• 2024 (Vol.38)
  • 1990 (Vol.4)
  • 1989 (Vol.3)
  • 1988 (Vol.2)
  • 1987 (Vol.1)

Volume 35 #4

Contents

  1. Age-related difference in perception of aesthetics and visual complexity of graphical user interfaces
  2. Theoretical analysis of factors that determine features of visual attention in preterm children
  3. Differentiation of Reasoning Processes Based on Eye Movement Indicators
  4. Interaction of foveal and peripheral vision in the implementation of systemic visual functions
  5. Generation of synthetic porous images for data augmentation to train machine learning algorithms

Age-related difference in perception of aesthetics and visual complexity of graphical user interfaces

© 2021 M. A. Bakaev, O. M. Razumnikova

Novosibirsk State Technical University 630073 Novosibirsk, pr. K. Marksa, 20, Russia

Received 18 May 2021

Visual complexity of graphical user interfaces (GUIs) is believed to be closely linked to their aesthetic perception. Today's image analysis technologies can automatically assess visual complexity, but the exact form of dependence between the two characteristics is being actively researched. The current article is a review of various approaches and algorithms for quantifying the aesthetics and complexity of graphical user interfaces. The role of the subjective assessment of GUI aesthetics and of instrumental assessment of the profile of the activation state and the amplitude of emotional reactivity to visual stimuli of different emotional valence and information complexity is considered. We also overview age-related particulars of visual perception and the neuropsychological mechanisms of the functional organization of neural networks in the brain, which underlies perception and decision-making about the visual complexity and aesthetics of images at different stages of ontogenesis and during sensory deprivation. A list of existing software tools for quantifying the visual complexity and aesthetics of graphical interfaces is presented. The article can be useful both for researchers in the field of image analysis and the creation of human-machine systems, and for designers of user interfaces of IT products for choosing their optimal complexity.

Key words: Human-machine interaction, image analysis, image recognition, user behavior models, ageing changes

DOI: 10.31857/S0235009221040028

Cite: Bakaev M. A., Razumnikova O. M. Vozrastnye osobennosti vospriyatiya estetichnosti i vizualnoi slozhnosti graficheskikh interfeisov [Age-related difference in perception of aesthetics and visual complexity of graphical user interfaces]. Sensornye sistemy [Sensory systems]. 2021. V. 35(4). P. 267–293 (in Russian). doi: 10.31857/S0235009221040028

References:

  • Bakaev M.A., Razumnikova O.M. Opredelenie slozhnosti zadach dlya zritel'no-prostranstvennoi pamyati i propusknoi sposobnosti cheloveka-operatora [Defining complexity for visual-spatial memory tasks and human operator's throughput]. Upravlenie bolshimi sistemami [Large-Scale Systems Control]. 2017. V. 70. P. 25–57 (in Russian). https://doi.org/10.25728/ubs.2017.70.2
  • Gracheva M.A., Bozhkova V.P., Kazakova A.A., Rozhkova G.I. Sub'ektivnaya otsenka kachestva staticheskih i videoizobrazhenii: metodologicheskii obzor [Subjective assessment of quality of static and video images: methodological review]. Sensornie sistemi [Sensory systems]. 2019. V. 33 (4). P. 287–304 (in Russian).
  • Razumnikova O.M., Krivonogova K.D. Sensornaya deprivaciya kak model' realizacii kompensatornikh resursov mozga: obzor zarubezhnikh issledovanij [Sensory deprivation as a model for implementing the brain's compensatory resources: review of foreign studies]. Sovremennaya zarubezhnaya psikhologiya [Modern psychology abroad]. 2020. V. 9 (2). P. 57–67 (in Russian). https://doi.org/10.17759/jmfp.2020090205
  • Razumnikova O.M., Nikolaeva E.I. Tormoznie funktsii mozga i vozrastnie osobennosti organizatsii kognitivnoi deyatelnosti [Braking functions of the brain and age-related particulars in organization of cognitive activity]. Uspehi fiziologicheskih nauk [Advances of physiological sciences]. 2019. V. 50 (1). P. 75–89 (in Russian).
  • Açık A., Sarwary A., Schultze-Kraft R., Onat S., König P. Developmental changes in natural viewing behavior: Bottom-up and top-down differences between children, young adults and older adults. Front Psychol. 2010. V. 1. P. 207. https://doi.org/10.3389/fpsyg.2010.00207
  • Aleem H., Correa-Herran I., Grzywacz N.M. A Theoretical framework for how we learn aesthetic values. Front. Hum. Neurosci. 2020. V. 14. P. 345. https://doi.org/10.3389/fnhum.2020.00345
  • Alemerien K., Magel K. GUIEvaluator: A metric-tool for evaluating the complexity of graphical user interfaces. SEKE. 2014. P. 13–18.
