The Universal law of generalisation: evolution drives learning

    Research output: Contribution to conferenceAbstract


    When an organism's behaviour produces a reward in one stimulus situation (called S+), it typically exhibits that behaviour in similar but recognisably different situations, the ubiquitous phenomenon of stimulus generalisation. Shepard (1987, Science, 237, 1317-1323) proposed a universal law of generalisation based on functional considerations. The problem for the learner is to decide whether a particular stimulus situation has the same consequences of interest (reward-producing) as S+. The animal has learned that S+ is in 'the consequential region'. The problem is: what is the probability that another stimulus, X, is also in the consequential region? Given a range of assumptions about the consequential region, this probability is an exponential function of the appropriately scaled 'distance' x between X and S+: y - e-kx, a curve concave upward in shape. Data from a number of species in a number of experimental paradigms fit this functional prediction. Especially important are recent data from spatial generalisation in honeybees, the only evidence in an inverterbrate animal. Shepard's law suggests that the probabilistic structure of the world has driven the evolution of learning in diverse animals.
    Original languageEnglish
    Number of pages1
    Publication statusPublished - 2000
    EventAustralasian Society for the Study of Animal Behaviour Conference (27th : 2000) - Sydney
    Duration: 27 Apr 200030 Apr 2000


    ConferenceAustralasian Society for the Study of Animal Behaviour Conference (27th : 2000)


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