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Bayesian action&perception: representing the world in the brain.

Loeb GE, Fishel JA - Front Neurosci (2014)

Bottom Line: Theories of perception seek to explain how sensory data are processed to identify previously experienced objects, but they usually do not consider the decisions and effort that goes into acquiring the sensory data.In previous studies, a simple robot equipped with a biomimetic tactile sensor and operated according to Bayesian Exploration performed in a manner similar to and actually better than humans on a texture identification task.The biomimetic design of this mechatronic system may provide insights into the neuronal basis of biological action and perception.

View Article: PubMed Central - PubMed

Affiliation: SynTouch LLC Los Angeles, CA, USA ; Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA.

ABSTRACT
Theories of perception seek to explain how sensory data are processed to identify previously experienced objects, but they usually do not consider the decisions and effort that goes into acquiring the sensory data. Identification of objects according to their tactile properties requires active exploratory movements. The sensory data thereby obtained depend on the details of those movements, which human subjects change rapidly and seemingly capriciously. Bayesian Exploration is an algorithm that uses prior experience to decide which next exploratory movement should provide the most useful data to disambiguate the most likely possibilities. In previous studies, a simple robot equipped with a biomimetic tactile sensor and operated according to Bayesian Exploration performed in a manner similar to and actually better than humans on a texture identification task. Expanding on this, "Bayesian Action&Perception" refers to the construction and querying of an associative memory of previously experienced entities containing both sensory data and the motor programs that elicited them. We hypothesize that this memory can be queried (i) to identify useful next exploratory movements during identification of an unknown entity ("action for perception") or (ii) to characterize whether an unknown entity is fit for purpose ("perception for action") or (iii) to recall what actions might be feasible for a known entity (Gibsonian affordance). The biomimetic design of this mechatronic system may provide insights into the neuronal basis of biological action and perception.

No MeSH data available.


Related in: MedlinePlus

Theory of computation for Bayesian Action&Perception. The associative memory in the various areas of cerebral cortex interprets incoming sensory data in the light of current hypotheses about the potential identity of objects (Perceptual) and selects an output that it expects will confirm that hypothesis by generating new sensory data (Proactive). Orderly development, use and refinement of this cortical database requires three major supporting functions that also require some form of learning: Value judgments are required to decide what level of certainty is acceptable for the identity and expected behavior of an unknown object, tentatively ascribed to the basal ganglia (Bornstein and Daw, 2011). If no acceptable identification is possible, then these unreconciled associations of motor strategies and sensory feedback that have been experienced with the unknown object must be remembered and eventually turned into a compressed, efficient representation of a new entity in cerebral cortex, tentatively ascribed to hippocampus (Winocur et al., 2010; Petrantonakis and Poirazi, 2014). The learned, abstract motor strategies need to be coordinated with lower level sensorimotor systems (e.g., spinal cord and brainstem) that can activate and stabilize complex body movements, tentatively ascribed to cerebellum (Thach, 2014).
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Figure 5: Theory of computation for Bayesian Action&Perception. The associative memory in the various areas of cerebral cortex interprets incoming sensory data in the light of current hypotheses about the potential identity of objects (Perceptual) and selects an output that it expects will confirm that hypothesis by generating new sensory data (Proactive). Orderly development, use and refinement of this cortical database requires three major supporting functions that also require some form of learning: Value judgments are required to decide what level of certainty is acceptable for the identity and expected behavior of an unknown object, tentatively ascribed to the basal ganglia (Bornstein and Daw, 2011). If no acceptable identification is possible, then these unreconciled associations of motor strategies and sensory feedback that have been experienced with the unknown object must be remembered and eventually turned into a compressed, efficient representation of a new entity in cerebral cortex, tentatively ascribed to hippocampus (Winocur et al., 2010; Petrantonakis and Poirazi, 2014). The learned, abstract motor strategies need to be coordinated with lower level sensorimotor systems (e.g., spinal cord and brainstem) that can activate and stabilize complex body movements, tentatively ascribed to cerebellum (Thach, 2014).

Mentions: The confidence level for our texture discrimination task was set arbitrarily to 99%, but an acceptable level would normally depend on motivation, e.g., the trade-off between the need for a speedy decision vs. the consequences of a mistaken identification (see example of throwing a stone above). If an object is sufficiently dissimilar to any previously experienced, the confidence threshold will never be exceeded. At some point, the organism must decide to create in its database a new entity consisting of the percepts (i.e., motor commands and sensory feedback) associated with exploring that entity. This requires some way to save the motor and sensory data associated with those unresolved explorations, as opposed to simply reinforcing the associative memory with the uninteresting observations of familiar objects. Valuation against prior experience and acquisition of new memories are functions that predate the evolution of cerebral cortex and that require global access to multimodal sensory and motor information. This suggests that these supportive functions are performed by subcortical structures; one such scheme is described in Figure 5 and its legend.