  • Almeida-Rocha T., Peixoto F., Jesus S.N. Aesthetic development in children, adolescents and young adults. Analise Psicologica. 2020. V. 1. P. 1–13. https://doi.org/10.14417/ap.1657
  • Anderson J.S., Ferguson M.A., Lopez-Larson M., Yurgelun-Todd D. Topographic maps of multisensory attention. Proc. Nat. Acad. Sci. 2010. V. 107 (46). P. 20110–20114.
  • Bakaev M. Impact of familiarity on information complexity in human-computer interfaces. MATEC Web Conf. 2016. V. 75. P. 803–809.
  • Bakaev M., Heil S., Khvorostov V., Gaedke M. HCI vision for automated analysis and mining of web user interfaces. Intern. Conf. Web Engineer. 2018a. P. 136–144. Bakaev M., Heil S., Khvorostov V., Gaedke M. Auto-extraction and integration of metrics for web user interfaces. J. Web Engineer. 2018b. V. 17(6). P. 561–590.
  • Bankson B.B., Hebart M.N., Groen I.I.A., Baker C.I. The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks. Neuroimage. 2018. V. 178. P. 172–182.
  • Barbot A., Liu S., Kimchi R., Carrasco M. Attention enhances apparent perceptual organization. Psychonomic bull. review. 2018. V. 25 (5). P.1824–1832. https://doi.org/10.3758/s13423-017-1365-x
  • Berger A., Henik A., Rafal R. Competition between endogenous and exogenous orienting of visual attention. J. Exp. Psychol: General. 2005. V. 134 (2). P. 207–209. https://doi.org/10.1037/0096-3445.134.2.207
  • Berlyne D.E. Aesthetics and Psychobiology. Appleton-Century-Crofts New York: New York, NY, USA. 1971. V. 336.
  • Bertamini M., Palumbo L., Gheorghes T.N., Galatsidas M. Do observers like curvature or do they dislike angularity? British J. Psychol. 2016. V. 107 (1). P. 154–178. https://doi.org/10.1111/bjop.12132
  • Bertamini M., Rampone G., Makin A.D., Jessop A. Symmetry preference in shapes, faces, flowers and landscapes. Peer J. 2019. V. 7. e7078. https://doi.org/10.7717/peerj.7078
  • Bo Y., Yu J., Zhang K. Computational aesthetics and applications. Vis Comput Ind Biomed Art. 2018. V. 1. Art. 6. https://doi.org/10.1186/s42492-018-0006-1
  • Bölte J., Hösker T.M., Hirschfeld G., Thielsch, M.T. Electrophysiological correlates of aesthetic processing of webpages: a comparison of experts and laypersons. Peer J. 2017. V. 5. e3440. https://doi.org/10.7717/peerj.3440
  • Borst A., Theunissen, F.E. Information theory and neural coding. Nature neuroscience. 1999. V. 2 (11). P. 947–957.
  • Boyce W.P., Lindsay A., Zgonnikov A., Rañó I., WongLin K. Optimality and limitations of audio-visual integration for cognitive systems. Frontiers in robotics and AI. 2020. V. 7. P. 94. https://doi.org/10.3389/frobt.2020.00094
  • Brachmann A., Redies C. Computational and experimental approaches to visual aesthetics. Front. Comput. Neurosci. 2017. V. 11. P. 102–110. https://doi.org/10.3389/fncom.2017.00102
  • Brachmann A., Barth E., Redies C. Using CNN features to better understand what makes visual artworks special. Frontiers in psychology. 2017. V. 8. P. 830–835. https://doi.org/10.3389/fpsyg.2017.00830
  • Braddick O., Atkinson J. Development of human visual function. Vision research. 2011. V. 51 (13). P. 1588–1609. https://doi.org/10.1016/j.visres.2011.02.018
  • Bradley M.M., Houbova P., Miccoli L., Costa V.D., Lang P.J. Scan patterns when viewing natural scenes: Emotion, complexity, and repetition. Psychophysiol. 2011. V. 48 (11). P. 1544–1553. https://doi.org/10.1111/j.1469-8986.2011.01223.x
  • Britz J., Díaz Hernàndez L., Ro T., Michel C.M. EEG-microstate dependent emergence of perceptual awareness. Frontiers in behavioral neuroscience. 2014. V. 8. P. 163–169.
  • Calvert G.A., Thesen T. Multisensory integration: methodological approaches and emerging principles in the human brain. J. Physiol. Paris. 2004. V. 98 (1–3). P. 191–205. https://doi.org/10.1016/j.jphysparis.2004.03.018
  • Carballal A., Santos A., Romero J., Machado P., Correia J., Castro L. Distinguishing paintings from photographs by complexity estimates. Neural Comp. Appl. 2018. V. 30 (6). P. 1957–1969.
  • Cattaneo Z., Lega C., Ferrari C., Vecchi T., CelaConde C.J., Silvanto J., Nadal M. The role of the lateral occipital cortex in aesthetic appreciation of representational and abstract paintings: A TMS study. Brain and cognition. 2015. V. 95. P. 44–53. https://doi.org/10.1016/j.bandc.2015.01.008
  • Chaitin G.J. Algorithmic information theory. IBM journal of research and development. 1977. V. 21 (4). P. 350–359.
