Rethinking simultaneous suppression in visual cortex via compressive spatiotemporal population receptive fields.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
11 Aug 2024
11 Aug 2024
Historique:
received:
01
07
2023
accepted:
24
07
2024
medline:
12
8
2024
pubmed:
12
8
2024
entrez:
11
8
2024
Statut:
epublish
Résumé
When multiple visual stimuli are presented simultaneously in the receptive field, the neural response is suppressed compared to presenting the same stimuli sequentially. The prevailing hypothesis suggests that this suppression is due to competition among multiple stimuli for limited resources within receptive fields, governed by task demands. However, it is unknown how stimulus-driven computations may give rise to simultaneous suppression. Using fMRI, we find simultaneous suppression in single voxels, which varies with both stimulus size and timing, and progressively increases up the visual hierarchy. Using population receptive field (pRF) models, we find that compressive spatiotemporal summation rather than compressive spatial summation predicts simultaneous suppression, and that increased simultaneous suppression is linked to larger pRF sizes and stronger compressive nonlinearities. These results necessitate a rethinking of simultaneous suppression as the outcome of stimulus-driven compressive spatiotemporal computations within pRFs, and open new opportunities to study visual processing capacity across space and time.
Identifiants
pubmed: 39128923
doi: 10.1038/s41467-024-51243-7
pii: 10.1038/s41467-024-51243-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
6885Subventions
Organisme : NEI NIH HHS
ID : R01 EY023915
Pays : United States
Informations de copyright
© 2024. The Author(s).
Références
Duncan, J. The locus of interference in the perception of simultaneous stimuli. Psychol. Rev. 87, 272–300 (1980).
pubmed: 7384344
doi: 10.1037/0033-295X.87.3.272
Scharff, A., Palmer, J. & Moore, C. M. Evidence of fixed capacity in visual object categorization. Psychon. Bull. Rev. 18, 713–721 (2011).
pubmed: 21538202
doi: 10.3758/s13423-011-0101-1
Pelli, D. G., Palomares, M. & Majaj, N. J. Crowding is unlike ordinary masking: distinguishing feature integration from detection. J. Vis. 4, 1136–1169 (2004).
pubmed: 15669917
doi: 10.1167/4.12.12
Luck, S. J. & Vogel, E. K. The capacity of visual working memory for features and conjunctions. Nature 390, 279–281 (1997).
pubmed: 9384378
doi: 10.1038/36846
Miller, E. K., Gochin, P. M. & Gross, C. G. Suppression of visual responses of neurons in inferior temporal cortex of the awake macaque by addition of a second stimulus. Brain Res. 616, 25–29 (1993).
pubmed: 8358617
doi: 10.1016/0006-8993(93)90187-R
Recanzone, G. H., Wurtz, R. H. & Schwarz, U. Responses of MT and MST neurons to one and two moving objects in the receptive field. J. Neurophysiol. 78, 2904–2915 (1997).
pubmed: 9405511
doi: 10.1152/jn.1997.78.6.2904
Reynolds, J. H., Chelazzi, L. & Desimone, R. Competitive mechanisms subserve attention in macaque areas V2 and V4. J. Neurosci. 19, 1736–1753 (1999).
pubmed: 10024360
doi: 10.1523/JNEUROSCI.19-05-01736.1999
Kastner, S., de Weerd, P., Desimone, R. & Ungerleider, L. G. Mechanisms of directed attention in the human extrastriate cortex as revealed by functional MRI. Science 282, 108–111 (1998).
pubmed: 9756472
doi: 10.1126/science.282.5386.108
Kastner, S. et al. Modulation of sensory suppression: implications for receptive field sizes in the human visual cortex. J. Neurophysiol. 86, 1398–1411 (2001).
pubmed: 11535686
doi: 10.1152/jn.2001.86.3.1398
Beck, D. M. & Kastner, S. Stimulus context modulates competition in human extrastriate cortex. Nat. Neurosci. 8, 1110–1116 (2005).
pubmed: 16007082
doi: 10.1038/nn1501
McMains, S. A. & Kastner, S. Interactions of top–down and bottom–up mechanisms in human visual cortex. J. Neurosci. 31, 587–597 (2011).
