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Socioeconomic Status, the Home Language Environment, Noise Exposure, and the Mismatch Response in Infancy.

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The authors' code

Python · 173 lines · 6.4 KB · no license

  1. import argparse
  2. import os.path
  3. import csv
  4. import ipdb
  6. # the "nap" needs to be greater than or equal to this time in order to be filtered out
  7. NAP_TIME_MIN = '600'
  9. INDEX_PARTICIPANT_ID = 22
  10. INDEX_AGE = 1
  11. INDEX_DURATION = 4 #was a time
  12. INDEX_MEANINGFUL = 9 #was a time
  13. INDEX_DISTANT = 11 #was a time
  14. INDEX_TV = 12 #was a time
  15. INDEX_NOISE = 14 #was a time
  16. INDEX_SILENCE = 15 #was a time
  17. INDEX_AWC_ACTUAL = 5
  18. INDEX_CTC_ACTUAL = 6
  19. INDEX_CVC_ACTUAL = 7
  21. def open_source_file(parser, arg):
  22. if not os.path.exists(arg):
  23. parser.error("The file %s does not exist" % arg)
  24. else:
  25. return open(arg, 'r')
  27. def open_dest_file(parser, arg):
  28. if os.path.exists(arg):
  29. parser.error("The file %s exists already" % arg)
  30. else:
  31. return open(arg, 'w', newline='\r\n')
  33. def write_stats(raw_stats, filtered_stats, stat_file, filtered_line_count):
  34. if (stat_file is not None) and (len(raw_stats) != 0):
  35. stat_file.write(','.join(raw_stats) + '\r\n')
  36. stat_file.write(','.join(filtered_stats) + '\r\n')
  37. print('Processed visit: PARTICIPANT_ID=' + raw_stats[INDEX_PARTICIPANT_ID] + ', AGE=' + raw_stats[INDEX_AGE])
  38. print('Filtered out ' + str(filtered_line_count) + ' lines')
  40. # AWC is 0, CVC is 0, CTC is 0, AND at least 3 of the 5 min are either noise or silence
  41. def line_passes_filter(parsed_list):
  42. noise_or_silence = add_numbers(parsed_list[INDEX_NOISE], parsed_list[INDEX_SILENCE])
  43. return (int(parsed_list[INDEX_AWC_ACTUAL]) != 0) or (int(parsed_list[INDEX_CTC_ACTUAL]) != 0) or (int(parsed_list[INDEX_CVC_ACTUAL]) > 10) or (int(noise_or_silence) < 180)
  45. def add_numbers(number1, number2):
  46. #ipdb.set_trace()
  47. return str(int(number1) + int(number2))
  49. def is_time_gte(time1, time2):
  50. if int(time1 > time2):
  51. return True
  52. if int(time1) < int(time2):
  53. return False
  54. return True
  56. def add_to_stats(parsed_list, stats):
  57. stats[INDEX_DURATION] = add_numbers(stats[INDEX_DURATION], parsed_list[INDEX_DURATION])
  58. stats[INDEX_MEANINGFUL] = add_numbers(stats[INDEX_MEANINGFUL], parsed_list[INDEX_MEANINGFUL])
  59. stats[INDEX_DISTANT] = add_numbers(stats[INDEX_DISTANT], parsed_list[INDEX_DISTANT])
  60. stats[INDEX_TV] = add_numbers(stats[INDEX_TV], parsed_list[INDEX_TV])
  61. stats[INDEX_NOISE] = add_numbers(stats[INDEX_NOISE], parsed_list[INDEX_NOISE])
  62. stats[INDEX_SILENCE] = add_numbers(stats[INDEX_SILENCE], parsed_list[INDEX_SILENCE])
  63. stats[INDEX_AWC_ACTUAL] = add_numbers(stats[INDEX_AWC_ACTUAL], parsed_list[INDEX_AWC_ACTUAL])
  64. stats[INDEX_CTC_ACTUAL] = add_numbers(stats[INDEX_CTC_ACTUAL], parsed_list[INDEX_CTC_ACTUAL])
  65. stats[INDEX_CVC_ACTUAL] = add_numbers(stats[INDEX_CVC_ACTUAL], parsed_list[INDEX_CVC_ACTUAL])
  68. # python cli arguments
  69. parser = argparse.ArgumentParser(description='Remove nap rows from LENA data file.')
  70. parser.add_argument("-s", dest="source_file", required=True,
  71. help="source file", metavar="FILE",
  72. type=lambda x: open_source_file(parser, x))
  73. parser.add_argument("-d", dest="dest_file", required=True,
  74. help="destination file", metavar="FILE",
  75. type=lambda x: open_dest_file(parser, x))
  76. parser.add_argument("-t", dest="stat_file", required=True,
  77. help="stats file", metavar="FILE",
  78. type=lambda x: open_dest_file(parser, x))
  80. args = parser.parse_args()
  82. # write out header
  83. header = next(args.source_file)
  84. args.dest_file.write(header)
  85. args.stat_file.write(header)
  87. # loop through source file
  88. raw_stats = []
  89. filtered_stats = []
  90. visit_count = 0
  91. filtered_line_count = 0
  92. parsed_list = []
  93. nap_raw_lines = []
  94. nap_parsed_lists = []
  95. print('Processing visits...')
  96. for raw_line in args.source_file:
  97. if len(raw_line.strip()) == 0:
  98. continue
  99. parsed_line = csv.reader([raw_line], delimiter=',', quotechar='"')
  100. parsed_list = next(parsed_line)
  102. line_passed = line_passes_filter(parsed_list)
  104. # this line is for a different visit from the previous
  105. is_new_visit = len(raw_stats) == 0 or (raw_stats[INDEX_PARTICIPANT_ID] != parsed_list[INDEX_PARTICIPANT_ID]) or (raw_stats[INDEX_AGE] != parsed_list[INDEX_AGE])
  106. if is_new_visit:
  108. # handle accumulated nap lines
  109. nap_length_accum = '0'
  110. for nap_parsed_list in nap_parsed_lists:
  111. nap_length_accum = add_numbers(nap_length_accum, nap_parsed_list[INDEX_DURATION])
  112. if is_time_gte(nap_length_accum, NAP_TIME_MIN):
  113. filtered_line_count += len(nap_parsed_lists)
  114. else:
  115. for nap_parsed_list in nap_parsed_lists:
  116. add_to_stats(nap_parsed_list, filtered_stats)
  117. for nap_raw_line in nap_raw_lines:
  118. args.dest_file.write(nap_raw_line)
  119. nap_parsed_lists = []
  120. nap_raw_lines = []
  122. # write out stats for the previous visit
  123. write_stats(raw_stats, filtered_stats, args.stat_file, filtered_line_count)
  124. visit_count += 1
  126. # start fresh with the new visit
  127. raw_stats = parsed_list.copy()
  128. filtered_stats = parsed_list.copy()
  129. filtered_line_count = 0
  130. if not line_passed:
  131. nap_parsed_lists.append(parsed_list.copy())
  132. nap_raw_lines.append(raw_line)
  133. filtered_stats[INDEX_DURATION] = '0'
  134. filtered_stats[INDEX_MEANINGFUL] = '0'
  135. filtered_stats[INDEX_DISTANT] = '0'
  136. filtered_stats[INDEX_TV] = '0'
  137. filtered_stats[INDEX_NOISE] = '0'
  138. filtered_stats[INDEX_SILENCE] = '0'
  139. filtered_stats[INDEX_AWC_ACTUAL] = '0'
  140. filtered_stats[INDEX_CTC_ACTUAL] = '0'
  141. filtered_stats[INDEX_CVC_ACTUAL] = '0'
  142. else:
  143. args.dest_file.write(raw_line)
  144. # this line is for the same visit as the previous
  145. else:
  146. add_to_stats(parsed_list, raw_stats)
  147. if line_passed:
  149. # handle accumulated nap lines
  150. nap_length_accum = '0'
  151. for nap_parsed_list in nap_parsed_lists:
  152. nap_length_accum = add_numbers(nap_length_accum, nap_parsed_list[INDEX_DURATION])
  153. if is_time_gte(nap_length_accum, NAP_TIME_MIN):
  154. filtered_line_count += len(nap_parsed_lists)
  155. else:
  156. for nap_parsed_list in nap_parsed_lists:
  157. add_to_stats(nap_parsed_list, filtered_stats)
  158. for nap_raw_line in nap_raw_lines:
  159. args.dest_file.write(nap_raw_line)
  160. nap_parsed_lists = []
  161. nap_raw_lines = []
  163. add_to_stats(parsed_list, filtered_stats)
  164. args.dest_file.write(raw_line)
  166. else:
  167. nap_parsed_lists.append(parsed_list.copy())
  168. nap_raw_lines.append(raw_line)
  170. # process final visit
  171. write_stats(raw_stats, filtered_stats, args.stat_file, filtered_line_count)
  173. print('Processed ' + str(visit_count) + ' visits')

