Dataset Information

Embodied cognition and STEM learning: overview of a topical collection in CR:PI.

ABSTRACT: Embodied learning approaches emphasize the use of action to support pedagogical goals. A specific version of embodied learning posits an action-to-abstraction transition supported by gesture, sketching, and analogical mapping. These tools seem to have special promise for bolstering learning in science, technology, engineering, and mathematics (STEM) disciplines, but existing efforts need further theoretical and empirical development. The topical collection in Cognitive Research: Principles includes articles aiming to formalize and test the effectiveness of embodied learning in STEM. The collection provides guideposts, staking out the terrain that should be surveyed before larger-scale efforts are undertaken. This introduction provides a broader context concerning mechanisms that can support embodied learning and make it especially well suited to the STEM disciplines.

SUBMITTER: Weisberg SM

PROVIDER: S-EPMC5596025 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Embodied cognition and STEM learning: overview of a topical collection in CR:PI.

Weisberg Steven M SM Newcombe Nora S NS

Cognitive research: principles and implications 20170912 1

Embodied learning approaches emphasize the use of action to support pedagogical goals. A specific version of embodied learning posits an action-to-abstraction transition supported by gesture, sketching, and analogical mapping. These tools seem to have special promise for bolstering learning in science, technology, engineering, and mathematics (STEM) disciplines, but existing efforts need further theoretical and empirical development. The topical collection in <i>Cognitive Research: Principles</i ...[more]

PMID: 28959709

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Project description:Scene understanding and decomposition is a crucial challenge for intelligent systems, whether it is for object manipulation, navigation, or any other task. Although current machine and deep learning approaches for object detection and classification obtain high accuracy, they typically do not leverage interaction with the world and are limited to a set of objects seen during training. Humans on the other hand learn to recognize and classify different objects by actively engaging with them on first encounter. Moreover, recent theories in neuroscience suggest that cortical columns in the neocortex play an important role in this process, by building predictive models about objects in their reference frame. In this article, we present an enactive embodied agent that implements such a generative model for object interaction. For each object category, our system instantiates a deep neural network, called Cortical Column Network (CCN), that represents the object in its own reference frame by learning a generative model that predicts the expected transform in pixel space, given an action. The model parameters are optimized through the active inference paradigm, i.e., the minimization of variational free energy. When provided with a visual observation, an ensemble of CCNs each vote on their belief of observing that specific object category, yielding a potential object classification. In case the likelihood on the selected category is too low, the object is detected as an unknown category, and the agent has the ability to instantiate a novel CCN for this category. We validate our system in an simulated environment, where it needs to learn to discern multiple objects from the YCB dataset. We show that classification accuracy improves as an embodied agent can gather more evidence, and that it is able to learn about novel, previously unseen objects. Finally, we show that an agent driven through active inference can choose their actions to reach a preferred observation.

Dataset Information

Embodied cognition and STEM learning: overview of a topical collection in CR:PI.

Publications

Embodied cognition and STEM learning: overview of a topical collection in CR:PI.

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