pubmed: 21228167
doi: 10.1523/JNEUROSCI.3766-10.2011
Kim, N. Y., Pinsk, M. A. & Kastner, S. Neural basis of biased competition in development: sensory competition in visual cortex of school-aged children. Cereb. Cortex 31, 3107–3121 (2021).
pubmed: 33565579
doi: 10.1093/cercor/bhab009
Desimone, R. & Duncan, J. Neural mechanisms of selective visual attention. Annu Rev. Neurosci. 18, 193–222 (1995).
pubmed: 7605061
doi: 10.1146/annurev.ne.18.030195.001205
Usher, M. & Niebur, E. Modeling the temporal dynamics of IT neurons in visual search: a mechanism for top-down selective attention. J. Cogn. Neurosci. 8, 311–327 (1996).
pubmed: 23971503
doi: 10.1162/jocn.1996.8.4.311
Deco, G. & Rolls, E. T. Neurodynamics of biased competition and cooperation for attention: a model with spiking neurons. J. Neurophysiol. 94, 295–313 (2005).
pubmed: 15703227
doi: 10.1152/jn.01095.2004
Scalf, P. E. & Beck, D. M. Competition in visual cortex impedes attention to multiple items. J. Neurosci. 30, 161–169 (2010).
pubmed: 20053898
doi: 10.1523/JNEUROSCI.4207-09.2010
Maunsell, J. H. & Newsome, W. T. Visual processing in monkey extrastriate cortex. Annu Rev. Neurosci. 10, 363–401 (1987).
pubmed: 3105414
doi: 10.1146/annurev.ne.10.030187.002051
Dumoulin, S. O. & Wandell, B. A. Population receptive field estimates in human visual cortex. Neuroimage 39, 647–660 (2008).
pubmed: 17977024
doi: 10.1016/j.neuroimage.2007.09.034
Wandell, B. A. & Winawer, J. Computational neuroimaging and population receptive fields. Trends Cogn. Sci. 19, 349–357 (2015).
pubmed: 25850730
doi: 10.1016/j.tics.2015.03.009
Reynolds, J. H. & Heeger, D. J. The normalization model of attention. Neuron 61, 168–185 (2009).
pubmed: 19186161
doi: 10.1016/j.neuron.2009.01.002
Albrecht, D. G. & Hamilton, D. B. Striate cortex of monkey and cat: contrast response function. J. Neurophysiol. 48, 217–237 (1982).
pubmed: 7119846
doi: 10.1152/jn.1982.48.1.217
Press, W. A., Brewer, A. A., Dougherty, R. F., Wade, A. R. & Wandell, B. A. Visual areas and spatial summation in human visual cortex. Vision Res. 41, 1321–1332 (2001).
pubmed: 11322977
doi: 10.1016/S0042-6989(01)00074-8
Hansen, K. A., David, S. V. & Gallant, J. L. Parametric reverse correlation reveals spatial linearity of retinotopic human V1 BOLD response. NeuroImage 23, 233–241 (2004).
pubmed: 15325370
doi: 10.1016/j.neuroimage.2004.05.012
Solomon, S. G., White, A. J. & Martin, P. R. Extraclassical receptive field properties of parvocellular, magnocellular, and koniocellular cells in the primate lateral geniculate nucleus. J. Neurosci. 22, 338–349 (2002).
pubmed: 11756517
doi: 10.1523/JNEUROSCI.22-01-00338.2002
Pihlaja, M., Henriksson, L., James, A. C. & Vanni, S. Quantitative multifocal fMRI shows active suppression in human V1. Hum. Brain Mapp. 29, 1001–1014 (2008).
pubmed: 18381768
doi: 10.1002/hbm.20442
Vanni, S. & Rosenstrom, T. Local non-linear interactions in the visual cortex may reflect global decorrelation. J. Comput. Neurosci. 30, 109–124 (2011).
pubmed: 20422445
doi: 10.1007/s10827-010-0239-2
Kay, K. N., Winawer, J., Mezer, A. & Wandell, B. A. Compressive spatial summation in human visual cortex. J. Neurophysiol. 110, 481–494 (2013).
