A hierarchical emotion regulated sensorimotor model: Case studies

Inspired by the hierarchical cognitive architecture and the perception-action model (PAM), we propose that the internal status acts as a kind of common-coding representation which affects, mediates and even regulates the sensorimotor behaviours. These regulation can be depicted in the Bayesian framework, that is why cognitive agents are able to generate behaviours with subtle differences according to their emotion or recognize the emotion by perception. A novel recurrent neural network called recurrent neural network with parametric bias units (RNNPB) runs in three modes, constructing a two-level emotion regulated learning model, was further applied to testify this theory in two different cases.

[1]  Maja J. Matarić,et al.  Reinforcement Learning and Animat Emotions , 1996 .

[2]  B. Gelder,et al.  The Body Action Coding System II: muscle activations during the perception and expression of emotion , 2014, Front. Behav. Neurosci..

[3]  S. Preston,et al.  A perception-action model for empathy , 2007 .

[4]  R. D. Walk,et al.  Emotion and dance in dynamic light displays , 1984 .

[5]  Jun Tani,et al.  Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB , 2004, Neural Networks.

[6]  William James,et al.  The consciousness of self. , 1890 .

[7]  K. Scherer,et al.  The Body Action and Posture Coding System (BAP): Development and Reliability , 2012 .

[8]  B. Gelder,et al.  The Body Action Coding System I: muscle activations during the perception and expression of emotion. , 2014 .

[9]  Beatrice de Gelder,et al.  The Body Action Coding System I: Muscle activations during the perception and expression of emotion , 2014, Social neuroscience.

[10]  Ian Wright,et al.  Reinforcement Learning and Animat Emotions , 1996 .

[11]  Jun Tani,et al.  Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[12]  D. Thistlethwaite.  A critical review of latent learning and related experiments. , 1951, Psychological bulletin.

[13]  R. Novianto,et al.  Flexible attention-based cognitive architecture for robots , 2014 .

[14]  N. Hadjikhani,et al.  Seeing Fearful Body Expressions Activates the Fusiform Cortex and Amygdala , 2003, Current Biology.

[15]  Angelo Cangelosi,et al.  Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives , 2014, Front. Behav. Neurosci..

[16]  Jun Tani,et al.  Generalization in Learning Multiple Temporal Patterns Using RNNPB , 2004, ICONIP.

[17]  M. Argyle.  Bodily communication, 2nd ed. , 1988 .

[18]  Jun Tani,et al.  Learning Semantic Combinatoriality from the Interaction between Linguistic and Behavioral Processes , 2005, Adapt. Behav..

[19]  A. Chaudhuri,et al.  The Many Faces of a Neutral Face: Head Tilt and Perception of Dominance and Emotion , 2003 .

[20]  Claire L. Roether,et al.  Critical features for the perception of emotion from gait. , 2009, Journal of vision.

[21]  Lola Cañamero,et al.  Are Discrete Emotions Useful in Human-Robot Interaction? Feedback from Motion Capture Analysis , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[22]  Michael Davis,et al.  The amygdala , 2000, Current Biology.

[23]  Stefan Wermter,et al.  Robot Trajectory Prediction and Recognition Based on a Computational Mirror Neurons Model , 2011, ICANN.

[24]  W. Prinz.  Perception and Action Planning , 1997 .

[25]  C. Darwin.  The Expression of the Emotions in Man and Animals , .

[26]  A. Greenwald,et al.  Sensory feedback mechanisms in performance control: with special reference to the ideo-motor mechanism. , 1970, Psychological review.

[27]  R. D. Walk,et al.  Perception of emotion from body posture , 1986 .

[28]  S. Preston,et al.  Empathy: Its ultimate and proximate bases. , 2001, The Behavioral and brain sciences.

[29]  Junpei Zhong,et al.  Artificial Neural Models for Feedback Pathways for Sensorimotor Integration , 2015 .

[30]  Lola Cañamero,et al.  From continuous affective space to continuous expression space: Non-verbal behaviour recognition and generation , 2014, 4th International Conference on Development and Learning and on Epigenetic Robotics.