Neuromodelling based on evolutionary robotics: on the importance of motor control for spatial attention

Abstract

Mainstream approaches to modelling cognitive processes have typically focused on (1) reproducing their neural underpinning, without regard to sensory-motor systems and (2) producing a single, ideal computational model. Evolutionary robotics is an alternative possibility to bridge the gap between neural substrate and behavior by means of a sensory-motor apparatus, and a powerful tool to build a population of individuals rather than a single model. We trained 4 populations of neurorobots, equipped with a pan/tilt/zoom camera, and provided with different types of motor control in order to perform a cancellation task, often used to tap spatial cognition. Neurorobots' eye movements were controlled by (a) position, (b) velocity, (c) simulated muscles and (d) simulated muscles with fixed level of zoom. Neurorobots provided with muscle and velocity control showed better performances than those controlled in position. This is an interesting result since muscle control can be considered a particular type of position control. Finally, neurorobots provided with muscle control and zoom outperformed those without zooming ability.

Pseudoneglect in Visual Search: Behavioral Evidence and Connectional Constraints in Simulated Neural Circuitry

“…Nonetheless, the present study shares with Di Ferdinando et al (2005) and other work from the Zorzi group ( Casarotti et al, 2012 ) the stress on accounts of attentional phenomena relying on sensory-motor transformations, as stated by the premotor theory of attention ( Rizzolatti et al, 1987 ). Specifically, our results support the hypothesis that the way in which the movements of the actuators are controlled affects the performance on a cancellation task ( Gigliotta et al, 2015 ).…”

“…While this article was under review, two theoretical papers were published that also took into account the dorsal/ventral architecture of the attentional networks ( Parr and Friston, 2017 ; Seidel Malkinson and Bartolomeo, 2017 ), but neither endeavored to simulate pseudoneglect. Another original feature of the present models is the embodiment factor, consisting of the explicit modeling of eye movements ( Bartolomeo et al, 2002 ; Lanyon and Denham, 2004 ; Di Ferdinando et al, 2007 ; Miglino et al, 2009 ; Gigliotta et al, 2015 ). In particular, the present models extended the models devised by Di Ferdinando et al (2007) , by increasing the complexity of the organisms’ retina, the biological plausibility of the motor system and that of the neural controllers.…”

Pseudoneglect in visual search: Behavioral evidence and connectional constraints in simulated neural circuitry

“…Di (2005) and other work from the Zorzi group (Casarotti, Lisi, Umiltà, & Zorzi, 2012) the stress on accounts of attentional phenomena relying on sensory-motor transformations, as stated by the premotor theory of attention (Rizzolatti, Riggio, Dascola, & Umilta, 1987). Specifically, our results support the hypothesis that the way in which the movements of the actuators are controlled affects the performance on a cancellation task (Gigliotta, Bartolomeo, & Miglino, 2015).…”

“…To the best of our knowledge, this is the first attempt to simulate the dorsal and ventral attention networks in the two hemispheres of the human brain. Another original feature of the present models is the embodiment factor, consisting of the explicit modeling of eye and hand movements (see also Bartolomeo, Pagliarini, & Parisi, 2002;Di Ferdinando et al, 2007;Gigliotta et al, 2015;Lanyon & Denham, 2004;Miglino, Ponticorvo, & Bartolomeo, 2009). In particular, the present models extended the models devised by Di Ferdinando et al (2007), by increasing the complexity of the organisms' retina, the biological plausibility of the motor system and that of the neural controllers.…”

Creating a Truly Conscious AI – A Novel Approach

“…Another field designed to achieve this ambition is evolutionary robotics (ER), a methodology which uses evolutionary computation (algorithms designed for global optimization that are inspired by biological evolution) to develop controllers and/or hardware for truly autonomous robots. Artificial neural networks (which are discussed later in the article) and other forms of reinforcement learning have been used to some success in the context of ER [7][8][9] .…”