pubmed: 23615546
doi: 10.1152/jn.00105.2013
Tolhurst, D. J., Walker, N. S., Thompson, I. D. & Dean, A. F. Non-linearities of temporal summation in neurones in area 17 of the cat. Exp. Brain Res. 38, 431–435 (1980).
pubmed: 6244972
doi: 10.1007/BF00237523
Boynton, G. M., Engel, S. A., Glover, G. H. & Heeger, D. J. Linear systems analysis of functional magnetic resonance imaging in human V1. J. Neurosci. 16, 4207–4221 (1996).
pubmed: 8753882
doi: 10.1523/JNEUROSCI.16-13-04207.1996
Birn, R. M., Saad, Z. S. & Bandettini, P. A. Spatial heterogeneity of the nonlinear dynamics in the FMRI BOLD response. Neuroimage 14, 817–826 (2001).
pubmed: 11554800
doi: 10.1006/nimg.2001.0873
Miller, K. L. et al. Nonlinear temporal dynamics of the cerebral blood flow response. Hum. Brain Mapp. 13, 1–12 (2001).
pubmed: 11284042
doi: 10.1002/hbm.1020
Motter, B. C. Modulation of transient and sustained response components of V4 neurons by temporal crowding in flashed stimulus sequences. J. Neurosci. 26, 9683–9694 (2006).
pubmed: 16988039
doi: 10.1523/JNEUROSCI.5495-05.2006
Hasson, U., Yang, E., Vallines, I., Heeger, D. J. & Rubin, N. A hierarchy of temporal receptive windows in human cortex. J. Neurosci. 28, 2539–2550 (2008).
pubmed: 18322098
doi: 10.1523/JNEUROSCI.5487-07.2008
Yesilyurt, B., Ugurbil, K. & Uludag, K. Dynamics and nonlinearities of the BOLD response at very short stimulus durations. Magn. Reson Imaging 26, 853–862 (2008).
pubmed: 18479876
doi: 10.1016/j.mri.2008.01.008
Horiguchi, H., Nakadomari, S., Misaki, M. & Wandell, B. A. Two temporal channels in human V1 identified using fMRI. Neuroimage 47, 273–280 (2009).
pubmed: 19361561
doi: 10.1016/j.neuroimage.2009.03.078
Nishimoto, S. & Gallant, J. L. A three-dimensional spatiotemporal receptive field model explains responses of area MT neurons to naturalistic movies. J. Neurosci. 31, 14551–14564 (2011).
pubmed: 21994372
doi: 10.1523/JNEUROSCI.6801-10.2011
Honey, C. J. et al. Slow cortical dynamics and the accumulation of information over long timescales. Neuron 76, 423–434 (2012).
pubmed: 23083743
doi: 10.1016/j.neuron.2012.08.011
Mattar, M. G., Kahn, D. A., Thompson-Schill, S. L. & Aguirre, G. K. Varying timescales of stimulus integration unite neural adaptation and prototype formation. Curr. Biol. 26, 1669–1676 (2016).
pubmed: 27321999
doi: 10.1016/j.cub.2016.04.065
Stigliani, A., Jeska, B. & Grill-Spector, K. Encoding model of temporal processing in human visual cortex. Proc. Natl. Acad. Sci. USA 114, E11047–E11056 (2017).
pubmed: 29208714
doi: 10.1073/pnas.1704877114
Zhou, J., Benson, N. C., Kay, K. N. & Winawer, J. Compressive temporal summation in human visual cortex. J. Neurosci. 38, 691–709 (2018).
pubmed: 29192127
doi: 10.1523/JNEUROSCI.1724-17.2017
Stigliani, A., Jeska, B. & Grill-Spector, K. Differential sustained and transient temporal processing across visual streams. PLoS Comput. Biol. 15, e1007011 (2019).
pubmed: 31145723
doi: 10.1371/journal.pcbi.1007011
Zhou, J., Benson, N. C., Kay, K. & Winawer, J. Predicting neuronal dynamics with a delayed gain control model. PLoS Comput. Biol. 15, e1007484 (2019).
pubmed: 31747389
doi: 10.1371/journal.pcbi.1007484
Hendrikx, E., Paul, J. M., van Ackooij, M., van der Stoep, N. & Harvey, B. M. Visual timing-tuned responses in human association cortices and response dynamics in early visual cortex. Nat. Commun. 13, 3952 (2022).