remove_naps_hub_No_IT_sec.py at commit 3ee2e09, no license · at the source

Overview

Authors: Katrina R. Simon1, Belen Azofra Macarron2, Aislinn Sandre3, Melina Amarante4, Sonya V. Troller-Renfree5, Kimberly G. Noble5
ORCID iDs: Katrina R. Simon
  1. Mailman School of Public Health, Columbia University, New York City, New York, USA
  2. Universite Paris Cité, Paris, France
  3. University of Western Ontario, London, Ontario, Canada
  4. Fordham University, New York City, New York, USA
  5. Teachers College, Columbia University, New York City, New York, USA
Institutions: Columbia University (United States); Université Paris Cité (France); Western University (Canada); Fordham University (United States)
Journal: Developmental psychobiology, volume 68, issue 2, article e70128
Dates: published online 1 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/dev.70128 · PMID 41619203 · PMCID PMC13005931 · OpenAlex W7126465781
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), developmental (subfield)
Methods: Smoothing, state filtering, decompositions, Statistics, Preprocessing, Spectral & time-frequency, Evoked potentials, Connectivity
MeSH: Auditory Perception*, Evoked Potentials*, Evoked Potentials, Auditory*, Home Environment*, Language Development*, Noise*, Social Class*, Electroencephalography, Female, Humans, Infant, Longitudinal Studies, Male, Socioeconomic Disparities in Health (* major topic)
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Canadian Institutes of Health Research Fellowship (187925); NICHD NIH HHS (R00 HD104923, K99 HD104923, R01 HD093707); Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD093707, R00HD10492, K99HD104923)
Citations: not cited yet (Europe PMC); 87 references in the paper

