Mind mapping through computational speech analysis
Mental mapping; Computational analysis; language; cognitive
The understanding of complex human behaviors such as language and its variations in different
conditions and contexts has been an important research aim for many decades. Naturalistic and
quantitative approaches to precisely measure language variations from the structural and semantic
points of view have recently emerged, allowing the measurement of variations manifested in free
speech that reflect atypical cognitive decline in pathological situations such as psychoses, or typical
cognitive development in healthy children during alphabetization, and even the processing of memories
in different states of consciousness, such as waking and dreaming. In this work we will start discussing 1)
the construction of tools for the analysis of speech structure inspired by the psychopathological
descriptions of mental illnesses, 2) their application to the differential diagnosis of psychosis and
dementias, and 3) the application of semantic tools to predict psychotic episodes. We will proceed by
widening this view away from pathology, so as to determine 4) how graph-theoretical measures of
language structure vary across healthy cognitive development, and 5) how they relate to indices of
academic achievement. Next we will investigate 6) how speech structure varies within a large sample of
healthy and psychotic subjects with large age and educational variation (N=200, ages 2-58, kindergarten
to PhD), to 7) evaluate the impact of years of education and 8) compare with the development of
literature across 5,000 years (N=448 texts; Syro-Mesopotamian (N=62), Egyptian (N=49), Hinduist (N=37), Persian (N=19), Judeo-Christian (N=76), Greek-Roman (N=134), Medieval (n=20), Modern (n=20)
and Contemporary (n=31)). We will conclude by applying tools of semantic similarity as a strategy to 9)
measure memory reverberation during dreams and their electrophysiological correlates in a sleep
transition experiment. The results indicate that the structural and semantic tools used in this work can
greatly improve the precision of naturalistic measurements of the complex human behaviors expressed
in speech.