Grounding Psychological Shape Space in Convolutional Neural Networks
- URL: http://arxiv.org/abs/2111.08409v1
- Date: Tue, 16 Nov 2021 12:21:07 GMT
- Title: Grounding Psychological Shape Space in Convolutional Neural Networks
- Authors: Lucas Bechberger and Kai-Uwe K\"uhnberger
- Abstract summary: We use convolutional neural networks to learn a generalizable mapping between perceptual inputs and a recently proposed psychological similarity space for the shape domain.
Our results indicate that a classification-based multi-task learning scenario yields the best results, but that its performance is relatively sensitive to the dimensionality of the similarity space.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Shape information is crucial for human perception and cognition, and should
therefore also play a role in cognitive AI systems. We employ the
interdisciplinary framework of conceptual spaces, which proposes a geometric
representation of conceptual knowledge through low-dimensional interpretable
similarity spaces. These similarity spaces are often based on psychological
dissimilarity ratings for a small set of stimuli, which are then transformed
into a spatial representation by a technique called multidimensional scaling.
Unfortunately, this approach is incapable of generalizing to novel stimuli. In
this paper, we use convolutional neural networks to learn a generalizable
mapping between perceptual inputs (pixels of grayscale line drawings) and a
recently proposed psychological similarity space for the shape domain. We
investigate different network architectures (classification network vs.
autoencoder) and different training regimes (transfer learning vs. multi-task
learning). Our results indicate that a classification-based multi-task learning
scenario yields the best results, but that its performance is relatively
sensitive to the dimensionality of the similarity space.
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