Abstract

Socioeconomic resources have long been associated with children’s language development. Several proximal factors have been suggested as candidate mechanisms underlying socioeconomic disparities in language development, including differences in the home language environment and home noise levels. These experiences may in part shape auditory discrimination skills, a key component of language comprehension. To index early auditory discrimination, researchers measured brain function in relation to the detection of different sounds with an event-related potential (ERP) called the mismatch response (MMR). The current study aimed to examine associations among socioeconomic circumstances, the home language environment, home noise exposure, and the MMR in a socioeconomically, racially, and ethnically diverse longitudinal sample of 6- and 12-month-old infants. Socioeconomic circumstances were measured prenatally via parent report. The home language environment and home noise levels were measured using digital language processing devices when infants were approximately 6 months of age. The MMR was elicited during a passive auditory oddball task at two timepoints—6 and 12 months of age. Results showed that neither SES, the home language environment, nor home noise levels predicted infant MMR at either age. These findings add to a growing body of literature examining the role of distal and proximal factors in shaping infant brain activity related to language development.

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

Repository

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

trollerrenfr/LENA_Scripts

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 3ee2e09f87e797c2a3c7d03941ae290addc18b13, 31 March 2022
Languages: Python (3)
Size: 3 files, 3 scripts
Software Heritage: not archived
Found in: the text, “Home Language Environment”
Not found: README, license file, 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
3 files
At the source: github.com/trollerrenfr/LENA_Scripts

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;
  • 3 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.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, KGN, upon reasonable request.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 14 MeSH terms, 3 funders, 74 references.

Cite

This paper

Simon, K. R., Macarron, B. A., Sandre, A., Amarante, M., Troller-Renfree, S. V., & Noble, K. G. (2026). Socioeconomic Status, the Home Language Environment, Noise Exposure, and the Mismatch Response in Infancy. Developmental psychobiology, 68(2), e70128. https://doi.org/10.1002/dev.70128

BibTeX

@article{simon2026socioeconomic,
author = {Simon, Katrina R. and Macarron, Belen Azofra and Sandre, Aislinn and Amarante, Melina and Troller-Renfree, Sonya V. and Noble, Kimberly G.},
title = {{Socioeconomic Status, the Home Language Environment, Noise Exposure, and the Mismatch Response in Infancy}},
journal = {Developmental psychobiology},
year = {2026},
month = mar,
volume = {68},
number = {2},
pages = {e70128},
publisher = {Wiley},
issn = {0012-1630},
doi = {10.1002/dev.70128},
url = {https://doi.org/10.1002/dev.70128},
pmid = {41619203},
pmcid = {PMC13005931}
}

RIS

TY - JOUR
AU - Simon, Katrina R.
AU - Macarron, Belen Azofra
AU - Sandre, Aislinn
AU - Amarante, Melina
AU - Troller-Renfree, Sonya V.
AU - Noble, Kimberly G.
TI - Socioeconomic Status, the Home Language Environment, Noise Exposure, and the Mismatch Response in Infancy
T2 - Developmental psychobiology
J2 - Dev Psychobiol
PY - 2026
DA - 2026/03/01
VL - 68
IS - 2
SP - e70128
SN - 0012-1630
PB - Wiley
DO - 10.1002/dev.70128
UR - https://doi.org/10.1002/dev.70128
LA - en
ER -

CSL-JSON

{
"id": "10.1002/dev.70128",
"type": "article-journal",
"title": "Socioeconomic Status, the Home Language Environment, Noise Exposure, and the Mismatch Response in Infancy",
"container-title": "Developmental psychobiology",
"author": [
{
"family": "Simon",
"given": "Katrina R."
},
{
"family": "Macarron",
"given": "Belen Azofra"
},
{
"family": "Sandre",
"given": "Aislinn"
},
{
"family": "Amarante",
"given": "Melina"
},
{
"family": "Troller-Renfree",
"given": "Sonya V."
},
{
"family": "Noble",
"given": "Kimberly G."
}
],
"container-title-short": "Dev Psychobiol",
"volume": "68",
"issue": "2",
"page": "e70128",
"DOI": "10.1002/dev.70128",
"PMID": "41619203",
"PMCID": "PMC13005931",
"ISSN": "0012-1630",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/dev.70128",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
1
]
]
}
}

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