Animal neurophysiological studies have identified neural signals within dorsal frontoparietal areas that trace a perceptual decision by accumulating sensory evidence over time and trigger action upon reaching a threshold. Although analogous accumulation-to-bound signals are identifiable on extracranial human electroencephalography, their cortical origins remain unknown. Here neural metrics of human evidence accumulation, predictive of the speed of perceptual reports, were isolated using electroencephalography and related to dorsal frontoparietal network (dFPN) connectivity using diffusion and resting-state functional magnetic resonance imaging. The build-up rate of evidence accumulation mediated the relationship between the white matter macrostructure of dFPN pathways and the efficiency of perceptual reports. This association between steeper build-up rates of evidence accumulation and the dFPN was recapitulated in the resting-state networks. Stronger connectivity between dFPN regions is thus associated with faster evidence accumulation and speeded perceptual decisions. Our findings identify an integrated network for perceptual decisions that may be targeted for neurorehabilitation in cognitive disorders.
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The EEG dataset from the current study is available at the following repository: https://figshare.com/s/8d6f461834c47180a444. These data are open access and available under a Creative Commons attribution: NonCommercial-ShareAlike 3.0 international licence. Raw data for the diffusion and resting-state imaging were collected at the Turner Institute for Brain and Mental Health as part of a large-scale project. Derived diffusion and resting-state MRI data supporting the findings of this study are available from the corresponding author upon request.
All EEG pre-processing scripts implemented in this study are available from https://github.com/gerontium/big_dots. These scripts are open access and available under a Creative Commons attribution: NonCommercial-ShareAlike international license. Custom code for the resting-state MRI and EEG data that the support the conclusion of this article are available from the corresponding author upon request.
Fish, S. et al. Modelling reaction time distribution of fast decision tasks in schizophrenia: evidence for novel candidate endophenotypes. Psychiatry Res. 269, 212–220 (2018).
Fosco, W. D., White, C. N. & Hawk, L. W. Acute stimulant treatment and reinforcement increase the speed of information accumulation in children with ADHD. J. Abnorm. Child Psychol. 45, 911–920 (2016).
Huang, Y.-T. et al. Different effects of dopaminergic medication on perceptual decision-making in Parkinson’s disease as a function of task difficulty and speed–accuracy instructions. Neuropsychologia 75, 577–587 (2015).
Kelly, S. P. & O’Connell, R. G. Internal and external influences on the rate of sensory evidence accumulation in the human brain. J. Neurosci. 33, 19434–19441 (2013).
Twomey, D. M., Kelly, S. P. & O’Connell, R. G. Abstract and effector-selective decision signals exhibit qualitatively distinct dynamics before delayed perceptual reports. J. Neurosci. 36, 7346–7352 (2016).
White, C. N., Ratcliff, R., Vasey, M. W. & McKoon, G. Using diffusion models to understand clinical disorders. J. Math. Psychol. 54, 39–52 (2010).
Starns, J. J. The effects of aging on the speed–accuracy compromise: boundary optimality in the diffusion model. Psychol. Aging 2, 277–390 (2010).
Hanks, T. D. & Summerfield, C. Perceptual decision making in rodents, monkeys, and humans. Neuron 93, 15–31 (2017).
Roitman, J. D. & Shadlen, M. N. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22, 9475–9489 (2002).
Shadlen, M. N. & Shohamy, D. Decision making and sequential sampling from memory. Neuron 90, 927–939 (2016).
Ding, L. & Gold, J. I. Caudate encodes multiple computations for perceptual decisions. J. Neurosci. 30, 15747–15759 (2010).
Ratcliff, R., Cherian, A. & Segraves, M. A comparison of macaque behavior and superior colliculus neuronal activity to predictions from models of two-choice decisions. J. Neurophysiol. 90, 1392–1407 (2003).
Cisek, P. & Kalaska, J. F. Neural correlates of reaching decisions in dorsal premotor cortex: specification of multiple direction choices and final selection of action. Neuron 45, 801–814 (2005).
Pape, A.-A. & Siegel, M. Motor cortex activity predicts response alternation during sensorimotor decisions. Nat. Commun. 7, 13098 (2016).
