A neural network model of causative actions
Identifieur interne : 000B52 ( Main/Merge ); précédent : 000B51; suivant : 000B53A neural network model of causative actions
Auteurs : Jeremy Lee-Hand ; Alistair KnottSource :
- Frontiers in Neurorobotics [ 1662-5218 ] ; 2015.
Abstract
A common idea in models of action representation is that actions are represented in terms of their perceptual effects (see e.g., Prinz,
Url:
DOI: 10.3389/fnbot.2015.00004
PubMed: 26175685
PubMed Central: 4485058
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A neural network model of causative actions</title>
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<front><div type="abstract" xml:lang="en"><p>A common idea in models of action representation is that actions are represented in terms of their perceptual effects (see e.g., Prinz, <xref rid="B48" ref-type="bibr">1997</xref>
; Hommel et al., <xref rid="B29" ref-type="bibr">2001</xref>
; Sahin et al., <xref rid="B54" ref-type="bibr">2007</xref>
; Umiltà et al., <xref rid="B67" ref-type="bibr">2008</xref>
; Hommel, <xref rid="B28" ref-type="bibr">2013</xref>
). In this paper we extend existing models of effect-based action representations to account for a novel distinction. Some actions bring about effects that are independent events in their own right: for instance, if John <italic>smashes</italic>
a cup, he brings about the event of <italic>the cup smashing</italic>
. Other actions do not bring about such effects. For instance, if John <italic>grabs</italic>
a cup, this action does not cause the cup to “do” anything: a grab action has well-defined perceptual effects, but these are not registered by the perceptual system that detects independent events involving external objects in the world. In our model, effect-based actions are implemented in several distinct neural circuits, which are organized into a hierarchy based on the complexity of their associated perceptual effects. The circuit at the top of this hierarchy is responsible for actions that bring about independently perceivable events. This circuit receives input from the perceptual module that recognizes arbitrary events taking place in the world, and learns movements that reliably cause such events. We assess our model against existing experimental observations about effect-based motor representations, and make some novel experimental predictions. We also consider the possibility that the “causative actions” circuit in our model can be identified with a motor pathway reported in other work, specializing in “functional” actions on manipulable tools (Bub et al., <xref rid="B11" ref-type="bibr">2008</xref>
; Binkofski and Buxbaum, <xref rid="B7" ref-type="bibr">2013</xref>
).</p>
</div>
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</TEI>
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