Perceptual categorisation, Bayesian inference and psychological similarity
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Date
26/06/2020Author
Poth, Nina Laura
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Abstract
At the heart of this thesis is the following question: why do we categorise two
objects (e.g., an apple and a banana) as instances of the same concept (e.g., the
concept fruit) despite their perceptual differences? This is the problem of perceptual
categorisation. One way of dealing with this problem is to appeal to the
notion of psychological similarity: the apple and the banana belong to the same
concept because they look similar. However, there is no scientific agreement on
what entity or mechanism the notion of psychological similarity refers to and how
this notion explains our ability to categorise both objects as fruit. A promising
alternative approach to the problem is Bayesian modelling, whereby perceptual
categorisation is analysed as a generalisation and concept-learning task: when
categorising the apple and the banana as fruit, we compute the conditional
probability that the banana is an instance of the concept fruit, given the background
knowledge that the apple is an instance of this concept.
This thesis argues for a combination of a Bayesian and a similarity-based approach
to perceptual categorisation. I argue that a Bayesian model of concept
learning by Tenenbaum and Griffiths (2001) can help us to comprehend a variety
of behaviours associated with perceptual categorisation. These were difficult
to understand in light of two previous competing theories of psychological
similarity—Shepard’s (1987) geometric and Tversky’s (1977) feature-matching
theories. One of the behaviours that the Bayesian model can help us comprehend
is the tendency to, for example, seek out mushrooms that look similar to edible
ones and avoid those that look different from edible ones. The Bayesian model
can help us understand why this tendency becomes stronger or weaker depending
on how similar or different the mushrooms are. Another of these behaviours is a
‘directionality effect’: we are sometimes more likely to judge Tel Aviv to be similar
to New York than vice versa. I argue that the Bayesian approach predicts,
systematises and summarises the data on both types of behaviours, whereby it
becomes a useful tool to understand perceptual categorisation as a unified phenomenon.
The second argument is that the advocated Bayesian approach implicitly relies
on a theory of psychological similarity when characterising the hypotheses in
the Bayesian inference of perceptual categories. The role of such a similaritybased
theory is to explain how a concept such as fruit should be represented
in a Bayesian model and how this concept’s representational content is active in
producing the subjective probabilities that are associated with hypotheses in a
Bayesian inference task.