In self-regulated learning (SRL), students often set goals that influence how they subsequently perform on tasks. Furthermore, emotions have been considered to play a key role in this process, yet the exact dynamic relationship between goals, performance, and emotions have not been specified. In the present study, we employed computational modeling to delineate the specific dynamic interplay of goals, performance, and emotions in multiple goal-striving episodes. We developed and applied our computational model of self-regulated learning to data collected from an online math task (Study 1) and from an online learning app used to study for a high-stakes state exam in Germany (Study 2). Across both studies, we found that students who set higher goals (compared to their previous performance) had higher subsequent performance, highlighting the importance of setting high goals for learning. Furthermore, emotions are not only influenced by previous goals and performance, but they also influence subsequent goal setting and performance. While the effects of emotions sometimes differed across the two studies, stronger positive emotions (particularly enjoyment) led to higher levels of goals, but to lower performance in the online math task and higher performance when studying for the high-stakes exam. Our work highlights computational modeling as a valuable tool to theorize and empirically analyze the processes of SRL in education research.
In human action control, executing a response is assumed to integrate features of the response and the stimulus into short-term episodic traces known as event files. Repeating any of the features comprised in the event file in a later episode leads to retrieval of other integrated features and can influence current behavior. Event files can include task-relevant features, and task-irrelevant features also can be bound to responses, termed distractor–response binding. Distractor–response binding effects have been shown in multimodal settings, such as for auditory distractors and visual targets. Recently, it has been suggested that silence can be perceived rather than just being cognitively inferred as the absence of sound. In the present study (combined N = 120), we used auditory distractors in a distractor–response binding task and investigated whether silence as a distractor (i.e., the absence of presented sound) can be bound to a response and subsequently retrieve this response from an event file. We found that silence as a distractor produced a typical distractor–response binding effect, which, furthermore, did not differ in size from the distractor–response binding effect when two sounds were used as distractors. The present findings indicate that silence operates similarly to sound as an auditory distractor in binding and retrieval in action control and support the notion that moments of absence can elicit perceptual event representations.
In everyday behaviour, the ability to stop an already initiated action is critical for ensuring both your safety and that of others; for example, when stopping a reaching movement towards a hot stove-top after realising it is hot. Neuroscientific evidence points towards the critical role of several regions in the right prefrontal cortex in the coordination and execution of this response inhibition—specifically the right inferior frontal gyrus (rIFG) and the right dorsolateral prefrontal cortex (rDLPFC). The present study investigated the effects of different transcranial magnetic stimulation (TMS) protocols on stop-signal task (SST) performance. We hypothesized that TMS over one or both of these areas would be detrimental to performance. However, contrary to our hypothesis, TMS significantly facilitated performance regardless of the stimulation condition. We applied both frequentist and Bayesian methods to assess the robustness of these effects, revealing consistent reductions in stop-signal reaction time (SSRT) across active conditions. Our results add to the growing body of results that suggest TMS effects may not be as straight-forward as usually assumed and that so-called “inhibitory protocols” can facilitate performance. This result could be explained by a shift in the signal-to-noise ratio depending on the pre-activation of the area. Put differently, TMS may have primed task-related activity in the target areas to a level that was optimal for task performance. Alternatively, the observed effect may reflect an (over)compensation by other parts of the network or disruption of competing resources. Future studies may provide further support for these hypotheses.
Digital prompts are brief instructional cues designed to guide student learning. The variety of ways in which prompts are designed and labelled across education result in conceptual and terminological inconsistencies that make it difficult to integrate research findings. Furthermore, artificial intelligence (AI) has introduced a new dominant meaning of the term “prompt” that complicates the discoverability of relevant literature. To address these challenges, we propose a guiding framework that conceptualizes digital prompts as dynamic scaffolds and characterizes their instructional design through a typology of content, function, presentation, and source. Using a bibliometric approach, we analyzed 1,238 publications to examine how well this framework captures the structure of the field. We identified 283 distinct prompt labels, most of which were used in only one publication. The mapping of these labels onto the proposed typology led to a refined understanding of prompt design characteristics. Furthermore, we examined co-occurrences between labels and identified research topics and citation practices. The resulting patterns revealed two major research clusters centered on metacognitive and self-explanation prompts, which structure much of the literature. However, these two clusters lack integration. We show how these separate research traditions can be integrated into our framework of dynamic scaffolding through prompts. Finally, we demonstrate how our typology of prompt types can foster greater terminological coherence and improve search strategies in the age of AI.
Human action control relies on the close interconnection of action and perception. This is possible through a binding mechanism that integrates distributed features of perceptual and action-related events within sensorimotor representations (event files). Encountering any one of these features later on can retrieve previously integrated features from memory and influence current action. Since actions are represented as their sensory consequences rather than their motor pattern, previous studies suggest that actions can be integrated into and retrieved from event representations even without being executed. However, it is still unclear whether binding and retrieval processes for omitted actions are highly automatic processes or if they can be influenced by higher-order strategies. Here we used sequential tasks to investigate whether binding and retrieval regarding omitted responses is affected by the time to prepare a response and by the likelihood of response omissions. Results indicate that binding and retrieval are highly adaptive processes that rely on the action planning of responses but operate beyond immediate action contingencies, facilitating efficient action control in future behavior.
