Selection-for-action emerges in neural networks trained to learn spatial associations between stimuli and actions.

The objects present in our environment evoke multiple conflicting actions at every moment. Thus, a mechanism that resolves this conflict is needed in order to avoid the production of chaotic ineffective behaviours. A plausible candidate for such role is the selective attention, capable of inhibiting the neural representations of the objects irrelevant in the ongoing context and as a consequence the actions they afford. In this paper, we investigated whether a selective attention mechanism emerges spontaneously during the learning of context-dependent behaviour, whereas most neurocomputational models of selective attention and action selection imply the presence of architectural constraints. To this aim, we trained a deep neural network to learn context-dependent visual-action associations. Our main result was the spontaneous emergence of an inhibitory mechanism aimed to solve conflicts between multiple afforded actions by directly suppressing the irrelevant visual stimuli eliciting the incorrect actions for the current context. This suggests that such an inhibitory mechanism emerged as a result of the incorporation of context-independent probabilistic regularities occurring between stimuli and afforded actions.