OSCR

Cortical dynamics of icon perception: effects of concreteness and attractiveness.

Code ↔ Paper

16 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 16 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › Statistical analysis ↔ code/15f-lowlevel-visual-metrics.py, lines 1–40 · score 0.93 · pixel intensities, edge pixels, edge density, luminance variance, icon stimuli, detector
  2. [2] § Materials and methods › Data Preprocessing ↔ code/12-inverse.py, the whole file · a weak match · score 0.85 · inverse operator, pick_ori, source space, FreeSurfer, ico4, depth
  3. [3] § Materials and methods › Data Preprocessing ↔ code/15a-RSA-model-diagnostics.py, lines 361–423 · score 0.81 · variance inflation factors, rank transformed, correlation matrix, model RDMs, partial Spearman, multicollinearity
  4. [4] § Materials and methods › Statistical analysis ↔ code/config_Iconmind.py, lines 45–60 · score 0.81 · 200–300 ms, 150–200 ms, 80–130 ms, 600–1000 ms, 300–600 ms, 150 ms
  5. [5] § Materials and methods › Data Preprocessing ↔ code/15b-run-RSA.py, lines 97–186 · score 0.77 · temporal radius, inverse epochs, MNE RSA, ROIs, artifact, RDM
  6. [6] § Materials and methods › Data Preprocessing ↔ code/15b-run-RSA.py, lines 97–186 · score 0.75 · inverse operator, PICK_ORI, FreeSurfer, ico4, depth, loose
  7. [7] § Results › Concreteness, attractiveness, and interaction effects in icon perception › Concreteness effect (abstract vs. concrete) ↔ code/config_Iconmind.py, lines 45–60 · score 0.74 · 200–300 ms, 150–200 ms, 600–1000 ms, 300–600 ms, 150 ms, 130 ms
  8. [8] § Materials and methods › Data Preprocessing ↔ code/utils_rsa_models.py, lines 203–218 · score 0.71 · raw grayscale luminance, pixel RDM, vectors, modeled, RSA
  9. [9] § Results › Concreteness, attractiveness, and interaction effects in icon perception › Priming control (congruency interactions) ↔ code/14e-plot-priming-control-figure.py, lines 1–66 · score 0.71 · attractiveness ANOVA, concreteness ANOVA, 300–600 ms, Priming, TFCE, congruency
  10. [10] § Results › Low-level visual properties analysis of icon stimuli ↔ code/15f-lowlevel-visual-metrics.py, lines 1–40 · score 0.69 · visual metrics, edge density, Luminance variance, energy, mid, ANOVAs
  11. [11] § Materials and methods › Data Preprocessing ↔ code/04-reject-ICA.py, the whole file · a weak match · score 0.57 · pass filtered, ICA, ECG, EOG, component, MNE
  12. [12] § Materials and methods › Statistical analysis ↔ code/15c-RSA-stats.py, lines 1–42 · score 0.55 · model RDM, partial Spearman, Fisher, permutation, transformed, cluster
  13. [13] § Results › Concreteness, attractiveness, and interaction effects in icon perception › Interaction effect (concreteness × attractiveness) ↔ code/14f-ANOVA-figure.py, lines 852–947 · score 0.53 · interaction windows, 180 ms, unattractive icons, concrete icons, 100 ms, sensor
  14. [14] § Materials and methods › Data Preprocessing ↔ code/05-remove-bad-trials.py, the whole file · a weak match · score 0.53 · pass filtered, ICA, EOG, artifacts, MNE, channels
  15. [15] § Materials and methods › Data acquisition ↔ code/05-remove-bad-trials.py, the whole file · a weak match · score 0.51 · band pass filtered, EOG, buttons, channel, MEG
  16. [16] § Materials and methods › Data Preprocessing ↔ code/utils_rsa_models.py, lines 324–423 · score 0.51 · model RDMs, unfamiliar, 1–7, binary, survey, unattractive

