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Art's hidden topology: A window into human perception.

Code ↔ Paper

6 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 6 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § 2 Materials and methods › 2.3 Images and preprocessing › 2.3.1 Art images. ↔ scripts/config.jl, lines 107–156 · score 0.95 · Czarne dziury pami, Jelita czerni, Ucho czerni, Wibracje czasu, Wn trze, uca czerni
  2. [2] § 2 Materials and methods › 2.7 Statistical analysis › 2.7.2 Statistical Image Properties (SIP). ↔ scripts/14ca_visualize_image_complexity_metrics.jl, lines 1–28 · score 0.65 · Fourier slope, edge density, PHOG, Anisotropy, orientation
  3. [3] § 2 Materials and methods › 2.4 Experimental procedure › 2.4.4 Eye tracking recording and data analysis. ↔ src/statistics_utils.jl, the whole file · a weak match · score 0.61 · Kruskal Wallis, Mann Whitney, ANOVA, FDR
  4. [4] § 2 Materials and methods › 2.4 Experimental procedure › 2.4.4 Eye tracking recording and data analysis. ↔ scripts/17ig3lb2_art_vs_fake_looking_all_metrics_ver2.jl, lines 217–262 · score 0.61 · Kruskal Wallis, Mann Whitney, ANOVA, FDR
  5. [5] § 2 Materials and methods › 2.7 Statistical analysis › 2.7.2 Statistical Image Properties (SIP). ↔ scripts/14c_load_image_complexity_metrics.jl, lines 1–83 · score 0.56 · Fourier slope, entropy, PHOG, orientation, edge, persistent
  6. [6] § 3 Results › 3.5 Comparison of the persistent homology with current state-of-the-art methods ↔ scripts/14ca_visualize_image_complexity_metrics.jl, lines 90–167 · score 0.56 · Fourier slope, edge density, anisotropy, metrics, landscape, persistent

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Julia · 268 lines · 6 KB · no license · 2 matches

