Abstract
Mental rotation, a classic experimental paradigm of cognitive psychology, tests the capacity of humans to mentally rotate a seen object to decide if it matches a target object. In recent years, mental rotation has been investigated with brain imaging techniques to identify the brain areas involved. Mental rotation has also been investigated through the development of neural-network models, used to identify the specific mechanisms that underlie its process, and with neurorobotics models to investigate its embodied nature. Current models, however, have limited capacities to relate to neuro-scientific evidence, to generalise mental rotation to new objects, to suitably represent decision making mechanisms, and to allow the study of the effects of overt gestures on mental rotation. The work presented in this study overcomes these limitations by proposing a novel neurorobotic model that has a macro-architecture constrained by knowledge held on brain, encompasses a rather general mental rotation mechanism, and incorporates a biologically plausible decision making mechanism. The model was tested using the humanoid robot iCub in tasks requiring the robot to mentally rotate 2D geometrical images appearing on a computer screen. The results show that the robot gained an enhanced capacity to generalise mental rotation to new objects and to express the possible effects of overt movements of the wrist on mental rotation. The model also represents a further step in the identification of the embodied neural mechanisms that may underlie mental rotation in humans and might also give hints to enhance robots' planning capabilities.
DOI
10.1016/j.neunet.2015.09.010
Publication Date
2015-12-01
Publication Title
Neural Networks
Volume
72
Publisher
Elsevier BV
ISSN
0893-6080
Embargo Period
2024-11-22
First Page
31
Last Page
47
Recommended Citation
Seepanomwan, K., Caligiore, D., Cangelosi, A., & Baldassarre, G. (2015) 'Generalisation, decision making, and embodiment effects in mental rotation: A neurorobotic architecture tested with a humanoid robot', Neural Networks, 72, pp. 31-47. Elsevier BV: Available at: https://doi.org/10.1016/j.neunet.2015.09.010
