Get out of the corner: Inhibition and the effect of location type and number on perceptron and human reorientation

Abstract

Spatial learning and navigation have frequently been investigated using a reorientation task paradigm (Cheng, Cognition, 23(2), 149-78, 1986). However, implementing this task typically involves making tacit assumptions about the nature of spatial information. This has important theoretical consequences: Theories of reorientation typically focus on angles at corners as geometric cues and ignore information present at noncorner locations. We present a neural network model of reorientation that challenges these assumptions and use this model to generate predictions in a novel variant of the reorientation task. We test these predictions against human behavior in a virtual environment. Networks and humans alike exhibit reorien tation behavior even when goal locations are not present at corners. Our simulated and our experimental results suggest that angles are processed in a manner more similar to features, acting as a focal point for reorientation, and that the mechanisms governing reorientation behavior may be inhibitory rather than excitatory.

Key-finding by artificial neural networks that learn about key profiles.

“…Some of our previous research using perceptrons has demonstrated this paradigm. For instance, after using simulations and formal analyses to establish strong general links between perceptrons and associative learning (Dawson, 2008), and to then use these networks to model phenomena in a particular associative learning task, reorientation (Dawson et al, 2010), perceptrons generated interesting hypotheses concerning reorientation which were then explored and confirmed experimentally (Dupuis & Dawson, 2013b).…”