Bayesian action&perception: representing the world in the brain.

Area of Science:

• Cognitive Science
• Robotics
• Neuroscience

Background:

• Traditional perception theories overlook sensory data acquisition effort.
• Object identification via touch necessitates active exploration, with movement details influencing data quality.
• Human exploratory movements are variable and seemingly unpredictable.

Purpose of the Study:

• To introduce Bayesian Action&Perception, integrating sensory data with motor programs in an associative memory.
• To explore querying this memory for action selection in perception (action for perception).
• To investigate using the memory for evaluating object suitability (perception for action) and recalling affordances.

Main Methods:

• Utilizing a biomimetic tactile sensor and a Bayesian Exploration algorithm.
• Developing an associative memory linking sensory experiences with motor commands.
• Testing the system's ability to guide exploration and characterize objects.

Main Results:

• Previous studies showed Bayesian Exploration matching or exceeding human performance in texture identification.
• The proposed Bayesian Action&Perception framework enables querying memory for exploratory actions and object evaluation.
• The system demonstrates potential for understanding biological action-perception loops.

Conclusions:

• Bayesian Action&Perception provides a framework for active perception and action selection.
• The biomimetic mechatronic system offers a novel approach to studying the neuronal basis of perception and action.
• This research bridges computational models with biological systems for a deeper understanding of perception-action cycles.