Ding, L. & Gold, J. I. Neural correlates of perceptual decision making before, during, and after decision commitment in monkey frontal eye field. Cereb. Cortex 22, 1052–1067 (2012).
Huk, A. C. Neural activity in macaque parietal cortex reflects temporal integration of visual motion signals during perceptual decision making. J. Neurosci. 25, 10420–10436 (2005).
Hanks, T., Kiani, R. & Shadlen, M. N. A neural mechanism of speed–accuracy tradeoff in macaque area LIP. eLife 3, e02260 (2014).
Heitz, R. P. & Schall, J. D. Neural mechanisms of speed–accuracy tradeoff. Neuron 76, 616–628 (2012).
Thura, D. & Cisek, P. Modulation of premotor and primary motor cortical activity during volitional adjustments of speed–accuracy trade-offs. J. Neurosci. 36, 938–956 (2016).
O’Connell, R. G., Dockree, P. M. & Kelly, S. P. A supramodal accumulation-to-bound signal that determines perceptual decisions in humans. Nat. Neurosci. 15, 1729–1735 (2012).
De Lange, F. P., Rahnev, D. A., Donner, T. H. & Lau, H. Prestimulus oscillatory activity over motor cortex reflects perceptual expectations. J. Neurosci. 33, 1400–1410 (2013).
Steinemann, N. A., O’Connell, R. G. & Kelly, S. P. Decisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy. Nat. Commun. 9, 3627 (2018).
De Lange, F. P., Jensen, O. & Dehaene, S. Accumulation of evidence during sequential decision making: the importance of top–down factors. J. Neurosci. 30, 731–738 (2010).
Donner, T. H., Siegel, M., Fries, P. & Engel, A. K. Buildup of choice-predictive activity in human motor cortex during perceptual decision making. Curr. Biol. 19, 1581–1585 (2009).
Loughnane, G. M. et al. Target selection signals influence perceptual decisions by modulating the onset and rate of evidence accumulation. Curr. Biol. 26, 496–502 (2016).
Wyart, V., de Gardelle, V., Scholl, J. & Summerfield, C. Rhythmic fluctuations in evidence accumulation during decision making in the human brain. Neuron 76, 847–858 (2012).
Murphy, P. R., Robertson, I. H., Harty, S. & O’Connell, R. G. Neural evidence accumulation persists after choice to inform metacognitive judgments. eLife 4, e11946 (2015).
Philiastides, M. G., Heekeren, H. R. & Sajda, P. Human scalp potentials reflect a mixture of decision-related signals during perceptual choices. J. Neurosci. 34, 16877–16889 (2014).
Rungratsameetaweemana, N., Itthipuripat, S., Salazar, A. & Serences, J. T. Expectations do not alter early sensory processing during perceptual decision-making. J. Neurosci. 38, 5632–5648 (2018).
Spitzer, B., Waschke, L. & Summerfield, C. Selective overweighting of larger magnitudes during noisy numerical comparison. Nature 1, 0145 (2017).
Von Lautz, A., Herding, J. & Blankenburg, F. Neuronal signatures of a random-dot motion comparison task. NeuroImage 193, 57–66 (2019).
Van Vugt, M. K., Beulen, M. A. & Taatgen, N. A. Relation between centro-parietal positivity and diffusion model parameters in both perceptual and memory-based decision making. Brain Res. 1715, 1–12 (2019).
Afacan-Seref, K., Steinemann, N. A., Blangero, A. & Kelly, S. P. Dynamic interplay of value and sensory information in high-speed decision making. Curr. Biol. 28, 795–802.e6 (2018).
Herding, J., Ludwig, S., von Lautz, A., Spitzer, B. & Blankenburg, F. Centro-parietal EEG potentials index subjective evidence and confidence during perceptual decision making. NeuroImage 201, 116011 (2019).
Tagliabue, C. F. et al. The EEG signature of sensory evidence accumulation during decision formation closely tracks subjective perceptual experience. Sci. Rep. 9, 4949 (2019).
Jurkiewicz, M. T., Gaetz, W. C., Bostan, A. C. & Cheyne, D. Post-movement beta rebound is generated in motor cortex: evidence from neuromagnetic recordings. NeuroImage 32, 1281–1289 (2006).
