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Beyond flatland: naturalistic three-dimensional stimuli and visual working memory processing.

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The authors' code

Jupyter notebook · 91 lines · 3.2 KB · no license

  1. # %% [markdown]
  2. # ## Figure Generation
  3. #
  4. # **Description:** This script loads the preprocessed data (`Processed_Data.csv`), reshapes it for visualization, and generates the figures used in the manuscript.
  5. #
  7. # %%
  8. import pandas as pd
  9. import numpy as np
  10. import matplotlib.pyplot as plt
  11. from scipy import stats
  13. # 1. Load the data
  14. df = pd.read_csv('Processed_Data.csv')
  16. # 2. Calculate Costs
  17. # Mapping: A = 3D, B = 2D
  18. # RT Cost = (RT(6) - RT(3)) * 1000 to convert to ms
  19. df['RT_Cost_3D'] = (df['A_ResponseTime6'] - df['A_ResponseTime3']) * 1000
  20. df['RT_Cost_2D'] = (df['B_ResponseTime6'] - df['B_ResponseTime3']) * 1000
  22. # Accuracy Cost = Acc(3) - Acc(6) (percentage)
  23. df['Acc_Cost_3D'] = (df['A_acc3'] - df['A_acc6']) * 100
  24. df['Acc_Cost_2D'] = (df['B_acc3'] - df['B_acc6']) * 100
  26. # 3. Calculate Stats for bars
  27. n = len(df)
  28. mean_rt_2d = df['RT_Cost_2D'].mean()
  29. mean_rt_3d = df['RT_Cost_3D'].mean()
  30. # Back to SEM
  31. sem_rt_2d = stats.sem(df['RT_Cost_2D'])
  32. sem_rt_3d = stats.sem(df['RT_Cost_3D'])
  34. mean_acc_2d = df['Acc_Cost_2D'].mean()
  35. mean_acc_3d = df['Acc_Cost_3D'].mean()
  36. # Back to SEM
  37. sem_acc_2d = stats.sem(df['Acc_Cost_2D'])
  38. sem_acc_3d = stats.sem(df['Acc_Cost_3D'])
  40. # 4. Plotting
  41. fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 6))
  43. conditions = ['2D Stimuli', '3D Stimuli']
  44. colors = ['#2E86AB', '#A23B72']
  45. bar_width = 0.6
  47. # --- Panel 1: RT Cost (ms) ---
  48. # Bars with SEM
  49. ax1.bar(conditions, [mean_rt_2d, mean_rt_3d], yerr=[sem_rt_2d, sem_rt_3d],
  50. capsize=8, color=colors, alpha=0.5, edgecolor='black', width=bar_width, zorder=1)
  52. # Jittered X coordinates
  53. np.random.seed(42)
  54. jitter_amount = 0.08
  55. x_2d = np.random.normal(0, jitter_amount, n)
  56. x_3d = np.random.normal(1, jitter_amount, n)
  58. # Lines connecting subjects
  59. for i in range(n):
  60. ax1.plot([x_2d[i], x_3d[i]], [df['RT_Cost_2D'][i], df['RT_Cost_3D'][i]],
  61. color='gray', alpha=0.3, linewidth=0.8, zorder=2)
  63. # Scatter points
  64. ax1.scatter(x_2d, df['RT_Cost_2D'], color='#1a4e63', alpha=0.6, s=25, zorder=3, edgecolors='white', linewidth=0.5)
  65. ax1.scatter(x_3d, df['RT_Cost_3D'], color='#5e2242', alpha=0.6, s=25, zorder=3, edgecolors='white', linewidth=0.5)
  67. ax1.set_ylabel('RT Cost (ms)', fontsize=12, fontweight='bold')
  68. ax1.set_title('Response Time Cost\n(6 items - 3 items)', fontsize=14, fontweight='bold')
  69. ax1.grid(True, alpha=0.3, linestyle='--', axis='y')
  71. # --- Panel 2: Accuracy Cost (%) ---
  72. # Bars with SEM
  73. ax2.bar(conditions, [mean_acc_2d, mean_acc_3d], yerr=[sem_acc_2d, sem_acc_3d],
  74. capsize=8, color=colors, alpha=0.5, edgecolor='black', width=bar_width, zorder=1)
  76. # Lines connecting subjects
  77. for i in range(n):
  78. ax2.plot([x_2d[i], x_3d[i]], [df['Acc_Cost_2D'][i], df['Acc_Cost_3D'][i]],
  79. color='gray', alpha=0.3, linewidth=0.8, zorder=2)
  81. # Scatter points
  82. ax2.scatter(x_2d, df['Acc_Cost_2D'], color='#1a4e63', alpha=0.6, s=25, zorder=3, edgecolors='white', linewidth=0.5)
  83. ax2.scatter(x_3d, df['Acc_Cost_3D'], color='#5e2242', alpha=0.6, s=25, zorder=3, edgecolors='white', linewidth=0.5)
  85. ax2.set_ylabel('Accuracy Cost (%)', fontsize=12, fontweight='bold')
  86. ax2.set_title('Accuracy Cost\n(3 items - 6 items)', fontsize=14, fontweight='bold')
  87. ax2.grid(True, alpha=0.3, linestyle='--', axis='y')
  89. plt.tight_layout()
  90. plt.savefig('Cost_Analysis_SEM_ms_Updated.svg', format='svg')
  91. plt.show()

