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2004 > |
Course Description |
Situation
Models and Embodied Language Processes
Franz Schmalhofer
U.
Osnabrueck, Germany
This course will be focussed on mental
models or situation models and their role in memory and language understanding
processes. Situation models represent the state of affairs of some
circumscribed world situation in the human mind. Situations are assumed to be
encoded by perceptual symbols (Barsalou, 1999) which constitute indices to the
objects and structures of the external world.
They may be formed by direct perception as well as by language or text
understanding. Situation models form a central concept in theories of situated
cognition and may guide studies about the embodiment of mental representations
and processes.
After an introduction to the
traditional experimental paradigms on memory and language, the pivotal role of
situation models in human cognition will be discussed. A particular focus will
be on inference processes which contribute to build situation models from texts
and how computational theories, such as Kintsch`s (1998)
construction-integration theory account for different aspects of experimental
findings. It will be argued that the goal of using and modifying a unified
theory is very promising. Thereby, computational modelling techniques are
applied to combine the insights obtained from behavioural studies and the
results about neural correlates of cognitive processes. After an explanation of
event-related potentials and functional Magnetic Resonance Imaging, several
experiments will be presented and it will be discussed which difficulties arise
when a variety of different measures are to be integrated into a model. This course will thus provide
insights on how general frameworks of cognition, computational modelling,
behavioural and neuroscience experiments are related to each other in the realm
of memory and language processes.
Day 1: Introduction to situation models - PowerPoint Presentation
Reference:
Kintsch, W. (1998) Comprehension: A paradigm
for cognition. Cambridge University Press. Pp 1-92.
Additional readings:
Zwaan, R. A. & Radvansky, G.
A. (1998) Situation models in language comprehension and
memory. Psychological Bulletin, 123 (2) 162-185.
Day 2: Computational modelling of inference processes - PowerPoint Presentation
Reference:
Schmalhofer, F., McDaniel, M.A
& Keefe, D. (2002) A unified model of predictive and bridging
inferences. Discourse Processes. 33 (2), 105-132.
Additional readings:
Frank, S.L., Koppen, M., Noordman,
L.G., & Vonk, W. (2003) Modeling knowledge-based inferences in story
comprehension. Cognitive Science, 27, 875-910.
Graesser, A. C., Singer, M.,
Trabasso, T. (1994) Constructing inferences during narrative text
comprehension. Psychological Review, 101, 371-395.
Schmalhofer, F. (1998)
Constructive knowledge acquisition: A computational model and experimental
evaluation. Mahwah, N.J. Chapter 3: The levels approach toward cognitive
modelling. pp 49-68
Day 3: What memory and language are for - PowerPoint Presentation
Reference:
Additional readings:
Barsalou, L. W. (1999)
Perceptual Symbol Systems. Behavioral and Brain Sciences, 22, 577-660.
Glenberg, A.M. & Kaschak, M.
P. (2002) Grounding language in action. Psychonomic Bulletin
& Review, 9, 558-565.
Zwaan, R. A., Stanfield, R.A.
& Yaxley, R. H. (2002) Language comprehenders mentally represent the
shapes of objects. Psychological Science. 13(2), 168-171.
Day 4: Neural correlates of text comprehension - PowerPoint Presentation
Reference:
Ferstl, E. C. & von Cramon, D.
Y. (2001) The role of
coherence and cohesion in text comprehension: an event-related fMRI study.
Cognitive Brain Research, 11, 325-340.
Additional readings:
Perfetti, C. A. (1999)
Comprehending written language: A blueprint of the reader. In C. M. Brown &
P. Hagoort (Eds) The neurocognition of language processing (pp. 167-208) Oxford
University Press.
Gazzaniga, M.S., Ivry, R. B. &
Mangun, G. R. (2002) Cognitive Neuroscience: The biology of the
mind. New York: W.W. Norton &
Company, Chapter 4: The methods of cognitive neuroscience (in particular pp
129-147)
Schmalhofer, F., Raabe, M.,
Friese, u., Pietruska, K., & Rutschmann, R. (2004)
Evidence from an fMRI experiment for the minimal encoding and subsequent
substantiation of predictive inferences. 1-page abstract.
Day 5: The integration of behavioural experiments,
computational modelling and neural correlates - PowerPoint Presentation
Reference:
Anderson, J. R., Qin, Y., Sohn,
M-H, Stenger, V. A. & Carter, C. S. (2003) An
information-processing model of the BOLD response in symbol manipulation tasks.
Psychonomic Bulletin & Review.
10(2) 241-261.
Franz Schmalhofer
Franz Schmalhofer
studied psychology, mathematics and computer science at Regensburg
University/Germany and the University of Colorado at Boulder. He received a PhD
from the University of Colorado in 1982 with a thesis on "The
comprehension of a technical text as a function of expertise".From 1982 to
1989 he has held academic positions at the University of Heidelberg and the
University of Freiburg and was assistant professor at the Cognitive Science
Centre at McGill University/Montreal. From 1989 to 2000 he was a senior
scientist at the German Research Center for Artificial Intelligence (DFKI) in
Kaiserslautern and a lecturer in psychology at the University of Heidelberg. In
2000, he became professor of cognitive psychology at the University of
Osnabrueck. His current interests are in text comprehension, situation models,
neural correlates of inferencing, computational modeling and how people develop
shared situation models.