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run or duck or attack before it is “too late”. The cost is that we have to accept a
fairly high rate of false positives—leading to people commonly seeing images of
figures in toast or rock formations, or sending in photographs of their “rude” veg-
etables to television shows. This also leads to people seeing potential attackers in
the dark and children seeing evil characters in wallpaper patterns at night. This is
relevant to science learning, as it start to explain some of the common ways that
people interpret phenomena that are at odds with scientific understanding.
Research is starting to explore where these effects are operating in the cognitive
system. Some aspects of pattern recognition may actually be genetically determined.
For example, humans appear to have evolved to be “hard-wired” to readily detect
face-like patterns—and sure enough we see faces readily: not only the “man in the
Moon”, but even on a crater on Mars. We are able to effectively communicate the
face with a simple emoticon, i.e. :-)
Other pattern recognition systems may develop in response to environmental
stimuli. For example, Andrea diSessa (1993) has undertaken work to explore how
what he terms
phenomenological primitives
channel student understanding and
explanations according to common patterns that are abstracted, and has used this
approach to offer explanations of many conceptions elicited from college students.
This work has mainly been carried out in physics, but certainly has potential to
inform learning in other sciences (García Franco & Taber, 2009) and probably
beyond. It suggests that research that is able to identify the types of patterns that
are readily abstracted from the perceptual field could help teachers design teaching
to use, rather than be thwarted by, such mechanisms (Taber, 2008).
Constructing Knowledge
When thinking about the learning of complex conceptual material, work such as
that of diSessa offers very useful insights, suggesting that processing elements in
the pre-conscious part of the cognitive system may be highly significant for how
learners come to understand science concepts (or those of other subject areas). In
particular, it implies that we need to recognise that our brains hold some “knowl-
edge” at intuitive levels: that is, that we make systematic discriminations based on
processing elements that are well “below” conscious awareness. DiSessa believes
that conceptual knowledge may be built upon networks of such intuitive knowledge
elements.
A model developed by Annette Karmiloff-Smith (1996) posits at least four dis-
crete levels of the cognitive system: one purely implicit level to which we have no
access through introspection, and three more explicit levels. The highest of these
levels allows us to access knowledge in verbal propositional forms: an elephant is a
mammal; transitions metals can demonstrate a range of oxidation states in their com-
pounds, and so forth. The intermediate levels do not reflect knowledge elements that
are themselves “in” verbal, propositional form. This again links to science education