  • Charles R.L., Nixon J. Measuring mental workload using physiological measures: A systematic review. Applied ergonomics. 2019. V. 74. P. 221–232. https://doi.org/10.1016/j.apergo.2018.08.028
  • Chater N., Vitányi P. Simplicity: A unifying principle in cognitive science? Trends in cognitive sciences. 2003. V. 7 (1). P. 19–22.
  • Chaumon M., Bishop D.V., Busch N.A. A practical guide to the selection of independent components of the electroencephalogram for artifact correction. J. Neurosci. methods. 2015. V. 250. P. 47–63.
  • Chella F., Pizzella V., Zappasodi F., Marzetti L. Impact of the reference choice on scalp EEG connectivity estimation. J. Neural Engineering. 2016. V. 13 (3). P. 036016.
  • Chikhman V., Bondarko V., Danilova M., Goluzina A., Shelepin Y. Complexity of images: Experimental and computational estimates compared. Perception. 2012. V. 41 (6). P. 631–647.
  • Cohen M.R., Maunsell J.H. Attention improves performance primarily by reducing interneuronal correlations. Nature neuroscience. 2009. V. 12 (12). P. 1594. https://doi.org/10.1038/nn.2439
  • Colonius H., Diederich A. The race model inequality: interpreting a geometric measure of the amount of violation. Psychological Review. 2006. V. 113 (1). P. 148–154.
  • Corchs S.E., Ciocca G., Bricolo E., Gasparini F. Predicting complexity perception of real world images. PloS one. 2016. V. 11 (6). e0157986.
  • Cotter K.N., Silvia P.J., Bertamini M., Palumbo L., Vartanian O. Curve appeal: Exploring individual differences in preference for curved versus angular objects. i-Perception. 2017. V. 8 (2). P. 1–17. https://doi.org/10.1177/2041669517693023
  • de Dieuleveult A.L., Siemonsma P.C., van Erp J.B., Brouwer A.M. Effects of aging in multisensory integration: a systematic review. Frontiers in aging neuroscience. 2017. V. 9. P. 80–89. https://doi.org/10.3389/fnagi.2017.00080
  • Donderi D.C., McFadden, S. Compressed file length predicts search time and errors on visual displays. Displays. 2005. V. 26 (2). P. 71–78. https://doi.org/10.1016/j.displa.2005.02.002
  • Donderi D.C. Visual complexity: a review. Psychological bulletin. 2006. V. 132 (1). P. 73–78.
  • Dou Q., Zheng X.S., Sun T., Heng, P.A. Webthetics: quantifying webpage aesthetics with deep learning. Intern. J. Human-Computer Studies. 2019. V. 124. P. 56–66.
  • Driver J., Noesselt T. Multisensory interplay reveals crossmodal influences on ‘sensory-specific' brain regions, neural responses, and judgments. Neuron. 2008. V. 57 (1). P. 11–23.
  • Emery K.J., Webster M.A. Individual differences and their implications for color perception. Current opinion in behavioral sciences. 2019. V. 30. P. 28–33. https://doi.org/10.1016/j.cobeha.2019.05.002
  • Erel H., Levy D.A. Orienting of visual attention in aging. Neurosci. Biobehav. Rev. 2016. V. 69. P. 357–380. https://doi.org/10.1016/j.neubiorev.2016.08.010
  • Erez Y., Duncan J. Discrimination of visual categories based on behavioral relevance in widespread regions of frontoparietal cortex. J. Neurosci. 2015. V. 35 (36). P. 12383–12393.
  • Fairbairn D. Measuring map complexity. The Cartographic Journal. 2006. V. 43 (3). P. 224–238.
  • Fister J.K., Stevenson R.A., Nidiffer A.R., Barnett Z.P., Wallace M.T. Stimulus intensity modulates multisensory temporal processing. Neuropsychologia. 2016. V. 88. P. 92–100. https://doi.org/10.1016/j.neuropsychologia.2016.02.016
  • Freyer F., Becker R., Dinse H.R., Ritter P. State-dependent perceptual learning. J. Neurosci. 2013. V. 33. P. 2900–2907. https://doi.org/10.1523/JNEUROSCI.4039-12.2013
  • Gao Z., Gao Q., Tang N., Shui R., Shen M. Organization principles in visual working memory: Evidence from sequential stimulus display. Cognition. 2016. V. 146. P. 277–288.
