Low-level image statistics in natural scenes influence perceptual decision-making.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
29 06 2020
29 06 2020
Historique:
received:
02
01
2020
accepted:
08
06
2020
entrez:
1
7
2020
pubmed:
1
7
2020
medline:
1
7
2020
Statut:
epublish
Résumé
A fundamental component of interacting with our environment is gathering and interpretation of sensory information. When investigating how perceptual information influences decision-making, most researchers have relied on manipulated or unnatural information as perceptual input, resulting in findings that may not generalize to real-world scenes. Unlike simplified, artificial stimuli, real-world scenes contain low-level regularities that are informative about the structural complexity, which the brain could exploit. In this study, participants performed an animal detection task on low, medium or high complexity scenes as determined by two biologically plausible natural scene statistics, contrast energy (CE) or spatial coherence (SC). In experiment 1, stimuli were sampled such that CE and SC both influenced scene complexity. Diffusion modelling showed that the speed of information processing was affected by low-level scene complexity. Experiment 2a/b refined these observations by showing how isolated manipulation of SC resulted in weaker but comparable effects, with an additional change in response boundary, whereas manipulation of only CE had no effect. Overall, performance was best for scenes with intermediate complexity. Our systematic definition quantifies how natural scene complexity interacts with decision-making. We speculate that CE and SC serve as an indication to adjust perceptual decision-making based on the complexity of the input.
Identifiants
pubmed: 32601499
doi: 10.1038/s41598-020-67661-8
pii: 10.1038/s41598-020-67661-8
pmc: PMC7324621
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
10573Références
Heekeren, H. R., Marrett, S. & Ungerleider, L. G. The neural systems that mediate human perceptual decision making. Nat. Rev. Neurosci. 9, 467–479 (2008).
doi: 10.1038/nrn2374
Gold, J. I. & Shadlen, M. N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007).
doi: 10.1146/annurev.neuro.29.051605.113038
Ratcliff, R. & McKoon, G. The diffusion decision model: theory and data for two-choice decision tasks. Neural Comput. 29, 997–1003 (2008).
Jahfari, S., Waldorp, L., Ridderinkhof, K. R. & Scholte, H. S. Visual information shapes the dynamics of corticobasal ganglia pathways during response selection and inhibition. J. Cogn. Neurosci. 1, 1344–1359 (2015).
doi: 10.1162/jocn_a_00792
Malcolm, G. L., Groen, I. I. A. & Baker, C. I. Making sense of real-world scenes. Trends Cogn. Sci. 20, 843–856 (2016).
doi: 10.1016/j.tics.2016.09.003
Greene, M. R. & Oliva, A. Recognition of natural scenes from global properties: Seeing the forest without representing the trees. Cogn. Psychol. 58, 137–176 (2009).
doi: 10.1016/j.cogpsych.2008.06.001
Greene, M. R. & Oliva, A. The briefest of glances: the time course of natural scene understanding. Psychol. Sci. 20, 464–472 (2009).
doi: 10.1111/j.1467-9280.2009.02316.x
Oliva, A. & Torralba, A. Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42, 145–175 (2001).
doi: 10.1023/A:1011139631724
Brady, N. & Field, D. J. Local contrast in natural images: normalisation and coding efficiency. Perception 29, 1041–1055 (2000).
doi: 10.1068/p2996
Scholte, H. S., Ghebreab, S., Waldorp, L., Smeulders, A. W. M. & Lamme, V. A. F. Brain responses strongly correlate with Weibull image statistics when processing natural images. J. Vis. 9, 29–29 (2009).
doi: 10.1167/9.4.29
Ghebreab, S., Scholte, S., Lamme, V. & Smeulders, A. A biologically plausible model for rapid natural scene identification. Adv. Neural Inf. Process. Syst. 1, 629–637 (2009).
Groen, I. I. A., Ghebreab, S., Lamme, V. A. F. & Scholte, H. S. Spatially pooled contrast responses predict neural and perceptual similarity of naturalistic image categories. PLoS Comput. Biol. 8, e1002726 (2012).
doi: 10.1371/journal.pcbi.1002726
Rosenholtz, R., Huang, J., Raj, A., Balas, B. J. & Ilie, L. A summary statistic representation in peripheral vision explains visual search. J. Vis. 1, 12 (2012).
Groen, I., Ghebreab, S., Lamme, V. & Scholte, S. The role of Weibull image statistics in rapid object detection in natural scenes. J. Vis. 10, 992–992 (2010).
doi: 10.1167/10.7.992
Jegou, H., Douze, M. & Schmid, C. Hamming embedding and weak geometric consistency for large scale image search. Eur. Conf. Comput. Vis. 5302, 304–317 (2008).
