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Including motivational variables such as interest in the cognitive load framework is an ongoing process. Of particular interest is the question of how motivational variables influence the investment of mental effort. In this study, we investigated how topic interest affects the investment of mental effort in simple tasks. A total of 1543 students’ judgments regarding invested mental effort, perceived task difficulty, and topic interest for 32 tasks of a chemistry test were analyzed at the task level based on item response theory parameters. Additionally, objective task difficulty was calculated. The Rasch parameters were used for correlation and moderated regression analyses. The results indicated that when perceived task difficulty was low, students invested more mental effort in solving tasks of low topic interest compared to tasks of high topic interest. With increasing perceived task difficulty, the amount of invested mental effort rose for tasks of low as well as high topic interest. However, the difference between tasks of low and high topic interest in the amount of invested mental effort decreased as perceived task difficulty increased and even vanished when perceived task difficulty roughly corresponded to students’ performance capability. These results are in line with flow theory and the expectancy-value-cost model of motivation. When solving tasks that match their performance capability, students can experience a flow situation. However, when solving rather easy tasks of low interest, students can experience motivational costs in terms of additional effort, such as an increased need for motivational self-regulation. The results of this study provide a basis for systematically investigating and better understanding the relationship between interest, task difficulty, invested mental effort, flow experience, and emotional costs.

We examined the hypothesis that people can modulate their mind wandering on the basis of their expectations of upcoming challenges in a task. To this end, we developed a novel paradigm in which participants were presented with an analog clock, via a computer monitor, and asked to push a button every time the clock’s hand was pointed at 12:00. Importantly, the time at which the clock’s hand was pointed at 12:00 was completely predictable and occurred at 20-s intervals. During some of the 20-s intervals, we presented thought probes to index participants’ rates of mind wandering. Results indicated that participants decreased their levels of mind wandering as they approached the predictable upcoming target. Critically, these results suggest that people can and do modulate their mind wandering in anticipation of changes in task demands.

Self-regulated learning depends on task difficulty and on learners’ resources and cognitive load, as described by an inverted U-shaped relationship in Seufert’s (2018) model: for easy tasks, resources are high and load is low, so there is no need to regulate, whereas for difficult tasks, load is too high and resources are too low to regulate. Only at moderate task difficulty do learners regulate, as resources and load are in equilibrium. The purpose of this study is to validate this model, i.e., the inverted U-shaped relationship between task difficulty and self-regulatory activities, as well as learner resources and cognitive load as mediators. In the within-subject study, 67 participants reported their cognitive and metacognitive strategy use for four exams of varying difficulty. For each exam task difficulty, cognitive load, and available resources (such as prior knowledge, interest, etc.) were assessed. Multilevel analysis revealed an inverted U-shaped relationship between task difficulty and the use of cognitive strategies. For metacognitive strategies, only a linear relationship was found. Increasing cognitive load mediated these relationship patterns. For learner resources we found a competitive mediation, indicating that further mediators could be relevant. In future investigations a broader range of task difficulty should be examined.

In this article, we outline our view of how concentration shields against distraction. We argue that higher levels of concentration make people less susceptible to distraction for two reasons. One reason is that the undesired processing of the background environment is reduced. For example, when people play a difficult video game, as opposed to an easy game, they are less likely to notice what people in the background are saying. The other reason is that the locus of attention becomes more steadfast. For example, when people are watching an entertaining episode of their favorite television series, as opposed to a less absorbing show, attention is less likely to be diverted away from the screen by a ringing telephone. The theoretical underpinnings of this perspective, and potential implications for applied settings, are addressed.

Driving safety hinges on the dynamic interplay between task demand and driving capability, yet these concepts lack a unified, quantifiable formulation. In this work, we present a framework based on a multimodal large language model that transforms heterogeneous driving signals—scene images, maneuver descriptions, control inputs, and surrounding traffic states—into low-dimensional embeddings of task demand and driving capability. By projecting both embeddings into a shared latent space, the framework yields an interpretable measurement of task difficulty that alerts to capability shortfalls before unsafe behavior arises. Built upon a customized BLIP 2 backbone and fine-tuned on diverse simulated driving scenarios, the model respects consistency within tasks, captures impairment-related capability degradation, and can transfer to real-world motorway data without additional training. These findings endorse the framework as a concise yet effective step toward proactive, explainable risk assessment in intelligent vehicles.