  • Gao J., Soranzo A. Individual differences in aesthetic preferences for multi-sensorial stimulation. Vision. 2020. V. 4 (1). P. 6. https://doi.org/10.3390/vision4010006
  • Gheorghiu E., Kingdom F.A., Petkov N. Contextual modulation as de-texturizer. Vision Research. 2014. V. 104. P. 12–23. https://doi.org/10.1016/j.visres.2014.08.013
  • Grady C., Sarraf S., Saverino C., Campbell K. Age differences in the functional interactions among the default, frontoparietal control, and dorsal attention networks. Neurobiol. Aging. 2016. V. 41. P. 159–172. https://doi.org/10.1016/j.neurobiolaging.2016.02.020
  • Graf L.K., Landwehr J.R. A dual-process perspective on fluency-based aesthetics: The pleasure-interest model of aesthetic liking. Personality and Social Psychology Review. 2015. V. 19 (4). P. 395–410. https://doi.org/10.1177/1088868315574978
  • Greene M.R., Baldassano C., Esteva A., Beck D.M., FeiFei L. Visual scenes are categorized by function. J. Exp. Psychol. Gen. 2016. V. 145. P. 82–94.
  • Greenlee M.W., Sekuler A.B. Visual perception and visual cognition in healthy and pathological ageing. Frontiers in psychology. 2014. V. 5. P. 348–355. https://doi.org/10.3389/fpsyg.2014.00348
  • Grill-Spector K., Weiner K.S. The functional architecture of the ventral temporal cortex and its role in categorization. Nat. Rev. Neurosci. 2014. V. 15. P. 536–548.
  • Groen I.I., Silson E.H., Baker C.I. Contributions of lowand high-level properties to neural processing of visual scenes in the human brain. Phil Trans R Soc B. 2017. V. 372–381.
  • Güçlütürk Y., Güçlü U., van Gerven M. Representations of naturalistic stimulus complexity in early and associative visual and auditory cortices. Sci Rep. 2018. V. 8. P. 3439–3445. https://doi.org/10.1038/s41598-018-21636-y
  • Habak C., Seghier M.L., Brûlé J., Fahim M.A., Monchi O. Age Affects how task difficulty and complexity modulate perceptual decision-making. Front. Aging Neurosci. 2019. V. 11. P. 28–35. https://doi.org/10.3389/fnagi.2019.00028
  • Haegerstrom-Portnoy G., Schneck M.E., Lott L.A., Hewlett S.E., Brabyn J.A. Longitudinal increase in anisometropia in older adults. Optomet. Vis.Sci. 2014. V. 91 (1). P. 60–65. https://doi.org/10.1097/OPX.0000000000000114
  • Harrie L., Stigmar H., Djordjevic M. Analytical estimation of map readability. ISPRS Intern. J. Geo-Inform. 2015. V. 4 (2). P. 418–446.
  • Helo A., Pannasch S., Sirri L., Rämä P. The maturation of eye movement behavior: scene viewing characteristics in children and adults. Vision Res. 2014. V. 103. P. 83–91.
  • Hsieh S., Yao Z.F., Yang M.H., Yang C.T., Wang C.H. Diffusion tensor imaging revealing the relation of age-related differences in the corpus callosum with cognitive style. Frontiers in human neuroscience. 2020. V. 14. P. 285–290. https://doi.org/10.3389/fnhum.2020.00285
  • Hübner R., Fillinger M.G. Perceptual balance, stability, and aesthetic appreciation: Their relations depend on the picture type. i-Perception. 2019. V. 10 (3). Art.2041669519856040. https://doi.org/10.1177/2041669519856040
  • Ichikawa K., Yokoyama S., Tanaka Y., Nakamura H., Smith R.T., Tanabe S. The change in color vision with normal aging evaluated on standard pseudoisochromatic plates part-3. Current Eye Research. 2020. V. 12. P. 1–9. https://doi.org/10.1080/02713683.2020.1843683
  • Iemi L., Chaumon M., Crouzet S.M., Busch N.A. Spontaneous neural oscillations bias perception by modulating baseline excitability. J. Neurosci. 2017. V. 37 (4). P. 807–819. https://doi.org/10.1523/JNEUROSCI.1432-16.2016
  • Jacobsen T. Beauty and the brain: culture, history and individual differences in aesthetic appreciation. J. Anat. 2010. V. 216. P. 184–191.
  • Johnson S.P. Development of visual perception. Wiley Interdisciplinary Reviews: Cognitive Science. 2011. V. 2 (5). P. 515–528. https://doi.org/10.1002/wcs.128
  • Kałamała P., Sadowska A., Ordziniak W., Chuderski A. Gestalt effects in visual working memory. Exp. Psychol. 2017. V. 64 (1). P. 5–13.
  • van Kerkoerle T., Self M.W., Dagnino B., Gariel-Mathis M.A., Poort J., Van Der Togt C., Roelfsema P.R. Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex. Proc. Nat. Acad. Sci. 2014. V. 111 (40). P. 14332–14341. https://doi.org/10.1073/pnas.1402773111
  • King M.L., Groen I.I., Steel A., Kravitz D.J., Baker C.I. Similarity judgments and cortical visual responses reflect different properties of object and scene categories in naturalistic images. NeuroImage. 2019. V. 197. P. 368–382.
  • Koenderink J., Van Doorn A., Pinna B. Measures of Prägnanz? Gestalt Theory. 2018. V. 40 (1). P. 7–28.
  • Konecni V.J. Determinants of aesthetic preference and effects of exposure to aesthetic stimuli: Social, emotional, and cognitive factors. 1979. Univ. California San Diego.