Opelt, A., Pinz, A., Fussenegger, M. & Auer, P. Generic object recognition with boosting. IEEE Trans. Pattern Anal. Mach. Intell. 28, 416–431 (2006).
doi: 10.1109/TPAMI.2006.54
Deng, J. D. J. et al. ImageNet: a large-scale hierarchical image database, in 2009 IEEE Conference on Computer Vision and Pattern Recognition 2–9 (2009).
Olmos, A. & Kingdom, F. A. A. A biologically inspired algorithm for the recovery of shading and reflectance images. Perception 33, 1463–1473 (2004).
doi: 10.1068/p5321
Groen, I. I. A., Ghebreab, S., Prins, H., Lamme, V. A. F. & Scholte, H. S. From image statistics to scene gist: evoked neural activity reveals transition from low-level natural image structure to scene category. J. Neurosci. 33, 18814–18824 (2013).
doi: 10.1523/JNEUROSCI.3128-13.2013
Thorpe, S., Fize, D. & Marlot, C. Speed of processing in the human visual system. Nature 381, 520 (1996).
doi: 10.1038/381520a0
Ratcliff, R. Measuring psychometric functions with the diffusion model. J. Exp. Psychol. Hum. Percept. Perform. 40, 870 (2014).
doi: 10.1037/a0034954
Wiecki, T. V., Sofer, I. & Frank, M. J. HDDM: hierarchical bayesian estimation of the drift-diffusion model in python. Front. Neuroinform. 7, 14 (2013).
doi: 10.3389/fninf.2013.00014
Ratcliff, R. & Childers, R. Individual differences and fitting methods for the two-choice diffusion model of decision making. Decision (Wash DC) 2015, (2015).
Spiegelhalter, D. J., Best, N. G., Carlin, B. P. & Van Der Linde, A. Bayesian measures of model complexity and fit. J. R. Stat. Soc. Ser. B Stat. Methodol. 64, 583–639 (2002).
doi: 10.1111/1467-9868.00353
Groen, I. I. A., Ghebreab, S., Lamme, V. A. F. & Scholte, H. S. The time course of natural scene perception with reduced attention. J. Neurophysiol. 115, 931–946 (2016).
doi: 10.1152/jn.00896.2015
Wolfe, J. M. Guided search 2.0: a revised model of visual search. Psychon. Bull. Rev. 1, 202–238 (1994).
doi: 10.3758/BF03200774
Jahfari, S., Ridderinkhof, K. R. & Scholte, H. S. Spatial frequency information modulates response inhibition and decision-making processes. PLoS ONE 8, e76467 (2013).
doi: 10.1371/journal.pone.0076467
Felsen, G. & Dan, Y. A natural approach to studying vision. Nat. Neurosci. 8, 1643–1646 (2005).
doi: 10.1038/nn1608
Geisler, W. S. & Diehl, R. L. A Bayesian approach to the evolution of perceptual and cognitive systems. Cogn. Sci. 27, 379–402 (2003).
doi: 10.1207/s15516709cog2703_3
Olshausen, B. A. & Field, D. J. Natural image statistics and efficient coding. Netw. Comput. Neural Syst. 7, 333–339 (1996).
doi: 10.1088/0954-898X_7_2_014
Davenport, J. L. & Potter, M. C. Scene consistency in object and background perception. Psychol. Sci. 15, 559–564 (2004).
doi: 10.1111/j.0956-7976.2004.00719.x
Neider, M. B. & Zelinsky, G. J. Scene context guides eye movements during visual search. Vis. Res. 46, 614–621 (2006).
doi: 10.1016/j.visres.2005.08.025
Greene, M. R., Botros, A. P., Beck, D. M. & Fei-Fei, L. What you see is what you expect: rapid scene understanding benefits from prior experience. Atten. Percept. Psychophys. 1, 1239–1251 (2015).
doi: 10.3758/s13414-015-0859-8
Mulder, M. J., Wagenmakers, E.-J., Ratcliff, R., Boekel, W. & Forstmann, B. U. Bias in the brain: a diffusion model analysis of prior probability and potential payoff. J. Neurosci. 32, 2335–2343 (2012).
doi: 10.1523/JNEUROSCI.4156-11.2012
Crouzet, S. M. & Serre, T. What are the Visual Features Underlying Rapid Object Recognition?. Front. Psychol. 2, 326 (2011).
pubmed: 22110461
pmcid: 3216029
Groen, I. I. A. et al. Scene complexity modulates degree of feedback activity during object detection in natural scenes. PLoS Comput. Biol. 14, e1006690 (2018).
doi: 10.1371/journal.pcbi.1006690