Lapses in Attention and Mind-Wandering are Related but Distinct Constructs

Post by Shireen Parimoo

The takeaway

Lapses in attention are more common in people who are prone to boredom, have poor attentional control, and a tendency to let their mind wander. Mind-wandering, on the other hand, is more strongly related to low motivation and alertness, as well as personality traits like conscientiousness and neuroticism.

What's the science?

We have all experienced days at work where we find it challenging to stay focused on the simplest of tasks. Lapses of attention can occur when we are disengaged from a task or when we let our mind wander, often negatively impacting our performance. The degree to which different causes of attentional lapses are related to each other, as well as to other cognitive abilities and personality traits is unclear. This week in Journal of Experimental Psychology: General, Unsworth and colleagues used latent variable analysis techniques to investigate the underlying causes of lapses in attention and whether individual differences might make some people more prone to experiencing attentional lapses than others.

How did they do it?

Participants were 358 young adults who completed a battery of cognitive tasks that assessed their working memory capacity (e.g., reading span), attentional control abilities (e.g., anti-saccade task), and lapses in attention (e.g., sustained attention to response task – SART). Participants also rated the degree to which they experienced task-unrelated mind-wandering by responding to infrequently presented thought probes during some of the tasks, as well as their level of motivation and alertness. Lastly, they filled out a series of self-report questionnaires assessing aspects of their personality (Big Five Inventory), proneness to boredom, daily cognitive failures including lapses in attention and memory, and sleep habits.

The authors first performed confirmatory factor analyses in which all the measures from the lapses of attention tasks were hypothesized to load onto a single latent factor (i.e., the construct of lapses in attention). In subsequent analyses, they tested whether the lapses of attention measures loaded onto the same factor as mind-wandering thoughts and attentional control or whether those were separable constructs. They then tested how all the cognitive factors were related to each other and to the questionnaire measures. Finally, the authors used structural equation modeling to determine which of the self-reported measures and cognitive factors uniquely contributed to (i) in-lab lapses in attention, (ii) daily cognitive failures, and (ii) task-unrelated mind-wandering, after accounting for the shared contribution of the remaining variables.

What did they find?

Behavioral measures of in-lab attentional lapses loaded onto a single latent factor, which means that those measures do arise from lapses in attention. Importantly, the factor of lapses in attention was distinct from both mind-wandering and attentional control, despite being correlated with them. Reduced attentional control and greater mind-wandering contributed to increased lapses in attention. Moreover, those who were more prone to boredom and lapses in attention in their daily lives were also more likely to experience greater lapses in attention on the cognitive tasks in the lab. In contrast, none of the cognitive factors predicted daily cognitive failures, only boredom proneness, conscientiousness, and neuroticism. These findings demonstrate that although in-lab lapses in attention are associated with boredom proneness, cognitive abilities, and everyday cognitive failures, everyday cognitive failures are primarily driven by personality traits.

Mind-wandering was not only distinct from lapses in attention but also showed a different pattern of correlations with other variables. For example, mind-wandering was associated with greater neuroticism and lower conscientiousness, whereas these personality traits were not related to lapses in attention. Compared to lapses in attention, mind-wandering was weakly related to attentional control and working memory but more strongly correlated with motivation and alertness. Lastly, greater lapses in attention, greater attentional control, and low alertness predicted greater mind-wandering. Thus, cognitive variables and personality traits differentially contribute to every day and in-lab lapses in attention and mind-wandering.

What's the impact?

This study found that lapses in attention and mind-wandering are related but separate constructs that arise from a distinct combination of cognitive abilities and personality traits. These findings provide greater insight into the different reasons why people have difficulty focusing on tasks and pave the way for developing effective interventions for improving task focus and performance.

Access the original scientific publication here.

P.S. This post is a part of our new BrainPost Behavior series. For more posts like this check out BrainPost Behavior.

Identifying the Neural Mechanism Behind Team Flow

Post by Lincoln Tracy

The takeaway

People can get “in the zone” when playing sports, listening to music, or working — either alone or as part of a team or group. Now, researchers have identified the neural mechanism responsible for getting “in the zone” during a team-based activity.

What's the science?

“Getting in the zone”—or entering a flow state—is a psychological phenomenon characterized by intense attention and effortless reflexes, leading to a reduced sense of external awareness and a reduced sense of time. Developing a flow state can occur during individual or team-based activities, with previous research reporting the flow state from team-based activities as being more intense than individual flow states. However, the neural mechanism underlying team-based flow states is unknown. This week in eNeuro, Shehata and colleagues propose a model of these mechanisms by investigating the neural activity of partners in a team-based activity.

How did they do it?

