| Summary: | Living systems process sensory data to facilitate adaptive behavior. A given sensor can be stimulated as the result of internally driven activity, or by purely external (environmental) sources. It is clear that these inputs are processed differently—have you ever tried tickling yourself? Self-caused stimuli have been shown to be attenuated compared to externally caused stimuli. A classical explanation of this effect is that when the brain sends a signal that would result in motor activity, it uses a copy of that signal to predict the sensory consequences of the resulting motor activity. The predicted sensory input is then subtracted from the actual sensory input, resulting in attenuation of the stimuli. To critically evaluate the utility of this predictive approach for coping with self-caused stimuli, and investigate when non-predictive solutions may be viable, we implement a computational model of a simple embodied system with self-caused sensorimotor dynamics, and use a genetic algorithm to explore the solutions possible in this model. We find that in this simple system the solutions that emerge modify their behavior to shape or avoid self-caused sensory inputs, rather than predicting these self-caused inputs and filtering them out. In some cases, solutions take advantage of the presence of these self-caused inputs. The existence of these non-predictive solutions demonstrates that embodiment provides possibilities for coping with self-caused sensory interference without the need for an internal, predictive model.
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