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

Python · 309 lines · 11 KB · no license · 2 matches

  1. """
  2. 15f-lowlevel-visual-metrics.py
  4. Compute low-level visual metrics for each icon stimulus and test whether
  5. they differ across experimental conditions (Concreteness × Attractiveness).
  7. Metrics computed per icon:
  8. 1. Edge density – proportion of edge pixels (Canny detector)
  9. 2. Luminance variance – variance of grayscale pixel intensities
  10. 3. Low SF energy – mean log spectral power in the low SF band
  11. 4. Mid SF energy – mean log spectral power in the mid SF band
  12. 5. High SF energy – mean log spectral power in the high SF band
  14. Output directory:
  15. - results/15f-lowlevel-visual-metrics/lowlevel_visual_metrics.csv
  16. - results/15f-lowlevel-visual-metrics/lowlevel_visual_metrics_ANOVA.csv
  17. - results/15f-lowlevel-visual-metrics/lowlevel_visual_metrics.pdf
  18. - results/15f-lowlevel-visual-metrics/lowlevel_visual_metrics.png
  20. Related write-up:
  21. - results/15f-lowlevel-visual-metrics/lowlevel_visual_metrics.md
  22. """
  24. import os
  25. import os.path as op
  26. import numpy as np
  27. import pandas as pd
  28. from PIL import Image
  29. import matplotlib
  30. matplotlib.use('Agg')
  31. import matplotlib.pyplot as plt
  32. import matplotlib as mpl
  33. from matplotlib.patches import Patch
  34. import statsmodels.api as sm
  35. from statsmodels.formula.api import ols
  36. from skimage.feature import canny
  38. from config_Iconmind import stim_folder, stimuli_list_file, results_folder
  40. output_folder = op.join(results_folder, '15f-lowlevel-visual-metrics')
  42. # ============================================================
  43. # 1. Get unique icon list and extract condition labels
  44. # ============================================================
  45. stimuli_list = pd.read_csv(stimuli_list_file)
  47. unique_icons = stimuli_list[stimuli_list['picture_code'] <= 140].copy()
  48. unique_icons = unique_icons.drop_duplicates(subset='Icon name').reset_index(drop=True)
  50. def parse_condition(icon_name):
  51. parts = icon_name.split('_')
  52. concreteness = 'Concrete' if parts[0] == 'Con' else 'Abstract'
  53. attractiveness = 'High' if parts[1] == 'ha' else 'Low'
  54. category = parts[2].rstrip('0123456789')
  55. return concreteness, attractiveness, category
  57. unique_icons[['Concreteness', 'Attractiveness', 'Category']] = unique_icons['Icon name'].apply(
  58. lambda x: pd.Series(parse_condition(x))
  59. )
  61. print(f"Unique icons: {len(unique_icons)}")
  62. print(f"Conditions:\n{unique_icons.groupby(['Concreteness', 'Attractiveness']).size()}")
  64. # ============================================================
  65. # 2. Metric computation
  66. # ============================================================
  68. def compute_metrics(img_gray):
  69. """Compute the five low-level visual metrics."""
  70. metrics = {}
  72. # Edge density (Canny)
  73. metrics['Edge density'] = canny(img_gray, sigma=2.0).mean()
  75. # Luminance variance
  76. metrics['Luminance variance'] = img_gray.var()
  78. # Spatial frequency distribution (3 bands via 2D FFT)
  79. f_shift = np.fft.fftshift(np.fft.fft2(img_gray))
  80. log_power = np.log1p(np.abs(f_shift) ** 2)
  82. rows, cols = img_gray.shape
  83. cy, cx = rows // 2, cols // 2
  84. Y, X = np.ogrid[:rows, :cols]
  85. r = np.sqrt((X - cx) ** 2 + (Y - cy) ** 2)
  86. r_max = min(cy, cx)
  88. band_edges = np.linspace(0, r_max, 4)
  89. for i, label in enumerate(['Low SF energy', 'Mid SF energy', 'High SF energy']):
  90. mask = (r >= band_edges[i]) & (r < band_edges[i + 1])
  91. metrics[label] = log_power[mask].mean() if mask.sum() > 0 else np.nan
  93. return metrics
  96. # ============================================================
  97. # 3. Main loop over icons
  98. # ============================================================