pubmed: 35804026
doi: 10.1038/s41467-022-31675-9
Groen, I. I. A. et al. Temporal dynamics of neural responses in human visual cortex. J. Neurosci. 42, 7562–7580 (2022).
pubmed: 35999054
doi: 10.1523/JNEUROSCI.1812-21.2022
Friston, K. J., Mechelli, A., Turner, R. & Price, C. J. Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics. Neuroimage 12, 466–477 (2000).
pubmed: 10988040
doi: 10.1006/nimg.2000.0630
Henson, R. N., Price, C. J., Rugg, M. D., Turner, R. & Friston, K. J. Detecting latency differences in event-related BOLD responses: application to words versus nonwords and initial versus repeated face presentations. Neuroimage 15, 83–97 (2002).
pubmed: 11771976
doi: 10.1006/nimg.2001.0940
Lewis, L. D., Setsompop, K., Rosen, B. R. & Polimeni, J. R. Fast fMRI can detect oscillatory neural activity in humans. Proc. Natl. Acad. Sci. USA 113, E6679–E6685 (2016).
pubmed: 27729529
doi: 10.1073/pnas.1608117113
Zuiderbaan, W., Harvey, B. M. & Dumoulin, S. O. Modeling center-surround configurations in population receptive fields using fMRI. J. Vis. 12, 10 (2012).
pubmed: 22408041
doi: 10.1167/12.3.10
DeSimone, K. & Schneider, K. A. Distinguishing hemodynamics from function in the human LGN using a temporal response model. Vision (2019).
Aqil, M., Knapen, T. & Dumoulin, S. O. Divisive normalization unifies disparate response signatures throughout the human visual hierarchy. Proc. Natl. Acad. Sci. USA (2021).
Kim, I., Kupers, E. R., Lerma-Usabiaga, G. & Grill-Spector, K. Characterizing spatiotemporal population receptive fields in human visual cortex with fMRI. J. Neurosci. 44, e0803232023 (2024).
pubmed: 37963768
doi: 10.1523/JNEUROSCI.0803-23.2023
Maunsell, J. H., Nealey, T. A. & DePriest, D. D. Magnocellular and parvocellular contributions to responses in the middle temporal visual area (MT) of the macaque monkey. J. Neurosci. 10, 3323–3334 (1990).
pubmed: 2213142
doi: 10.1523/JNEUROSCI.10-10-03323.1990
De Valois, R. L. & Cottaris, N. P. Inputs to directionally selective simple cells in macaque striate cortex. Proc. Natl. Acad. Sci. USA 95, 14488–14493 (1998).
pubmed: 9826727
doi: 10.1073/pnas.95.24.14488
Ray, S. & Maunsell, J. H. Different origins of gamma rhythm and high-gamma activity in macaque visual cortex. PLoS Biol. 9, e1000610 (2011).
pubmed: 21532743
doi: 10.1371/journal.pbio.1000610
Jacques, C. et al. Corresponding ECoG and fMRI category-selective signals in human ventral temporal cortex. Neuropsychologia 83, 14–28 (2016).
pubmed: 26212070
doi: 10.1016/j.neuropsychologia.2015.07.024
Finzi, D. et al. Differential spatial computations in ventral and lateral face-selective regions are scaffolded by structural connections. Nat. Commun. 12, 2278 (2021).
pubmed: 33859195
doi: 10.1038/s41467-021-22524-2
Croner, L. J. & Kaplan, E. Receptive fields of P and M ganglion cells across the primate retina. Vision Res. 35, 7–24 (1995).
pubmed: 7839612
doi: 10.1016/0042-6989(94)E0066-T
Henry, C. A., Jazayeri, M., Shapley, R. M. & Hawken, M. J. Distinct spatiotemporal mechanisms underlie extra-classical receptive field modulation in macaque V1 microcircuits. Elife (2020).
