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Towards a theory of stimulus control
Stockholm University, Faculty of Science, Department of Zoology.
2001 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

If an animal performs a behaviour in response to a `familiar' stimulus, it is often found that the behaviour is elicited also by somewhat different, novel stimuli. It is said that behaviour `generalises' from familiar to novel stimuli. Studies of generalisation (or `stimulus control') are a valuable source of empirical knowledge and a powerful tool to evaluate theories of behaviour (since different theories often make different predictions about generalisation). Recently, the importance of generalisation for the evolution of communication systems has also been acknowledged, and stimulus control is now widely researched in evolutionary biology as well.

Despite such extensive, and still growing empirical knowledge a comprehensive theory of generalisation is lacking. In this thesis, I first review empirical data in an attempt to establish a firm foundation for theoretical reasoning. Among the topics considered are the shape of generalisation gradients along different stimulus dimensions and response biases (peak-shift, supernormal stimulation). I then evaluate a number of theoretical models (both existing and novel ones), investigating their properties by computer simulations and formal mathematical analysis. One result is that models must take into account the information real nervous systems receive from the sense organs. Models that ignore the sense organs are found either unable to handle some phenomena or too vague to be tested concretely. Examples of concepts that are commonly used but often not precise enough are stimulus `elements' or `features', stimulus `similarity', `common elements of stimulation'. A further result is that, when sensory information is modelled realistically, very simple models can account for surprisingly many phenomena. These models have the form of simple artificial neural networks. Rather than being treated as `black-box' models, their computational principles are analysed and their abilities in realistic settings are determined. These models are judged superior to other popular models such as the Spence-Hull theory of gradient interaction and Shepard's theory of generalisation, because 1) they account for more data, 2) they are potentially biologically realistic, and 3) they follow the complete causal path from sensory information to behaviour.

Finally, I consider how stimulus control theory can be applied to the study of human facial attractiveness. A model about how we form judgments of attractiveness is proposed. According to the model, facial beauty emerges from an interaction among memories of faces, in a way fully compatible with general knowledge about stimulus control. Many major findings about attractiveness are reproduced by the model, including preferences for certain symmetries, for moderate exaggerations of sexually dimorphic traits, and for average values of non-dimorphic traits. The model is also linked with a model of the ontogeny of sexual preferences. The resulting theory is potentially able to account for both uniformity and diversity of preferences within and between human populations. The theory provides a challenge to claims that sexual preferences arise from face-specific genetic adaptations evolved to detect quality cues in potential partners. Keywords: ethology, experimental psychology, learning, stimulus control, peak-shift, receiver bias.

Place, publisher, year, edition, pages
Stockholm: Department of Zoology, Stockholm University , 2001. , 114 p.
Keyword [sv]
Etologi
National Category
Zoology
Identifiers
URN: urn:nbn:se:su:diva-141704ISBN: 91-87272-87-3 (print)OAI: oai:DiVA.org:su-141704DiVA: diva2:1089382
Public defence
2001-08-31, 13:00 (English)
Opponent
Note

S. i-xxx: sammanfattning, s.1-114: 5 uppsatser

Stockholms universitetsbiblioteks retrospektiva digitalisering. Avhandlingar 1906-2003.

Available from: 2017-04-19 Created: 2017-04-19 Last updated: 2017-07-10Bibliographically approved

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