Soltani, M. & Knight, R. T. Neural origins of the P300. Crit. Rev. Neurobiol. 14, 199–224 (2000).
Linden, D. E. J. The P300: where in the brain is it produced and what does it tell us? Neuroscientist 11, 563–576 (2005).
Stuss, D. T. & Knight, R. T. Principles of Frontal Lobe Function (Oxford Univ. Press, 2002).
Tang, Y.-Y. et al. Short-term meditation induces white matter changes in the anterior cingulate. Proc. Natl Acad. Sci. USA 107, 15649–15652 (2010).
Tang, Y.-Y., Lu, Q., Fan, M., Yang, Y. & Posner, M. I. Mechanisms of white matter changes induced by meditation. Proc. Natl Acad. Sci. USA 109, 10570–10574 (2012).
Newsome, W. T., Britten, K. H. & Movshon, J. A. Neuronal correlates of a perceptual decision. Nature 341, 52–54 (1989).
Dienes, Z. How Bayes factors change scientific practice. J. Math. Psychol. 72, 78–89 (2016).
De Schotten, M. T. et al. A lateralized brain network for visuospatial attention. Nat. Neurosci. 14, 1245–1246 (2011).
Hayes, A. F. PROCESS: A Versatile Computational Tool for Observed Variable Mediation, Moderation, and Conditional Process Modeling White Paper. https://api.semanticscholar.org/CorpusID:22220661 (2012).
Hayes, A. F. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (Guilford Press, 2013).
Fox, M. D., Corbetta, M., Snyder, A. Z., Vincent, J. L. & Raichle, M. E. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc. Natl Acad. Sci. USA 103, 10046–10051 (2006).
Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3, 215–229 (2002).
Vincent, J. L., Kahn, I., Snyder, A. Z., Raichle, M. E. & Buckner, R. L. Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. J. Neurophysiol. 100, 3328–3342 (2008).
Nelson, S. M. et al. A parcellation scheme for human left lateral parietal cortex. Neuron 67, 156–170 (2010).
Grefkes, C., Wang, L. E., Eickhoff, S. B. & Fink, G. R. Noradrenergic modulation of cortical networks engaged in visuomotor processing. Cereb. Cortex 20, 783–797 (2010).
Corbetta, M., Patel, G. & Shulman, G. L. The reorienting system of the human brain: from environment to theory of mind. Neuron 58, 306–324 (2008).
Corbetta, M. & Shulman, G. L. Spatial neglect and attention networks. Annu. Rev. Neurosci. 34, 569–599 (2011).
Hanks, T. D. et al. Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature 520, 220–223 (2015).
O’Connell, R. G., Shadlen, M. N., Wong-Lin, K. & Kelly, S. P. Bridging neural and computational viewpoints on perceptual decision-making. Trends Neurosci. 41, 838–852 (2018).
Romo, R. & de Lafuente, V. Conversion of sensory signals into perceptual decisions. Prog. Neurobiol. 103, 41–75 (2013).
Shadlen, M. N. & Kiani, R. Decision making as a window on cognition. Neuron 80, 791–806 (2013).
Assaf, Y., Johansen-Berg, H. & de Schotten, M. T. The role of diffusion MRI in neuroscience. NMR Biomed. 11, e3762 (2017).
Johansen-Berg, H. Behavioural relevance of variation in white matter microstructure. Curr. Opin. Neurol. 23, 351–358 (2010).
Kanai, R. & Rees, G. The structural basis of inter-individual differences in human behaviour and cognition. Nat. Rev. Neurosci. 12, 231–242 (2011).
Zatorre, R. J., Fields, R. D. & Johansen-Berg, H. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat. Neurosci. 15, 528–536 (2012).
Hursh, J. B. Conduction velocity diameter of nerve fibers. Am. J. Physiol. 127, 131–139 (1939).
Waxman, S. G. & Bennett, M. V. L. Relative conduction velocities of small myelinated and non-myelinated fibres in the central nervous system. Nat. New Biol. 238, 217–219 (1972).
