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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

Spatial and sensorimotor trajectories of habits that have emerged from a randomly initialized IDSM. The spatial plots (Plot A) indicate the spatial trajectories taken by the agent during the last 25% of the trial indicated in the lower right corner. Plot (B) shows a PCA dimensional reduction projection of the sensorimotor trajectories for these same trajectories, with colors used to group those trials that have a similar spatial trajectory.
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Figure 8: Spatial and sensorimotor trajectories of habits that have emerged from a randomly initialized IDSM. The spatial plots (Plot A) indicate the spatial trajectories taken by the agent during the last 25% of the trial indicated in the lower right corner. Plot (B) shows a PCA dimensional reduction projection of the sensorimotor trajectories for these same trajectories, with colors used to group those trials that have a similar spatial trajectory.

Mentions: The experiment consists of a sequence of trials, where for each trial we observe the pattern of behavior that the robot falls into after having had its sensorimotor state and position randomized. Each trial starts with the robot being placed at a random location within the arena, with its motors set to random values selected from the flat distribution [−1, 1]. The IDSM then controls the motors of the robot for 100 time-units, and we record the sensorimotor and spatial trajectories. At the end of the experiment, we categorized the trials by hand, by comparing plots of the spatial trajectories taken during the last 25 time-units of the trial. This was accomplished by looking at the spatial trajectories plotted in Figure 8A and selecting by hand which trajectories seemed similar to each other. Five categories were identified, and colored red, green, blue, magenta and cyan. Figures 8B and 9 show the sensorimotor trajectories for the same trials as plotted in Figure 8A.


Modeling habits as self-sustaining patterns of sensorimotor behavior.

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

Spatial and sensorimotor trajectories of habits that have emerged from a randomly initialized IDSM. The spatial plots (Plot A) indicate the spatial trajectories taken by the agent during the last 25% of the trial indicated in the lower right corner. Plot (B) shows a PCA dimensional reduction projection of the sensorimotor trajectories for these same trajectories, with colors used to group those trials that have a similar spatial trajectory.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Spatial and sensorimotor trajectories of habits that have emerged from a randomly initialized IDSM. The spatial plots (Plot A) indicate the spatial trajectories taken by the agent during the last 25% of the trial indicated in the lower right corner. Plot (B) shows a PCA dimensional reduction projection of the sensorimotor trajectories for these same trajectories, with colors used to group those trials that have a similar spatial trajectory.
Mentions: The experiment consists of a sequence of trials, where for each trial we observe the pattern of behavior that the robot falls into after having had its sensorimotor state and position randomized. Each trial starts with the robot being placed at a random location within the arena, with its motors set to random values selected from the flat distribution [−1, 1]. The IDSM then controls the motors of the robot for 100 time-units, and we record the sensorimotor and spatial trajectories. At the end of the experiment, we categorized the trials by hand, by comparing plots of the spatial trajectories taken during the last 25 time-units of the trial. This was accomplished by looking at the spatial trajectories plotted in Figure 8A and selecting by hand which trajectories seemed similar to each other. Five categories were identified, and colored red, green, blue, magenta and cyan. Figures 8B and 9 show the sensorimotor trajectories for the same trials as plotted in Figure 8A.

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