  99. records = []
  100. for _, row in unique_icons.iterrows():
  101. icon_name = row['Icon name']
  102. img_path = op.join(stim_folder, icon_name)
  104. if not op.exists(img_path):
  105. print(f" WARNING: missing {icon_name}")
  106. continue
  108. img = Image.open(img_path).convert('L')
  109. img_gray = np.array(img, dtype=np.float64) / 255.0
  111. m = compute_metrics(img_gray)
  112. m['Icon name'] = icon_name
  113. m['Concreteness'] = row['Concreteness']
  114. m['Attractiveness'] = row['Attractiveness']
  115. m['Category'] = row['Category']
  116. records.append(m)
  118. df_metrics = pd.DataFrame(records)
  119. print(f"\nComputed metrics for {len(df_metrics)} icons.")
  121. os.makedirs(output_folder, exist_ok=True)
  122. csv_path = op.join(output_folder, 'lowlevel_visual_metrics.csv')
  123. df_metrics.to_csv(csv_path, index=False)
  124. print(f"Saved metrics to: {csv_path}")
  126. # ============================================================
  127. # 4. 2×2 ANOVA (Concreteness × Attractiveness) per metric
  128. # ============================================================
  129. metric_cols = [
  130. 'Edge density', 'Luminance variance',
  131. 'Low SF energy', 'Mid SF energy', 'High SF energy'
  132. ]
  134. anova_results = []
  136. print("\n" + "=" * 75)
  137. print("2 × 2 ANOVA: Concreteness × Attractiveness")
  138. print("=" * 75)
  140. for metric in metric_cols:
  141. safe_name = metric.replace(' ', '_')
  142. df_metrics[safe_name] = df_metrics[metric]
  144. formula = f'{safe_name} ~ C(Concreteness) * C(Attractiveness)'
  145. model = ols(formula, data=df_metrics).fit()
  146. table = sm.stats.anova_lm(model, typ=2)
  148. ss_total = table['sum_sq'].sum()
  150. row = {'Metric': metric}
  151. for effect, label in [('C(Concreteness)', 'Concreteness'),
  152. ('C(Attractiveness)', 'Attractiveness'),
  153. ('C(Concreteness):C(Attractiveness)', 'Interaction')]:
  154. row[f'F_{label}'] = table.loc[effect, 'F']
  155. row[f'p_{label}'] = table.loc[effect, 'PR(>F)']
  156. row[f'eta2_{label}'] = table.loc[effect, 'sum_sq'] / ss_total
  158. anova_results.append(row)
  160. means = df_metrics.groupby(['Concreteness', 'Attractiveness'])[metric].agg(['mean', 'std'])
  161. print(f"\n--- {metric} ---")
  162. print(means.to_string())
  163. print(f" Concreteness: F = {row['F_Concreteness']:.3f}, p = {row['p_Concreteness']:.4f}, η² = {row['eta2_Concreteness']:.4f}")
  164. print(f" Attractiveness: F = {row['F_Attractiveness']:.3f}, p = {row['p_Attractiveness']:.4f}, η² = {row['eta2_Attractiveness']:.4f}")
  165. print(f" Interaction: F = {row['F_Interaction']:.3f}, p = {row['p_Interaction']:.4f}, η² = {row['eta2_Interaction']:.4f}")
  167. df_metrics.drop(columns=[safe_name], inplace=True)
  169. df_anova = pd.DataFrame(anova_results)
  170. anova_path = op.join(output_folder, 'lowlevel_visual_metrics_ANOVA.csv')
  171. df_anova.to_csv(anova_path, index=False)
  172. print(f"\nSaved ANOVA results to: {anova_path}")
  174. # ============================================================
  175. # 5. Publication-quality figure
  176. # ============================================================
  177. mpl.rcParams.update({
  178. 'font.family': 'sans-serif',
  179. 'font.sans-serif': ['Arial', 'DejaVu Sans'],
  180. 'font.size': 8,
  181. 'figure.dpi': 300,
  182. 'savefig.dpi': 300,
  183. 'savefig.bbox': 'tight',
  184. 'pdf.fonttype': 42,
  185. 'ps.fonttype': 42,
  186. })
  188. condition_order = [
  189. ('Abstract', 'High'),
  190. ('Abstract', 'Low'),
  191. ('Concrete', 'High'),
  192. ('Concrete', 'Low'),
  193. ]
  194. cond_colors = ['#4393c3', '#a8cfe2', '#d6604d', '#f0a992']
  195. group_positions = [0, 0.35, 0.95, 1.30]
  196. group_centers = [0.175, 1.125]
  199. def format_p(p):
  200. if p < 0.001:
  201. return '$p$ < .001'
  202. return f'$p$ = {p:.3f}'.replace('0.', '.')
  205. def format_anova_line(F_val, p_val, eta2, label):
  206. stars = ''
