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The Spatial Similarity Task: A cross-species approach to spatial memory.

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Paper

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

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The authors' code

MATLAB · 64 lines · 2.5 KB · no license

  1. function[compiled_data, compiled_performance]=x_CompileTrials_Performance_forFilesInFolder(todaysdate, path_to_data)
  3. %% INPUT %%
  4. % todaysdate = string (e.g. '20231122')
  5. % path_to_data= give path to data
  6. % e.g.path_to_data='/Volumes/human/dataset for methods paper/Trial Logging/New Scale/No Arms Version/SPS Task';
  7. %%
  9. cd(path_to_data)
  10. % Get a list of CSV files in the current directory
  11. delete '._SONA*.csv'
  12. csv_files = dir('*.csv');
  14. % Initialize an empty cell array to hold the combined data
  15. compiled_data = {}; %[];
  16. compiled_performance = cell(length(csv_files), 2); %zeros(size(SPS_csv_files,1),2);
  18. % Loop over the files
  19. for iFiles = 1:length(csv_files)
  20. % Read the data from the current file
  21. data = readtable(csv_files(iFiles).name);
  22. % Split the filename into parts by "_"
  23. filename_parts = strsplit(csv_files(iFiles).name, '_');
  24. % Add a new column with the first part of the filename
  25. data.filename = repmat(filename_parts(1), height(data), 1);
  26. % Concatenate the data into the compiled_data cell array
  27. compiled_data = [compiled_data; table2cell(data)];
  30. correct_responses=sum(table2array(data(:,4)));
  31. trial_num=size(data(:,1),1);
  32. %not sure if overall_accuracy should be out of 1 or 100
  33. overall_accuracy=(correct_responses)/trial_num*100; %need to match this with MST output
  34. %overall_accuracy=(correct_responses)/trial_num*100;
  35. % Split the filename into parts by "_"
  36. filename_parts = strsplit(csv_files(iFiles).name, '_');
  37. % Assume the subject name is the first part of the filename
  38. subject_name = filename_parts{1};
  39. % Store the results
  40. compiled_performance{iFiles, 1} = subject_name;
  41. compiled_performance{iFiles, 2} = overall_accuracy;
  42. end
  44. % Convert the accuracy column to a numeric array
  45. accuracy_array = cell2mat(compiled_performance(:, 2));
  47. % Calculate the mean and standard deviation of the accuracies
  48. mean_accuracy = mean(accuracy_array, 'omitnan');
  49. std_accuracy = std(accuracy_array, 'omitnan');
  51. % Calculate the z-score for each subject's accuracy
  52. for iFiles = 1:length(csv_files)
  53. if isnan(compiled_performance{iFiles, 2})
  54. % If the accuracy is NaN, skip the z-score calculation or assign a default value
  55. compiled_performance{iFiles, 3} = NaN; % or some default value
  56. else
  57. compiled_performance{iFiles, 3} = (compiled_performance{iFiles, 2} - mean_accuracy) / std_accuracy;
  58. end
  59. end
  61. % Save the combined data into a .mat file
  62. save_filename=['compiled_trials_performance_' todaysdate];
  63. save(save_filename, 'compiled_data','compiled_performance');

x_CompileTrials_Performance_forFilesInFolder.m at commit fcb46c4, no license · at the source

Overview

Authors: Mia Borzello1, William Supian1, Andrea A. Chiba1
ORCID iDs: Mia Borzello
  1. Department of Cognitive Science, University of California,San Diego, 9500 Gilman Drive, Mail Code 0515, La Jolla, CA 92093-0515 USA
Institutions: University of California San Diego (United States)
Journal: Behavior research methods, volume 58, issue 9, article 273
Dates: received 18 July 2024; accepted 11 June 2026; published online 18 August 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3758/s13428-026-03127-5 · PMID 42613502 · PMCID PMC13486031 · OpenAlex W7203709402
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Keywords: Mnemonic discrimination, Spatial memory, Virtual reality, Cross-species
MeSH: Spatial Memory*, Adult, Animals, Female, Humans, Male, Maze Learning, Space Perception, User-Computer Interface, Virtual Reality, Young Adult (* major topic)
Journal subjects: Original Manuscript
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institutes of Health (2t32mh020002-16a1)
Citations: not cited yet (Europe PMC); 66 references in the paper

