Andersson, M. B. Sexual Selection. (Princeton University Press, 1994).
Cummings, M. E. & Endler, J. A. 25 Years of sensory drive: the evidence and its watery bias. Curr. Zool. 64, 471–484 (2018).
Seehausen, O. et al. Speciation through sensory drive in cichlid fish. Nature 455, 620–626 (2008).
Endler, J. A. & Basolo, A. L. Sensory ecology, receiver biases and sexual selection. Trends Ecol. Evol. 13, 415–420 (1998).
Ryan, M. J. Sexual selection, sensory systems and sensory exploitation. Oxf. Surv. Evol. Biol. 7, 157–195 (1990).
Endler, J. A. & Mappes, J. The current and future state of animal coloration research. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160352 (2017).
Ryan, M. J. & Rand, A. S. The sensory basis of sexual selection for complex calls in the Túngara frog, Physalaemus pustulosus (sexual selection for sensory exploitation). Evolution 44, 305–314 (1990).
Justin Marshall, N. Communication and camouflage with the same ‘bright’ colours in reef fishes. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 355, 1243–1248 (2000).
Fleishman, L. J. The influence of the sensory system and the environment on motion patterns in the visual displays of anoline lizards and other vertebrates. Am. Nat. 139, S36–S61 (1992).
Palmer, S. E., Schloss, K. B. & Sammartino, J. Visual aesthetics and human preference. Annu. Rev. Psychol. 64, 77–107 (2013).
Graham, D. J. & Redies, C. Statistical regularities in art: relations with visual coding and perception. Vis. Res. 50, 1503–1509 (2010).
Redies, C., Hänisch, J., Blickhan, M. & Denzler, J. Artists portray human faces with the Fourier statistics of complex natural scenes. Netw. Comput. Neural Syst. 18, 235–248 (2007).
Redies, C., Hasenstein, J. & Denzler, J. Fractal-like image statistics in visual art: similarity to natural scenes. Spat. Vis. 21, 137–148 (2007).
Graham, D. & Field, D. Statistical regularities of art images and natural scenes: spectra, sparseness and nonlinearities. Spat. Vis. 21, 149–164 (2008).
Menzel, C., Hayn-Leichsenring, G. U., Langner, O., Wiese, H. & Redies, C. Fourier power spectrum characteristics of face photographs: attractiveness perception depends on low-level image properties. PLoS ONE 10, e0122801 (2015).
Renoult, J. P., Bovet, J. & Raymond, M. Beauty is in the efficient coding of the beholder. R. Soc. Open Sci. 3, 160027 (2016).
Penacchio, O. & Wilkins, A. J. Visual discomfort and the spatial distribution of Fourier energy. Vis. Res. 108, 1–7 (2015).
Juricevic, I., Land, L., Wilkins, A. & Webster, M. A. Visual discomfort and natural image statistics. Perception 39, 884–899 (2010).
Changizi, M. A. The optimal human ventral stream from estimates of the complexity of visual objects. Biol. Cybern. 94, 415–426 (2006).
Redies, C. A universal model of esthetic perception based on the sensory coding of natural stimuli. Spat. Vis. 21, 97–117 (2007).
Barlow, H. B. Possible principles underlying the transformations of sensory messages. in Sensory Communication (ed Rosenblith, W. A.) 216–234 (The MIT Press, 1961).
Simoncelli, E. P. & Olshausen, B. A. Natural image statistics and neural representation. Annu. Rev. Neurosci. 24, 1193–1216 (2001).
Olshausen, B. A. & Field, D. J. Sparse coding of sensory inputs. Curr. Opin. Neurobiol. 14, 481–487 (2004).
Graf, L. K. M. & Landwehr, J. R. A dual-process perspective on fluency-based aesthetics: the pleasure-interest model of aesthetic liking. Personal. Soc. Psychol. Rev. 19, 395–410 (2015).
Reber, R., Schwarz, N. & Winkielman, P. Processing fluency and aesthetic pleasure: is beauty in the perceiver’s processing experience? Personal. Soc. Psychol. Rev. 8, 364–382 (2004).
Reber, R., Winkielman, P. & Schwarz, N. Effects of perceptual fluency on affective judgments. Psychol. Sci. 9, 45–48 (1998).
Renoult, J. P. & Mendelson, T. C. Processing bias: extending sensory drive to include efficacy and efficiency in information processing. Proc. R. Soc. B Biol. Sci. 286, 20190165 (2019).
