Predicting the practice effects on the blood oxygenation level-dependent (BOLD) function of fMRI in a symbolic manipulation task

Based on adaptive control of thought-rational (ACT-R), a cognitive architecture for cognitive modeling, researchers have developed an information-processing model to predict the blood oxygenation level-dependent (BOLD) response of functional MRI in symbol manipulation tasks. As an extension of this research, the current event-related functional MRI study investigates the effect of relatively extensive practice on the activation patterns of related brain regions. The task involved performing transformations on equations in an artificial algebra system. This paper shows that the base-level activation learning in the ACT-R theory can predict the change of the BOLD response in practice in a left prefrontal region reflecting retrieval of information. In contrast, practice has relatively little effect on the form of BOLD response in the parietal region reflecting imagined transformations to the equation or the motor region reflecting manual programming.

[1]  Erik D. Reichle,et al.  The Neural Bases of Strategy and Skill in Sentence–Picture Verification , 2000, Cognitive Psychology.

[2]  John R. Anderson,et al.  An information-processing model of the BOLD response in symbol manipulation tasks , 2003, Psychonomic bulletin & review.

[3]  M. Farah,et al.  Role of left inferior prefrontal cortex in retrieval of semantic knowledge: a reevaluation. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Mark S. Cohen,et al.  Parametric Analysis of fMRI Data Using Linear Systems Methods , 1997, NeuroImage.

[5]  T. Rickard.  Bending the power law : A CMPL theory of strategy shifts and the automatization of cognitive skills , 1997 .

[6]  M. Metz-Lutz.  Handbook of neuropsychology, Vol 6 et 7, Rapin I, Segalowitz SJ (Eds.). Elsevier (1992) , 1993 .

[7]  John R. Anderson,et al.  Working Memory: Activation Limitations on Retrieval , 1996, Cognitive Psychology.

[8]  W. K. Estes,et al.  Journals, Journals, Journals , 1990 .

[9]  T. SHALLICE,et al.  Learning and Memory , 1970, Nature.

[10]  John R. Anderson,et al.  How people learn to skip steps. , 1996 .

[11]  Jordan Grafman,et al.  Handbook of Neuropsychology , 1991 .

[12]  Scott D. Brown,et al.  The power law repealed: The case for an exponential law of practice , 2000, Psychonomic bulletin & review.

[13]  D E Kieras,et al.  A computational theory of executive cognitive processes and multiple-task performance: Part 1. Basic mechanisms. , 1997, Psychological review.

[14]  S. Petersen,et al.  The effects of practice on the functional anatomy of task performance. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[15]  S. Petersen,et al.  Practice-related changes in human brain functional anatomy during nonmotor learning. , 1994, Cerebral cortex.

[16]  P. Andersen,et al.  [Learning and memory]. , 1995, Tidsskrift for den Norske laegeforening : tidsskrift for praktisk medicin, ny raekke.

[17]  C. Lebiere,et al.  The Atomic Components of Thought , 1998 .

[18]  A. Dale,et al.  Selective averaging of rapidly presented individual trials using fMRI , 1997, Human brain mapping.

[19]  D. Heeger,et al.  Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1 , 1996, The Journal of Neuroscience.

[20]  R. Henson,et al.  Frontal lobes and human memory: insights from functional neuroimaging. , 2001, Brain : a journal of neurology.

[21]  Jonathan D. Cohen,et al.  Improved Assessment of Significant Activation in Functional Magnetic Resonance Imaging (fMRI): Use of a Cluster‐Size Threshold , 1995, Magnetic resonance in medicine.

[22]  R. Cabeza,et al.  Imaging Cognition II: An Empirical Review of 275 PET and fMRI Studies , 2000, Journal of Cognitive Neuroscience.