Abstract

Memory serves as the cornerstone of cognitive function and specifically enables the critical ability to disambiguate and encode similar experiences as distinct memories. Mnemonic discrimination of near spaces and places is a process crucially underpinned by a computational feature of the hippocampus, pattern separation. Whereas this refined aspect of spatial memory has been addressed in abundance in the rodent literature, fewer spatial tasks are designed to specifically query memory for spatial similarities in human subjects. To address this, we introduce an open-source virtual maze suite developed on the Unity platform, freely available for researchers to use, modify, and extend. The suite is designed to bridge the behavioral translation gap by assessing mnemonic discrimination in humans through a spatial delayed match-to-sample task, carefully modeled after rodent spatial memory tasks. Rather than relying on fully immersive systems, the suite leverages desktop-based virtual navigation to combine parametric control of experimental conditions with the ability to collect each participant’s exploratory data. The pilot experiments are presented to validate the final suite design, alongside providing representative results from a subset of participants. Mnemonic discrimination was measured by parametric manipulation of spatial distances, resulting in a spatial distance memory function in which participants made more errors in remembering the correct location amongst similar or adjacent locations relative to distant locations. Given the prevalence of spatial memory deficits in age-related cognitive decline and neurological and psychiatric conditions, and the widespread use of spatial tasks in rodent models of these disorders, the Spatial Similarity Task (SST) suite offers a translationally grounded tool with direct potential for customization towards clinical application.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.3758/s13428-026-03127-5.

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

Repositories

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

Spatial-Similarity-Task

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Measured parameters”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: github.com/Spatial-Similarity-Task

Spatial-Similarity-Task/SST

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: fcb46c4f78dfca67afb3c2a4478b9f8ffb169c26, 18 July 2024
Languages: MATLAB (4)
Size: 1,405 files, 4 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, documentation
Not found: license file, CITATION.cff, environment file, tests, continuous integration
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/Spatial-Similarity-Task/SST

Code availability

Experiment code and analysis code for the example study are available in a GitHub repository: https://github.com/Spatial-Similarity-Task/SST.

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

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 4 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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.

Availability of data and materials

Anonymized sample dataset and materials are available in a GitHub repository: https://github.com/Spatial-Similarity-Task/SST.

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 2, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media
  • Funding: added National Institutes of Health: 2t32mh020002-16a1

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 4 keywords, 11 MeSH terms, 61 references.

Cite

This paper

Borzello, M., Supian, W., & Chiba, A. A. (2026). The Spatial Similarity Task: A cross-species approach to spatial memory. Behavior research methods, 58(9), 273. https://doi.org/10.3758/s13428-026-03127-5

BibTeX

@article{borzello2026spatial,
author = {Borzello, Mia and Supian, William and Chiba, Andrea A.},
title = {{The Spatial Similarity Task: A cross-species approach to spatial memory}},
journal = {Behavior research methods},
year = {2026},
month = aug,
volume = {58},
number = {9},
pages = {273},
publisher = {Springer Science+Business Media},
issn = {1554-351X},
doi = {10.3758/s13428-026-03127-5},
url = {https://doi.org/10.3758/s13428-026-03127-5},
pmid = {42613502},
pmcid = {PMC13486031}
}

RIS

TY - JOUR
AU - Borzello, Mia
AU - Supian, William
AU - Chiba, Andrea A.
TI - The Spatial Similarity Task: A cross-species approach to spatial memory
T2 - Behavior research methods
J2 - Behav Res Methods
PY - 2026
DA - 2026/08/18
VL - 58
IS - 9
SP - 273
SN - 1554-351X
PB - Springer Science+Business Media
DO - 10.3758/s13428-026-03127-5
UR - https://doi.org/10.3758/s13428-026-03127-5
LA - en
ER -

CSL-JSON

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"container-title-short": "Behav Res Methods",
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"DOI": "10.3758/s13428-026-03127-5",
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"PMCID": "PMC13486031",
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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