Optimal feedback improves behavioral focus during self-regulated computer-based work

Distractions are omnipresent and can derail our attention, which is a precious and very limited resource. To achieve their goals in the face of distractions, people need to regulate their attention, thoughts, and behavior; this is known as self-regulation. How can self-regulation be supported or strengthened in ways that are relevant for everyday work and learning activities? To address this question, we introduce and evaluate a desktop application that helps people stay focused on their work and train self-regulation at the same time. Our application lets the user set a goal for what they want to do during a defined period of focused work at their computer, then gives negative feedback when they get distracted, and positive feedback when they reorient their attention towards their goal. After this so-called focus session, the user receives overall feedback on how well they focused on their goal relative to previous sessions. While existing approaches to attention training often use artificial tasks, our approach transforms real-life challenges into opportunities for building strong attention control skills. Our results indicate that optimal attentional feedback can generate large increases in behavioral focus, task motivation, and self-control-benefitting users to successfully achieve their long-term goals.

Workload-dependent hemispheric asymmetries during the emotion-cognition interaction: a close-to-naturalistic fNIRS study

Introduction

We investigated brain activation patterns of interacting emotional distractions and cognitive processes in a close-to-naturalistic functional near-infrared spectroscopy (fNIRS) study.

Methods

Eighteen participants engaged in a monitoring-control task, mimicking common air traffic controller requirements. The scenario entailed experiencing both low and high workload, while concurrently being exposed to emotional speech distractions of positive, negative, and neutral valence.

Results

Our investigation identified hemispheric asymmetries in prefrontal cortex (PFC) activity during the presentation of negative and positive emotional speech distractions at different workload levels. Thereby, in particular, activation in the left inferior frontal gyrus (IFG) and orbitofrontal cortex (OFC) seems to play a crucial role. Brain activation patterns revealed a cross-over interaction indicating workload-dependent left hemispheric inhibition processes during negative distractions and high workload. For positive emotional distractions under low workload, we observed left-hemispheric PFC recruitment potentially associated with speech-related processes. Furthermore, we found a workload-independent negativity bias for neutral distractions, showing brain activation patterns similar to those of negative distractions.

Discussion

In conclusion, lateralized hemispheric processing, regulating emotional speech distractions and integrating emotional and cognitive processes, is influenced by workload levels and stimulus characteristics. These findings advance our understanding of the factors modulating hemispheric asymmetries during the processing and inhibition of emotional distractions, as well as the interplay between emotion and cognition. Moreover, they emphasize the significance of exploring emotion-cognition interactions in more naturalistic settings to gain a deeper understanding of their implications in real-world application scenarios (e.g., working and learning environments).

Decoding Mental Effort in a Quasi-Realistic Scenario: A Feasibility Study on Multimodal Data Fusion and Classification

Humans' performance varies due to the mental resources that are available to successfully pursue a task. To monitor users' current cognitive resources in naturalistic scenarios, it is essential to not only measure demands induced by the task itself but also consider situational and environmental influences. We conducted a multimodal study with 18 participants (nine female, M = 25.9 with SD = 3.8 years). In this study, we recorded respiratory, ocular, cardiac, and brain activity using functional near-infrared spectroscopy (fNIRS) while participants performed an adapted version of the warship commander task with concurrent emotional speech distraction. We tested the feasibility of decoding the experienced mental effort with a multimodal machine learning architecture. The architecture comprised feature engineering, model optimisation, and model selection to combine multimodal measurements in a cross-subject classification. Our approach reduces possible overfitting and reliably distinguishes two different levels of mental effort. These findings contribute to the prediction of different states of mental effort and pave the way toward generalised state monitoring across individuals in realistic applications.

Affective Response Categories—Toward Personalized Reactions in Affect-Adaptive Tutoring Systems

Affect-adaptive tutoring systems detect the current emotional state of the learner and are capable of adequately responding by adapting the learning experience. Adaptations could be employed to manipulate the emotional state in a direction favorable to the learning process; for example, contextual help can be offered to mitigate frustration, or lesson plans can be accelerated to avoid boredom. Safety-critical situations, in which wrong decisions and behaviors can have fatal consequences, may particularly benefit from affect-adaptive tutoring systems, because accounting for affecting responses during training may help develop coping strategies and improve resilience. Effective adaptation, however, can only be accomplished when knowing which emotions benefit high learning performance in such systems. The results of preliminary studies indicate interindividual differences in the relationship between emotion and performance that require consideration by an affect-adaptive system. To that end, this article introduces the concept of Affective Response Categories (ARCs) that can be used to categorize learners based on their emotion-performance relationship. In an experimental study, N = 50 subjects (33% female, 19–57 years, M = 32.75, SD = 9.8) performed a simulated airspace surveillance task. Emotional valence was detected using facial expression analysis, and pupil diameters were used to indicate emotional arousal. A cluster analysis was performed to group subjects into ARCs based on their individual correlations of valence and performance as well as arousal and performance. Three different clusters were identified, one of which showed no correlations between emotion and performance. The performance of subjects in the other two clusters benefitted from negative arousal and differed only in the valence-performance correlation, which was positive or negative. Based on the identified clusters, the initial ARC model was revised. We then discuss the resulting model, outline future research, and derive implications for the larger context of the field of adaptive tutoring systems. Furthermore, potential benefits of the proposed concept are discussed and ethical issues are identified and addressed.