  • Kravitz D.J., Saleem K.S., Baker C.I., Ungerleider L.G., Mishkin M. The ventral visual pathway: an expanded neural framework for the processing of object quality. Trends Cogn. Sci. (Regul Ed). 2013. V. 17. P. 26–49.
  • Kuai S.G., Kourtzi Z. Learning to see, but not discriminate, visual forms is impaired in aging. Psychol.Sci. 2013. V. 24 (4). P. 412–422. https://doi.org/10.1177/0956797612459764
  • Leder H., Belke B., Oeberst A., Augustin D. A model of aesthetic appreciation and aesthetic judgments. British J. Psychol. 2004. V. 95 (4). P. 489–508. https://doi.org/10.1348/0007126042369811
  • Leeuwenberg E.L. Structural information of visual patterns. The Hague: Mouton de Gruyter. 1968.
  • Leeuwenberg E.L. Quantitative specification of information in sequential patterns. Psychological Review. 1969. V. 76 (2). P. 216–220.
  • Li H.J., Hou X.H., Liu H.H., Yue C.L., Lu G.M., Zuo X.N. Putting age-related task activation into large-scale brain networks: a metaanalysis of 114 fMRI studies on healthy aging. Neurosci. Biobehav. Rev. 2015. V. 57. P. 156–174. https://doi.org/10.1016/j.neubiorev.2015.08.013
  • Li R., Zhang J. Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science. Brain Informatics. 2020. V. 7 (1). P. 1–17. https://doi.org/10.1186/s40708-020-00118-w
  • Locher P.J. Experimental techniques for investigating the contribution of pictorial balance to the creation and perception of visual displays. Empirical Studies of the Arts. 2003. V. 21 (2). P. 127–135.
  • Löw A., Bradley M.M., Lang P.J. Perceptual processing of natural scenes at rapid rates: effects of complexity, content, and emotional arousal. Cogn., Affect.Behav. Neurosc. 2013. V. 13 (4). P. 860–868. https://doi.org/10.3758/s13415-013-0179-1
  • Luce R.D. Whatever happened to information theory in psychology? Rev.General Psychol. 2003. V. 7 (2). P. 183–188.
  • Machado P., Romero J., Nadal M., Santos A., Correia J., Carballal A. Computerized measures of visual complexity. Acta Psychol. 2015. V. 160. P. 43–57.
  • Mahoney J.R., Verghese J. Does cognitive impairment influence visual-somatosensory integration and mobility in older adults? The journals of gerontology. Series A, Biological sciences and medical sciences. 2020. V. 75 (3). P. 581–588. https://doi.org/10.1093/gerona/glz117
  • Mahoney J.R., Verghese J. Using the race model inequality to guantify behavioral multisensory integration effects. J. Visualized Experim: JoVE. 2019. V. 147–150. https://doi.org/10.3791/59575
  • Mahoney J.R., Holtzer R., Verghese J. Visual-somatosensory integration and balance: evidence for psychophysical integrative differences in aging. Multisensory research. 2014. V. 27 (1). P. 17–42. https://doi.org/10.1163/22134808-00002444
  • Mahoney J.R., Li P.C.C., Oh-Park M., Verghese J., Holtzer R. Multisensory integration across the senses in young and old adults. Brain research. 2011. V. 1426. P. 43–53. https://doi.org/10.1016/j.brainres.2011.09.017
  • Mangun G.R. Neural mechanisms of visual selective attention. Psychophysiology. 1995. V. 32 (1). P. 4–18. 1995. tb03400. x. https://doi.org/10.1111/j.1469-8986
  • Marin M.M., Leder H. Examining complexity across domains: relating subjective and objective measures of affective environmental scenes, paintings and music. PloS one. 2013. V. 8 (8), e72412. https://doi.org/10.1371/journal.pone.0072412
  • Martindale C., Moore K., Borkum J. Aesthetic preference: Anomalous findings for Berlyne's psychobiological theory. The Amer. J. Psychol. 1990. P. 53–80.
  • Mateus C., Lemos R., Silva M.F., Reis A., Fonseca P., Oliveiros B., Castelo-Branco M. Aging of low and high level vision: from chromatic and achromatic contrast sensitivity to local and 3D object motion perception. PloS one. 2013. V. 8 (1). e55348. https://doi.org/10.1371/journal.pone.0055348
  • Meng Q., Wang B., Cui D., Liu N., Huang Y., Chen L., Ma Y. Age-related changes in local and global visual perception. J.Vision. 2019. V. 19 (1). P. 10–11. https://doi.org/10.1167/19.1.10
  • Michailidou E., Eraslan S., Yesilada Y., Harper S. Automated prediction of visual complexity of web pages: Tools and evaluations. Intern. J. Human-Comp. Studies. 2021. V. 145, Art. 102523.
  • Michailidou E., Harper S., Bechhofer S. Visual complexity and aesthetic perception of web pages. In Proc. 26th Annual ACM Intern. Conf. Des. Comm. 2008. P. 215–224.
