Simione Luca, Nolfi Stefano
Institute of Cognitive Sciences and Technologies, CNR, Viale S. Martino della Battaglia 44, Rome, Italy,
Cogn Process. 2015 Sep;16 Suppl 1:393-7. doi: 10.1007/s10339-015-0679-8.
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.
我们环境中的物体每时每刻都会引发多种相互冲突的行为。因此，需要一种解决这种冲突的机制，以避免产生混乱无效的行为。一个可能扮演这种角色的候选者是选择性注意，它能够抑制当前情境中无关物体的神经表征，从而抑制这些物体所引发的行为。在本文中，我们研究了在依赖情境的行为学习过程中，选择性注意机制是否会自发出现，而大多数选择性注意和动作选择的神经计算模型都暗示了结构约束的存在。为此，我们训练了一个深度神经网络来学习依赖情境的视觉-动作关联。我们的主要结果是自发出现了一种抑制机制，该机制旨在通过直接抑制引发与当前情境不匹配动作的无关视觉刺激，来解决多种可行动作之间的冲突。这表明，这种抑制机制的出现是由于刺激与可行动作之间存在的与情境无关的概率规律所致。
