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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

The influence of a single node. This plot shows the combined influence of single node, located at Np = (0.5, 0.5) with Nv = (0, 0.1) in a hypothetical 2-motor, 0-sensor IDSM. The Nv is exactly vertical, so all horizontal motion is due to the attraction factor, and vertical motion is due to the velocity factor. See Equations (6–9) and main text for details.
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Figure 2: The influence of a single node. This plot shows the combined influence of single node, located at Np = (0.5, 0.5) with Nv = (0, 0.1) in a hypothetical 2-motor, 0-sensor IDSM. The Nv is exactly vertical, so all horizontal motion is due to the attraction factor, and vertical motion is due to the velocity factor. See Equations (6–9) and main text for details.

Mentions: The influence of a node upon the motors can be broken down into two factors: a “velocity” factor and an “attraction” factor. The velocity factor (Equation 6) is simply the motor components of the Nv vector. The attraction factor (Equation 7), is slightly more complicated. It is a “force” that draws the system toward the node. This tends to result in a motion in SM-space toward regions of SM-space that are familiar, i.e., for which there is a higher density of nodes. Figure 2 provides a visualization of the influence of a single, activated node, located at Np = (0.5, 0.5) with Nv = (0, 0.1) in a hypothetical 2-motor, 0-sensor IDSM. Because Nv is exactly vertical in this example, all horizontal motion is due to the “attractive force” of the node. The attraction influence draws the SM-state toward the node and the velocity influence pushes the SM-state away from the node. To prevent the attraction influence from interfering with the velocity influence, the component of the attraction influence that is parallel to the node's velocity vector is removed [as described by the Γ function used in Equations (7 and 10) and defined in Equation (8)].


Modeling habits as self-sustaining patterns of sensorimotor behavior.

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

The influence of a single node. This plot shows the combined influence of single node, located at Np = (0.5, 0.5) with Nv = (0, 0.1) in a hypothetical 2-motor, 0-sensor IDSM. The Nv is exactly vertical, so all horizontal motion is due to the attraction factor, and vertical motion is due to the velocity factor. See Equations (6–9) and main text for details.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: The influence of a single node. This plot shows the combined influence of single node, located at Np = (0.5, 0.5) with Nv = (0, 0.1) in a hypothetical 2-motor, 0-sensor IDSM. The Nv is exactly vertical, so all horizontal motion is due to the attraction factor, and vertical motion is due to the velocity factor. See Equations (6–9) and main text for details.
Mentions: The influence of a node upon the motors can be broken down into two factors: a “velocity” factor and an “attraction” factor. The velocity factor (Equation 6) is simply the motor components of the Nv vector. The attraction factor (Equation 7), is slightly more complicated. It is a “force” that draws the system toward the node. This tends to result in a motion in SM-space toward regions of SM-space that are familiar, i.e., for which there is a higher density of nodes. Figure 2 provides a visualization of the influence of a single, activated node, located at Np = (0.5, 0.5) with Nv = (0, 0.1) in a hypothetical 2-motor, 0-sensor IDSM. Because Nv is exactly vertical in this example, all horizontal motion is due to the “attractive force” of the node. The attraction influence draws the SM-state toward the node and the velocity influence pushes the SM-state away from the node. To prevent the attraction influence from interfering with the velocity influence, the component of the attraction influence that is parallel to the node's velocity vector is removed [as described by the Γ function used in Equations (7 and 10) and defined in Equation (8)].

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