  207. if p_val < 0.001: stars = '***'
  208. elif p_val < 0.01: stars = '**'
  209. elif p_val < 0.05: stars = '*'
  210. if not stars:
  211. return None
  212. return f'{label}{stars}: $F$ = {F_val:.1f}, {format_p(p_val)}, η² = {eta2:.3f}'
  215. fig, axes = plt.subplots(1, 5, figsize=(10.0, 2.8))
  217. for idx, metric in enumerate(metric_cols):
  218. ax = axes[idx]
  219. data_groups = [
  220. df_metrics[(df_metrics['Concreteness'] == c) &
  221. (df_metrics['Attractiveness'] == a)][metric].values
  222. for c, a in condition_order
  223. ]
  225. # Violins
  226. vp = ax.violinplot(data_groups, positions=group_positions,
  227. showmedians=False, showextrema=False, widths=0.30)
  228. for i, body in enumerate(vp['bodies']):
  229. body.set_facecolor(cond_colors[i])
  230. body.set_edgecolor('none')
  231. body.set_alpha(0.55)
  233. # Individual data points (jittered)
  234. rng = np.random.default_rng(42)
  235. for i, d in enumerate(data_groups):
  236. jitter = rng.uniform(-0.06, 0.06, size=len(d))
  237. ax.scatter(group_positions[i] + jitter, d, s=6, alpha=0.4,
  238. color=cond_colors[i], edgecolors='none', zorder=2)
  240. # Box summary (IQR + median)
  241. for i, d in enumerate(data_groups):
  242. q1, med, q3 = np.percentile(d, [25, 50, 75])
  243. ax.vlines(group_positions[i], q1, q3, color='black', linewidth=1.5, zorder=4)
  244. ax.scatter(group_positions[i], med, color='white', edgecolors='black',
  245. s=18, zorder=5, linewidths=0.8)
  247. ax.set_title(metric, fontsize=8.5, fontweight='bold', pad=6)
  249. # ANOVA annotation below title
  250. row = df_anova[df_anova['Metric'] == metric].iloc[0]
  251. ann_parts = []
  252. for effect, short in [('Concreteness', 'Conc'), ('Attractiveness', 'Attr'),
  253. ('Interaction', 'Int')]:
  254. line = format_anova_line(row[f'F_{effect}'], row[f'p_{effect}'],
  255. row[f'eta2_{effect}'], short)
  256. if line:
  257. ann_parts.append(line)
  258. ann = '\n'.join(ann_parts) if ann_parts else 'n.s.'
  259. ann_color = '#222222' if ann_parts else '#aaaaaa'
  260. ax.text(0.03, 0.97, ann, transform=ax.transAxes, fontsize=5.5,
  261. verticalalignment='top', color=ann_color, style='italic',
  262. linespacing=1.4)
  264. ax.set_xticks(group_centers)
  265. ax.set_xticklabels(['Abstract', 'Concrete'], fontsize=7.5)
  266. ax.set_xlim(-0.25, 1.55)
  267. ax.spines['top'].set_visible(False)
  268. ax.spines['right'].set_visible(False)
  269. ax.tick_params(axis='x', length=0)
  270. ax.tick_params(axis='y', labelsize=7)
  272. # Shared legend
  273. legend_elements = [
  274. Patch(facecolor=cond_colors[0], alpha=0.7, label='High attractiveness'),
  275. Patch(facecolor=cond_colors[1], alpha=0.7, label='Low attractiveness'),
  276. ]
  277. fig.legend(handles=legend_elements, loc='lower center', ncol=2, frameon=False,
  278. fontsize=7.5, bbox_to_anchor=(0.5, -0.02))
  280. fig.tight_layout(w_pad=1.5, rect=[0, 0.06, 1, 1])
  282. fig_path = op.join(output_folder, 'lowlevel_visual_metrics.pdf')
  283. fig.savefig(fig_path)
  284. fig_path_png = fig_path.replace('.pdf', '.png')
  285. fig.savefig(fig_path_png)
  286. plt.close(fig)
  287. print(f"\nSaved figure to: {fig_path}")
  289. # ============================================================
  290. # 6. Print summary
  291. # ============================================================
  292. print("\n" + "=" * 75)
  293. print("SUMMARY")
  294. print("=" * 75)
  296. any_sig = False
  297. for _, row in df_anova.iterrows():
  298. for effect in ['Concreteness', 'Attractiveness', 'Interaction']:
  299. if row[f'p_{effect}'] < 0.05:
  300. any_sig = True
  301. print(f" SIGNIFICANT: {row['Metric']} – {effect}: "
  302. f"F = {row[f'F_{effect}']:.2f}, p = {row[f'p_{effect}']:.4f}, "
  303. f"η² = {row[f'eta2_{effect}']:.4f}")
  305. if not any_sig:
  306. print(" No significant effects found (all p > .05)")
  308. print("\nSCRIPT 23 FINISHED")
  309. print("=" * 75)