  • Michel C.M., Seeck M., Murray M.M. The speed of visual cognition. Supplements to Clinical neurophysiology. 2004. V. 57. P. 617–627. https://doi.org/10.1016/s1567-424x(09)70401-5
  • Miniukovich A., de Angeli A. Quantification of interface visual complexity. In Proceedings of the 2014 international working conference on advanced visual interfaces. 2014. P. 153–160.
  • Miniukovich A., Marchese M. Relationship between visual complexity and aesthetics of webpages. In Proc. 2020 CHI Conf. Human Factors in Comp. Systems. 2020. P. 1–13.
  • Miniukovich A., Sulpizio S., de Angeli A. Visual complexity of graphical user interfaces. In Proc. 2018 Intern. Conf. Adv. Visual Interfaces. 2018. P. 1–9.
  • Misselhorn J., Göschl F., Higgen F.L., Hummel F.C., Gerloff C., Engel A.K. Sensory capability and information integration independently explain the cognitive status of healthy older adults. Scientific reports. 2020. V. 10 (1). Art. 22437. https://doi.org/10.1038/s41598-020-80069-8
  • Moshagen M., Thielsch M.T. Facets of visual aesthetics. Intern. J. Human-Computer Studies. 2010. V. 68 (10). P. 689–709.
  • Nadal M., Munar E., Marty G., Cela-Conde C.J. Visual complexity and beauty appreciation: Explaining the divergence of results. Empirical Studies of the Arts. 2010. V. 28 (2). P. 173–191. https://doi.org/10.2190/EM.28.2.d
  • Nagle F., Lavie N. Predicting human complexity perception of real-world scenes. R. Soc. Open Sci. 2020. V. 7. Art. 191487. https://doi.org/10.1098/rsos.191487
  • Noppeney U. The effects of visual deprivation on functional and structural organization of the human brain. Neurosci. Biobehav. Rev. 2007. V. 31 (8). P. 1169–1180. https://doi.org/10.1016/j.neubiorev.2007.04.012
  • Norman D. Emotion & design: attractive things work better. Interactions. 2002. V. 9 (4). P. 36–42. https://doi.org/10.1145/543434.543435
  • Norman D.A. Emotional design: Why we love (or hate) everyday things. Basic Civitas Books. 2004.
  • Norman J.F., Higginbotham A.J. Aging and the perception of global structure. Plos one. 2020. V. 15 (5). e0233786. https://doi.org/10.1371/journal.pone.0233786
  • Nuthmann A., Schütz I., Einhäuser W. Salience-based object prioritization during active viewing of naturalistic scenes in young and older adults. Sci Rep. 2020. V. 10 (1). P. 1–18. https://doi.org/10.1038/s41598-020-78203-7
  • Ogawa N., Motoyoshi I. Differential effects of orientation and spatial-frequency spectra on visual unpleasantness. Frontiers in psychology. 2020. V. 11. P. 1342–1348. https://doi.org/10.3389/fpsyg.2020.01342
  • Oostenveld R., Fries P., Maris E., Schoffelen J.M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comp. Intel. Neurosci. 2011.
  • Oulasvirta A., De Pascale S., Koch J., Langerak T., Jokinen J., Todi K., Laine M., Kristhombuge M., Zhu Y., Miniukovich A., Palmas G. October. Aalto Interface Metrics (AIM) A service and codebase for computational GUI evaluation. In The 31st Annual ACM Symposium on User Interface Software and Technology Adjunct Proceedings. 2018. P. 16–19.
  • Owsley C. Vision and aging. Annu Rev Vis Sci. 2016. V. 14 (2). P. 255–271. https://doi.org/10.1146/annurev-vision-111815-114550
  • Oyibo K., Adaji I., Vassileva J. The effect of age and information design on the perception of visual aesthetics. In Proc. 32nd Intern. BCS Human Comp. Interact. Conf. 2018. P. 1–5. https://doi.org/10.14236/ewic/HCI2018.208
  • Palmer S.E. Vision science: Photons to phenomenology. MIT press. 1999.
  • Pearce M.T., Zaidel D.W., Vartanian O., Skov M., Leder H., Chatterjee A., Nadal M. Neuroaesthetics. Perspectives on Psychological Science. 2016. V. 11 (2). P. 265–279. https://doi.org/10.1177/1745691615621274
  • Pessoa L. On the relationship between emotion and cognition. Nature reviews neuroscience. 2008. V. 9 (2). P. 148–158. https://doi.org/10.1038/nrn2317
  • Peterson D.J., Berryhill M.E. The Gestalt principle of similarity benefits visual working memory. Psychonomic bulletin & review. 2013. V. 20 (6). P. 1282–1289.
  • Pugach C., Leder H., Graham D.J. How stable are human aesthetic preferences across the lifespan? Front. Hum. Neurosci. 2017. V. 11. Art. 289. https://doi.org/10.3389/fnhum.2017.00289
  • Quiroga R.Q., Panzeri S. Extracting information from neuronal populations: information theory and decoding approaches. Nature Rev. Neurosci. 2009. V. 10 (3). P. 173–185.
