Name
Abstract | ||
|---|---|---|
Attempting to model human categorization and similarity judgements is both a very interesting but also an exceedingly difficult challenge. Some of the diffi- culty 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 which potentially large subsets of stimuli are represented metrically with a small subset causing a global violation of metricity. |
Year | Venue | Keywords |
|---|---|---|
2006 | NIPS | mental representation |
Field | DocType | Citations |
Categorization,Appeal,Computer science,Judgement,Artificial intelligence,Euclidean geometry,Machine learning,Mental representation | Conference | 3 |
PageRank | References | Authors |
0.43 | 8 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julian Laub | 1 | 182 | 14.21 |
| jakob h macke | 2 | 24 | 3.08 |
| kr muller | 3 | 3 | 0.43 |
| F A Wichmann | 4 | 231 | 17.54 |
| scholkopf | 5 | 216 | 13.73 |
| John Platt | 6 | 6611 | 1100.14 |
| Thomas Hofmann | 7 | 10064 | 1001.83 |