15f-lowlevel-visual-metrics.py at commit 1fd951d, no license · at the source

Overview

Authors: Jiaqi Zheng1,2,3, Weiyong Xu3,4, Johanna Silvennoinen1, Fengyu Cong1,2,5,6, Tiina Parviainen3,4, Tuomo Kujala1
ORCID iDs: Jiaqi Zheng, Weiyong Xu, Johanna Silvennoinen, Tiina Parviainen, Tuomo Kujala
  1. Faculty of Information Technology, University of Jyväskylä, Mattilanniemi 2, P.O. Box 35, FI-40014, Jyväskylä, Finland
  2. School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian 116024, Liaoning Province, China
  3. Centre for Interdisciplinary Brain Research, University of Jyväskylä, Mattilanniemi 6, FI-40014, Jyväskylä, Finland
  4. Department of Psychology, University of Jyväskylä, PO BOX 35, Mattilanniemi 6, FI-40014, Jyväskylä, Finland
  5. Key Laboratory of Social Computing and Cognitive Intelligence, Dalian University of Technology, Ministry of Education, Dalian 116024, Liaoning Province, China
  6. School of Software Engineering, Dalian University, Dalian 116622, Liaoning Province, China
Institutions: Dalian University of Technology (China); University of Jyväskylä (Finland); Dalian University (China)
Journal: Cerebral cortex (New York, N.Y. : 1991), volume 36, issue 6, article bhag075
Dates: received 19 December 2025; accepted 19 May 2026; published online 22 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/cercor/bhag075 · PMID 42330320 · PMCID PMC13286001 · OpenAlex W7165550683
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: MEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Connectivity, fMRI & imaging, Physiology & signal measures, Machine learning
Keywords: attractiveness, concreteness, icon perception, magnetoencephalography (MEG), representational similarity analysis (RSA)
MeSH: Cerebral Cortex*, Pattern Recognition, Visual*, Visual Perception*, Adult, Brain Mapping, Female, Humans, Magnetoencephalography, Male, Photic Stimulation, Young Adult (* major topic)
Topic: Multisensory perception and integration (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: Research Council of Finland; Faculty of Information Technology at the University of Jyväskylä, Finland; China Scholarship Council (201906060240)
Citations: not cited yet (Europe PMC); 98 references in the paper

