Flexible information coding in frontoparietal cortex across the functional stages of cognitive processing

Neural activity in frontoparietal cortex shows overlap across cognitive domains and has been proposed to reflect flexible information processing according to current task demands (Dosenbach et al., 2007; Duncan, 2001). However, a strong assertion of flexibility requires investigating activity across stages of cognitive processing. The current study assessed neural activity in Multiple Demand (MD) regions across the stages of processing that form the core of long-standing cognitive models (Welford, 1952). Specifically, many complex tasks share a comparable structure of subsequent operations: target selection, stimulus-response (SR) mapping, and response execution. We independently manipulated the difficulty of target selection and SR mapping in identical stimulus displays and assessed changes in frontoparietal activity with increased demands in either stage. The results confirmed flexibility in MD regions, with enhanced information representation during difficult target selection as well as SR mapping. Additionally, anterior insula (AI) and anterior cingulate cortex (ACC) showed preferential representation of SR stage information, whereas the medial frontal gyrus (MFG) and inferior parietal sulcus (IPS) showed preferential representation of target selection-stage information. Together these results suggest that MD regions dynamically alter the information they represent with changing task demands. This is the first study to demonstrate that MD regions support flexible goal-directed cognition across multiple processing stages. At the same time we show a preference for the representation of information from a specific processing stage in a subset of MD regions. Significance Statement Goal-directed cognition in complex tasks is critical to key life outcomes including longevity and academic performance. Nevertheless, the mechanisms underlying cognition in complex tasks are not well understood. Distinct neural networks are critical to the navigation of specific cognitive domains (e.g. attention), but frontoparietal activity shows cross-domain and -task overlap and supports flexible representation of goal-critical information. This study links flexible frontoparietal processing to longstanding models of meta-cognition that propose a unifying structure of operations underlying most tasks: target selection, SR mapping, and response execution. Our results demonstrate that flexible information representation in frontoparietal cortex is not limited to the SR mapping stage, but applies across the functional stages of cognitive processing, thus maximizing neural efficiency and supporting flexible cognition.

[1]  M. Botvinick,et al.  Motivation and cognitive control: from behavior to neural mechanism. , 2015, Annual review of psychology.

[2]  Anina N. Rich,et al.  Flexible Coding of Task Rules in Frontoparietal Cortex: An Adaptive System for Flexible Cognitive Control , 2015, Journal of Cognitive Neuroscience.

[3]  Sean M. Polyn,et al.  Beyond mind-reading: multi-voxel pattern analysis of fMRI data , 2006, Trends in Cognitive Sciences.

[4]  John Duncan,et al.  Dynamic Construction of a Coherent Attentional State in a Prefrontal Cell Population , 2013, Neuron.

[5]  Jonathan D. Cohen,et al.  Confounds in multivariate pattern analysis: Theory and rule representation case study , 2013, NeuroImage.

[6]  James T. Townsend,et al.  Issues and Models Concerning the Processing of a Finite Number of Inputs 1 , 2021, Human Information Processing.

[7]  J. Duncan The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour , 2010, Trends in Cognitive Sciences.

[8]  Thomas L. Webb,et al.  The neural basis of monitoring goal progress , 2014, Front. Hum. Neurosci..

[9]  C. Blair,et al.  Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. , 2007, Child development.

[10]  Gilles Faÿ,et al.  Características inmunológicas claves en la fisiopatología de la sepsis. Infectio , 2009 .

[11]  Seth A. Herd,et al.  A Unified Framework for Inhibitory Control Opinion , 2022 .

[12]  Jonathan D. Cohen,et al.  The Expected Value of Control: An Integrative Theory of Anterior Cingulate Cortex Function , 2013, Neuron.

[13]  C. Beste,et al.  A causal role of the right inferior frontal cortex in implementing strategies for multi-component behaviour , 2015, Nature Communications.

[14]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[15]  J. Hoffman Hierarchical stages in the processing of visual information , 1975 .

[16]  J. Duncan,et al.  Discrimination of Visual Categories Based on Behavioral Relevance in Widespread Regions of Frontoparietal Cortex , 2015, The Journal of Neuroscience.

[17]  Karl J. Friston,et al.  Psychophysiological and Modulatory Interactions in Neuroimaging , 1997, NeuroImage.