Researchers recruited 15 participants (five males, 18-35 years) to form 10 sets of pairs—meaning some participants were paired twice. Participants played the music rhythm game “O2JAM U”, an iPad game in the same vein as Guitar Hero, under three different conditions designed to manipulate how easy it would be for participants to get “in the zone” while playing as a team. During the Team Flow condition participants played a particular song while they could see their partner and the area on the screen they had to tap to “play” the song. The Team Only condition had the same setup, but participants played a reversed and shuffled version of the song. Finally, the Flow Only condition played the same song as the Team Flow condition, but participants could see neither their partner nor the tapping area. Irrelevant beeping sounds were played throughout the songs in all conditions to test how much attention participants were paying to the game. Researchers specifically recruited people who were good at the game (i.e., they missed less than 10 cues during a song with nearly 300 cues during a practice round) and preferred playing the game with someone else, rather than by themselves.

Flow state—or how much participants felt they were “in the zone”—during the task was measured in two ways. The first was by a series of ratings that participants completed after each trial (feeling in control, enjoyment, time perception, etc.). The second was via electroencephalography (EEG) hyperscanning—where brain activity from both participants was recorded at the same time. The researchers were specifically interested in the auditory-evoked potentiations (AEP), or the brain activity that occurred in response to the irrelevant beeps played during the tasks. The more brain activity in response to the beeps, the less “in the zone” the participant was. The researchers looked at the EEG data for participants individually, as well as looking at if the level and timing of brain activity were similar between the two participants in each of the pairs.

What did they find?

First, the authors found that the AEP response was greater during the Team Only condition compared to the Team Flow and Flow Only conditions, meaning that participants were less engaged in the task during that condition. Second, they found that the AEP displayed the strongest correlation with the participant’s flow ratings during the Team Flow condition. This suggests participants were more in the zone during the Team Flow condition. Third, the authors found the beta-gamma EEG band (brain waves) had the highest power when participants were in team flow, meaning the neural signature for team flow had been identified. Finally, they found that the Team Flow condition was associated with higher interbrain neural synchrony. This means that both individuals displayed higher levels of similar brain activity when completing the task—consistent with the phenomenological experience of team flow. 

What's the impact?

This is the first study to identify an objective neural measure of team flow. These results provide a proof of concept that team flow is a distinct brain state from solo or individual flow states. The novel method used in this study will be a useful tool for future research in this area.

Shehata et al. Team flow is a unique brain state associated with enhanced information integration and inter-brain synchrony. eNeuro (2021). Access the original scientific publication here. 

Remote Work: What’s the Impact on Team Collaboration?

Post by Ifrah Khanyaree

What's the science?

The COVID-19 pandemic has accelerated digital transformation across many industries and organizations. Within a matter of weeks of the onset of the pandemic, many office-based working adults shifted to working remotely full time. This week in Nature Human Behaviour, Yang and colleagues analyzed communication and working hours data from a large US tech company to find out the impact of remote work on employee collaboration and communication.                  

How did they do it?

The authors used anonymized email, instant message (IM), calendar, video/audio call, working hour data of 61,182 US Microsoft employees from December 2019 - June 2020, collected using Microsoft’s Workplace Analytics product. The authors then analyzed this data using a modified version of the traditional Difference-in-Difference model (DiD), which is a technique used in econometrics that measures causal effect between at least two sets of longitudinal data, where one group receives a ‘treatment’ and the other does not (the control group). This works because many of Microsoft’s employees were remote even before the pandemic hit; that group acts as the control group that also experiences the effects of working during COVID, but not the treatment (switching to remote work). 

They used a modified version of DiD both because COVID affected both the treatment and control groups and because their model measured the effects of changes in two different treatment variables instead of one - an employee’s remote work status and also their colleague’s remote work status.                            

What did they find?

The authors found that the shift to remote work for all employees caused the communication network to become siloed: a decrease in cross-group communication but an increase in the connectedness of one’s own group. Remote work led to a substantial increase in unscheduled calls, emails, and instant messages, but a decrease in meeting hours, and total video/audio call hours. Synchronous collaboration, where more complex information can be conveyed, such as video calls, was decreased overall in favour of asynchronous communication, like emails or messages. Further, the total hours worked were increased. These changes were particularly enhanced for managers. Finally, connections between employees became more static, with fewer social connections being added or lost over time.

What's the impact?

The authors suggest that the increase in asynchronous communication and more siloed networks could negatively affect workers’ productivity and innovation because of the difficulty in collaboration and sharing of information. They propose that firms carry on more qualitative and quantitative research before finalizing any remote work policies. Based on their analyses, firms that want to continue with full-time remote work need to be intentional about strengthening cross-group ties in their organizations. The sudden shift to remote work has brought about a much-needed acceleration and transformation to support working remotely, and it is likely that some version of remote work will continue to prevail even after the pandemic is over. Therefore more research needs to be done to understand the long-term effects of remote work on team communication and collaboration and what the downstream impact might be.

Yang et al. The effects of remote work on collaboration among information workers (2021). Access the original scientific publication here.