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on a human recognition of a “significant” pattern is somewhat unfortunate, as we
will bring our own biases to the process. At worst we only pay attention to those
patterns that confirm our current prejudices: what Wyszomirski et al. (1999) call
the WYWIWYG—What You Want is What You Get—fallacy. At best, we will only
recognise those patterns that match the wiring of the human visual system and
our cultural experiences. The existence of visualisation techniques generally points
up the fact that humans are better at perceiving some patterns than others, and in
some media than others—it is easier to see an event as a movie and not a binary
representation of the movie file displayed as text. However, in addition to standard
physiological and psychological restrictions on pattern recognition consistent to all
people, it is also increasing apparent there are cultural differences in perceptions.
Whether there is some underlying biological norm for the perception of time and
space is still moot (Nisbett and Masuda 2003; Boroditsky 2001), but it is clear that
some elements of pattern recognition vary by either culture or genetics (Nisbett
and Masuda 2003; Chua et al. 2005). Even when one looks at the representation of
patterns and elements like colour, there are clear arguments for a social influence
on the interpretation of even very basic stimuli into perceptions (Roberson et al.
2004). Indeed, while there is a clear and early ability of humans to perceive moving
objects in a scene as associated in a pattern (e.g. Baird et al. 2002), there are
cultural traits associated with the age at which even relatively universal patterns are
appreciated (Clement et al. 1970). The more we can objectify the process, therefore,
the less our biases will impinge on our understanding. In many respects it is easier
to remove human agents from data comparison and knowledge development than
pattern hunting, as patterns are not something machines deal with easily. The
unsupervised recognition of even static patterns repeated in different contexts is far
from computationally solved (Bouvrie and Sinha 2007), though significant advances
have been made in recent years (Druzhkov and Kustikova 2016). Most pattern-
hunting algorithms try to replicate the process found in humans, and in that sense
one suspects we would do better to skip the pattern hunting and concentrate on data
consistency and the comparison of full datasets directly. At best we might say that
an automated “pattern” hunter that wasn’t trying to reproduce the human ability
would instead seek to identify attractors within the data.
Figure 10.1 presents several visualisation methods that are commonly found in
the literature, ranging from 1D time-series representation (a) to contour plots (d)
that could be potentially used for 4D representation.
Visualisations are plainly extremely useful. Here we’ll look at a couple of
techniques that are of use in deciphering individual-level data: phase maps and
recurrence plots. Both techniques focus on the representation of individual-level
states and the relationships between stated individuals.