Selen, L. P. J., Shadlen, M. N. & Wolpert, D. M. Deliberation in the motor system: reflex gains track evolving evidence leading to a decision. J. Neurosci. 32, 2276–2286 (2012).
Barbas, H. & Pandya, D. N. Architecture and frontal cortical connections of the premotor cortex (area 6) in the rhesus monkey. J. Comp. Neurol. 256, 211–228 (1987).
Muakkassa, K. F. & Strick, P. L. Frontal lobe inputs to primate motor cortex: evidence for four somatotopically organized ‘premotor’ areas. Brain Res. 177, 176–182 (1979).
Shushruth, S., Mazurek, M. & Shadlen, M. N. Comparison of decision-related signals in sensory and motor preparatory responses of neurons in area LIP. J. Neurosci. 38, 6350–6365 (2018).
Wyart, V., Myers, N. E. & Summerfield, C. Neural mechanisms of human perceptual choice under focused and divided attention. J. Neurosci. 35, 3485–3498 (2015).
Lorteije, J. A. M. et al. The formation of hierarchical decisions in the visual cortex. Neuron 87, 1344–1356 (2015).
Ditterich, J., Mazurek, M. E. & Shadlen, M. N. Microstimulation of visual cortex affects the speed of perceptual decisions. Nat. Neurosci. 6, 891–898 (2003).
Gazzaley, A. & Nobre, A. C. Top-down modulation: bridging selective attention and working memory. Trends Cogn. Sci. 16, 129–135 (2012).
Brosnan, M. B. et al. Prefrontal modulation of visual processing and sustained attention in aging, a transcranial direct current stimulation–electroencephalogram coregistration approach. J. Cogn. Neurosci. 30, 1–16 (2018).
Twomey, D. M., Murphy, P. R., Kelly, S. P. & O’Connell, R. G. The classic P300 encodes a build‐to‐threshold decision variable. Eur. J. Neurosci. 42, 1636–1643 (2015).
Hillyard, S. A., Squires, K. C., Bauer, J. W. & Science, P. L. Evoked potential correlates of auditory signal detection. Science 172, 1357–1360 (1971).
Squires, K. C., Hillyard, S. A. & Lindsay, P. H. Vertex potentials evoked during auditory signal detection: relation to decision criteria. Percept. Psychophys. 14, 265–272 (1973).
Sutton, S., Braren, M., Zubin, J. & John, E. R. Evoked-potential correlates of stimulus uncertainty. Science 150, 1187–1188 (1965).
Courchesne, E., Hillyard, S. A. & Courchesne, R. Y. P3 waves to the discrimination of targets in homogeneous and heterogeneous stimulus sequences. Psychophysiology 14, 590–597 (1977).
Johnson, C. C. D. & Donchin, E. On quantifying surprise: the variation of event‐related potentials with subjective probability. Psychophysiology 14, 456–467 (1977).
Polich, J. Updating P300: an integrative theory of P3a and P3b. Clin. Neurophysiol. 118, 2128–2148 (2007).
Nieuwenhuis, S., Aston-Jones, G. & Cohen, J. D. Decision making, the P3, and the locus coeruleus–norepinephrine system. Psychol. Bull. 131, 510–532 (2005).
Szuromi, B., Czobor, P., Komlósi, S. & Bitter, I. P300 deficits in adults with attention deficit hyperactivity disorder: a meta-analysis. Psychol. Med. 41, 1529–1538 (2011).
Ford, J. M., Mathalon, D. H., Kalba, S., Marsh, L. & Pfefferbaum, A. N1 and P300 abnormalities in patients with schizophrenia, epilepsy, and epilepsy with schizophrenialike features. Biol. Psychiatry 49, 848–860 (2001).
Frodl, T. et al. Value of event-related P300 subcomponents in the clinical diagnosis of mild cognitive impairment and Alzheimer’s disease. Psychophysiology 39, 175–181 (2002).
Ziegler, D. A. et al. Closed-loop digital meditation improves sustained attention in young adults. Nat. Hum. Behav. 3, 746–757 (2019).