Evaluating students' engagement with an online learning environment during and after COVID-19 related school closures: A survival analysis approach

Background

Due to the COVID-19 pandemic schools all over the world were closed and thereby students had to be instructed from distance. Consequently, the use of online learning environments for online distance learning increased massively. However, the perseverance of using online learning environments during and after school closures remains to be investigated.

Method

We examined German students’ (n ≈ 300,000 students; ≈ 18 million computed problem sets) engagement in an online learning environment for mathematics by means of survival analysis.

Results

We observed that the total number of students who registered increased considerably during and after school closures compared to the previous three years. Importantly, however, the proportion of students engaged also decreased more rapidly over time.

Conclusion

The application of survival analysis provided valuable insights into students’ engagement in online learning - or conversely students’ increased dropout rates - over time. Its application to educational settings allows to address a broader range of questions on students’ engagement in online learning environments in the future.

Memory-related cognitive load effects in an interrupted learning task: A model-based explanation

BACKGROUND

The Cognitive Load Theory provides a well-established framework for investigating aspects of learning situations that demand learners' working memory resources. However, the interplay of these aspects at the cognitive and neural level is still not fully understood.

METHOD

We developed four computational models in the cognitive architecture ACT-R to clarify underlying memory-related strategies and mechanisms. Our models account for human data of an experiment that required participants to perform a symbol sequence learning task with embedded interruptions. We explored the inclusion of subsymbolic mechanisms to explain these data and used our final model to generate fMRI predictions.

RESULTS

The final model indicates a reasonable fit for reaction times and accuracy and links the fMRI predictions to the Cognitive Load Theory.

CONCLUSIONS

Our work emphasizes the influence of task characteristics and supports a process-related view on cognitive load in instructional scenarios. It further contributes to the discussion of underlying mechanisms at a neural level.

Schema-related cognitive load influences performance, speech, and physiology in a dual-task setting: A continuous multi-measure approach

Schema acquisition processes comprise an essential source of cognitive demands in learning situations. To shed light on related mechanisms and influencing factors, this study applied a continuous multi-measure approach for cognitive load assessment. In a dual-task setting, a sample of 123 student participants learned visually presented symbol combinations with one of two levels of complexity while memorizing auditorily presented number sequences. Learners' cognitive load during the learning task was addressed by secondary task performance, prosodic speech parameters (pauses, articulation rate), and physiological markers (heart rate, skin conductance response). While results revealed increasing primary and secondary task performance over the trials, decreases in speech and physiological parameters indicated a reduction in the overall level of cognitive load with task progression. In addition, the robustness of the acquired schemata was confirmed by a transfer task that required participants to apply the obtained symbol combinations. Taken together, the observed pattern of evidence supports the idea of a logarithmically decreasing progression of cognitive load with increasing schema acquisition, and further hints on robust and stable transfer performance, even under enhanced transfer demands. Finally, theoretical and practical consequences consider evidence on desirable difficulties in learning as well as the potential of multimodal cognitive load detection in learning applications.

Embedded interruptions and task complexity influence schema-related cognitive load progression in an abstract learning task

Cognitive processes related to schema acquisition comprise an essential source of demands in learning situations. Since the related amount of cognitive load is supposed to change over time, plausible temporal models of load progression based on different theoretical backgrounds are inspected in this study. A total of 116 student participants completed a basal symbol sequence learning task, which provided insights into underlying cognitive dynamics. Two levels of task complexity were determined by the amount of elements within the symbol sequence. In addition, interruptions due to an embedded secondary task occurred at five predefined stages over the task. Within the resulting 2x5-factorial mixed between-within design, the continuous monitoring of efficiency in learning performance enabled assumptions on relevant resource investment. From the obtained results, a nonlinear change of learning efficiency over time seems most plausible in terms of cognitive load progression. Moreover, different effects of the induced interruptions show up in conditions of task complexity, which indicate the activation of distinct cognitive mechanisms related to structural aspects of the task. Findings are discussed in the light of evidence from research on memory and information processing.