Dissociable neural imprints of perception and grammar in auditory functional imaging

Abstract

In language processing, the relative contribution of early sensory and higher cognitive brain areas is still an open issue. A recent controversial hypothesis proposes that sensory cortices show sensitivity to syntactic processes, whereas other studies suggest a wider neural network outside sensory regions. The goal of the current event-related fMRI study is to clarify the contribution of sensory cortices in auditory syntactic processing in a 2 × 2 design. Two-word utterances were presented auditorily and varied both in perceptual markedness (presence or absence of an overt word category marking "-t"), and in grammaticality (syntactically correct or incorrect). A multivariate pattern classification approach was applied to the data, flanked by conventional cognitive subtraction analyses. The combination of methods and the 2 × 2 design revealed a clear picture: The cognitive subtraction analysis found initial syntactic processing signatures in a neural network including the left IFG, the left aSTG, the left superior temporal sulcus (STS), as well as the right STS/STG. Classification of local multivariate patterns indicated the left-hemispheric regions in IFG, aSTG, and STS to be more syntax-specific than the right-hemispheric regions. Importantly, auditory sensory cortices were only sensitive to the overt perceptual marking, but not to the grammaticality, speaking against syntax-inflicted sensory cortex modulations. Instead, our data provide clear evidence for a distinction between regions involved in pure perceptual processes and regions involved in initial syntactic processes.

Syntactic processing is distributed across the language system

“…Similar reasoning applies to other studies that have targeted syntactic processing and reported effects only in the inferior frontal and posterior temporal regions (e.g., syntactic violation manipulations : Embick, Marantz, Miyashita, O’Neil, & Sakai, 2000; Cooke et al , 2006; Friederici, Kotz, Scott, & Obleser, 2010; Herrmann, Obleser, Kalberlah, Haynes, & Friederici, 2012; or syntactic priming : Santi & Grodzinsky, 2010; Menenti, Segaert, & Hagoort, 2012; Segaert, Menenti, Weber, Petersson, & Hagoort, 2012). We hypothesize that those effects (and possibly other, non-syntactic, effects; e.g., Devlin et al , 2000), like the syntactic complexity effects studied here, are actually present throughout the language system.…”

Coding of Visual, Auditory, Rule, and Response Information in the Brain: 10 Years of Multivoxel Pattern Analysis

“…The technique has also been generalized to incorporate multiple classes to test more complex representational models (e.g., representational similarity analysis; Kriegeskorte, Mur, & Bandettini, 2008). It has been used to examine neural coding of a wide range of different task events including aspects of stimuli, task rules, participant responses, rewards, emotion, and language (e.g., McNamee, Rangel, & O'Doherty, 2013;Herrmann, Obleser, Kalberlah, Haynes, & Friederici, 2012;Woolgar, Thompson, Bor, & Duncan, 2011;Peelen & Vuilleumier, 2010;Haxby et al, 2001).…”

Recent developments in multivariate pattern analysis for functional MRI

“…Applications of the MVPA have rapidly developed, covering studies of neuronal mechanisms in various domains, such as perception [12][13][14][15][16][17] , learning and memory [18][19][20][21] , language [22] , intention [23] , decision-making [5,[24][25][26] , emotion [27][28][29][30] , and mental disorders [31][32][33] . Instead of serving as a tutorial on applying multivariate classifiers to fMRI data (see Pereira et al [34] , an excellent reference for this purpose), this review attempts to give a general introduction to recent promising developments branching off from the original idea of treating fMRI data as multivariate patterns.…”

“…Exploiting its information-mapping nature, a number of studies have applied this approach to a vast range of research topics [15,16,22,25,26,30,[58][59][60][61][62][63][64] . On the other hand, searchlight MVPA can be considered as a featureselection method that reflects which voxels (features) are most informative in distinguishing stimuli (see 'Section 7 Summary of MVPA algorithms and parameters').…”