In order for the information
contained by linguistic units (and, more
generally, psychological stimuli of all kinds) to influence cognition,
it is required that this information is processed by the mind.
This is to say, it is not sufficient to show that the stimuli contain a
certain amount of potentially relevant information and that in some
ways this affects processing, but it is also necessary to directly show
that this information is actually used by the cognitive system.
An intensely researched
topic in language processing is how different
corpus-derived measures of the frequency of linguistic constituents
(ranging from the basic morphemes up to larger discourse units)
influence their comprehension and production. An aspect of my research,
stemming from my
dissertation
work, has been to clarify the effects of these measures
on behavior. Starting from a characterization of the different
factors of word forms and word meanings that affect the recognition of
a word, I developed an information-theoretical account of morphological
complexity measures that influence word recognition
(Moscoso
del Prado, Kostić, & Baayen, 2004).
In further developments I have extended this work to show how
behaviorally relevant
measures of informativity can be fully derived from plain collections
of texts
(Moscoso
del Prado, 2007) and how it can be used to characterize larger
scale regularities of the morphological system
(Milin,
Filipović-Ɖurđević, & Moscoso del Prado, 2009).
A great
part of my recent work is concerned with developing a method to
determine the amount of information that has actually been processed by
the cognitive system. This has
resulted in a qualitatively different interpretation of reaction time
data than that traditionally used (
Moscoso
del
Prado, 2011; see also the highlight of this work that appeared
in
MIT Technology Review).
This new approach uses insights from physics to provide a fundamentally
new perspective on the study of cognition. This development has
unearthed important properties of the cognitive system and the
dynamical mechanisms that it employs for information processing. Among
other things, I find evidence for an adaptive system that
systematically adjusts online its rate of information processing to
adapt to the requirements of the information being processed.
Furthermore, a direct link can be established between measures of
informativity and actual behavioral mechanisms
(Moscoso
del Prado, submitted).
Integrating the measure of complexity of cognitive processing, with an
account of the complexity of words and texts is one my principal goals
in the immediate future. This development would enable the study of
linguistic, behavioral, and neurophysiological complexity, within the
same mathematical framework.