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--- abstract: "We evaluate the ability of artificial neural network models (multi-layer perceptrons) to predict stimulus-�response relationships. A variety of empirical results are considered, such as generalization, peak-shift (supernormality) and stimulus intensity effects. The networks were trained on the same tasks as the animals in the considered experiments. The subsequent generalization tests on the networks showed that the model replicates correctly the empirical results. It is concluded that these models are valuable tools in the study of animal behaviour.\n" altloc: [] chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: ~ confloc: ~ contact_email: ~ creators_id: [] creators_name: - family: Ghirlanda given: Stefano honourific: '' lineage: '' - family: Enquist given: Magnus honourific: '' lineage: '' date: 1998 date_type: published datestamp: 2006-12-03 department: ~ dir: disk0/00/00/52/71 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 5271 fileinfo: /style/images/fileicons/application_pdf.png;/5271/1/ghirlanda_enquist1998.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: 'neural networks, animal behavior, generalization' lastmod: 2011-03-11 08:56:43 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: 1383-1389 pubdom: FALSE publication: Animal Behaviour publisher: ~ refereed: TRUE referencetext: ~ relation_type: [] relation_uri: [] reportno: ~ rev_number: 12 series: ~ source: ~ status_changed: 2007-09-12 17:08:34 subjects: - bio-etho - comp-neuro-sci - comp-sci-neural-nets - bio-ani-behav succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: Artificial neural networks as models of stimulus control type: journalp userid: 6801 volume: 56