ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2014
A continuous-time neural model for sequential action.
Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Statistical signature of subtle behavioral changes in large-scale assays.PLoS computational biology · 2025Article
- Why motor imagery is not really motoric: towards a re-conceptualization in terms of effect-based action control.Psychological research · 2024Review
- GOALIATH: a theory of goal-directed behavior.Psychological research · 2022Article
- Layer-specific activation in human primary somatosensory cortex during tactile temporal prediction error processing.NeuroImage · 2022Article
- Deep Predictive Learning in Neocortex and Pulvinar.Journal of cognitive neuroscience · 2021Article
- Theory of Event Coding (TEC) V2.0: Representing and controlling perception and action.Attention, perception & psychophysics · 2019Article
- Layer-specific activation of sensory input and predictive feedback in the human primary somatosensory cortex.Science advances · 2019Article
- Simon effects in action sequences.Experimental brain research · 2018Article
- Predictive Movements and Human Reinforcement Learning of Sequential Action.Cognitive science · 2018Article
- The theory of event coding (TEC) as embodied-cognition framework.Frontiers in psychology · 2015Article
- The principles of goal-directed decision-making: from neural mechanisms to computation and robotics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2014Article
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5 authors.
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Abstract
Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions.
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