Rosenthal, G. G. Mate Choice: The Evolution of Sexual Decision Making from Microbes to Humans. (Princeton University Press, 2017).
Smith, T. A., Ciccotto, P. J., Mendelson, T. C. & Page, L. M. Dense taxon sampling using AFLPs leads to greater accuracy in phylogeny estimation and classification of darters (Percidae: Etheostomatinae). Copeia 2014, 257–268 (2014).
Near, T. J. et al. Phylogeny and temporal diversification of darters (Percidae: Etheostomatinae). Syst. Biol. 60, 565–595 (2011).
Martin, M. D. & Mendelson, T. C. Male behaviour predicts trait divergence and the evolution of reproductive isolation in darters (Percidae: Etheostoma). Anim. Behav. 112, 179–186 (2016).
Williams, T. H. & Mendelson, T. C. Male and female responses to species-specific coloration in darters (Percidae: Etheostoma). Anim. Behav. 85, 1251–1259 (2013).
Williams, T. H. & Mendelson, T. C. Behavioral isolation based on visual signals in a sympatric pair of darter species. Ethology 116, 1038–1049 (2010).
Williams, T. H. & Mendelson, T. C. Female preference for male coloration may explain behavioural isolation in sympatric darters. Anim. Behav. 82, 683–689 (2011).
Fuller, R. C. Disentangling female mate choice and male competition in the Rainbow Darter, Etheostoma caeruleum. Copeia 2003, 138–148 (2003).
Welsh, S. A. & Perry, S. A. Habitat partitioning in a community of darters in the Elk River, West Virginia. Environ. Biol. Fishes 51, 411–419 (1998).
Stauffer, J. R., Boltz, J. M., Kellogg, K. A. & van Snik, E. S. Microhabitat partitioning in a diverse assemblage of darters in the Allegheny River system. Environ. Biol. Fishes 46, 37–44 (1996).
Ultsch, G. R., Boschung, H. & Ross, M. J. Metabolism, critical oxygen tension, and habitat selection in darters (Etheostoma). Ecology 59, 99–107 (1978).
Brachmann, A. & Redies, C. Computational and experimental approaches to visual aesthetics. Front. Comput. Neurosci. 11, 102 (2017).
Hyvärinen, A., Hurri, J. & Hoyer, P. O. Natural Image Statistics: A Probabilistic Approach to Early Computational Vision. (Springer Science & Business Media, 2009).
Bosworth, R. G., Bartlett, M. S. & Dobkins, K. R. Image statistics of American Sign Language: comparison with faces and natural scenes. J. Opt. Soc. Am. Opt. Image Sci. Vis. 23, 2085–2096 (2006).
Spehar, B. et al. Beauty and the beholder: the role of visual sensitivity in visual preference. Front. Hum. Neurosci. 9, 514 (2015).
Koch, M., Denzler, J. & Redies, C. 1/f2 characteristics and isotropy in the Fourier power spectra of visual art, cartoons, comics, mangas, and different categories of photographs. PLoS ONE 5, e12268 (2010).
Bex, P. J., Solomon, S. G. & Dakin, S. C. Contrast sensitivity in natural scenes depends on edge as well as spatial frequency structure. J. Vis. 9, 1–1 (2009).
Daugman, J. G. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. Opt. Image Sci. Vis. 2, 1160–1169 (1985).
Párraga, C. A., Troscianko, T. & Tolhurst, D. J. The human visual system is optimised for processing the spatial information in natural visual images. Curr. Biol. 10, 35–38 (2000).
Srinivasan, M. V., Laughlin, S. B., Dubs, A. & Horridge, G. A. Predictive coding: a fresh view of inhibition in the retina. Proc. R. Soc. Lond. B Biol. Sci. 216, 427–459 (1982).
Pouli, T., Reinhard, E., Cunningham, D. W., Reinhard, E. & Cunningham, D. W. Image Statistics in Visual Computing. (A K Peters/CRC Press, 2013).
Balboa, R. M. & Grzywacz, N. M. Power spectra and distribution of contrasts of natural images from different habitats. Vis. Res. 43, 2527–2537 (2003).
Boughman, J. W. How sensory drive can promote speciation. Trends Ecol. Evol. 17, 571–577 (2002).
Rosenthal, G. G. Spatiotemporal dimensions of visual signals in animal communication. Annu. Rev. Ecol. Evol. Syst. 38, 155–178 (2007).
