Incidental learning across statistically-structured input in active tasks

主动任务中统计结构输入的附带学习

• 批准号: 1950054
• 负责人: Lori Holt
• 金额: $ 82.5万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1950054&HistoricalAwards=false
• 关键词: Incidental; learning; across; statistically; structured

The natural world is rich with patterns, and organisms learn these patterns through passive exposure. This presents a powerful and flexible means of learning about the world that does not involve explicit instruction that appears to play an important role in spoken language learning. However, not all patterns can be learned by passive exposure alone. This research project investigates how learning across patterns of experience proceeds when passive exposure is insufficient to drive learning and yet there is no explicit instruction. The prior work that this project builds on suggests that real-world statistical learning may capitalize on input regularities’ global temporal alignment with behaviorally-relevant actions and events to hasten learning. Learning across statistical regularities can be incidental, and not overtly driven by an intention to learn, while still taking place in the context of an active task that generates valuable predictions and rewarding outcomes. This perspective may be transformative in how we think about human learning of statistically-structured input in complex, naturalistic environments. Findings from this research will inform the design of learning interventions that capitalize on these learning principles to be useful for diverse communities of learners. The proposed research will advance a new research approach, empirical tests of mechanistic predictions, and complementary information from behavior, electrophysiology and functional magnetic resonance imaging to understand statistical learning under more natural circumstances involving interplay among active behavior, multimodal input, selective attention, and statistical input regularities. It pursues the twin hypotheses that (1) active engagement in a rich, environment can support statistical learning by virtue of loose temporal alignment of statistically-structured input with behaviorally-relevant actions, objects, and events and (2) that this incidental statistical learning drives the emergence of selective attention to behaviorally relevant regularities, creating a virtuous cycle that promotes later learning. The team will also conduct several outreach activities including collecting data from non-university samples via a “data-truck” and providing science of learning outreach to high school students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

然而，自然世界充满了模式，生物体通过被动接触来学习这些模式，这提供了一种强大而灵活的了解世界的方法，而无需涉及在口语学习中发挥重要作用的指令。并非所有模式都可以仅通过被动暴露来学习。这项研究预测了当被动暴露不足以驱动学习并且没有明确的指导时，跨经验模式的学习如何进行。统计学习可以利用输入规律的全局时间对齐促进学习的行为相关的行动和事件可能是偶然的，并且不是由学习意图明显驱动的，同时仍然发生在产生有价值的预测和奖励结果的活跃任务的背景下。这项研究的结果将改变我们对人类在复杂、自然的环境中学习技术结构输入的看法，该研究将为利用这些学习原则对不同学习者群体有用的学习干预措施的设计提供信息。推进新的研究方法、机械预测的实证检验以及来自行为、电生理学和功能磁共振成像的补充信息，以理解更自然情况下的统计学习，涉及主动行为、多模态输入、选择性注意和统计输入规律之间的相互作用。假设 (1) 积极参与丰富的环境可以通过分析结构输入与相关行为、对象和事件的松散时间对齐来支持统计学习行为，以及 (2) 这种偶然的统计学习推动了统计学习的出现选择性关注该团队还将开展多项推广活动，包括通过“数据卡车”从非大学样本中收集数据，并向高中生提供学习科学推广。通过使用基金会的智力价值和更广泛的影响审查标准进行评估，NSF 的法定使命被认为值得支持。

项目成果

A neural network model of the effect of prior experience with regularities on subsequent category learning

具有规律性的先验经验对后续类别学习影响的神经网络模型

• DOI: 10.1016/j.cognition.2021.104997
• 发表时间: 2022-05
• 期刊: Cognition
• 影响因子: 3.4
• 作者: Roark, Casey L.;Plaut, David C.;Holt, Lori L.
• 通讯作者: Holt, Lori L.

Long-term priors constrain category learning in the context of short-term statistical regularities

长期先验限制了短期统计规律背景下的类别学习

• DOI: 10.3758/s13423-022-02114-z
• 发表时间: 2022-01
• 期刊: Psychonomic Bulletin & Review
• 影响因子: 3.5
• 作者: Roark, Casey L.;Holt, Lori L.
• 通讯作者: Holt, Lori L.

Auditory category learning is robust across training regimes

听觉类别学习在整个培训体系中都很强大

• DOI: 10.1016/j.cognition.2023.105467
• 发表时间: 2023-08
• 期刊: Cognition
• 影响因子: 3.4
• 作者: Obasih, Chisom O.;Luthra, Sahil;Dick, Frederic;Holt, Lori L.
• 通讯作者: Holt, Lori L.

The representational glue for incidental category learning is alignment with task-relevant behavior.

附带类别学习的代表性粘合剂是与任务相关的行为保持一致。

• DOI: 10.1037/xlm0001078
• 发表时间: 2022-06
• 期刊: and Cognition
• 作者: Roark, Casey L.;Lehet, Matthew I.;Dick, Frederic;Holt, Lori L.
• 通讯作者: Holt, Lori L.

Incidental auditory category learning and visuomotor sequence learning do not compete for cognitive resources

附带听觉类别学习和视觉运动序列学习不竞争认知资源

• DOI: 10.3758/s13414-022-02616-x
• 发表时间: 2022-01
• 期刊: & Psychophysics
• 作者: Gabay, Yafit;Madlansacay, Michelle;Holt, Lori L.
• 通讯作者: Holt, Lori L.