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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 of phototactic and photophobic behaviors and the long term evolution of each of the trained behaviors. The square frames show the spatial trajectories taken by a robot trained with the behavior indicated to the left of the row, during the time indicated at the top of the column. Robots are relocated to a random position and assigned a random motor-state every 50 time-units. The light is fixed at the center of the arena. The bar chart shows the mean distance of the robot from the light for each behavior during each indicated time-period.
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Figure 7: Training of phototactic and photophobic behaviors and the long term evolution of each of the trained behaviors. The square frames show the spatial trajectories taken by a robot trained with the behavior indicated to the left of the row, during the time indicated at the top of the column. Robots are relocated to a random position and assigned a random motor-state every 50 time-units. The light is fixed at the center of the arena. The bar chart shows the mean distance of the robot from the light for each behavior during each indicated time-period.

Mentions: Figure 7, depicts the spatial trajectories of IDSM-controlled robots trained with the controllers described above. The square frames show the spatial trajectories of the robot during the time-period indicated at the top of the column, with the filled circles indicating the final position of the robot before a relocation took place. Plotted underneath these is a bar-chart indicating the mean distance of the robot from the light (located at the center of the arena). It is clear from evaluating the trajectories and the final location of the robots plotted in Figure 7 that the IDSM has been substantially influenced by the pattern it was exposed to during training. Both the two forms of phototaxis training result in robots that tends to approach the light and the photophobe training results in a robot that tends to avoid it. Moreover, the way that these behaviors are performed is similar in the way that it accomplishes the behavior; compare the sinusoidal approach engendered by the sinusoidal-phototactic training agent to the more direct approach to the light performed by the agent trained with the simple-phototaxis algorithm.


Modeling habits as self-sustaining patterns of sensorimotor behavior.

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

Training of phototactic and photophobic behaviors and the long term evolution of each of the trained behaviors. The square frames show the spatial trajectories taken by a robot trained with the behavior indicated to the left of the row, during the time indicated at the top of the column. Robots are relocated to a random position and assigned a random motor-state every 50 time-units. The light is fixed at the center of the arena. The bar chart shows the mean distance of the robot from the light for each behavior during each indicated time-period.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Training of phototactic and photophobic behaviors and the long term evolution of each of the trained behaviors. The square frames show the spatial trajectories taken by a robot trained with the behavior indicated to the left of the row, during the time indicated at the top of the column. Robots are relocated to a random position and assigned a random motor-state every 50 time-units. The light is fixed at the center of the arena. The bar chart shows the mean distance of the robot from the light for each behavior during each indicated time-period.
Mentions: Figure 7, depicts the spatial trajectories of IDSM-controlled robots trained with the controllers described above. The square frames show the spatial trajectories of the robot during the time-period indicated at the top of the column, with the filled circles indicating the final position of the robot before a relocation took place. Plotted underneath these is a bar-chart indicating the mean distance of the robot from the light (located at the center of the arena). It is clear from evaluating the trajectories and the final location of the robots plotted in Figure 7 that the IDSM has been substantially influenced by the pattern it was exposed to during training. Both the two forms of phototaxis training result in robots that tends to approach the light and the photophobe training results in a robot that tends to avoid it. Moreover, the way that these behaviors are performed is similar in the way that it accomplishes the behavior; compare the sinusoidal approach engendered by the sinusoidal-phototactic training agent to the more direct approach to the light performed by the agent trained with the simple-phototaxis algorithm.

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