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Inducing metric violations in human similarity judgements

: Laub, J.; MacKe, J.; Müller, K.-R.; Wichmann, F.A.

Schölkopf, B.:
Advances in Neural Information Processing Systems 19 : Proceedings of the 20th Conference on Advances in Neural Information Processing Systems (NIPS), which took place in Vancouver, British Columbia, Canada, on December 4 - 7, 2006
Cambridge, MA: MIT Press, 2007 (A Bradford book)
ISBN: 0-262-19568-2
ISBN: 978-0-262-19568-3
Annual Conference on Neural Information Processing Systems (NIPS) <20, 2006, Vancouver>
Conference Paper
Fraunhofer FIRST ()

Attempting to model human categorization and similarity judgements is both a very interesting but also an exceedingly difficult challenge. Some of the difficulty arises because of conflicting evidence whether human categorization and similarity judgements should or should not be modelled as to operate on a mental representation that is essentially metric. Intuitively, this has a strong appeal as it would allow (dis)similarity to be represented geometrically as distance in some internal space. Here we show how a single stimulus, carefully constructed in a psychophysical experiment, introduces l2 violations in what used to be an internal similarity space that could be adequately modelled as Euclidean. We term this one influential data point a conflictual judgement. We present an algorithm of how to analyse such data and how to identify the crucial point. Thus there may not be a strict dichotomy between either a metric or a non-metric internal space but rather degrees to w hich potentially large subsets of stimuli are represented metrically with a small subset causing a global violation of metricity.