[18]  N. Sigala,et al.  Dynamic Coding for Cognitive Control in Prefrontal Cortex , 2013, Neuron.

[19]  Daniel J Mitchell,et al.  Task Encoding across the Multiple Demand Cortex Is Consistent with a Frontoparietal and Cingulo-Opercular Dual Networks Distinction , 2016, The Journal of Neuroscience.

[20]  Ian J. Deary,et al.  Intelligence Predicts Health and Longevity, but Why? , 2004 .

[21]  Nancy Kanwisher,et al.  Broad domain generality in focal regions of frontal and parietal cortex , 2013, Proceedings of the National Academy of Sciences.

[22]  D. Sharp,et al.  Contrasting network and modular perspectives on inhibitory control , 2015, Trends in Cognitive Sciences.

[23]  Keith E. Stanovich,et al.  Encoding, stimulus-response compatibility, and stages of processing , 1977 .

[24]  Katharina N. Seidl-Rathkopf,et al.  Functions of the human frontoparietal attention network: Evidence from neuroimaging , 2015, Current Opinion in Behavioral Sciences.

[25]  Walter Schneider,et al.  The cognitive control network: Integrated cortical regions with dissociable functions , 2007, NeuroImage.

[26]  Jonathan D. Cohen,et al.  Conflict monitoring and anterior cingulate cortex: an update , 2004, Trends in Cognitive Sciences.

[27]  T. Braver The variable nature of cognitive control: a dual mechanisms framework , 2012, Trends in Cognitive Sciences.

[28]  A. Welford THE ‘PSYCHOLOGICAL REFRACTORY PERIOD’ AND THE TIMING OF HIGH‐SPEED PERFORMANCE—A REVIEW AND A THEORY , 1952 .

[29]  Jonathan D. Cohen,et al.  The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers , 2014, Cogn. Sci..

[30]  S. Yantis,et al.  Abrupt visual onsets and selective attention: evidence from visual search. , 1984, Journal of experimental psychology. Human perception and performance.

[31]  Justin L. Vincent,et al.  Distinct brain networks for adaptive and stable task control in humans , 2007, Proceedings of the National Academy of Sciences.

[32]  D G Pelli,et al.  The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.

[33]  H Pashler,et al.  Processing stages in overlapping tasks: evidence for a central bottleneck. , 1984, Journal of experimental psychology. Human perception and performance.

[34]  Timothy E. J. Behrens,et al.  Tools of the trade: psychophysiological interactions and functional connectivity. , 2012, Social cognitive and affective neuroscience.

[35]  Saul Sternberg,et al.  The discovery of processing stages: Extensions of Donders' method , 1969 .

[36]  Jonathan D. Power,et al.  Multi-task connectivity reveals flexible hubs for adaptive task control , 2013, Nature Neuroscience.

[37]  N. Logothetis What we can do and what we cannot do with fMRI , 2008, Nature.

[38]  Cameron S. Carter,et al.  The neural circuitry supporting goal maintenance during cognitive control: a comparison of expectancy AX-CPT and dot probe expectancy paradigms , 2015, Cognitive, Affective, & Behavioral Neuroscience.

[39]  A. Kleinschmidt,et al.  Brain Networks and α-Oscillations: Structural and Functional Foundations of Cognitive Control , 2016, Trends in Cognitive Sciences.

[40]  John Duncan,et al.  Hierarchical Organization of Cognition Reflected in Distributed Frontoparietal Activity , 2012, The Journal of Neuroscience.

[41]  Eric L. Denovellis,et al.  Synchronous Oscillatory Neural Ensembles for Rules in the Prefrontal Cortex , 2012, Neuron.

[42]  G. Glover,et al.  Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control , 2007, The Journal of Neuroscience.

[43]  James L. McClelland On the time relations of mental processes: An examination of systems of processes in cascade. , 1979 .

[44]  Nadja Tschentscher,et al.  Frontal Cortex Supports the Early Structuring of Multiple Solution Steps in Symbolic Problem-solving , 2017, Journal of Cognitive Neuroscience.

[45]  Polina Golland,et al.  Coping with confounds in multivoxel pattern analysis: What should we do about reaction time differences? A comment on Todd, Nystrom & Cohen 2013 , 2014, NeuroImage.

[46]  R. Knight,et al.  Insights into Human Behavior from Lesions to the Prefrontal Cortex , 2014, Neuron.