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Binding and Perspective Taking as Inference in a Generative Neural Network Model
[article]
2020
arXiv
pre-print
The ability to flexibly bind features into coherent wholes from different perspectives is a hallmark of cognition and intelligence. Importantly, the binding problem is not only relevant for vision but also for general intelligence, sensorimotor integration, event processing, and language. Various artificial neural network models have tackled this problem with dynamic neural fields and related approaches. Here we focus on a generative encoder-decoder architecture that adapts its perspective and
arXiv:2012.05152v1
fatcat:aue6snwgyzcfplwm4gcahywdum