This study aimed to evaluate the effect of transcutaneous auricular vagus nerve stimulation (taVNS) on cognitive flexibility under different levels of task complexity. The hypothesis was that taVNS would enhance cognitive flexibility more effectively under demanding task conditions. A within-subject design was used, involving 24 healthy adults who completed a Dimensional Change Card Sorting task combined with an auditory task of varying difficulty levels (low, medium, high). Participants underwent both active and sham taVNS conditions while performing the tasks. The complexity of the auditory task served to reduce cognitive resources available for the cognitive flexibility task, allowing an assessment of how taVNS modulates cognitive flexibility under different task difficulty conditions. The results show that switch costs in the Dimensional Change Card Sorting task increase with task difficulty. In addition, active taVNS reduced switch costs significantly in the high complexity condition, while no differences were observed in the low and medium complexity conditions. This indicates that taVNS is particularly effective in conditions of higher cognitive demand. The findings suggest that taVNS enhances cognitive flexibility, especially in more complex tasks, providing a better understanding of the effects of taVNS on cognitive control.

Metacognitive awareness is knowing about learners’ own thinking and learning, facilitated by introspection and self-evaluation. Although metacognitive functions are personal, they cannot be explained simply by individual conceptions, especially in a collaborative group learning context. This study considers metacognitive awareness on multiple levels. It investigates how metacognitive awareness at the individual, social, and environmental levels are associated with collaborative problem solving (CPS). Seventy-seven higher education students collaborated in triads on a computer-based simulation about running a fictional company for 12 simulated months. The individual level of metacognitive awareness was measured using the Metacognitive Awareness Inventory. The social level of metacognitive awareness was measured multiple times during CPS through situated self-reports, that is, metacognitive judgements and task difficulty. The environmental level of metacognitive awareness was measured via a complex CPS process so that group members’ interactions were video recorded and facial expression data were created by post-processing video-recorded data. Perceived individual and group performance were measured with self-reports at the end of the CPS task. In the analysis, structural equation modelling was conducted to observe the relationships between multiple levels of metacognitive awareness and CPS task performance. Three-level multilevel modelling was also used to understand the effect of environmental-level metacognitive awareness. The results reveal that facial expression recognition makes metacognitive awareness visible in a collaborative context. This study contributes to research on metacognition by displaying both the relatively static and dynamic aspects of metacognitive awareness in CPS.

Attentional focus plays a crucial role in motor performance; however, its impact on young adolescent females across varying task difficulties remains unclear. This study aims to answer the following question: how do attentional focus strategies affect motor performance in young adolescents, and does task difficulty modify this effect? A sample of 112 healthy girls aged 10–12 years (M = 10.98, SD = 0.82) was randomly assigned to one of four conditions: external focus (focus on the part of the ball contacted during the kick), holistic focus (focus on feeling solid contact during the kick), internal focus (focus on the part of the foot making contact during the kick), or control (no focus instruction). Participants completed 40 soccer kicking trials under two levels of task difficulty: kicking a stationary ball and kicking a moving ball. Findings revealed that under low task difficulty, performance was significantly better with an external focus compared to an internal focus ( p  = .001), and no instruction ( p  = .004). Similarly, a holistic focus led to significantly better performance than an internal focus ( p  = .0001) and no instruction ( p  = .0001). However, under higher task difficulty, only the holistic focus group sustained superior performance, outperforming all other groups ( p  ≤ .05). These results highlight the moderating role of task difficulty in attentional focus research and suggest the potential advantages of holistic focus in complex motor tasks.

Subjective cognitive load (CL) rating scales are widely used in educational research. However, there are still some open questions regarding the point of time at which such scales should be applied. Whereas some studies apply rating scales directly after each step or task and use an average of these ratings, others assess CL only once after the whole learning or problem-solving phase. To investigate if these two approaches are comparable indicators of experienced CL, two experiments were conducted, in which 168 and 107 teacher education university students, respectively, worked through a sequence of six problems. CL was assessed by means of subjective ratings of mental effort and perceived task difficulty after each problem and after the whole process. Results showed that the delayed ratings of both effort and difficulty were significantly higher than the average of the six ratings made during problem solving. In addition, the problems we assumed to be of higher complexity seemed to be the best predictors for the delayed ratings. Interestingly, for ratings of affective variables, such as interest and motivation, the delayed rating did not differ from the average of immediate ratings.

In rule-based (RB) category-learning tasks, the optimal strategy is a simple explicit rule, whereas in information-integration (II) tasks, the optimal strategy is impossible to describe verbally. Many studies have reported qualitative dissociations between training and performance in RB and II tasks. Virtually all of these studies were testing predictions of the dual-systems model of category learning called COVIS. The most prominent alternative account to COVIS is that humans have one learning system that is used in all tasks, and that the observed dissociations occur because the II task is more difficult than the RB task. This article describes the first attempt to test this difficulty hypothesis against anything more than a single set of data. First, two novel predictions are derived that discriminate between the difficulty and multiple-systems hypotheses. Next, these predictions are tested against a wide variety of published categorization data. Overall, the results overwhelmingly reject the difficulty hypothesis and instead strongly favor the multiple-systems account of the many RB versus II dissociations.