Children's scale errors are a natural consequence of learning to associate objects with actions: A computational model
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Number of pages
SourceDevelopmental Science, 22, 4, (2019), article e12777
Article / Letter to editor
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SW OZ DCC AI
SubjectCognitive artificial intelligence
Young children sometimes attempt an action on an object, which is inappropriate because of the object size - they make scale errors. Existing theories suggest that scale errors may result from immaturities in children's action planning system, which might be overpowered by increased complexity of object representations or developing teleofunctional bias. We used computational modelling to emulate children's learning to associate objects with actions and to select appropriate actions, given object shape and size. A computational Developmental Deep Model of Action and Naming (DDMAN) was built on the dual-route theory of action selection, in which actions on objects are selected via a direct (non-semantic or visual) route, or an indirect (semantic) route. As in case of children, DDMAN produced scale errors: the number of errors was high at the beginning of training and decreased linearly but did not disappear completely. Inspection of emerging object-action associations revealed that these were coarsely organized by shape, hence leading DDMAN to initially select actions based on shape rather than size. With experience, DDMAN gradually learned to use size in addition to shape when selecting actions. Overall, our simulations demonstrate that children's scale errors are a natural consequence of learning to associate objects with actions. This article is protected by copyright.
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