Distinct neural markers for intentional and unintentional task unrelated thought

Abstract

Studies suggest that generating thought unrelated to the task in hand is accompanied by a reduction of attention to external task-relevant information. This observations led contemporary theory to suggest multiple component processes contribute to patterns of ongoing thought. The present study used EEG to seek support for these component-process accounts by examining the neural correlates of deliberate and spontaneous task unrelated thought. EEG activity was compared prior to reports of ongoing thought during a test of sustained attention. Event-related potentials, such as the P3, were attenuated during off-task states, regardless of whether they were intentional or not. In contrast, increased alpha power and cortical phase-locking were linked to deliberate off-task thoughts, while reductions of evoked sensory response were prevalent in spontaneous off-task episodes. These data suggest off-task thought shares common and distinct neural features that are differentiated through their relationship to intention, supporting component process accounts of ongoing thought.

Predicting intentional and unintentional task unrelated thought with EEG

“…Evidence for this view comes from studies showing that different facets of the off-task experience are a function of both shared and distinct neurocognitive profiles. For example, a recent study by the authors of the present paper (Martel et al, 2019) shows that one of the features of off-task thought, the degree of intentionality, can be understood as the interplay between bottom-up and top-down features of attention. More specifically, of the two processes that are assumed by contemporary accounts to be critical during off-task mental activity (Smallwood, 2013a), perceptual decoupling and executive control, the latter is thought to contribute exclusively to off-task thought when engaged in a deliberate manner.…”

“…Although the role of perceptual decoupling is now well understood, the role of executive control in off-task states remains more elusive and a central question of the literature McVay & Kane, 2010;Smallwood, 2013b;Smallwood & Schooler, 2006;Watkins, 2008), with evidence for its involvement in both off-task and task-related thought. The recent study led by the authors of the current work (Martel et al, 2019) investigating the intentionality dimension of off-task experiences found that spontaneous and deliberate forms of off-task states differ on several EEG features. Among these markers, alpha synchronisation, associated with perceptual inhibition (Foxe & Snyder, 2011) and internal attention (Benedek, Schickel, Jauk, Fink, & Neubauer, 2014;Cooper, Croft, Dominey, Burgess, & Gruzelier, 2003;Katahira et al, 2018), was found to be the most prominent marker of deliberate off-task states and interpreted as reflecting top down inhibition of irrelevant sensory input.…”

“…Most often, the focus of our attention is not tied to the present moment or the immediate surroundings but rather engrossed in information generated from internal representations, such as autobiographical memories (Poerio et al, 2017;Smallwood et al, 2016;Smallwood & Schooler, 2006 or prospective thinking (Baird, Smallwood, & Schooler, 2011;Schooler et al, 2011;Spreng, Mar, & Kim, 2009;Stawarczyk, Cassol, & D'Argembeau, 2013). Ubiquitous both in daily life (Kane et al, 2007(Kane et al, , 2017Killingsworth & Gilbert, 2010;Poerio, Totterdell, & Miles, 2013;Risko, Anderson, Sarwal, Engelhardt, & Kingstone, 2012;Seli, Beaty, et al, 2018) and in the lab (McVay, Kane, & Kwapil, 2009;Smallwood, Davies, et al, 2004;Smallwood, Obonsawin, & Heim, 2003;Smallwood, O'Connor, Sudberry, Haskell, & Ballantyne, 2004;Varao-Sousa, Smilek, & Kingstone, 2018), these off-task experiences can vary along different dimensions such as content (Ruby, Smallwood, Engen, & Singer, 2013;Smallwood, Nind, & O'Connor, 2009;Smallwood & O'Connor, 2011), intrinsic or extrinsic constraints imposed on cognition (Christoff, Irving, Fox, Nathan Spreng, & Andrews-Hanna, 2016;Mills, Raffaelli, Irving, Stan, & Christoff, 2018), metacognitive awareness (Drescher, Van den Bussche, & Desender, 2018;Schooler, 2002;Schooler et al, 2011;Zedelius, Broadway, & Schooler, 2015) and degrees of intentionality (Martel, Arvaneh, Robertson, Smallwood, & Dockree, 2019;Robison & Unsworth, 2018;Seli, Ralph, Konishi, Smilek, & Schacter, 2017;Seli, Ralph, Risko, et al, 2017;…”

“…This effect has proven to be robust across experimental paradigms and sensory modalities (Baldwin et al, 2017;Braboszcz & Delorme, 2011;Broadway, Franklin, & Schooler, 2015/4;Kam, Xu, & Handy, 2014;Martel, Dähne, & Blankertz, 2014;O'Connell et al, 2009). Generally speaking, perceptual decoupling during off-task states is associated in the EEG with a decrease of sensory and cognitive event-related potentials (ERPs (Hopfinger, Buonocore, & Mangun, 2000;Kam et al, 2011;Smallwood et al, 2008), an attenuation of inter-trial phase coherence (ITPC (Baird et al, 2014;Martel et al, 2019) and an upswing of low-frequency oscillations (Broadway et al, 2015/4;Jin, Borst, & van Vugt, 2019;Macdonald, Mathan, & Yeung, 2011;Martel et al, 2014;O'Connell et al, 2009). Taken together, these neurocognitive dynamics substantiate the notion that the perceptual decoupling process serves as an insulation mechanism, shielding cognition from competing input and allowing off-task thoughts to unfold (Smallwood, 2013a(Smallwood, , 2013b.…”

“…Previous work identified several measures modulated by off-task states, including behavioral measures such as speech patterns (Drummond & Litman, 2010), response time (Bastian & Sackur, 2013;Cheyne, Carriere, & Smilek, 2006;Stawarczyk, Majerus, Maquet, & D'Argembeau, 2011)), body movement Farley, Risko, & Kingstone, 2013;Seli, Carriere, Thomson, et al, 2014), reading speed , and peripheral physiological measures such as eye movement (Frank, Nara, Zavagnin, Touron, & Kane, 2015;Reichle, Reineberg, & Schooler, 2010;Uzzaman & Joordens, 2011;Zhang, Miller, Sun, & Cortina, 2018), gaze (Faber et al, 2018;Huang, Li, Ngai, Leong, & Bulling, 2019;Mills, Bixler, Wang, & D'Mello, 2016), blinks (Grandchamp, Braboszcz, & Delorme, 2014;McIntire, McKinley, Goodyear, & McIntire, 2014;Smilek, Carriere, & Cheyne, 2010), pupil dilation (Franklin, Broadway, Mrazek, Smallwood, & Schooler, 2013;Konishi, Brown, Battaglini, & Smallwood, 2017;, and neurophysiological measures (Baird et al, 2014;Broadway et al, 2015/4;Jin et al, 2019;Kam et al, 2011;A. Martel, Arvaneh, Taylor, Dockree, & Robertson, 2017;Martel et al, 2019;O'Connell et al, 2009;Smallwood et al, 2008). The next step in the triangulation approach after the collection of various measures is to integrate these into machine learning models to obtain a continuous index of attention.…”