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Although real-world environments are often multisensory, visual scientists typically study visual learning in unisensory environments containing visual signals only. Here, we use deep or artificial neural networks to address the question, Can multisensory training aid visual learning? We examine a network's internal representations of objects based on visual signals in two conditions: (a) when the network is initially trained with both visual and haptic signals, and (b) when it is initially trained with visual signals only. Our results demonstrate that a network trained in a visual-haptic environment (in which visual, but not haptic, signals are orientation-dependent) tends to learn visual representations containing useful abstractions, such as the categorical structure of objects, and also learns representations that are less sensitive to imaging parameters, such as viewpoint or orientation, that are irrelevant for object recognition or classification tasks. We conclude that researchers studying perceptual learning in vision-only contexts may be overestimating the difficulties associated with important perceptual learning problems. Although multisensory perception has its own challenges, perceptual learning can become easier when it is considered in a multisensory setting.
This article was published in the following journal.
Name: Journal of vision
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Process in which individuals take the initiative, in diagnosing their learning needs, formulating learning goals, identifying resources for learning, choosing and implementing learning strategies and evaluating learning outcomes (Knowles, 1975)
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.
SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.
A controlled learning environment that closely represents reality.