figures.ipynb, no license · at the source

Overview

Authors: Gilad Schrift1, Shachar Lando2, Roy Luria1,2, Nitzan Censor1,2
ORCID iDs: Nitzan Censor
  1. Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 69978 Israel
  2. School of Psychological Sciences, Tel Aviv University, Tel Aviv, 69978 Israel
Institutions: Tel Aviv University (Israel)
Journal: Virtual reality, volume 30, issue 2, article 93
Dates: received 29 August 2025; accepted 5 February 2026; published online 21 March 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s10055-026-01343-0 · PMID 41890983 · PMCID PMC13013113 · OpenAlex W7139981335
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: cognitive (subfield)
Methods: Statistics, Single-unit activity, calcium imaging
Keywords: Visual working memory, 3D perception, Virtual reality, Change detection, Cognitive load, Depth cues, Ecological validity
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (866093, ERC2019-COG 866093)
Citations: not cited yet (Europe PMC); 34 references in the paper

Abstract

Visual working memory (VWM) enables the temporary storage and manipulation of visual information, yet its limited capacity makes it sensitive to the amount and structure of the information it must retain. The vast majority of previous VWM research has used two-dimensional stimuli, while real-world visual perception incorporates depth-related spatial cues which may affect performance. Prior work suggests that depth could enhance VWM performance by enabling perceptual enrichment and better individuation, but may also introduce additional cognitive processing costs and impair performance. Here, we directly tested how dimensionality influences VWM under different memory loads using a virtual reality adaptation of the change detection task, enabling the presentation of ecologically valid, real-world 2D and 3D objects. While accuracy was comparable across stimulus dimensionality, response times for 3D stimuli showed larger increases at higher memory loads. These results suggest that even though 3D stimuli may enrich perceptual input, they introduce processing costs that become more apparent under high memory load, possibly demanding additional neural resources associated with depth processing.

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

Repositories

Its files are read in the Code ↔ Paper reader above.

OSF hkuys

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: Jupyter (2)
Size: 85 files, 2 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (2 files), NumPy (2 files), pandas (2 files), SciPy (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
2 files
At the source: osf.io/hkuys

Giladsc/Object-Change-Detection-VR

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 469c730246cd9a88859251f3b857a548450e89d7, 14 December 2025
Size: 2 files, 0 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
2 files
At the source: github.com/Giladsc/Object-Change-Detection-VR

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

Tracing map

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Data

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

Data availability

The experimental data and analysis code have been deposited on the Open Science Framework (OSF) and are publicly available as of the date of publication at https://osf.io/hkuys .An executable version of the task is available at https://github.com/Giladsc/Object-Change-Detection-VR .

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 7 keywords, 1 funder, 33 references.

Cite

This paper

Schrift, G., Lando, S., Luria, R., & Censor, N. (2026). Beyond flatland: naturalistic three-dimensional stimuli and visual working memory processing. Virtual reality, 30(2), 93. https://doi.org/10.1007/s10055-026-01343-0

BibTeX

@article{schrift2026beyond,
author = {Schrift, Gilad and Lando, Shachar and Luria, Roy and Censor, Nitzan},
title = {{Beyond flatland: naturalistic three-dimensional stimuli and visual working memory processing}},
journal = {Virtual reality},
year = {2026},
month = mar,
volume = {30},
number = {2},
pages = {93},
publisher = {Springer Science+Business Media},
issn = {1359-4338},
doi = {10.1007/s10055-026-01343-0},
url = {https://doi.org/10.1007/s10055-026-01343-0},
pmid = {41890983},
pmcid = {PMC13013113}
}

RIS

TY - JOUR
AU - Schrift, Gilad
AU - Lando, Shachar
AU - Luria, Roy
AU - Censor, Nitzan
TI - Beyond flatland: naturalistic three-dimensional stimuli and visual working memory processing
T2 - Virtual reality
J2 - Virtual Real
PY - 2026
DA - 2026/03/21
VL - 30
IS - 2
SP - 93
SN - 1359-4338
PB - Springer Science+Business Media
DO - 10.1007/s10055-026-01343-0
UR - https://doi.org/10.1007/s10055-026-01343-0
LA - en
ER -

CSL-JSON

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"given": "Roy"
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{
"family": "Censor",
"given": "Nitzan"
}
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"container-title-short": "Virtual Real",
"volume": "30",
"issue": "2",
"page": "93",
"DOI": "10.1007/s10055-026-01343-0",
"PMID": "41890983",
"PMCID": "PMC13013113",
"ISSN": "1359-4338",
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