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We investigated whether, during visual word recognition, semantic processing is modulated by attentional control mechanisms directed at matching semantic information with task-relevant goals. In previous research, we analyzed the semantic Stroop interference as a function of response latency (delta-plot analyses) and found that this phenomenon mainly occurs in the slowest responses. Here, we investigated whether this pattern is due to reduced ability to proactively maintain the task goal in these slowest trials. In two pairs of experiments, participants completed two semantic Stroop tasks: a classic semantic Stroop task (Experiment 1A and 2A) and a semantic Stroop task combined with an n-back task (Experiment 1B and 2B). The two pairs of experiments only differed in the trial pace, which was slightly faster in Experiments 2A and 2B than in Experiments 1A and 1B. By taxing the executive control system, the n-back task was expected to hinder proactive control. Delta-plot analyses of the semantic Stroop task replicated the enhanced effect in the slowest responses, but only under sufficient time pressure. Combining the semantic Stroop task with the n-back task produced a change in the distributional profile of semantic Stroop interference, which we ascribe to a general difficulty in the use of proactive control. Our findings suggest that semantic Stroop interference is, to some extent, dependent on the available executive resources, while also being sensitive to subtle variations in task conditions. [ABSTRACT FROM AUTHOR]

A sample of 33 experiments was extracted from the Web-of-Science database over a 5-year period (2016–2020) that used physiological measures to measure intrinsic cognitive load. Only studies that required participants to solve tasks of varying complexities using a within-subjects design were included. The sample identified a number of different physiological measures obtained by recording signals from four main body categories (heart and lungs, eyes, skin, and brain), as well as subjective measures. The overall validity of the measures was assessed by examining construct validity and sensitivity. It was found that the vast majority of physiological measures had some level of validity, but varied considerably in sensitivity to detect subtle changes in intrinsic cognitive load. Validity was also influenced by the type of task. Eye-measures were found to be the most sensitive followed by the heart and lungs, skin, and brain. However, subjective measures had the highest levels of validity. It is concluded that a combination of physiological and subjective measures is most effective in detecting changes in intrinsic cognitive load.

A sample of 33 experiments was extracted from the Web-of-Science database over a 5-year period (2016–2020) that used physiological measures to measure intrinsic cognitive load. Only studies that required participants to solve tasks of varying complexities using a within-subjects design were included. The sample identified a number of different physiological measures obtained by recording signals from four main body categories (heart and lungs, eyes, skin, and brain), as well as subjective measures. The overall validity of the measures was assessed by examining construct validity and sensitivity. It was found that the vast majority of physiological measures had some level of validity, but varied considerably in sensitivity to detect subtle changes in intrinsic cognitive load. Validity was also influenced by the type of task. Eye-measures were found to be the most sensitive followed by the heart and lungs, skin, and brain. However, subjective measures had the highest levels of validity. It is concluded that a combination of physiological and subjective measures is most effective in detecting changes in intrinsic cognitive load. [ABSTRACT FROM AUTHOR]

Multisensory integration is considered a crucial mechanism underlying the sense of body ownership, but the everyday experience of feeling our own bodies seems automatic and effortless, which implies that the process of integrating multisensory signals for bodily self-awareness may require minimal cognitive effort. To test this, we developed an n-back rubber-hand illusion. The n-back component refers to tracking the identity of the rubber-hand fingers being stroked as the illusion is induced. This embedded n-back task allowed us to parametrically manipulate the levels of cognitive load while maintaining the integrity of the rubber-hand illusion. Across two experiments, we show that the strength of hand embodiment is sensitive only to the multisensory congruency of signals (as elicited by the finger stroking on the rubber hand) but not to the increasingly higher load placed on the performance of the n-back tasks. These results suggest that multisensory representation of self is robust to cognitive depletion and requires minimal effort. [ABSTRACT FROM AUTHOR]