  1. using DrWatson
  2. @quickactivate "arttopopaper"
  4. # ===-===-===-===-
  5. "14c_load_image_complexity_metrics.jl" |> scriptsdir |> include
  7. # ===-===-===-===-
  8. using CairoMakie
  10. # ===-===-===-
  11. # Load data
  12. category_labels1 = FSlope_df[:, :set]
  13. data_array1 = FSlope_df[:, "Fourier slope"]
  14. data_array1 = abs.(data_array1)
  16. category_labels2 = PHOG_df[:, :set]
  17. data_array2 = PHOG_df[:, "Self-Similarity"]
  18. data_array3 = PHOG_df[:, "Complexity"]
  19. data_array4 = PHOG_df[:, "Anisotropy"]
  20. data_array4 = abs.(data_array4)
  22. category_labels3 = category_labels2
  23. data_array5 = EdgeOrientation_df[:, "avg-shannon20-80"]
  24. data_array6 = EdgeOrientation_df[:, "edge-density"]
  26. category_labels4 = (@pipe land_areas_df |> filter(:dim => ==(0), _))[:, :dataset]
  27. data_array7 = (@pipe land_areas_df |> filter(:dim => ==(0), _))[:, :pland_area]
  28. data_array8 = (@pipe land_areas_df |> filter(:dim => ==(1), _))[:, :pland_area]
  30. ## ===-===-===-===-===-===-===-===-===-===-===-===-===-===-===-===-===-===-
  31. # Plot the data
  32. use_print_size = true
  34. total_metrics = size(all_data_df, 2) - 2
  35. total_datasets = length(all_data_df.set |> unique)
  37. total_cols = 2
  39. if length(data_names_vec) < 4
  40. scaling_factor = 0.8
  41. else
  42. scaling_factor = length(data_names_vec)
  43. end
  45. if use_print_size
  46. plt_width = 300 + 150 * total_datasets
  47. plt_height = 700
  48. else
  49. plt_height = round(Int, 1700 * scaling_factor ÷ total_cols)
  50. plt_width = 100 + 150 * total_datasets
  51. end
  53. area_axis_args = (yscale = log10,)
  54. #
  55. f = Figure(resolution = (plt_width, plt_height));
  56. fgl = GridLayout(f[1, 1])
  57. axis_vec = []
  59. Box(
  60. fgl[4, 1],
  61. strokecolor = (:red, 0.7),
  62. linestyle = :solid,
  63. strokewidth = 4,
  64. color = :white,
  65. alignmode = Outside(),
  66. halign = :left,
  67. )
  68. Box(
  69. fgl[4, 2],
  70. strokecolor = (:red, 0.7),
  71. linestyle = :solid,
  72. strokewidth = 4,
  73. color = :white,
  74. alignmode = Outside(),
  75. halign = :left,
  76. )
  77. for k = 1:total_metrics
  78. if k <= 4
  79. col = 1
  80. row = k
  81. else
  82. col = 2
  83. row = k - 4
  84. end
  85. args = (yticklabelrotation = pi / 2, xtrimspine = true, area_axis_args...)
  87. push!(axis_vec, CairoMakie.Axis(fgl[row, col]; args...))
  88. end
  90. axis_fl,
  91. axis_selfsim,
  92. axis_complex,
  93. axis_pland0,
  94. axis_aniso,
  95. axis_avg_shannon_20_80,
  96. axis_edge_density,
  97. axis_pland1 = axis_vec
  99. colors = [Makie.wong_colors(8)...];
  101. for (i, (ax, labels, data_arr, title)) in
  102. zip(
  103. [
  104. axis_fl,
  105. axis_selfsim,
  106. axis_complex,
  107. axis_pland0,
  108. axis_aniso,
  109. axis_avg_shannon_20_80,
  110. axis_edge_density,
  111. axis_pland1,
  112. ],
  113. [
  114. category_labels1,
  115. category_labels2,
  116. category_labels2,
  117. category_labels4,
  118. category_labels2,
  119. category_labels3,
  120. category_labels3,
  121. category_labels4,
  122. ],
  123. [
  124. data_array1,
  125. data_array2,
  126. data_array3,
  127. data_array7,
  128. data_array4,
  129. data_array5,
  130. data_array6,
  131. data_array8,
  132. ],
  133. [
  134. "Abs. Fourier slope",
  135. "Self-Similarity",
  136. "Complexity",
  137. "Persistence landscapes'\narea, dim 0",
  138. "Abs. Anisotropy",
  139. "Avg. Shannon20-80",
  140. "Edge density",
  141. "Persistence landscapes'\narea, dim 1",
  142. ],
  143. ) |> enumerate
  145. @info "Working on a metric: $(i)"
  147. y_label = title
  148. img_title = ""
  149. if i == total_metrics
  150. x_label = "Category"
  151. else
  152. x_label = ""
  153. hidexdecorations!(
  154. ax,
  155. label = true,
  156. ticklabels = false,
  157. ticks = false,
  158. grid = false,
  159. minorgrid = false,
  160. minorticks = false,
  161. )
  162. end
  163. or = :vertical
  164. ax.xgridvisible = false
  165. ax.ygridvisible = false
  166. ax.rightspinevisible = false
  167. ax.topspinevisible = false
  169. # ===-
  170. colours_vec = colors[indexin(category_labels1, unique(category_labels1)) .* 3]
  171. CairoMakie.rainclouds!(
  172. ax,
  173. labels,
  174. data_arr;
  175. # jitter_width=0.2,
  176. plot_boxplots = true,
  177. clouds = nothing,
  178. orientation = or,
  179. gap = 0.1,
  180. center_boxplot = false,
  181. boxplot_nudge = -0.15,
  182. boxplot_width = 0.4,
  183. markersize = 10,
  184. color = colours_vec,
  185. )
  186. f
  187. CairoMakie.xlims!(ax, low = 0.6, high = total_datasets + 0.4)
  188. ax.xticks = 1:total_datasets
  189. ax.ylabel = y_label
  191. CairoMakie.ylims!(ax, low = 0.0001, high = 1e8)
  193. f
  194. ax.yticklabelsize = 10
  195. ax.ylabelsize = 12
  197. hidexdecorations!(
  198. ax,
  199. label = true,
  200. ticklabels = true,
  201. ticks = false,
  202. grid = true,
  203. minorgrid = true,
  204. minorticks = true,
  205. )
  206. end
  208. overlapping_elements =
  209. data_array5[1:12] .< 1e6 .&&
  210. data_array5[1:12] .> 1e2 .&&
  211. data_array6[1:12] .< 1e6 .&&
  212. data_array6[1:12] .> 1e4
  214. @info "The elements overlapping in their landscapes area are $(land_areas_df[1:12, :][overlapping_elements, :file])"
  216. group_color =
  217. [PolyElement(color = color, strokecolor = :transparent) for color in colors[3:3:6]]
  219. Legend(
  220. fgl[end+1, :],
  221. group_color,
  222. ["Artist", "Pseudo-art"],
  223. tellwidth = false,
  224. tellheight = true,
  225. nbanks = 2,
  226. framevisible = false,
  227. )
  229. rowgap!(fgl, 30)
  230. colgap!(fgl, 10)
  231. f
  232. ## ===-===-===-===-===-===-===-===-===-===-===-===-===-===-===-===-===-===-
  233. # Save image
  234. @info "Saving..."
  236. script_prefix = "14ca"
  237. plot_14c_dir(args...) =
  238. plotsdir("section14", script_prefix * "-metrics-comparison", args...)
  240. savename_val = @savename useddata
  242. if use_print_size
  243. out_name0 =
  244. plot_14c_dir("print", "$(script_prefix)_metrics_comparison_$(savename_val).png")
  245. safesave(out_name0, f)
  247. out_name1 = plot_14c_dir(
  248. "print",
  249. "pdf",
  250. "$(script_prefix)_metrics_comparison_$(savename_val).pdf",
  251. )
  252. safesave(out_name1, f)
  254. @info "Saved."
  255. else
  257. out_name0 = plot_14c_dir("metrics_comparison_$(savename_val).png")
  258. safesave(out_name0, f)
  260. out_name1 = plot_14c_dir("pdf", "metrics_comparison_$(savename_val).pdf")
  261. safesave(out_name1, f)
  263. @info "Saved."
  264. end
  266. ## ===-===-===-===-===-===-===-===-===-===-===-===-===-===-===-===-===-===-
  267. #
  268. do_nothing = "ok"