Abstract

Icons, as simplified visual symbols, play a key role in visual communication, yet little neuroimaging research has addressed how icons are represented in the brain. We investigated how concreteness and attractiveness modulate the spatiotemporal dynamics of icon processing in a 2 × 2 factorial design in 35 adults using magnetoencephalography. Source-level event-related field (ERF) analysis and representational similarity analysis (RSA) were used to characterize neural responses, with partial RSA isolating each feature’s unique contribution after controlling for low-level visual similarity. ERF results showed that concreteness exerted a robust and sustained influence on neural dynamics, with concrete icons eliciting stronger responses than abstract ones from 90 to 1,000 ms, emerging in bilateral occipital cortices and extending to occipitotemporal, temporal, and parietal regions. RSA confirmed concreteness as a representational dimension across processing stages. Attractiveness showed an early but transient effect in occipital and ventral occipitotemporal regions (80 to 130 ms), though this did not survive RSA after controlling for low-level visual property models. A concreteness × attractiveness interaction modulated early occipital and later parietal processing (100 to 185 ms), indicating these features do not operate independently. Our results reveal that semantic content outweighs esthetic appeal in shaping the neural representation of icons.

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 16 matches between paragraphs and lines of code.

weiyongxu/iconmind-analysis

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1fd951d22b7098f0f1dea320e3a9382ac0c0d45b, 21 May 2026
Languages: Python (32)
Size: 34 files, 32 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: MNE-Python (28 files), NumPy (19 files), pandas (14 files), Matplotlib (8 files), Pillow (2 files), scikit-image (2 files), SciPy (2 files), scikit-learn (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
33 files
At the source: github.com/weiyongxu/iconmind-analysis

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;
  • 32 scripts, each with its path and the digest of its content;
  • 16 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

No dataset and no data link were found in the paper.

Data availability

The experimental paradigm, data preprocessing, and data analysis scripts are freely available at the following Github repository: https://github.com/weiyongxu/iconmind-analysis.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 11 MeSH terms, 3 funders, 93 references.

Cite

This paper

Zheng, J., Xu, W., Silvennoinen, J., Cong, F., Parviainen, T., & Kujala, T. (2026). Cortical dynamics of icon perception: effects of concreteness and attractiveness. Cerebral cortex (New York, N.Y. : 1991), 36(6), bhag075. https://doi.org/10.1093/cercor/bhag075

BibTeX

@article{zheng2026cortical,
author = {Zheng, Jiaqi and Xu, Weiyong and Silvennoinen, Johanna and Cong, Fengyu and Parviainen, Tiina and Kujala, Tuomo},
title = {{Cortical dynamics of icon perception: effects of concreteness and attractiveness}},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
year = {2026},
month = jun,
volume = {36},
number = {6},
pages = {bhag075},
publisher = {Oxford University Press},
issn = {1047-3211},
doi = {10.1093/cercor/bhag075},
url = {https://doi.org/10.1093/cercor/bhag075},
pmid = {42330320},
pmcid = {PMC13286001}
}

RIS

TY - JOUR
AU - Zheng, Jiaqi
AU - Xu, Weiyong
AU - Silvennoinen, Johanna
AU - Cong, Fengyu
AU - Parviainen, Tiina
AU - Kujala, Tuomo
TI - Cortical dynamics of icon perception: effects of concreteness and attractiveness
T2 - Cerebral cortex (New York, N.Y. : 1991)
J2 - Cereb Cortex
PY - 2026
DA - 2026/06/01
VL - 36
IS - 6
SP - bhag075
SN - 1047-3211
PB - Oxford University Press
DO - 10.1093/cercor/bhag075
UR - https://doi.org/10.1093/cercor/bhag075
LA - en
ER -