Storebø, O. J. et al. Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents. Cochrane Database Syst. Rev. https://doi.org//10.1002/14651858.CD012069.pub2 (2012)
Loughnane, G. M. et al. Catecholamine modulation of evidence accumulation during perceptual decision formation: a randomized trial. J. Cogn. Neurosci. 31, 1044–1053 (2019).
Chiang, M.-C. et al. Genetics of brain fiber architecture and intellectual performance. J. Neurosci. 29, 2212–2224 (2009).
Newman, D. P., Loughnane, G. M., Kelly, S. P., O’Connell, R. G. & Bellgrove, M. A. Visuospatial asymmetries arise from differences in the onset time of perceptual evidence accumulation. J. Neurosci. 37, 3378–3385 (2017).
Marshall, T. R., Bergmann, T. O. & Jensen, O. Frontoparietal structural connectivity mediates the top-down control of neuronal synchronization associated with selective attention. PLoS Biol. 13, e1002272 (2015).
Chechlacz, M., Gillebert, C. R., Vangkilde, S. A., Petersen, A. & Humphreys, G. W. Structural variability within frontoparietal networks and individual differences in attentional functions: an approach using the theory of visual attention. J. Neurosci. 35, 10647–10658 (2015).
Pelli, D. G. The VideoToolbox software for visual psychophysics: transforming numbers into movies. Spatial Vis. 10, 437–442 (1997).
Brainard, D. H. The psychophysics toolbox. Spat. Vis. 10, 433–436 (1997).
Cornelissen, F. W., Peters, E. M. & Palmer, J. The Eyelink Toolbox: eye tracking with MATLAB and the Psychophysics Toolbox. Behav. Res. Methods Instrum. Comput. 34, 613–617 (2002).
Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004).
Morel, P. Gramm: grammar of graphics plotting in Matlab. J. Open. Source. Softw. 3, 568 (2018).
Kayser, J. & Tenke, C. E. Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: I. Evaluation with auditory oddball tasks. Clin. Neurophysiol. 117, 348–368 (2006).
Foxe, J. J. & Simpson, G. V. Flow of activation from V1 to frontal cortex in humans. Exp. Brain Res. 142, 139–150 (2002).
Kelly, S. P., Gomez-Ramirez, M. & Foxe, J. J. Spatial attention modulates initial afferent activity in human primary visual cortex. Cereb. Cortex 18, 2629–2636 (2008).
Thut, G., Nietzel, A., Brandt, S. A. & Pascual-Leone, A. α-Band electroencephalographic activity over occipital cortex indexes visuospatial attention bias and predicts visual target detection. J. Neurosci. 26, 9494–9502 (2006).
Andersson, J. L. R. & Sotiropoulos, S. N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage 125, 1063–1078 (2016).
Jenkinson, M. & Smith, S. A global optimisation method for robust affine registration of brain images. Med. Image. Anal. 2, 143–156 (2001).
Smith, R. E., Tournier, J.-D., Calamante, F. & Connelly, A. Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage 62, 1924–1938 (2012).
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59, 2142–2154 (2012).
Ball, G. et al. Multimodal structural neuroimaging markers of brain development and ADHD symptoms. Am. J. Psychiatry 176, 57–66 (2019).
D’Albis, M.-A. Local structural connectivity is associated with social cognition in autism spectrum disorder. Brain 141, 3472–3481 (2018).
Esteban, O. et al. MRIQC: advancing the automatic prediction of image quality in MRI from unseen sites. PLoS ONE 12, e0184661 (2017).
Griffanti, L. et al. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. NeuroImage 95, 232–247 (2014).
Salimi-Khorshidi, G. et al. Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. NeuroImage 90, 449–468 (2014).
Boyacioğlu, R., Schulz, J., Koopmans, P. J., Barth, M. & Norris, D. G. Improved sensitivity and specificity for resting state and task fMRI with multiband multi-echo EPI compared to multi-echo EPI at 7T. NeuroImage 119, 352–361 (2015).
Griffanti, L. et al. Hand classification of fMRI ICA noise components. NeuroImage 154, 188–205 (2017).
Parkes, L., Fulcher, B., Yücel, M. & Fornito, A. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage 171, 415–436 (2018).
Burgess, G. C. et al. Evaluation of denoising strategies to address motion-correlated artifacts in resting-state functional magnetic resonance imaging data from the human connectome project. Brain Connect. 6, 669–680 (2016).