  • Reinecke K., Yeh T., Miratrix L., Mardiko R., Zhao Y., Liu J., Gajos K.Z. Predicting users' first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness. In Proc. SIGCHI Conf. Human Fact. Comp. Systems. 2013. P. 2049–2058.
  • Ren Y., Guo A., Xu Z., Wang T., Wu R., Yang W. Age-related functional brain connectivity during audio–visual hand-held tool recognition. Brain and behavior. 2020a. V. 10 (9). e01759. https://doi.org/10.1002/brb3.1759
  • Ren Y., Li S., Wang T., Yang W. Age-related shifts in theta oscillatory activity during audio-visual integration regardless of visual attentional load. Frontiers in aging neuroscience. 2020b. V. 12. P. 329. https://doi.org/10.3389/fnagi.2020.571950
  • Reppa I., McDougall S. When the going gets tough the beautiful get going: aesthetic appeal facilitates task performance. Psychonomic bulletin & review. 2015. V. 22 (5). P. 1243–1254. https://doi.org/10.3758/s13423-014-0794-z
  • Ritter P., Born J., Brecht M., Dinse H.R., Heinemann U., Pleger B., Schmitz D., Schreiber S., Villringer A., Kempter R. State-dependencies of learning across brain scales. Front. Comp. Neurosci. 2015. V. 9. P. 1. https://doi.org/10.3389/fncom.2015.00001
  • Ritterfeld U.T.E. Social heuristics in interior design preferences. J. Environment. Psychol. 2002. V. 22 (4). P. 369–386.
  • Rosenholtz R., Li Y., Nakano L. Measuring visual clutter. J. Vision. 2007. V. 7 (2). P. 17–19.
  • Roudaia E., Bennett P.J., Sekuler A.B. Contour integration and aging: the effects of element spacing, orientation alignment and stimulus duration. Front. Psychol. 2013. V. 4. P. 356. https://doi.org/10.3389/fpsyg.2013.00356
  • Schloss K.B., Palmer S.E. Aesthetic response to color combinations: preference, harmony, and similarity. Attention, Perception, & Psychophysics. 2011. V. 73 (2). P. 551–571. https://doi.org/10.3758/s13414-010-0027-0
  • Schmidt T., Wolff C. The Influence of User Interface Attributes on Aesthetics. i-com. 2018. V. 17 (1). P. 41–55.
  • Schomaker J., Walper D., Wittmann B.C., Einhäuser W. Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Res. 2017. V. 133. P. 161–175. https://doi.org/10.1016/j.visres.2017.02.003
  • Sedda A., Scarpina F. Dorsal and ventral streams across sensory modalities. Neurosci. Bull. 2012. V. 28 (3). P. 291–300.
  • Seow S.C. Information theoretic models of HCI: A comparison of the hick-hyman law and fitts' law. Humancomputer interaction. 2005. V. 20 (3). P. 315–352.
  • Shavit-Cohen K., Zion Golumbic E. The dynamics of attention shifts among concurrent speech in a naturalistic multi-speaker virtual environment. Front Hum Neurosci. 2019. V. 8 (13). Art.386. https://doi.org/10.3389/fnhum.2019.00386
  • Shigeto H., Ishiguro J., Nittono H. Effects of visual stimulus complexity on event-related brain potentials and viewing duration in a free-viewing task. Neuroscience letters. 2011. V. 497 (2). P. 85–89. https://doi.org/10.1016/j.neulet.2011.04.035
  • Sigala R., Haufe S., Roy D., Dinse H.R., Ritter P. The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models. Front. Comput. National. Neurosci. 2014. V. 8. P. 36–40. 00036 https://doi.org/10.3389/fncom.2014
  • Silvestre D., Guy J., Hanck J., Cornish K., Bertone A. Different luminance-and texture-defined contrast sensitivity profiles for school-aged children. Scientific Reports. 2020. V. 10 (1). P. 1–7. https://doi.org/10.1038/s41598-020-69802-5
  • Siu C.R., Murphy K.M. The development of human visual cortex and clinical implications. Eye and brain. 2018. V. 10. P. 25–36. https://doi.org/10.2147/EB.S130893
  • Sonderegger A., Sauer J. The influence of design aesthetics in usability testing: Effects on user performance and perceived usability. Applied ergonomics. 2010. V. 41 (3). P. 403–410. https://doi.org/10.1016/j.apergo.2009.09.002
  • Sonderegger A., Schmutz S., Sauer J. The influence of age in usability testing. Applied Ergonomics. 2016. V. 52. P. 291–300. https://doi.org/10.1016/j.apergo.2015.06.012
  • Spillmann L., Dresp-Langley B., Tseng C.H. Beyond the classical receptive field: the effect of contextual stimuli. J.Vision. 2015. V. 15 (9). P. 7–10. https://doi.org/10.1167/15.9.7
  • Srokova S., Hill P.F., Koen J.D., King D.R., Rugg M.D. Neural differentiation is moderated by age in scene-selective, but not face-selective, cortical regions. Eneuro. 2020. V. 7 (3). https://doi.org/10.1523/ENEURO.0142-20.2020
  • Stanischewski S., Altmann C.S., Brachmann A., Redies C. Aesthetic perception of line patterns: effect of edge-orientation entropy and curvilinear shape. i-Perception. 2020. V. 11 (5). Art. 2041669520950749. https://doi.org/10.1177/2041669520950749
  • Stickel C., Ebner M., Holzinger A. The XAOS metric–understanding visual complexity as measure of usability. In Symposium of the Austrian HCI and Usability Engineering Group. 2010. P. 278–290. Springer, Berlin, Heidelberg.