Winkielman, P., Schwarz, N., Fazendeiro, T. A. & Reber, R. The hedonic marking of processing fluency: implications for evaluative judgment. in The Psychology Of Evaluation: Affective Processes in Cognition And Emotion 189–217 (Lawrence Erlbaum Associates Publishers, 2003).
Zylinski, S., How, M. J., Osorio, D., Hanlon, R. T. & Marshall, N. J. To be seen or to hide: visual characteristics of body patterns for camouflage and communication in the Australian giant cuttlefish Sepia apama. Am. Nat. 177, 681–690 (2011).
Phillips, G. A. C., How, M. J., Lange, J. E., Marshall, N. J. & Cheney, K. L. Disruptive colouration in reef fish: does matching the background reduce predation risk? J. Exp. Biol. 220, 1962–1974 (2017).
Josef, N., Amodio, P., Fiorito, G. & Shashar, N. Camouflaging in a complex environment—octopuses use specific features of their surroundings for background matching. PLoS ONE 7, e37579 (2012).
Roberts, N. S. & Mendelson, T. C. Identifying female phenotypes that promote behavioral isolation in a sexually dimorphic species of fish (Etheostoma zonale). Preprint at https://www.biorxiv.org/content/10.1101/2020.04.20.051714v1 (2020).
Roberts, N. S. & Mendelson, T. C. Male mate choice contributes to behavioural isolation in sexually dimorphic fish with traditional sex roles. Anim. Behav. 130, 1–7 (2017).
Zhou, M., Loew, E. R. & Fuller, R. C. Sexually asymmetric colour-based species discrimination in orangethroat darters. Anim. Behav. 106, 171–179 (2015).
Moran, R. L., Zhou, M., Catchen, J. M. & Fuller, R. C. Male and female contributions to behavioral isolation in darters as a function of genetic distance and color distance. Evolution 71, 2428–2444 (2017).
Mendelson, T. C., Gumm, J. M., Martin, M. D. & Ciccotto, P. J. Preference for conspecifics evolves earlier in males than females in a sexually dimorphic radiation of fishes. Evolution 72, 337–347 (2018).
Graham, D. J., Friedenberg, J. D., McCandless, C. H. & Rockmore, D. N. Preference for art: similarity, statistics, and selling price. Hum. Vis. Electron. Imaging XV 7527, 75271A (2010).
Lennie, P. The cost of cortical computation. Curr. Biol. 13, 493–497 (2003).
Blickhan, M., Kaufmann, J. M., Denzler, J., Schweinberger, S. R. & Redies, C. 1/fp characteristics of the Fourier power spectrum affects ERP correlates of face learning and recognition. Biol. Psychol. 88, 204–214 (2011).
Menzel, C., Hayn-Leichsenring, G. U., Redies, C., Németh, K. & Kovács, G. When noise is beneficial for sensory encoding: noise adaptation can improve face processing. Brain Cogn. 117, 73–83 (2017).
Etnier, D. & Starnes, W. The Fishes of Tennessee. (University of Tennessee Press, 1993).
Kuehne, R. A. & Barbour, R. W. The American Darters. (University Press of Kentucky, 2015).
Bailey, R. M. & Etnier, D. A. Comments on the subgenera of darters (Percidae) with descriptions of two new species of Etheostoma (Ulocentra) from Southeastern United States. in Miscellaneous Publications (University of Michigan, USA, Museum of Zoology, 1988).
Gumm, J. M., Loew, E. R. & Mendelson, T. C. Differences in spectral sensitivity within and among species of darters (genus Etheostoma). Vis. Res. 55, 19–23 (2012).
Sigernes, F. et al. The absolute sensitivity of digital colour cameras. Opt. Express 17, 20211–20220 (2009).
Lennie, P., Pokorny, J. & Smith, V. C. Luminance. J. Opt. Soc. Am. Opt. Image Sci. Vis. 10, 1283–1293 (1993).
Melmer, T., Amirshahi, S. A., Koch, M., Denzler, J. & Redies, C. From regular text to artistic writing and artworks: Fourier statistics of images with low and high aesthetic appeal. Front. Hum. Neurosci. 7, 106 (2013).
Ives, A. R. & Garland, T. Phylogenetic regression for binary dependent variables. in (ed Garamszegi, L. Z.) Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology: Concepts and Practice. 231–261 (Springer, Berlin, Heidelberg, 2014).
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
Piel, W. H. et al. TreeBASE v. 2: a database of phylogenetic knowledge. e-Biosphere https://treebase.org/treebase-web/reference.html (2009).
Source: Ecology - nature.com