Abstract
Humans reliably attribute social interpretations and agency to highly impoverished stimuli, such as interacting geometrical shapes. While it has been proposed that this capability is based on high-level cognitive processes, such as probabilistic reasoning, we demonstrate that it might be accounted for also by rather simple physiologically plausible neural mechanisms. Our model is a hierarchical neural network architecture with two pathways that analyze form and motion features. The highest hierarchy level contains neurons that have learned combinations of relative position-, motion-, and body-axis features. The model reproduces psychophysical results on the dependence of perceived animacy on motion smoothness and the orientation of the body axis. In addition, the model correctly classifies six categories of social interactions that have been frequently tested in the psychophysical literature. For the generation of training data we propose a novel algorithm that is derived from dynamic human navigation models, and which allows to generate arbitrary numbers of abstract social interaction stimuli by self-organization.
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Notes
- 1.
A simple estimate of the encoded angle is given by \(\hat{\theta } = \arg \left( (\sum _m \exp (i\theta _m)\right. \)\(\left. u_{\theta }(\theta _m, t)) / (\sum _m u_{\theta }(\theta _m, t))\right) \), where the \(\theta _m\) are the preferred directions of the neurons.
- 2.
Here the estimator is \(\hat{v} = \arg \left( (\sum _m v_m u_{v}(v_m, t)) / (\sum _m u_{v}(v_m, t))\right) \), where the \(v_m\) are the preferred speeds of the neurons.
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Acknowledgments
This work was supported by: HFSP RGP0036/2016; the European Commission HBP FP7-ICT2013-FET-F/604102 and COGIMON H2020-644727, the DFG KA 1258/15-1, and BMBF CRNC FK: 01CQ1704.
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Hovaidi-Ardestani, M., Saini, N., Martinez, A.M., Giese, M.A. (2018). Neural Model for the Visual Recognition of Animacy and Social Interaction. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_17
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