Bayesian action&perception: representing the world in the brain.

Loeb GE, Fishel JA - Front Neurosci (2014)

Theory of computation for Bayesian Action&Perception. The associative memory in the various areas of cerebral cortex interprets incoming sensory data in the light of current hypotheses about the potential identity of objects (Perceptual) and selects an output that it expects will confirm that hypothesis by generating new sensory data (Proactive). Orderly development, use and refinement of this cortical database requires three major supporting functions that also require some form of learning: Value judgments are required to decide what level of certainty is acceptable for the identity and expected behavior of an unknown object, tentatively ascribed to the basal ganglia (Bornstein and Daw, 2011). If no acceptable identification is possible, then these unreconciled associations of motor strategies and sensory feedback that have been experienced with the unknown object must be remembered and eventually turned into a compressed, efficient representation of a new entity in cerebral cortex, tentatively ascribed to hippocampus (Winocur et al., 2010; Petrantonakis and Poirazi, 2014). The learned, abstract motor strategies need to be coordinated with lower level sensorimotor systems (e.g., spinal cord and brainstem) that can activate and stabilize complex body movements, tentatively ascribed to cerebellum (Thach, 2014).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4214374&req=5

Figure 5: Theory of computation for Bayesian Action&Perception. The associative memory in the various areas of cerebral cortex interprets incoming sensory data in the light of current hypotheses about the potential identity of objects (Perceptual) and selects an output that it expects will confirm that hypothesis by generating new sensory data (Proactive). Orderly development, use and refinement of this cortical database requires three major supporting functions that also require some form of learning: Value judgments are required to decide what level of certainty is acceptable for the identity and expected behavior of an unknown object, tentatively ascribed to the basal ganglia (Bornstein and Daw, 2011). If no acceptable identification is possible, then these unreconciled associations of motor strategies and sensory feedback that have been experienced with the unknown object must be remembered and eventually turned into a compressed, efficient representation of a new entity in cerebral cortex, tentatively ascribed to hippocampus (Winocur et al., 2010; Petrantonakis and Poirazi, 2014). The learned, abstract motor strategies need to be coordinated with lower level sensorimotor systems (e.g., spinal cord and brainstem) that can activate and stabilize complex body movements, tentatively ascribed to cerebellum (Thach, 2014).
Mentions: The confidence level for our texture discrimination task was set arbitrarily to 99%, but an acceptable level would normally depend on motivation, e.g., the trade-off between the need for a speedy decision vs. the consequences of a mistaken identification (see example of throwing a stone above). If an object is sufficiently dissimilar to any previously experienced, the confidence threshold will never be exceeded. At some point, the organism must decide to create in its database a new entity consisting of the percepts (i.e., motor commands and sensory feedback) associated with exploring that entity. This requires some way to save the motor and sensory data associated with those unresolved explorations, as opposed to simply reinforcing the associative memory with the uninteresting observations of familiar objects. Valuation against prior experience and acquisition of new memories are functions that predate the evolution of cerebral cortex and that require global access to multimodal sensory and motor information. This suggests that these supportive functions are performed by subcortical structures; one such scheme is described in Figure 5 and its legend.

Bottom Line: Theories of perception seek to explain how sensory data are processed to identify previously experienced objects, but they usually do not consider the decisions and effort that goes into acquiring the sensory data.In previous studies, a simple robot equipped with a biomimetic tactile sensor and operated according to Bayesian Exploration performed in a manner similar to and actually better than humans on a texture identification task.The biomimetic design of this mechatronic system may provide insights into the neuronal basis of biological action and perception.

View Article: PubMed Central - PubMed

Affiliation: SynTouch LLC Los Angeles, CA, USA ; Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA.

ABSTRACT
Theories of perception seek to explain how sensory data are processed to identify previously experienced objects, but they usually do not consider the decisions and effort that goes into acquiring the sensory data. Identification of objects according to their tactile properties requires active exploratory movements. The sensory data thereby obtained depend on the details of those movements, which human subjects change rapidly and seemingly capriciously. Bayesian Exploration is an algorithm that uses prior experience to decide which next exploratory movement should provide the most useful data to disambiguate the most likely possibilities. In previous studies, a simple robot equipped with a biomimetic tactile sensor and operated according to Bayesian Exploration performed in a manner similar to and actually better than humans on a texture identification task. Expanding on this, "Bayesian Action&Perception" refers to the construction and querying of an associative memory of previously experienced entities containing both sensory data and the motor programs that elicited them. We hypothesize that this memory can be queried (i) to identify useful next exploratory movements during identification of an unknown entity ("action for perception") or (ii) to characterize whether an unknown entity is fit for purpose ("perception for action") or (iii) to recall what actions might be feasible for a known entity (Gibsonian affordance). The biomimetic design of this mechatronic system may provide insights into the neuronal basis of biological action and perception.

No MeSH data available.


Related in: MedlinePlus