Rule and similarity in grammar: Their interplay and individual differences in the brain

Previous research on artificial grammar has indicated that the human ability to classify sentences or letter strings according to grammaticality relies on two types of knowledge. One is a superficial, familiarity-based understanding of a grammar the other is the knowledge of rules and critical features underlying a grammar. The fundamentally different characteristics of these systems permit an analysis of receiver-operating characteristics (ROC), which measures the extent to which each type of knowledge is used in grammaticality judgments. Furthermore, violations of a grammar can be divided into hierarchical and local violations. The present study is the first to combine the use of ROC analyses, fMRI and a grammaticality dichotomy. Based on previous neuroimaging studies, it was hypothesized that judgments based on rule knowledge, as extracted from individual ROC analyses, involve the left inferior frontal gyrus (IFG), whereas similarity would involve right IFG, as well as left hippocampal regions. With regards to violation types, it was hypothesized that hierarchical violations would recruit the opercular part of the left IFG as well as the posterior operculum, whereas local violations would bilaterally activate the premotor cortex (PMC). Results indicated that for greater reliance on rule knowledge, a ventral part of the left PMC was activated for ungrammatical items, whereas other PMC areas show a differentiated response for grammaticality for individuals less reliant on similarity. The right IFG was related to ungrammatical items as a function of similarity. Results are discussed with regards to possible error detection systems and differentiated efficiencies for respective classification strategies.

[1]  Michael T. Ullman,et al.  The neural basis of lexicon and grammar in first and second language: the declarative/procedural model , 2001 .

[2]  William A. Cunningham,et al.  Type I and Type II error concerns in fMRI research: re-balancing the scale. , 2009, Social cognitive and affective neuroscience.

[3]  F. Ashby,et al.  Category learning and multiple memory systems , 2005, Trends in Cognitive Sciences.

[4]  Joan Y. Chiao,et al.  An Event-related fMRI Study of Artificial Grammar Learning in a Balanced Chunk Strength Design , 2004, Journal of Cognitive Neuroscience.

[5]  A. Wills,et al.  Syntactic transfer in artificial grammar learning , 2010, Psychonomic bulletin & review.

[6]  R. Schvaneveldt,et al.  What Is Learned From Artificial Grammars ? Transfer Tests of Simple Association , 2004 .

[7]  E. Pothos.  Theories of artificial grammar learning. , 2007, Psychological bulletin.

[8]  Angela D. Friederici,et al.  Neural circuits of hierarchical visuo-spatial sequence processing , 2009, Brain Research.

[9]  Karl J. Friston,et al.  Anterior prefrontal cortex mediates rule learning in humans. , 2001, Cerebral cortex.

[10]  A. Friederici,et al.  Neural basis of processing sequential and hierarchical syntactic structures , 2007, Human brain mapping.

[11]  M. W. Brown,et al.  Episodic memory, amnesia, and the hippocampal–anterior thalamic axis , 1999, Behavioral and Brain Sciences.

[12]  M. Gluck,et al.  Basal ganglia and dopamine contributions to probabilistic category learning , 2008, Neuroscience & Biobehavioral Reviews.

[13]  Pierre Perruchet,et al.  Synthetic Grammar Learning : Implicit Rule Abstraction or Explicit Fragmentary Knowledge ? Pierre Perruchet and Chantal Pacteau , 1990 .

[14]  H. Spiers,et al.  Prefrontal and medial temporal lobe interactions in long-term memory , 2003, Nature Reviews Neuroscience.

[15]  R. A. Carlson,et al.  A case of syntactical learning and judgment: How conscious and how abstract? , 1984 .

[16]  Bertram Opitz,et al.  Interactions of the hippocampal system and the prefrontal cortex in learning language-like rules , 2003, NeuroImage.

[17]  A. Kinder,et al.  Learning artificial grammars: No evidence for the acquisition of rules , 2000, Memory & cognition.

[18]  M. Ullman.  Contributions of memory circuits to language: the declarative/procedural model , 2004, Cognition.

[19]  A. Reber.  Implicit learning of artificial grammars , 1967 .

[20]  A. Friederici,et al.  Brain Correlates of Language Learning: The Neuronal Dissociation of Rule-Based versus Similarity-Based Learning , 2004, The Journal of Neuroscience.

[21]  Matthias Schlesewsky,et al.  Processing linguistic complexity and grammaticality in the left frontal cortex. , 2005, Cerebral cortex.

[22]  A. Friederici,et al.  Syntactic learning by mere exposure - An ERP study in adult learners , 2009, BMC Neuroscience.

[23]  Karl J. Friston,et al.  Learning-related neuronal responses in prefrontal cortex studied with functional neuroimaging. , 1999, Cerebral cortex.

[24]  R. Schubotz.  Prediction of external events with our motor system: towards a new framework , 2007, Trends in Cognitive Sciences.

[25]  Guillén Fernández,et al.  Neural correlates of artificial syntactic structure classification , 2006, NeuroImage.

[26]  Colin M. Brown,et al.  Syntactic Processing in Left Prefrontal Cortex Is Independent of Lexical Meaning , 2001, NeuroImage.

[27]  S. Kotz,et al.  Ventral Premotor Cortex Lesions disrupt learning of sequential syntactic structures , 2009 .

[28]  J. C. Ogilvie,et al.  Maximum-likelihood estimation of receiver operating characteristic curve parameters , 1968 .

[29]  Nicola K. Ferdinand,et al.  Timing Matters: The Impact of Immediate and Delayed Feedback on Artificial Language Learning , 2010, Front. Hum. Neurosci..

[30]  Regina Bernhaupt,et al.  Application of implicit knowledge: deterministic or probabilistic? , 1997 .

[31]  M. Torrens.  Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268 , 1990 .