Ciric, R. et al. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. NeuroImage 154, 174–187 (2017).
Power, J. D., Plitt, M., Laumann, T. O. & Martin, A. Sources and implications of whole-brain fMRI signals in humans. NeuroImage 146, 609–625 (2017).
Fox, M. D., Zhang, D., Snyder, A. Z. & Raichie, M. E. The global signal and observed anticorrelated resting state brain networks. J. Neurophysiol. 101, 3270–3283 (2009).
Saad, Z. S. et al. Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect. 2, 25–32 (2012).
Li, J. et al. Global signal regression strengthens association between resting-state functional connectivity and behavior. NeuroImage 196, 126–141 (2019).
Satterthwaite, T. D. et al. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage 64, 240–256 (2013).
Seeley, W. W. et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27, 2349–2356 (2007).
Fox, M. D. et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl Acad. Sci. USA 102, 9673–9678 (2005).
Dai, H. et al. Resting‐state functional MRI: functional connectivity analysis of the visual cortex in primary open‐angle glaucoma patients. Hum. Brain Mapp. 34, 2455–2463 (2013).
Forstmann, B. U. et al. Cortico-subthalamic white matter tract strength predicts interindividual efficacy in stopping a motor response. NeuroImage 60, 370–375 (2012).
Rouder, J. N. & Morey, R. D. The nature of psychological thresholds. Psychol. Rev. 116, 655–660 (2009).
Baron, R. M. & Kenny, D. A. The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51, 1173–1182 (1986).
This work was supported by grants from the Australian Research Council (including DP150100986 and DP180102066) to M.A.B. and R.G.O. M.A.B. is supported by a Senior Research Fellowship from the Australian National Health and Medical Research Council (APP1154378). A.F. was supported by the Sylvia and Charles Viertel Foundation, National Health and Medical Research Council (1050504) and Australian Research Council (FT130100589). We thank N. Steinemann and D. McGovern for providing the additional data reported in Supplementary Results 1 and Supplementary Fig. 1, and J. Wiley and J. Matthews for statistical advice. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
The authors declare no competing interests.
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Extended Data Fig. 1 Consistency of an individual’s CPP build-up rate across experimental manipulations.
a, Steinemann et al.22 Nature Communications. b, McGovern et al (2018) Nature Human Behaviour. c, Loughnane et al.25 Current Biology. Note. For each sub-plot every data point depicts an individual’s CPP build-up rate for condition a (as described on the x-axis), and condition b (as described on the y-axis). r denotes the correlation between the two experimental conditions using Pearson’s r.
The EEG signals (pre-target alpha a, N2c (latency and amplitude) b, and FCN amplitude (c)) that offered a significant improvement in model fit for RT, over and above the signals that temporally preceded them but did not independently account for variance in RT. Please note data have been binned using a median split of participants’ RT for visualisation purposes only. Given the window of interest is the pre-stimulus epoch, alpha power has not been baseline-corrected here (but see Supplementary Fig. 3).
The left and right hemispheres of the dFPN (a) and vFPN (b) respectively depicted in the upper and lower panels. Note. For the purpose of visualisation, these images were thresholded using a clusterwise FWE rate of p < 0.001 (extent threshold of 10 voxels).
Extended Data Fig. 4 Regions of interest (ROI) used for the tractography of the three branches of the left and right superior longitudinal fasciculus on the cohort-specific T1 template.
At the level of the AC, three ROIs delineate the superior (light blue), middle (purple) and inferior frontal gyri (pink) in each hemisphere. At the level of the PC is a single large parietal ROI (green).
In line with previous work, the most ventral branch (SLF3) shows specific right lateralisation (Note, x-axis depicts right-left hemisphere volume, controlling for overall white matter volume, *** indicates p < 0.0005).
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Brosnan, M.B., Sabaroedin, K., Silk, T. et al. Evidence accumulation during perceptual decisions in humans varies as a function of dorsal frontoparietal organization. Nat Hum Behav (2020). https://doi.org/10.1038/s41562-020-0863-4
Nature Human Behaviour (2020)