CSL-JSON

{
"id": "10.1093/cercor/bhag075",
"type": "article-journal",
"title": "Cortical dynamics of icon perception: effects of concreteness and attractiveness",
"container-title": "Cerebral cortex (New York, N.Y. : 1991)",
"author": [
{
"family": "Zheng",
"given": "Jiaqi"
},
{
"family": "Xu",
"given": "Weiyong"
},
{
"family": "Silvennoinen",
"given": "Johanna"
},
{
"family": "Cong",
"given": "Fengyu"
},
{
"family": "Parviainen",
"given": "Tiina"
},
{
"family": "Kujala",
"given": "Tuomo"
}
],
"container-title-short": "Cereb Cortex",
"volume": "36",
"issue": "6",
"page": "bhag075",
"DOI": "10.1093/cercor/bhag075",
"PMID": "42330320",
"PMCID": "PMC13286001",
"ISSN": "1047-3211",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/cercor/bhag075",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
1
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1162/imag.a.1207 [code]
Investigating the temporal dynamics and modeling of mid-level feature representations in humans.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: MNE-Python, Pillow, statsmodels, 5 other tools, cognitive, 7 references
[2] doi:10.1093/cercor/bhag110 [code]
Flexibility of internal attention: divergent spatiotemporal profiles for feature and object cueing.
Journal: Cerebral cortex (New York, N.Y. : 1991)
In common: MNE-Python, pandas, SciPy, 2 other tools, MEG, cognitive, 2 references, author Tiina Parviainen
[3] doi:10.1371/journal.pone.0351872 [code]
Decoding visual object recognition from EEG signals.
Journal: PloS one
In common: MNE-Python, Pillow, scikit-learn, 4 other tools, 5 references
[4] doi:10.1162/nol.a.247 [code]
Same Sentences, Different Grammars, Different Brain Responses?: An MEG Study on Case and Agreement Encoding in Hindi and Nepali Split-Ergative Structures.
Journal: Neurobiology of language (Cambridge, Mass.)
In common: MNE-Python, statsmodels, pandas, 3 other tools, MEG, 6 references
[5] doi:10.1093/nc/niag029 [code]
A data-driven approach to identifying and evaluating connectivity-based neural correlates of conscious visual perception.
Journal: Neuroscience of consciousness
In common: MNE-Python, scikit-image, statsmodels, 5 other tools, MEG, cognitive, 3 references
[6] doi:10.1016/j.neuroimage.2026.122051 [code]
Determining hemispheric language dominance from MEG beta-power modulations: Concordance with fMRI.
Journal: NeuroImage
In common: MNE-Python, Pillow, scikit-learn, 4 other tools, MEG, cognitive, 4 references
[7] doi:10.1523/eneuro.0344-25.2026 [code]
Long-Term Variability in Visual Processing versus Perceptual Stability.
Journal: eNeuro
In common: MNE-Python, statsmodels, scikit-learn, 4 other tools, MEG, cognitive, 3 references
[8] doi:10.7554/elife.107933 [code]
Modality-agnostic decoding of vision and language from fMRI.
Journal: eLife
In common: scikit-image, Pillow, statsmodels, 5 other tools, cognitive, 3 references
[9] doi:10.1038/s41593-026-02285-1 [code]
Fixation duration on natural scenes is explained by memory encoding not processing demand.
Journal: Nature neuroscience
In common: MNE-Python, scikit-image, Pillow, 6 other tools, MEG, cognitive, 1 reference
[10] doi:10.1523/eneuro.0041-26.2026 [code]
Ocular Speech Tracking Persists in Blindness, but Its Dynamics and Oculo-Cerebral Connectivity Depend on Visual Status.
Journal: eNeuro
In common: MNE-Python, Pillow, statsmodels, 5 other tools, MEG, cognitive, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.