14ca_visualize_image_complexity_metrics.jl at commit cc56bf9, no license · at the source

Overview

Authors: Emil Dmitruk1, Beata Bajno2, Lidia Kot3, Joanna Dreszer4, Bibianna Bałaj4, Ewa Ratajczak4, Marcin Hajnowski4, Romuald A. Janik5, Marek Kuś6, Shabnam N. Kadir1, Jacek Rogala7
ORCID iDs: Bibianna Bałaj, Ewa Ratajczak, Marek Kuś, Shabnam N. Kadir, Jacek Rogala
  1. UH Biocomputation Research Group, Department of Computer Science, University of Hertfordshire, Hatfield, United Kingdom
  2. Independent Artist, Warsaw, Poland
  3. In Situ Contemporary Art Foundation, International Laboratory of Culture, Sokołowsko, Poland
  4. Institute of Psychology, Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University in Toruń, Toruń, Poland
  5. Institute of Theoretical Physics and Mark Kac Center for Complex Systems Research, Jagiellonian University, Kraków, Poland
  6. Center for Theoretical Physics, Polish Academy of Sciences, Warsaw, Poland
  7. Center of Trustworthy AI for Life Sciences – International Research Agendas Programme, University of Warsaw, Warsaw, Poland
Institutions: University of Hertfordshire (United Kingdom); Nicolaus Copernicus University (Poland); Jagiellonian University (Poland); Center for Theoretical Physics (Poland); Polish Academy of Sciences (Poland); University of Warsaw (Poland)
Journal: PLoS computational biology, volume 22, issue 5, article e1014156
Dates: received 21 January 2025; accepted 23 March 2026; published online 14 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1014156 · PMID 42133620 · PMCID PMC13175340 · OpenAlex W4403540011
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.10.16.618741
Status: code verified
Categories: human (organism), pain (population), cognitive (subfield)
Methods: Statistics, Spectral & time-frequency, Connectivity, Physiology & signal measures, Machine learning
MeSH: Art*, Paintings*, Visual Perception*, Computational Biology, Humans (* major topic)
Journal subjects: Biology and Life Sciences, Neuroscience, Cognitive Science, Cognitive Psychology, Perception, Sensory Perception, Vision, Psychology, Social Sciences, Physiology, Sensory Physiology, Visual System, Eye Movements, Sensory Systems, Research and Analysis Methods, Bioassays and Physiological Analysis, Electrophysiological Techniques, Brain Electrophysiology, Electroencephalography, Electrophysiology, Neurophysiology, Brain Mapping, Medicine and Health Sciences, Clinical Medicine, Clinical Neurophysiology, Imaging Techniques, Neuroimaging, Physical Sciences, Mathematics, Topology, Separation Processes, Filtration, Research Design, Survey Research, Questionnaires
Topic: Art, Technology, and Culture (Visual Arts and Performing Arts, Arts and Humanities), according to OpenAlex
Funding: University of Warsaw (N/A)
Citations: not cited yet (Europe PMC); 90 references in the paper

