A biologically based model for the integration of sensory–motor contingencies in rules and plans: A prefrontal cortex based extension of the Distributed Adaptive Control architecture

Duff, Armin; Sánchez-Fibla, Martí; Verschure, Paul F. M. J.

doi:10.1016/j.brainresbull.2010.11.008

Cited by 29 publications

(28 citation statements)

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“…Ashby, for example, in his concept of ultrastability (Ashby, 1960), formulated perhaps the first mechanistic account of open-ended learning, namely as the random exploration of a large space of sensorimotor loops with the aim of achieving homeostatic equilibrium (for use of this idea in more recent work also see Di Paolo, 2000, 2003, 2010; Harvey et al, 2005; Iizuka and Di Paolo, 2007, 2008; Di Paolo and Iizuka, 2008; Manicka and Di Paolo, 2009; Izquierdo et al, 2013). Parallels with reinforcement learning (Sutton and Barto, 2009) and related sensorimotor approaches (e.g., Duff et al, 2011; Maye and Engel, 2011, 2013) can be drawn as well. For instance, the exploration-exploitation trade-off characteristic of such approaches is related to the assimilation-accommodation dichotomy in equilibration; and the global equilibrium towards which these systems tend is one of maximum expected reward, in analogy with the state of maximum equilibration.…”

Section: Discussionmentioning

confidence: 99%

Learning to perceive in the sensorimotor approach: Piagetâ€™s theory of equilibration interpreted dynamically

Paolo

Barandiaran

Beaton

et al. 2014

Front. Hum. Neurosci.

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Learning to perceive is faced with a classical paradox: if understanding is required for perception, how can we learn to perceive something new, something we do not yet understand? According to the sensorimotor approach, perception involves mastery of regular sensorimotor co-variations that depend on the agent and the environment, also known as the “laws” of sensorimotor contingencies (SMCs). In this sense, perception involves enacting relevant sensorimotor skills in each situation. It is important for this proposal that such skills can be learned and refined with experience and yet up to this date, the sensorimotor approach has had no explicit theory of perceptual learning. The situation is made more complex if we acknowledge the open-ended nature of human learning. In this paper we propose Piaget’s theory of equilibration as a potential candidate to fulfill this role. This theory highlights the importance of intrinsic sensorimotor norms, in terms of the closure of sensorimotor schemes. It also explains how the equilibration of a sensorimotor organization faced with novelty or breakdowns proceeds by re-shaping pre-existing structures in coupling with dynamical regularities of the world. This way learning to perceive is guided by the equilibration of emerging forms of skillful coping with the world. We demonstrate the compatibility between Piaget’s theory and the sensorimotor approach by providing a dynamical formalization of equilibration to give an explicit micro-genetic account of sensorimotor learning and, by extension, of how we learn to perceive. This allows us to draw important lessons in the form of general principles for open-ended sensorimotor learning, including the need for an intrinsic normative evaluation by the agent itself. We also explore implications of our micro-genetic account at the personal level.

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Section: Discussionmentioning

confidence: 99%

Learning to perceive in the sensorimotor approach: Piagetâ€™s theory of equilibration interpreted dynamically

Paolo

Barandiaran

Beaton

et al. 2014

Front. Hum. Neurosci.

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“…The Distributed Adaptive Control (DAC) [9] presents a biologically inspired model, with a three-layered architecture. A reactive layer provides a pre-wired set of reflexive behaviors; an adaptive layer allows adaptive classification of sensory events; and a contextual layer uses long-term and short-term memory to support action sequences.…”

Section: Introductionmentioning

confidence: 99%

Interactive object classification using sensorimotor contingencies

Högman

Björkman

Kragić

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Understanding and representing objects and their function is a challenging task. Objects we manipulate in our daily activities can be described and categorized in various ways according to their properties or affordances, depending also on our perception of those. In this work, we are interested in representing the knowledge acquired through interaction with objects, describing these in terms of action-effect relations, i.e. sensorimotor contingencies, rather than static shape or appearance representations. We demonstrate how a robot learns sensorimotor contingencies through pushing using a probabilistic model. We show how functional categories can be discovered and how entropy-based action selection can improve object classification.

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“…Also related to our work, the Distributed Adaptive Control (DAC) [36] presents a biologically inspired model, with a three-layered architecture. A reactive layer provides a prewired set of reflexive behaviors; an adaptive layer allows adaptive classification of sensory events; and a contextual layer uses long-term and short-term memory to support action sequences.…”

Section: Related Workmentioning

confidence: 99%

A Sensorimotor Learning Framework for Object Categorization

Högman

Björkman

Maki

et al. 2016

IEEE Trans. Cogn. Dev. Syst.

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Abstract-This paper presents a framework that enables a robot to discover various object categories through interaction. The categories are described using action-effect relations, i.e. sensorimotor contingencies rather than more static shape or appearance representation. The framework provides a functionality to classify objects and the resulting categories, associating a class with a specific module. We demonstrate the performance of the framework by studying a pushing behavior in robots, encoding the sensorimotor contingencies and their predictability with Gaussian Processes. We show how entropy-based action selection can improve object classification and how functional categories emerge from the similarities of effects observed among the objects. We also show how a multidimensional action space can be realized by parameterizing pushing using both position and velocity.

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A biologically based model for the integration of sensory–motor contingencies in rules and plans: A prefrontal cortex based extension of the Distributed Adaptive Control architecture

Cited by 29 publications

References 100 publications

Learning to perceive in the sensorimotor approach: Piagetâ€™s theory of equilibration interpreted dynamically

Learning to perceive in the sensorimotor approach: Piagetâ€™s theory of equilibration interpreted dynamically

Interactive object classification using sensorimotor contingencies

A Sensorimotor Learning Framework for Object Categorization

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