21 papers · ranked by Valyu relevance
Kevin D. Himberger, Amy S. Finn, Christopher J. Honey
Humans can extract regularities from their environment, enabling them to recognize and predict sequences of events. The process of regularity extraction is called ‘statistical learning’ and is generally thought to occur rapidly and automatically; that is, regularities are extracted from repeated stimulus presentations…
Alana Collins, Michael M. Saling, Sarah J. Wilson, Graeme D. Jackson + 1 more
'Chris Tailby'] Objective The Spatial Learning Task of Lhermitte and Signoret is an object-location arbitrary associative learning task. The task was originally developed to evaluate adults with severe amnesia. It is currently used in populations where the memory system either is not yet fully developed or where it has…
Yu Yuan, Lili Zhao, Wei Chen, Guangting Zheng + 3 more
Capturing human learning behavior based on deep learning methods has become a major research focus in both psychology and intelligent systems. Recent approaches rely on controlled experiments or rule-based models to explore cognitive processes. However, they struggle to capture learning dynamics, track progress over…
Scott H. Fraundorf, Zachary A. Caddick, Timothy J. Nokes-Malach, Benjamin M. Rottman
Although tests and assessments-such as those used to maintain a physician’s Board certification-are often viewed merely as tools for decision-making about one’s performance level, strong evidence now indicates that the experience of being tested is a powerful learning experience in its own right: The act of retrieving…
Haopeng Chen, Pieter Verbeke, Stefania Mattioni, Cristian Buc Calderon + 1 more
Testing enhances memory more than studying. Although numerous studies have demonstrated the robustness of this classic effect, its neural and computational origin remains debated. Predictive learning is a potential mechanism behind this phenomenon: Because predictions and prediction errors (mismatch between predictions…
Ynès Hendriks, Bart Vogelaar, Roos van Heeswijk, Jochanan Veerbeek + 4 more
This study evaluated the feasibility of including a computerized dynamic test of analogical reasoning in standard neuropsychological assessments in a heterogeneous psychiatric population. The participants were 40 adult patients (Mage = 33.15 ± 12.27, range 19-68; 60% male) enrolled in specialized mental health and…
Laura Franzoi, Veronica Cembrani, Claudio Mulatti, Barbara Treccani
With the aim of bridging the gap between laboratory studies and real-world learning experiences, this research investigated the effects of combining retrieval and distributed practice in primary school settings. Retrieval and distributed practice were implemented through a testing procedure that provided accuracy…
Aaron Hu
Difficulties and Adaptive System to Overcome: A Qualitative and Conceptual Framework Authors: ['Aaron Hu'] Learning difficulties pose significant challenges for students, impacting their academic performance and overall educational experience. These difficulties could sometimes put students into a downward spiral that…
Kálmán Tót, Noémi Harcsa-Pintér, Gabriella Eördegh, Balázs Bodosi + 1 more
Introduction Associative equivalence learning, the ability to form connections between different stimuli based on shared outcomes, plays a fundamental role in human cognition. In this study, we investigated how stimulus modality (visual vs. audiovisual) and the semantic content of the visual stimuli affect performance…
Jiahao Zhao
Spaced repetition systems are fundamental to efficient learning and memory retention, but existing algorithms often struggle with semantic interference and personalized adaptation. We present LECTOR (LLM-Enhanced Concept-based Test-Oriented Repetition), a novel adaptive scheduling algorithm specifically designed for…
N. Menghi, S. Vigano’, W. J. Johnston, S. Elnagar + 2 more
Learning depends not only on the content of what we learn, but also on how we learn and on how experiences are structured over time. To investigate how task similarity and training regime interact during learning, we trained participants on spatial and conceptual learning tasks that shared either similar or distinct…
Steven C. Pan, Jia Yi Han, Fun Man Fung
Environmental Chemistry Course Authors: ['Steven C. Pan' 'Jia Yi Han' 'Fun Man Fung'] Prequestioning is an instructional strategy that involves taking practice tests on to-be-learned information followed by studying the correct answers. Despite promising results in laboratory studies, it has rarely been examined in…
Michelle L. Rivers
Practice testing is a highly robust learning strategy that promotes long-term retention, especially in comparison to more passive strategies such as restudying-a finding referred to as the testing effect. However, learners do not always appreciate the memorial benefits of practice testing over restudying, which could…
Mahir Akgun, Sacip Toker
Pretesting - attempting problems before instruction - supports learning by activating prior knowledge and sharpening attention to subsequent instruction. Recent work suggests that adaptive AI-assisted pretesting can yield further advantages, particularly for tasks requiring higher-order reasoning, yet it remains…
Li Zheng, Zachary Boogaart, Andrew McAvan, Joshua Garren + 7 more
Training cognitive skills, such as remembering a list of words or navigating a new city, has important implications for everyday life. Yet, understanding what brain changes underlie the acquisition of complex cognitive skills remains unresolved. Here, we developed and validated intensive multiweek interventions in…
Michael J. Lee, James J. DiCarlo
A core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images. One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains through two connected mechanisms: 1) the re-representation of incoming…
Phillip L. Ackerman
The main purpose of modern intelligence tests has been to predict individual differences in academic performance, first of children, then adolescents, and later extending to adults. From the earliest Binet-Simon scales to current times, most one-on-one omnibus intelligence assessments include both process subtests…
Paul Francoeur, Daniel Penaherrera, David Koes
The immense size of chemical space, the relative scarcity of high quality data, and the cost of running experiments to accurately measure molecular properties makes active learning (AL) an attractive approach to efficiently explore the space and train high-quality models for molecular property prediction. While AL is…
Dao Xuan-Quy, Le Ngoc-Bich, Vo The-Duy, Ngo Bac-Bien + 1 more
This study evaluates the potential and challenges of large langue models (LLMs) for education in chemistry. Specifically, we analyze the performance of two state -of-the art of LLMs, ChatGPT and Microsoft Bing AI Chat, on a quiz dataset consisting of 200 multiple-choice questions in chemistry at the high school level.…
Authors not listed
Large Language Models (LLMs) based on transformer architectures excel at internet-scale tasks. However, real-world scientific scenarios—such as synthetic chemistry laboratories and autonomous experimental setups—typically involve incremental data generation in batches as new chemical reactions are conducted, unlike…
Derek van Tilborg, Francesca Grisoni
Deep learning is accelerating drug discovery. However, current approaches are often affected by limitations in the available data, e.g., in terms of size or molecular diversity. Active deep learning has an untapped potential for low-data drug discovery, as it allows to improve a model iteratively during the screening…