Book contents
- Frontmatter
- Contents
- Contributors
- Introduction: Modelling perception with artificial neural networks
- Part I General themes
- Part II The use of artificial neural networks to elucidate the nature of perceptual processes in animals
- Part III Artificial neural networks as models of perceptual processing in ecology and evolutionary biology
- 9 Evolutionary diversification of mating behaviour: using artificial neural networks to study reproductive character displacement and speciation
- 10 Applying artificial neural networks to the study of prey colouration
- 11 Artificial neural networks in models of specialisation, guild evolution and sympatric speciation
- 12 Probabilistic design principles for robust multi-modal communication networks
- 13 Movement-based signalling and the physical world: modelling the changing perceptual task for receivers
- Part IV Methodological issues in the use of simple feedforward networks
- Index
- References
13 - Movement-based signalling and the physical world: modelling the changing perceptual task for receivers
from Part III - Artificial neural networks as models of perceptual processing in ecology and evolutionary biology
Published online by Cambridge University Press: 05 July 2011
- Frontmatter
- Contents
- Contributors
- Introduction: Modelling perception with artificial neural networks
- Part I General themes
- Part II The use of artificial neural networks to elucidate the nature of perceptual processes in animals
- Part III Artificial neural networks as models of perceptual processing in ecology and evolutionary biology
- 9 Evolutionary diversification of mating behaviour: using artificial neural networks to study reproductive character displacement and speciation
- 10 Applying artificial neural networks to the study of prey colouration
- 11 Artificial neural networks in models of specialisation, guild evolution and sympatric speciation
- 12 Probabilistic design principles for robust multi-modal communication networks
- 13 Movement-based signalling and the physical world: modelling the changing perceptual task for receivers
- Part IV Methodological issues in the use of simple feedforward networks
- Index
- References
Summary
13.1 Introduction
Consideration of the design and use of animal signals is of fundamental importance for our understanding of the social organisation and the perceptual and cognitive abilities of animals (e.g. Endler & Basolo, 1998). Movement-based visual signals have proven particularly difficult to understand because (in contrast to colour and auditory signals) perception, environmental conditions at the time of signalling and information content of motion signals cannot be easily modelled. Image motion has to be computed by the brain from the temporal and spatial correlations of photoreceptor signals. Although the computational structure of motion perception is well understood, in most situations it is still practically impossible to accurately quantify image motion signals under natural conditions from the animal's perspective. This undermines our ability to understand the perceptual constraints on movement-based signal design.
Extrapolating from other signalling systems, the diversity of movement-based signals between species is likely to be a function of the characteristics of competing, irrelevant sensory stimulation, or ‘noise’, and sensory system capabilities. The extent to which the spatiotemporal properties of signal and noise overlap remains unclear, however, and indeed, the motion characteristics that reliably lead to segmentation of the signal from noise are largely unresolved. It is therefore difficult to know the circumstances in which signal detection is compromised. In this chapter, I begin to generate the kind of data that will help explain movement-based signal evolution by modelling the changing perceptual task facing the Australian lizard Amphibolurus muricatus in detecting conspecific communicative displays.
- Type
- Chapter
- Information
- Modelling Perception with Artificial Neural Networks , pp. 269 - 292Publisher: Cambridge University PressPrint publication year: 2010
References
- 6
- Cited by