style and cognitive expertise. For example, individuals who are anxious or unhappy engage in greater off-task thought, often with repetitive or unpleasant features (Makovac et al., 2018, Ottaviani and Couyoumdjian, 2013). In the cognitive domain, individuals with a high capacity for executive control maintain attention more effectively during complex task environments (McVay and Kane, 2009, Unsworth and McMillan, 2013) and refrain from generating off-task thoughts until task environments are less demanding (Rummel and Boywitt, 2014, Turnbull et al., 2019). In contrast, individuals who excel at tasks that depend on memory tend to generate patterns of thought involving mental time travel with vivid detail (Wang et al., 2019). It has also been shown that individuals who do well on creativity tasks report high levels of daydreaming (Baird et al., 2012, Smeekens and Kane, 2016, Wang et al., 2018) and that those who report engaging in highly vivid and absorbent imagination perform better in mental visualisation tasks (Bregman-Hai et al., 2018). Finally, individuals with expertise in disciplines such as poetry or physics often identify solutions to problems when their mind wanders from the task they are performing (Gable, Hopper, & Schooler, 2019).
Together contemporary research highlights the influence of internal features of the individual and external features of the task environment on ongoing experience. However, no study to date has examined experience across a wide range of lab tasks and so little is known about the interplay between these factors. In the current study, we aimed to bridge this gap in the literature by examining how reported patterns of thought vary across a wide range of task environments. We chose a range of conditions, including conventional tasks that isolate discrete cognitive processes, as well as higher order tasks that rely on multiple task components (such as gambling or set-switching). We also included more naturalistic conditions such as television-viewing paradigms which are more engaging, dynamic and closely mimic the complexity of daily life (Sonkusare et al., 2019, Vanderwal et al., 2019, Vanderwal et al. 2017). To see whether thought reports during these tasks were related to measurements of individual affective style; we measured levels of anxiety (state and trait) and depression in our participants, since these have been linked to differences in both self-reported and psychophysiological correlates of thought patterns gained via experience sampling (Deng et al., 2012, Hoffmann et al., 2016, Makovac et al., 2018, Ottaviani et al., 2014, Poerio et al., 2013, Smallwood et al., 2007, Xu et al., 2017).
In our study, we used multidimensional experience sampling (MDES), a technique applied routinely in the work from our lab for the last five years to identify different features of thought patterns (Konu et al., 2020, Ruby et al., 2013, Ruby et al., 2013, Smallwood et al., 2016, Sormaz et al., 2018, Turnbull et al., 2019, Turnbull et al., 2019b). The experience sampling questions used in the current study had previously been applied in a brain imaging study (Konu et al., 2020). In that study we examined how the different patterns of thought were associated with ongoing neural activity during a low-demand sustained attention task using Functional Magnetic Resonance Imaging (fMRI). We found that reports of ongoing thoughts with episodic and social features were associated with increasing activity in a region of the ventromedial prefrontal cortex. In our MDES studies we employ dimension reduction techniques to create a common low-dimensional representation of the experience sampling data, thereby identifying “patterns of thought” (Konishi et al., 2017, Turnbull et al., 2019, Vatansever et al., 2019). Building on our prior work, in the current study we use Principal Component Analysis (PCA) with varimax rotation to determine the dimensions that make up the matrix of our experience sampling reports. We use these as a guide to explore (i) how our tasks evoke different patterns of thought and (ii) whether any of these patterns are also related to measures of the individual affective style assessed via questionnaire. To understand how the task environment influences the types of thoughts people have, we compare patterns of thought across the different task environments. To understand the impact of individual variation on thought patterns, we examine whether the distribution of the thought patterns were associated with participant affective style (anxiety and depression). Although we expected the different tasks to be associated with different thought patterns, our analysis was exploratory and we had no specific hypotheses about the specific patterns in each task. In summary, our study is the first to characterise how the thoughts people think vary across multiple task conditions, providing new insight about the variation of ongoing thought patterns across contexts that include both conventional and naturalistic situations.
Section snippets
Participants
Seventy participants took part in a two-part behavioural study (60 females; mean age: 20.60 years; standard deviation: 2.10 years, age range: 18–34 years). As no study to date has examined experience across a wide range of lab tasks, a sample of 100 participants was intended for collection which was guided by the sample sizes of prior studies in the literature that have investigated differences in ongoing thought across easy and hard task contexts (e.g. Ruby et al., 2013, Ruby et al., 2013,
Results
To provide a compact low-dimensional representation of the experience sampling data we applied Principal Component Analysis (PCA; see Methods). Based on the inflexion point of the scree plot and variance explained by the eigenvalues we selected four components (see Fig. 1 and Supplementary Table 2) which in total accounted for 53.22% of the total variance. The loadings on these components are presented as word clouds in Fig. 1 (also see Table 1 in the Supplementary Materials for specific
Discussion
Our study set out to understand how thought patterns vary across a wide range of task environments including those which encompass both simple and complex laboratory tasks, as well as more realistic everyday task situations such as watching TV programmes with varying affective components. We used MDES to characterise patterns of thought during blocks of task performance along multiple dimensions (see Table One) and applied Principal Component Analyses (PCA) to these data to identify the latent
Conclusion and limitations
Although our study establishes the role that both individual differences and situations play in patterns of ongoing thought, it leaves several important questions unanswered. First, our study was composed of university educated students and this limits the degree to which these results would generalise to older or clinical populations for whom patterns of thoughts are known to be different (Fox et al., 2018, Giambra and Grodsky, 1989). Second, although our design demonstrated the influence of
CRediT authorship contribution statement
Delali Konu: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing - original draft, Writing - review & editing. Brontë Mckeown: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Writing - original draft, Writing - review & editing. Adam Turnbull: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software,
Acknowledgements
This work was supported by European Research Council awarded to JS (WANDERINGMINDS − 646927).
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