A neural-network reinforcement-learning model of domestic chicks that learn to localize the centre of closed arenas

Previous experiments have shown that when domestic chicks (Gallus gallus) are first trained to locate food elements hidden at the centre of a closed square arena and then are tested in a square arena of double the size, they search for food both at its centre and at a distance from walls similar to the distance of the centre from the walls experienced during training. This paper presents a computational model that successfully reproduces these behaviours. The model is based on a neural-network implementation of the reinforcement-learning actor–critic architecture (in this architecture the ‘critic’ learns to evaluate perceived states in terms of predicted future rewards, while the ‘actor’ learns to increase the probability of selecting the actions that lead to higher evaluations). The analysis of the model suggests which type of information and cognitive mechanisms might underlie chicks' behaviours: (i) the tendency to explore the area at a specific distance from walls might be based on the processing of the height of walls' horizontal edges, (ii) the capacity to generalize the search at the centre of square arenas independently of their size might be based on the processing of the relative position of walls' vertical edges on the horizontal plane (equalization of walls' width), and (iii) the whole behaviour exhibited in the large square arena can be reproduced by assuming the existence of an attention process that, at each time, focuses chicks' internal processing on either one of the two previously discussed information sources. The model also produces testable predictions regarding the generalization capabilities that real chicks should exhibit if trained in circular arenas of varying size. The paper also highlights the potentialities of the model to address other experiments on animals' navigation and analyses its strengths and weaknesses in comparison to other models.