Polimeni, J. R. & Lewis, L. D. Imaging faster neural dynamics with fast fMRI: a need for updated models of the hemodynamic response. Prog. Neurobiol. 207, 102174 (2021).
pubmed: 34525404
doi: 10.1016/j.pneurobio.2021.102174
Macevoy, S. P. & Epstein, R. A. Decoding the representation of multiple simultaneous objects in human occipitotemporal cortex. Curr. Biol. 19, 943–947 (2009).
pubmed: 19446454
doi: 10.1016/j.cub.2009.04.020
Rust, N. C. & Dicarlo, J. J. Selectivity and tolerance (“invariance”) both increase as visual information propagates from cortical area V4 to IT. J. Neurosci. 30, 12978–12995 (2010).
pubmed: 20881116
doi: 10.1523/JNEUROSCI.0179-10.2010
Cheng, K., Hasegawa, T., Saleem, K. S. & Tanaka, K. Comparison of neuronal selectivity for stimulus speed, length, and contrast in the prestriate visual cortical areas V4 and MT of the macaque monkey. J. Neurophysiol. 71, 2269–2280 (1994).
pubmed: 7931516
doi: 10.1152/jn.1994.71.6.2269
Tootell, R. B. et al. Functional analysis of human MT and related visual cortical areas using magnetic resonance imaging. J. Neurosci. 15, 3215–3230 (1995).
pubmed: 7722658
doi: 10.1523/JNEUROSCI.15-04-03215.1995
Smith, A. T., Greenlee, M. W., Singh, K. D., Kraemer, F. M. & Hennig, J. The processing of first- and second-order motion in human visual cortex assessed by functional magnetic resonance imaging (fMRI). J. Neurosci. 18, 3816–3830 (1998).
pubmed: 9570811
doi: 10.1523/JNEUROSCI.18-10-03816.1998
Vanduffel, W. et al. Visual motion processing investigated using contrast agent-enhanced fMRI in awake behaving monkeys. Neuron 32, 565–577 (2001).
pubmed: 11719199
doi: 10.1016/S0896-6273(01)00502-5
An, X. et al. Distinct functional organizations for processing different motion signals in V1, V2, and V4 of macaque. J. Neurosci. 32, 13363–13379 (2012).
pubmed: 23015427
doi: 10.1523/JNEUROSCI.1900-12.2012
Nandy, A. S., Mitchell, J. F., Jadi, M. P. & Reynolds, J. H. Neurons in macaque area V4 are tuned for complex spatio-temporal patterns. Neuron 91, 920–930 (2016).
pubmed: 27499085
doi: 10.1016/j.neuron.2016.07.026
Mikellidou, K. et al. Cortical BOLD responses to moderate- and high-speed motion in the human visual cortex. Sci. Rep. 8, 8357 (2018).
pubmed: 29844426
doi: 10.1038/s41598-018-26507-0
Adelson, E. H. & Bergen, J. R. Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. A 2, 284–299 (1985).
pubmed: 3973762
doi: 10.1364/JOSAA.2.000284
Watson, A. B. & Ahumada, A. J. Jr. Model of human visual-motion sensing. J. Opt. Soc. Am. A 2, 322–341 (1985).
pubmed: 3973764
doi: 10.1364/JOSAA.2.000322
Heeger, D. J. Modeling simple-cell direction selectivity with normalized, half-squared, linear operators. J. Neurophysiol. 70, 1885–1898 (1993).
pubmed: 8294961
doi: 10.1152/jn.1993.70.5.1885
Simoncelli, E. P. & Heeger, D. J. A model of neuronal responses in visual area MT. Vision Res. 38, 743–761 (1998).
pubmed: 9604103
doi: 10.1016/S0042-6989(97)00183-1
Russ, B. E., Koyano, K. W., Day-Cooney, J., Perwez, N. & Leopold, D. A. Temporal continuity shapes visual responses of macaque face patch neurons. Neuron 111, 903–914 e903 (2023).
pubmed: 36630962
doi: 10.1016/j.neuron.2022.12.021
Wallis, G. & Bulthoff, H. Learning to recognize objects. Trends Cogn. Sci. 3, 22–31 (1999).
pubmed: 10234223
doi: 10.1016/S1364-6613(98)01261-3
Zhuang, C. et al. Unsupervised neural network models of the ventral visual stream. Proc. Natl. Acad. Sci. USA (2021).
