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        <identifier>oai:drops-oai.dagstuhl.de:1656</identifier>
        <datestamp>2024-03-06T11:07:57Z</datestamp>
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          <dc:title>Architectural and Representational Requirements for Seeing Processes, Proto-affordances and Affordances</dc:title>
          <dc:creator>Sloman, Aaron</dc:creator>
          <dc:subject>Vision</dc:subject>
          <dc:subject>affordances</dc:subject>
          <dc:subject>architectures</dc:subject>
          <dc:subject>development</dc:subject>
          <dc:subject>design space</dc:subject>
          <dc:description>This paper, combining the standpoints of philosophy and&#13;
Artificial Intelligence with theoretical psychology, summarises several&#13;
decades of investigation by the author of the variety of functions of vision&#13;
in humans and other animals, pointing out that biological evolution has&#13;
solved many more problems than are normally noticed. For example, the&#13;
biological functions of human and animal vision are closely related to the&#13;
ability of humans to do mathematics, including discovering and proving&#13;
theorems in geometry, topology and arithmetic. Many of the phenomena&#13;
discovered by psychologists and neuroscientists require sophisticated controlled&#13;
laboratory settings and specialised measuring equipment, whereas&#13;
the functions of vision reported here mostly require only careful attention&#13;
to a wide range of everyday competences that easily go unnoticed. Currently&#13;
available computer models and neural theories are very far from&#13;
explaining those functions, so progress in explaining how vision works&#13;
is more in need of new proposals for explanatory mechanisms than new&#13;
laboratory data. Systematically formulating the requirements for such&#13;
mechanisms is not easy. If we start by analysing familiar competences,&#13;
that can suggest new experiments to clarify precise forms of these competences,&#13;
how they develop within individuals, which other species have&#13;
them, and how performance varies according to conditions. This will&#13;
help to constrain requirements for models purporting to explain how the&#13;
competences work. For example, Gibson’s theory of affordances needs&#13;
a number of extensions, including allowing affordances to be composed&#13;
in several ways from lower level proto-affordances. The paper ends with&#13;
speculations regarding the need for new kinds of information-processing&#13;
machinery to account for the phenomena.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Aaron Sloman</dc:contributor>
          <dc:date>2008</dc:date>
          <dc:relation>Is Part Of Dagstuhl Seminar Proceedings, Volume 8091, Logic and Probability for Scene Interpretation (2008)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
          <dc:type>publishedVersion</dc:type>
          <dc:format>application/pdf</dc:format>
          <dc:identifier>doi:10.4230/DagSemProc.08091.4</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-16569</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08091.4</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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