Search · four archives
Search · four archives
23 papers · ranked by Valyu relevance
Sandra Chanraud, Thomas Michelet, Alexandre Zenon, Arnaud Boutin + 1 more
From a behavioral and neuronal perspective, observational and physical practice conditions have been theorized to be equivalent during motor task learning. However, some paradigms can challenge such a functional equivalence hypothesis. The perception of difficulties experienced by others may play a role in…
Ainsley Temudo, Owen Benzley, Bradley R. King, Genevieve Albouy
Everyday activities often require learning sequences that necessitate the involvement of both the declarative and the procedural memory domains. Previous research has shown that a learning structure that is common across tasks from different domains can improve learning and resistance to interference. However, it…
Chengzhang Yu, Zhenghua Lu, Chen Zheng, C Wang + 2 more
Large language models (LLMs) universally suffer from knowledge staleness and lack of interpretability due to their implicit knowledge storage paradigm, where information is distributed across network parameters in an entangled, nonaddressable manner. This fundamental limitation prevents targeted knowledge updates…
Li-Ann Leow, Jarrad Lum, Sara Johnson, Emily Corti + 1 more
Musicians demonstrate advantages in acquiring motor sequences, showing faster learning and better explicit sequence knowledge than non-musicians. However, it is unclear whether this advantage extends beyond acquisition to the consolidation phase, which is when newly learned skills stabilize and become resistant to…
Raphaëlle Malassis, Laura Moscado, Jérôme Sackur, Dezső Németh
Human adults can extract regularities through implicit learning, resulting in non-conscious knowledge, or through explicit learning, leading to conscious and reportable knowledge. Experiments aiming to disentangle implicit from explicit learning are limited by their heavy reliance on verbal instructions. This prevents…
Li-Ann Leow, Jarrad Lum, Sara Johnson, Emily Corti + 1 more
Musicians demonstrate advantages in acquiring motor sequences, showing faster learning and better explicit sequence knowledge than non-musicians. However, it is unclear whether this advantage extends beyond acquisition to the consolidation phase, which is when newly learned skills stabilize and become resistant to…
Tomoya Nakai, Tatsuya Daikoku, Yohei Oseki
Humans use various sequential signals, such as language, music, and mathematics, to convey complex information and facilitate communication. Previous research has identified two fundamental frameworks underlying human sequential signal processing: a structural framework, emphasizing rule-based hierarchical organization…
Emily Cordeiro, Daniela Herrera Chaves, Nima Talei, Iván Castro + 6 more
Statistical learning (SL) has been proposed to depend on the hippocampus, but traditional neuropsychological theories of long-term memory posit that the hippocampus is only necessary for explicit memory processes, not implicit memory processes. To reconcile these two accounts, we exposed 27 temporal lobe epilepsy (TLE)…
Keshu Wu, Chenchen Kuai, Zihao Li, Jiwan Jiang + 5 more
Retrieval-augmented generation (RAG) enhances large language models by grounding outputs in retrieved knowledge. However, existing RAG methods including graph- and hypergraph-based approaches treat retrieved evidence as an unordered set, implicitly assuming permutation invariance. This assumption is misaligned with…
Peiran Li
MeMo proposes language models with explicit multi-layer correlation matrix memories (CMMs), where memorization, retrieval, and forgetting are architectural operations. This paper asks how such memories can reduce the need for retraining when knowledge changes. For changes expressible as MeMo memory associations, the…
Sophie Thong, Joshua Hendrikse, Trevor T. -J. Chong, James P. Coxon
Title: Summary Motor and declarative memory systems have been traditionally considered distinct. However, a study by Mosha and Robertson (2016) reported striking evidence of “generalization” between motor and declarative learning. Specifically, learning improved if the current task (e.g., motor sequence) shared the…
Sophie Thong, Joshua Hendrikse, Trevor T. -J. Chong, James P. Coxon
Motor and declarative memory systems have been traditionally considered distinct. However, a study by 24 reported striking evidence of ‘generalisation’ between motor and declarative learning. Specifically, learning improved if the current task (e.g. motor sequence) shared the same high-level ordinal structure as an…
Authors not listed
Perovskite solar cell performance depends on the joint configuration of materials, interfaces, and layer-specific physical parameters, forming a structured design space that is naturally sequential but rarely modeled as such. This work introduces PervoTransformer, a transformer-based framework that represents complete…
Zikui Cai, Kaushal Janga, Tan Dat Dao, Seungjae Lee + 14 more
Embodied question answering (EQA) is traditionally evaluated under an episodic formulation, where agents solve each task independently and reset internal state between episodes. However, real-world robots operate continuously and must accumulate, retain, and selectively reuse information acquired from prior…
Xiaohui Shao, Weizheng Jiang, Khairul Nizam Osman
Ongoing debates in higher education regarding whether artificial intelligence should be further integrated or deliberately constrained call for empirical research that offers a more explanatory analytical framework. However, existing studies on the human-AI collaboration (HAC) paradox are largely grounded in a binary…
Xiuli Diao, Ruiqing Hu, Qingtian Zeng, Zhengguo Song + 1 more
Knowledge Tracing (KT) aims to dynamically model a student’s knowledge state to predict future learning performance. However, most existing approaches have two main limitations. On the one hand, they fail to capture the gradual evolution of knowledge over time, overlooking the stable nature of the learning process. As…
Authors not listed
Incorporating prior domain knowledge into Bayesian optimization (BO) remains difficult for statistical methods, which also typically suffer from limited interpretability. Large language models (LLMs) offer complementary strengths in reasoning and knowledge integration, but it remains unclear when and how they improve…
Changyuan Wang, Chubin Zhang, Zhenyu Wu, Runhao Li + 7 more
Embodied visuomotor models, including Diffusion Policy (DP) and Vision-Language-Action (VLA) models, have demonstrated promising performance on robotic manipulation benchmarks. However, their potential remains fundamentally constrained by the scarcity of large-scale embodied trajectory datasets, leading to insufficient…
Zonglin Han, Yichen Chen, Jiawen Jiang, Tongan Shi + 1 more
When a student must learn concepts connected by prerequisite dependencies, when does the order of instruction matter, and what does it cost to find the best one? We study instructional sequencing as a stochastic shortest-path problem in which attempting a concept succeeds with a state-dependent probability and failure…
Authors not listed
Terminally labeled DNA oligonucleotides have wide applications in modern biology and biotechnological applications. It has been observed that the fluorescent intensity of light released from these fluorescent labels is heavily influenced by the terminal sequence of nucleotides. Recent studies have assayed and published…
Yuxuan Wu, Guangming Wang, Zhiheng Yang, Maoqing Yao + 2 more
Developing general robot intelligence in open environments requires continual skill learning. Recent Vision-Language-Action (VLA) models leverage massive pretraining data to support diverse manipulation tasks, but they still depend heavily on task-specific fine-tuning, revealing a lack of continual learning capability.…
Andrea I. Costantino, Artem Platonov, Felipe Fontana Vieira, Emily Van Hove + 2 more
What transforms a novice into an expert? Decades of research show that expertise relies on domain-specific knowledge, but a neural account of this transformation has remained fragmentary: we lack an understanding of what information expert representations encode, how they are structured for efficient use, and where in…
Authors not listed
Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that…