According to Cognitive Load Theory, one of the crucial factors for successful learning is the type and amount of working-memory load (WML) learners experience while studying instructional materials. Optimal learning conditions are characterized by providing challenges for learners without inducing cognitive over- or underload. Thus, presenting instruction in a way that WML is constantly held within an optimal range with regard to learners’ current working-memory capacity might be a good method to provide these optimal conditions. The current paper elaborates how digital learning environments, which achieve this goal can be developed by combining approaches from Cognitive Psychology, Neuroscience, and Computer Science. One of the biggest obstacles that needs to be overcome is the lack of an unobtrusive method of continuously assessing learners’ WML in real-time. We propose to solve this problem by applying passive Brain-Computer Interface (BCI) approaches to realistic learning scenarios in digital environments. In this paper we discuss the methodological and theoretical prospects and pitfalls of this approach based on results from the literature and from our own research. We present a strategy on how several inherent challenges of applying BCIs to WML and learning can be met by refining the psychological constructs behind WML, by exploring their neural signatures, by using these insights for sophisticated task designs, and by optimizing algorithms for analyzing EEG data. Based on this strategy we applied machine-learning algorithms for cross-task classifications of different levels of WML to tasks that involve studying realistic instructional materials. We obtained very promising results that yield several recommendations for future work.

According to Cognitive Load Theory (CLT), one of the crucial factors for successful learning is the type and amount of working-memory load (WML) learners experience while studying instructional materials. Optimal learning conditions are characterized by providing challenges for learners without inducing cognitive over- or underload. Thus, presenting instruction in a way that WML is constantly held within an optimal range with regard to learners' working-memory capacity might be a good method to provide these optimal conditions. The current paper elaborates how digital learning environments, which achieve this goal can be developed by combining approaches from Cognitive Psychology, Neuroscience, and Computer Science. One of the biggest obstacles that needs to be overcome is the lack of an unobtrusive method of continuously assessing learners' WML in real-time. We propose to solve this problem by applying passive Brain-Computer Interface (BCI) approaches to realistic learning scenarios in digital environments. In this paper we discuss the methodological and theoretical prospects and pitfalls of this approach based on results from the literature and from our own research. We present a strategy on how several inherent challenges of applying BCIs to WML and learning can be met by refining the psychological constructs behind WML, by exploring their neural signatures, by using these insights for sophisticated task designs, and by optimizing algorithms for analyzing electroencephalography (EEG) data. Based on this strategy we applied machine-learning algorithms for cross-task classifications of different levels of WML to tasks that involve studying realistic instructional materials. We obtained very promising results that yield several recommendations for future work. [ABSTRACT FROM AUTHOR]

In this study we examined (a) whether verbal self-instructions can enhance task-switching performance in younger and older adults, and (b) whether verbal self-instruction benefits on task switching are smaller when memory demands on keeping track of the task sequence are reduced by spatial task cueing. Task-switching ability was measured as the difference in performance between single-task and mixed-task blocks (termed mixing costs), in which participants switched between two tasks A and B. One group of participants performed the switching tasks with spatial task cues, indicating which of the two tasks has to be performed, thereby reducing demands on the endogenous control of serial task order (low task-sequencing load). The other group switched between tasks without external task cues (high task-sequencing load). To investigate the influence of verbal self-instructions on task switching, participants either named aloud the next task during task preparation (task-naming condition) or they did not verbalize (control condition). Results indicated that age differences in verbalization benefits on mixing costs depend on early learning whereby benefits were generally larger when subjects had some prior practice in task switching alone, and that verbalization benefits did not differ between the two task-sequencing load conditions. These findings suggest that task naming is a suitable cognitive intervention for enhancing the control of task switching in younger and older adults, even if memory load is reduced, and that for the efficient application of this strategy it first has to be coordinated with task switching, which is easier when task switching is already practiced.

Recent studies have shown that many subject-verb agreement errors consist of making the verb agree with the immediately preceding noun, as in “The smell of the rubbish-bins are foul”. Assuming that it is the automaticity of the agreement operation which is responsible for these attraction errors in expert writers, the present studies aimed at demonstrating the gradual automatization of this operation in young writers by examining developmental changes in the occurrence of agreement errors. In three experiments we found that subjects' performance moves from systematic errors in number agreement in young children (e.g., no use of plural marks) to attraction errors in fifth graders and older adults through an intermediate phase characterized by an attention-demanding and easily disrupted computation of verb agreement displayed by some second graders. Attraction errors are a byproduct of the automatization of the implementation of the agreement process. [ABSTRACT FROM AUTHOR]