Weiner, K. S. & Grill-Spector, K. Neural representations of faces and limbs neighbor in human high-level visual cortex: evidence for a new organization principle. Psychol. Res. 77, 74–97 (2013).
pubmed: 22139022
doi: 10.1007/s00426-011-0392-x
Pitcher, D. & Ungerleider, L. G. Evidence for a third visual pathway specialized for social perception. Trends Cogn. Sci. 25, 100–110 (2021).
pubmed: 33334693
doi: 10.1016/j.tics.2020.11.006
Wurm, M. F. & Caramazza, A. Two ‘what’ pathways for action and object recognition. Trends Cogn. Sci. 26, 103–116 (2022).
pubmed: 34702661
doi: 10.1016/j.tics.2021.10.003
Mruczek, R. E. & Sheinberg, D. L. Context familiarity enhances target processing by inferior temporal cortex neurons. J. Neurosci. 27, 8533–8545 (2007).
pubmed: 17687031
doi: 10.1523/JNEUROSCI.2106-07.2007
Brady, T. F., Konkle, T. & Alvarez, G. A. Compression in visual working memory: using statistical regularities to form more efficient memory representations. J. Exp. Psychol. Gen. 138, 487–502 (2009).
pubmed: 19883132
doi: 10.1037/a0016797
Ihssen, N., Linden, D. E. & Shapiro, K. L. Improving visual short-term memory by sequencing the stimulus array. Psychon. Bull. Rev. 17, 680–686 (2010).
pubmed: 21037166
doi: 10.3758/PBR.17.5.680
Brainard, D. H. The psychophysics toolbox. Spat. Vis. 10, 433–436 (1997).
pubmed: 9176952
doi: 10.1163/156856897X00357
Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).
pubmed: 9931268
doi: 10.1006/nimg.1998.0395
Poltoratski, S., Kay, K., Finzi, D. & Grill-Spector, K. Holistic face recognition is an emergent phenomenon of spatial processing in face-selective regions. Nat. Commun. 12, 4745 (2021).
pubmed: 34362883
doi: 10.1038/s41467-021-24806-1
Smith, A. T., Singh, K. D., Williams, A. L. & Greenlee, M. W. Estimating receptive field size from fMRI data in human striate and extrastriate visual cortex. Cereb. Cortex 11, 1182–1190 (2001).
pubmed: 11709489
doi: 10.1093/cercor/11.12.1182
Witthoft, N. et al. Where is human V4? Predicting the location of hV4 and VO1 from cortical folding. Cereb. Cortex 24, 2401–2408 (2014).
pubmed: 23592823
doi: 10.1093/cercor/bht092
Amano, K., Wandell, B. A. & Dumoulin, S. O. Visual field maps, population receptive field sizes, and visual field coverage in the human MT+ complex. J. Neurophysiol. 102, 2704–2718 (2009).
pubmed: 19587323
doi: 10.1152/jn.00102.2009
Swisher, J. D., Halko, M. A., Merabet, L. B., McMains, S. A. & Somers, D. C. Visual topography of human intraparietal sulcus. J. Neurosci. 27, 5326–5337 (2007).
pubmed: 17507555
doi: 10.1523/JNEUROSCI.0991-07.2007
Watson, A. B. in Handbook of Perception and Human Performance (eds Boff, K., Kaufman, L. & Thomas, J.) Ch. 6 (Wiley, 1986).
Kupers, E. R., Kim, I. & Grill-Spector, K. Source data files for “Rethinking simultaneous suppression in visual cortex via compressive spatiotemporal population receptive fields”. Data repository at https://osf.io/rpuhs (2024).
Kupers, E. R., Kim, I. & Grill-Spector, K. Code repository of the paper “Rethinking simultaneous suppression in visual cortex via compressive spatiotemporal population receptive fields”. Code repository at https://doi.org/10.5281/zenodo.12658143 (2024).
SpatiotemporalPRFs: a MATLAB software toolbox to create spatiotemporal population receptive fields using fMRI. v. 1.0.2. Code repository at https://doi.org/10.5281/zenodo.12658232 (2024).
Hessels, R. S., Niehorster, D. C., Kemner, C. & Hooge, I. T. C. Noise-robust fixation detection in eye movement data: identification by two-means clustering. Behav. Res. Methods 49, 1802–1823 (2017).
pubmed: 27800582
doi: 10.3758/s13428-016-0822-1