Conflict between multiple sensory stimuli or potential motor responses is thought to be resolved via bias signals from prefrontal cortex (PFC). However, population codes in the PFC also represent abstract information, such as task rules. How is conflict between active abstract representations resolved? We used functional neuroimaging to investigate the mechanism responsible for resolving conflict between abstract representations of task rules. Participants performed two different tasks based on a cue. We manipulated the degree of conflict at the task-rule level by training participants to associate the color and shape dimensions of the cue with either the same task rule (congruent cues) or different ones (incongruent cues). Phonological and semantic tasks were used in which performance depended on learned, abstract representations of information, rather than sensory features of the target stimulus or on any habituated stimulus-response associations. In addition, these tasks activate distinct regions that allowed us to measure magnitude of conflict between tasks. We found that incongruent cues were associated with increased activity in several cognitive control areas, including the inferior frontal gyrus, inferior parietal lobule, insula, and subcortical regions. Conflict between abstract representations appears to be resolved by rule-specific activity in the inferior frontal gyrus that is correlated with enhanced activity related to the relevant information. Furthermore, multi-voxel pattern analysis of the activity in the inferior frontal gyrus was shown to carry information about both the currently relevant rule (semantic/phonological) and the currently relevant cue context (color/shape). Similar to models of attentional selection of conflicting sensory or motor representations, the current findings indicate part of the frontal cortex provides a bias signal, representing task rules, that enhances task-relevant information. However, the frontal cortex can also be the target of these bias signals in order to enhance abstract representations that are independent of particular stimuli or motor responses.
Pubmed ID: 27771559 RIS Download
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
A Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run. Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. This Python-based, cross-platform, open-source software toolbox software toolbox for the application of classifier-based analysis techniques to fMRI datasets makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages.
View all literature mentionsAn integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM) from the laboratory of Chih-Chung Chang and Chih-Jen Lin. It supports multi-class classification.
View all literature mentions