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Memories of control: One-shot episodic learning of item-specific stimulus-control associations.

ABSTRACT: The repeated pairing of a particular stimulus with a specific cognitive control process, such as task switching, can bind the two together in memory, resulting in the formation of stimulus-control associations. These bindings are thought to guide the context-sensitive application of cognitive control, but it is not presently known whether such stimulus-control associations are only acquired through slow, incremental learning or could also be mediated by episodic memories of a single experience, so-called one-shot learning. Here, we tested this episodic control-binding hypothesis by probing whether a single co-occurrence of a stimulus and the control process of task switching would lead to significant performance benefits (reduced task switch cost) when that stimulus later re-occurred under the same as opposed to different control demands. Across three experiments, we demonstrate that item-specific stimulus-control associations can be formed based on a single exposure, providing the first strong evidence for episodic memory guidance of cognitive control.

SUBMITTER: Whitehead PS

PROVIDER: S-EPMC7507789 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Memories of control: One-shot episodic learning of item-specific stimulus-control associations.

Whitehead Peter S PS Pfeuffer Christina U CU Egner Tobias T

Cognition 20200214

The repeated pairing of a particular stimulus with a specific cognitive control process, such as task switching, can bind the two together in memory, resulting in the formation of stimulus-control associations. These bindings are thought to guide the context-sensitive application of cognitive control, but it is not presently known whether such stimulus-control associations are only acquired through slow, incremental learning or could also be mediated by episodic memories of a single experience, ...[more]

PMID: 32065944

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Project description:ObjectivesTo characterize the complex interplay between multiple clinical conditions in a time-to-event analysis framework using data from multiple hospitals, we developed two novel one-shot distributed algorithms for competing risk models (ODACoR). By applying our algorithms to the EHR data from eight national children's hospitals, we quantified the impacts of a wide range of risk factors on the risk of post-acute sequelae of SARS-COV-2 (PASC) among children and adolescents.Materials and methodsOur ODACoR algorithms are effectively executed due to their devised simplicity and communication efficiency. We evaluated our algorithms via extensive simulation studies as applications to quantification of the impacts of risk factors for PASC among children and adolescents using data from eight children's hospitals including the Children's Hospital of Philadelphia, Cincinnati Children's Hospital Medical Center, Children's Hospital of Colorado covering over 6.5 million pediatric patients. The accuracy of the estimation was assessed by comparing the results from our ODACoR algorithms with the estimators derived from the meta-analysis and the pooled data.ResultsThe meta-analysis estimator showed a high relative bias (∼40%) when the clinical condition is relatively rare (∼0.5%), whereas ODACoR algorithms exhibited a substantially lower relative bias (∼0.2%). The estimated effects from our ODACoR algorithms were identical on par with the estimates from the pooled data, suggesting the high reliability of our federated learning algorithms. In contrast, the meta-analysis estimate failed to identify risk factors such as age, gender, chronic conditions history, and obesity, compared to the pooled data.DiscussionOur proposed ODACoR algorithms are communication-efficient, highly accurate, and suitable to characterize the complex interplay between multiple clinical conditions.ConclusionOur study demonstrates that our ODACoR algorithms are communication-efficient and can be widely applicable for analyzing multiple clinical conditions in a time-to-event analysis framework.

Dataset Information

Memories of control: One-shot episodic learning of item-specific stimulus-control associations.

Publications

Memories of control: One-shot episodic learning of item-specific stimulus-control associations.

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