.png){kind=icon}
返回
Decomposing dynamical subprocesses for compositional generalization
.png){kind=icon}
OA
AI
DOI：10.1073/pnas.2408134121.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A striking feature of human cognition is an exceptional ability to rapidly adapt to novel situations. It is proposed this relies on abstracting and generalizing past experiences. While previous research has explored how humans detect and generalize single sequential processes, we have a limited understanding of how humans adapt to more naturalistic scenarios, for example, complex, multisubprocess environments. Here, we propose a candidate computational mechanism that posits compositional generalization of knowledge about subprocess dynamics. In two samples (N = 238 and N = 137), we combined a novel sequence learning task and computational modeling to ask whether humans extract and generalize subprocesses compositionally to solve new problems. In prior learning, participants experienced sequences of compound images formed from two graphs' product spaces (group 1: G1 and G2, group 2: G3 and G4). In transfer learning, both groups encountered compound images from the product of G1 and G3, composed entirely of new images. We show that subprocess knowledge transferred between task phases, such that in a new task environment each group had enhanced accuracy in predicting subprocess dynamics they had experienced during prior learning. Computational models utilizing predictive representations, based solely on the temporal contiguity of experienced task states, without an ability to transfer knowledge, failed to explain these data. Instead, behavior was consistent with a predictive representation model that maps task states between prior and transfer learning. These results help advance a mechanistic understanding of how humans discover and abstract subprocesses composing their experiences and compositionally reuse prior knowledge as a scaffolding for new experiences.
Keyword:
structure learning
memory
generalization
abstraction
compositionality
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
P
IF:
9.1
论文数:
10.8W
被引数:
73.5W
机构
引用论文
Twisted gastrulation (Tsg) is regulated by Tob and enhances TGF-β signaling in activated T lymphocytes.png)
BloodOAAI
BloodIF0
OAAI分享收藏Biogeography and conservation of non-volant mammals from the Urucum Mountains: a Chiquitano dry forest ecoregion in western BrazilMammalia
MammaliaIF0
PREAI
分享收藏分享收藏Transferring structural knowledge across cognitive maps in humans and modelsNATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏分享收藏High content imaging-based assay to classify estrogen receptor-α ligands based on defined mechanistic outcomesGeneOAAI
GeneIF0
OAAI分享收藏
查看更多内容