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Modeling habits as self-sustaining patterns of sensorimotor behavior.

Egbert MD, Barandiaran XE - Front Hum Neurosci (2014)

Bottom Line: In the recent history of psychology and cognitive neuroscience, the notion of habit has been reduced to a stimulus-triggered response probability correlation.We present various environments and the resulting habits that form in them.Finally, we discuss how this model and extensions of it can help us understand aspects of behavioral self-organization, historicity and autonomy that remain out of the scope of contemporary representationalist frameworks.

View Article: PubMed Central - PubMed

Affiliation: Embodied Emotion, Cognition and (Inter-)Action Lab, School of Computer Science, University of Hertfordshire Hatfield, UK.

ABSTRACT
In the recent history of psychology and cognitive neuroscience, the notion of habit has been reduced to a stimulus-triggered response probability correlation. In this paper we use a computational model to present an alternative theoretical view (with some philosophical implications), where habits are seen as self-maintaining patterns of behavior that share properties in common with self-maintaining biological processes, and that inhabit a complex ecological context, including the presence and influence of other habits. Far from mechanical automatisms, this organismic and self-organizing concept of habit can overcome the dominating atomistic and statistical conceptions, and the high temporal resolution effects of situatedness, embodiment and sensorimotor loops emerge as playing a more central, subtle and complex role in the organization of behavior. The model is based on a novel "iterant deformable sensorimotor medium (IDSM)," designed such that trajectories taken through sensorimotor-space increase the likelihood that in the future, similar trajectories will be taken. We couple the IDSM to sensors and motors of a simulated robot, and show that under certain conditions, the IDSM conditions, the IDSM forms self-maintaining patterns of activity that operate across the IDSM, the robot's body, and the environment. We present various environments and the resulting habits that form in them. The model acts as an abstraction of habits at a much needed sensorimotor "meso-scale" between microscopic neuron-based models and macroscopic descriptions of behavior. Finally, we discuss how this model and extensions of it can help us understand aspects of behavioral self-organization, historicity and autonomy that remain out of the scope of contemporary representationalist frameworks.

No MeSH data available.


Related in: MedlinePlus

Training and performance of an oscillatory behavior. The top plot shows the position of the robot, and the bottom three plots indicate SM-trajectories and the motor components of activated IDSM-nodes (arrows) for different time-periods in normalized SM-space. See main text for details.
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Figure 5: Training and performance of an oscillatory behavior. The top plot shows the position of the robot, and the bottom three plots indicate SM-trajectories and the motor components of activated IDSM-nodes (arrows) for different time-periods in normalized SM-space. See main text for details.

Mentions: We start with the robot located at x = −2.5. For the first 20 time-units of the simulation, the motor is not controlled by the IDSM, but is instead determined by the training controller, which sets the motor state according to the time-dependent equation m = cos(t/2)/2. This causes the robot to move back and forth, but remain on one side of the light. The physical position and sensorimotor trajectory during this training phase are plotted as dotted curves in Figure 5. As the robot moves through the training trajectory, the IDSM adds nodes to its record, describing the change in SM-state for experienced SM-states. The motor component of activated nodes are shown as gray arrows in the SM-plots of Figure 5, with only every 25th node plotted for clarity.


Modeling habits as self-sustaining patterns of sensorimotor behavior.

Egbert MD, Barandiaran XE - Front Hum Neurosci (2014)

Training and performance of an oscillatory behavior. The top plot shows the position of the robot, and the bottom three plots indicate SM-trajectories and the motor components of activated IDSM-nodes (arrows) for different time-periods in normalized SM-space. See main text for details.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4126554&req=5

Figure 5: Training and performance of an oscillatory behavior. The top plot shows the position of the robot, and the bottom three plots indicate SM-trajectories and the motor components of activated IDSM-nodes (arrows) for different time-periods in normalized SM-space. See main text for details.
Mentions: We start with the robot located at x = −2.5. For the first 20 time-units of the simulation, the motor is not controlled by the IDSM, but is instead determined by the training controller, which sets the motor state according to the time-dependent equation m = cos(t/2)/2. This causes the robot to move back and forth, but remain on one side of the light. The physical position and sensorimotor trajectory during this training phase are plotted as dotted curves in Figure 5. As the robot moves through the training trajectory, the IDSM adds nodes to its record, describing the change in SM-state for experienced SM-states. The motor component of activated nodes are shown as gray arrows in the SM-plots of Figure 5, with only every 25th node plotted for clarity.

Bottom Line: In the recent history of psychology and cognitive neuroscience, the notion of habit has been reduced to a stimulus-triggered response probability correlation.We present various environments and the resulting habits that form in them.Finally, we discuss how this model and extensions of it can help us understand aspects of behavioral self-organization, historicity and autonomy that remain out of the scope of contemporary representationalist frameworks.

View Article: PubMed Central - PubMed

Affiliation: Embodied Emotion, Cognition and (Inter-)Action Lab, School of Computer Science, University of Hertfordshire Hatfield, UK.

ABSTRACT
In the recent history of psychology and cognitive neuroscience, the notion of habit has been reduced to a stimulus-triggered response probability correlation. In this paper we use a computational model to present an alternative theoretical view (with some philosophical implications), where habits are seen as self-maintaining patterns of behavior that share properties in common with self-maintaining biological processes, and that inhabit a complex ecological context, including the presence and influence of other habits. Far from mechanical automatisms, this organismic and self-organizing concept of habit can overcome the dominating atomistic and statistical conceptions, and the high temporal resolution effects of situatedness, embodiment and sensorimotor loops emerge as playing a more central, subtle and complex role in the organization of behavior. The model is based on a novel "iterant deformable sensorimotor medium (IDSM)," designed such that trajectories taken through sensorimotor-space increase the likelihood that in the future, similar trajectories will be taken. We couple the IDSM to sensors and motors of a simulated robot, and show that under certain conditions, the IDSM conditions, the IDSM forms self-maintaining patterns of activity that operate across the IDSM, the robot's body, and the environment. We present various environments and the resulting habits that form in them. The model acts as an abstraction of habits at a much needed sensorimotor "meso-scale" between microscopic neuron-based models and macroscopic descriptions of behavior. Finally, we discuss how this model and extensions of it can help us understand aspects of behavioral self-organization, historicity and autonomy that remain out of the scope of contemporary representationalist frameworks.

No MeSH data available.


Related in: MedlinePlus