  • Strother L., Kubovy M. Perceived complexity and the grouping effect in band patterns. Acta Psychologica. 2003. V. 114. P. 229–244.
  • Taba S.E.S., Keivanloo I., Zou Y., Ng J., Ng T. An exploratory study on the relation between user interface complexity and the perceived quality. In Intern. Conf. Web Engin. 2014. P. 370–379.
  • Thielsch M.T., Blotenberg I., Jaron R. User evaluation of websites: From first impression to recommendation. Interacting with Computers. 2014. V. 26 (1). P. 89–102. https://doi.org/10.1093/iwc/iwt033
  • Thielsch M.T., Haines R., Flacke L. Experimental investigation on the effects of website aesthetics on user performance in different virtual tasks. PeerJ. 2019. V. 7. e6516. https://doi.org/10.7717/peerj.6516
  • Tractinsky N., Katz A.S., Ikar D. What is beautiful is usable. Interacting with computers. 2000. V. 13 (2). P. 127–145.
  • van Uden C.E., Nastase S.A., Connolly A.C., Feilong M., Hansen I., Gobbini M.I., Haxby J.V. Modeling semantic encoding in a common neural representational space. Frontiers in neuroscience. 2018. V. 12. Art. 437. https://doi.org/10.3389/fnins.2018.00437
  • Vartanian O., Skov M. Neural correlates of viewing paintings: Evidence from a quantitative meta-analysis of functional magnetic resonance imaging data. Brain and cognition. 2014. V. 87. P. 52–56. https://doi.org/10.1016/j.bandc.2014.03.004
  • Wagemans J., Elder J.H., Kubovy M., Palmer S.E., Peterson M.A., Singh M., von der Heydt R. A century of Gestalt psychology in visual perception: I. Perceptual grouping and figure–ground organization. Psychological bulletin. 2012. V. 138 (6). Art. 1172.
  • Wagemans J., Feldman J., Gepshtein S., Kimchi R., Pomerantz J.R., Van der Helm P.A., Van Leeuwen C. A century of Gestalt psychology in visual perception: II. Conceptual and theoretical foundations. Psychological bulletin. 2012. V. 138 (6). Art. 1218.
  • Wang J., Hsu Y. The Relationship of symmetry, complexity, and shape in mobile interface aesthetics, from an emotional perspective. A case study of the smartwatch. Symmetry. 2020. V. 12 (9), Art. 1403.
  • Widmann A., Schröger E., Maess B. Digital filter design for electrophysiological data–a practical approach. J.Neurosci. Methods. 2015. V. 250. P. 34–46.
  • Wu O., Hu W., Shi L. Measuring the visual complexities of web pages. ACM Transactions on the Web (TWEB). 2013. V. 7 (1). P. 1–34.
  • Wuerger S., Xiao K., Chauhan T. Colour vision across the life span: effect of age, ambient illumination and individual differences. J. Vision. 2017. V. 17 (26). https://doi.org/10.1167/17.7.26
  • Xie W., Zhang W. Familiarity speeds up visual short-term memory consolidation: Electrophysiological evidence from contralateral delay activities. J. Cognitive Neurosci. 2018. V. 30 (1). P. 1–13.
  • Xing J. Measures of information complexity and the implications for automation design. 2004. Federal Aviation Administration Oklahoma City OK Civil Aeromedical Inst.
  • Yan F.F., Hou F., Lu H., Yang J., Chen L., Wu Y., Chen G., Huang C.B. Aging affects gain and internal noise in the visual system. Scientific reports. 2020. V. 10 (1). P. 1–10. https://doi.org/10.1038/s41598-020-63053-0
  • Yu H., Winkler S. Image complexity and spatial information. In 2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX). 2013. P. 12–17. IEEE.
  • Zhang J., Liu X.L., So M., Reder L.M. Familiarity acts as a reduction in objective complexity. Memory & Cognition. 2020. V. 48. P. 1376–1387. https://doi.org/10.3758/s13421-020-01055-z
  • Zhang S., Xu W., Zhu Y., Tian E., Kong W. Impaired multisensory integration predisposes the elderly people to fall: A Systematic Review. Front.Neurosci. 2020. V. 14. Art. 411. https://doi.org/10.3389/fnins.2020.00411