npj Science of Learning最新文献

Statistical learning is a core ability for individuals in extracting and integrating regularities and patterns from linguistic input. Yet, the developmental trajectory of visual statistical learning has not been fully examined in the orthographic learning domain. Employing an artificial orthographic learning task, we manipulated three levels of positional consistency of radicals, i.e., high (100%), moderate (80%), and low (60%), embedded in pseudocharacters to investigate visual statistical learning across a wide age range between 4-12-year-olds and adults. The non-linear power-function models indicated that the rates of improvement in acquiring varying positional consistencies increased with age, particularly for high and moderate levels. Specifically, we observed a significant enhancement in statistical learning abilities between the ages of 4-5 years and 5-6 years, followed by a stabilization of performance after 8-9 years. Our findings support the age-dependent perspective that individuals' visual statistical learning ability improves significantly in early childhood and then decelerates its improvement progressively until adulthood.

The COVID-19 pandemic resulted in significant disruption in schooling worldwide. Global test score data is used to estimate learning losses by modeling the effect of school closures on achievement by predicting the deviation of the most recent results from a linear trend using data from all rounds of PISA. Mathematics scores declined an average of 14 percent of a standard deviation, roughly equal to seven months of learning. Losses are greater for students in schools that faced relatively longer closures, for boys, immigrants, and disadvantaged students. Educational losses may translate into significant national income losses over time.

Observational learning enables us to make decisions by watching others' behaviors. The quality of such learning depends on the abilities of those we observe, but also on our beliefs about those abilities. We have previously demonstrated that observers learned better from demonstrators described as high vs. low in ability, regardless of their actual performance. The current study aimed to conceptually replicate these findings, and explore the neural mechanisms involved. Forty-five participants performed an observational learning task while undergoing functional magnetic resonance imaging (fMRI). We hypothesized that participants would perform better when demonstrators were described as having high vs. low ability. Unexpectedly, participants performed equally well regardless of described demonstrator ability. The behavioral effects of biased observational learning seem to be driven by mentalizing processes together with general learning and decision-making processes.

New information that is compatible with pre-existing knowledge can be learned faster. Such schema memory effect has been reported in declarative memory and in explicit motor sequence learning (MSL). Here, we investigated if sequences of key presses that were compatible to previously trained ones, could be learned faster in an implicit MSL task. Participants trained a motor sequence before switching to a completely new sequence, to a compatible sequence with high overlap in ordinal positions, or to an incompatible sequence with low overlap, while the compatible and incompatible sequences had the same overlap in movement transitions. We observed accelerated learning in the Compatible and Incompatible groups compared to the New group, if participants trained for 3 sessions before switching to the altered sequence. Overall, our study suggests facilitative learning of implicit motor sequences that are compatible in movement transitions, if the previous sequence has been trained extensively.

Rising interest in artificial intelligence in education reinforces the demand for evidence-based implementation. This study investigates how tutor agents' physical embodiment and anthropomorphism (student-reported sociability, animacy, agency, and disturbance) relate to affective (on-task enjoyment) and cognitive (task performance) learning within an intelligent tutoring system (ITS). Data from 56 students (M = 17.75 years, SD = 2.63 years; 30.4% female), working with an emotionally-adaptive version of the ITS "Betty's Brain", were analyzed. The ITS' agents were either depicted as on-screen robots (condition A) or as both on-screen avatars and physical robots (condition B). Physical presence of the tutor agent was not significantly related to task performance or anthropomorphism, but to higher initial on-task enjoyment. Student-reported disturbance was negatively related to initial on-task enjoyment, and student-reported sociability was negatively related to task performance. While physical robots may increase initial on-task enjoyment, students' perception of certain characteristics may hinder learning, providing implications for designing social robots for education.

This study evaluates the ability of large language models (LLMs) to deliver criterion-based grading and examines the impact of prompt engineering with detailed criteria on grading. Using well-established human benchmarks and quantitative analyses, we found that even free LLMs achieve criterion-based grading with a detailed understanding of the criteria, underscoring the importance of domain-specific understanding over model complexity. These findings highlight the potential of LLMs to deliver scalable educational feedback.

Generalization is central to motor learning. However, few studies are on the learning generalization of BCI-actuated supernumerary robotic finger (BCI-SRF) for human-machine interaction training, and no studies have explored its longitudinal neuroplasticity mechanisms. Here, 20 healthy right-handed participants were recruited and randomly assigned to BCI-SRF group or inborn finger group (Finger) for 4-week training and measured by novel SRF-finger opposition sequences and multimodal MRI. After training, the BCI-SRF group showed 350% times compared to the Finger group in the improvement of sequence opposition accuracy before and after training, and accompanied by significant functional connectivity increases in the sensorimotor region and prefrontal cortex, as well as in the intra- and inter-hemisphere of the sensorimotor network. Moreover, Granger Causality Analysis identified causal effect main transfer within the sensorimotor cortex-cerebellar-thalamus loop and frontal-parietal loop. The findings suggest that BCI-SRF training enhances motor sequence learning ability by influencing the functional reorganization of sensorimotor network.

Perception and perceptual memory play crucial roles in fear generalization, yet their dynamic interaction remains understudied. This research (N = 80) explored their relationship through a classical differential conditioning experiment. Results revealed that while fear context perception fluctuates over time with a drift effect, perceptual memory remains stable, creating a disjunction between the two systems. Surprisingly, this disjunction does not significantly impact fear generalization behavior. Although most participants demonstrated generalization aligned with perceptual rather than physical stimulus distances, incorporating perceptual memory data into perceptual distance calculations did not enhance model performance. This suggests a potential shift in the mapping of the perceptual memory component of fear context, occurring alongside perceptual dynamics. Overall, this work provides evidence for understanding fear generalization behavior through different stimulus representational processes. Such mechanistic investigations can enhance our understanding of how individuals behave when facing threats and potentially aid in developing mechanism-specific diagnoses and treatments.

The primary motor cortex (M1) is crucial for motor skill learning. We examined its role in interleaved practice, which enhances retention (vs. repetitive practice) through M1-dependent consolidation. We hypothesized that cathodal transcranial direct current stimulation (ctDCS) to M1 would disrupt retention. We found that ctDCS reduced retention due to weakened encoding during acquisition, not disrupted consolidation. These results highlight M1's broad role in encoding and retention of novel motor skills.