Abstract

Generations of researchers have sought a link between features of an artistic image and the audience’s experience. However, a direct link between the properties of an image and the responses evoked has still not been established. Given the importance of shape to human perception and artistic creation, it can be assumed that one of the most important aspects of an artistic image is the use of different visual structures. We show that a method from the field of computational topology, persistent homology, can be used to analyse properties of image structures and composition at multiple scales. In order to determine the reliability of this method as a tool for analysing visual artworks, we analysed two different sets of abstract paintings that revealed significant discrepancies in the eye tracking, electrical brain activity and the subjective experience of viewers. Our research showed that our newly developed method not only clearly distinguished between two sets of images but also allowed us to map topological features onto gaze fixation heat maps. Furthermore, the extent to which various artistic images violate a topological duality (Alexander duality) is significantly different from that of pseudo-art. It is intriguing that a diverse group of eminent abstract artists seem to favour a special rate of violation close to a specific value.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

edd26/arttoppaper

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: cc56bf96fd14fc76298a8ba9e6eb00ec2461372c, 6 October 2025
Languages: Julia (46)
Size: 58 files, 46 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, environment (Project.toml)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: Makie (18 files), DataFrames.jl (5 files), Distributions.jl (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
47 files
At the source: github.com/edd26/arttoppaper

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 46 scripts, each with its path and the digest of its content;
  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability

Data are available at: https://osf.io/6u4p2/?view_only=d38603b8105a4724afd3572a49daee8a. Code for the image analyses is available here: https://github.com/edd26/arttoppaper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 5 MeSH terms, 1 funder, 61 references.

Cite

This paper

Dmitruk, E., Bajno, B., Kot, L., Dreszer, J., Bałaj, B., Ratajczak, E., Hajnowski, M., Janik, R. A., Kuś, M., Kadir, S. N., & Rogala, J. (2026). Art's hidden topology: A window into human perception. PLoS computational biology, 22(5), e1014156. https://doi.org/10.1371/journal.pcbi.1014156

BibTeX

@article{dmitruk2026art,
author = {Dmitruk, Emil and Bajno, Beata and Kot, Lidia and Dreszer, Joanna and Bałaj, Bibianna and Ratajczak, Ewa and Hajnowski, Marcin and Janik, Romuald A. and Kuś, Marek and Kadir, Shabnam N. and Rogala, Jacek},
title = {{Art's hidden topology: A window into human perception}},
journal = {PLoS computational biology},
year = {2026},
month = may,
volume = {22},
number = {5},
pages = {e1014156},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1014156},
url = {https://doi.org/10.1371/journal.pcbi.1014156},
pmid = {42133620},
pmcid = {PMC13175340}
}

RIS

TY - JOUR
AU - Dmitruk, Emil
AU - Bajno, Beata
AU - Kot, Lidia
AU - Dreszer, Joanna
AU - Bałaj, Bibianna
AU - Ratajczak, Ewa
AU - Hajnowski, Marcin
AU - Janik, Romuald A.
AU - Kuś, Marek
AU - Kadir, Shabnam N.
AU - Rogala, Jacek
TI - Art's hidden topology: A window into human perception
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/05/14
VL - 22
IS - 5
SP - e1014156
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1014156
UR - https://doi.org/10.1371/journal.pcbi.1014156
LA - en
ER -

CSL-JSON

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"id": "10.1371/journal.pcbi.1014156",
"type": "article-journal",
"title": "Art's hidden topology: A window into human perception",
"container-title": "PLoS computational biology",
"author": [
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"family": "Dmitruk",
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{
"family": "Bajno",
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{
"family": "Kot",
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{
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{
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{
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"given": "Romuald A."
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"container-title-short": "PLoS Comput Biol",
"volume": "22",
"issue": "5",
"page": "e1014156",
"DOI": "10.1371/journal.pcbi.1014156",
"PMID": "42133620",
"PMCID": "PMC13175340",
"ISSN": "1553-734X",
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