Optimized feature gains explain and predict successes and failures of human selective listening.

Code ↔ Paper

17 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.

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[1] § Methods › Experiment 1: effect of distractors on attentional selection in monaural conditions › Stimuli ↔ corpus/saddler_word_rec.py, the whole file · a weak match · score 0.88 · IEEE AASP CASA, Spoken Wikipedia, Common Voice, Saddler, babble, music
[2] § Methods › Cochlear model stage ↔ src/audio_attention_transforms.py, lines 292–346 · score 0.77 · gammatone filter bank, half wave, convolved, domain, compressive, downsampled
[3] § Methods › Cochlear model stage ↔ src/audio_transforms.py, lines 309–360 · score 0.77 · gammatone filter bank, half wave, convolved, domain, compressive, downsampled
[4] § Methods › Cochlear model stage ↔ src/time_domain_cochleagram.py, lines 15–82 · score 0.76 · compressive nonlinearity, impulse response, filter bank, downsampled, cochlea, channels
[5] § Methods › Experiment 2: effect of harmonicity on attentional selection in monaural conditions › Stimuli ↔ src/get_swc_popham_expmt_stim_2024.py, lines 100–237 · score 0.72 · jitter pattern, Whispered speech, STRAIGHT, sinusoidal, cut, Inharmonic
[6] § Methods › Experiment 2: effect of harmonicity on attentional selection in monaural conditions › Stimuli ↔ src/get_swc_popham_expmt_stim_2024_for_model.py, lines 81–225 · score 0.71 · jitter pattern, Whispered speech, STRAIGHT, sinusoidal, Inharmonic, harmonics
[7] § Methods › Cochlear model stage ↔ src/audio_attention_transforms.py, lines 292–346 · score 0.71 · gammatone filter bank, half wave, compressive, downsampled, waveform, cochlea
[8] § Results › The model replicates effects of speech harmonicity ↔ src/get_swc_popham_expmt_stim_2024_for_model.py, lines 81–225 · score 0.67 · speech harmonicity, whispered speech, cue signals, human word, inharmonic, talker
[9] § Methods › Training data generation › Signal augmentations ↔ src/get_swc_popham_expmt_stim_2024.py, lines 100–237 · score 0.65 · target signals, RMS normalized, cue signal, onset, cut, duration
[10] § Methods › Training data generation › Signal augmentations ↔ src/get_swc_unfamiliar_distractor_stim_2024.py, lines 50–135 · score 0.64 · target signals, RMS normalized, cue signal, onset, cut, duration
[11] § Methods › Training data generation › Speech corpora ↔ corpus/saddler_word_rec.py, the whole file · a weak match · score 0.60 · Common Voice, word classes, word recognition, corpus, excerpts, train
[12] § Methods › Experiment 1: effect of distractors on attentional selection in monaural conditions › Stimuli ↔ src/get_swc_mono_expmt_stim_2024.py, lines 14–144 · score 0.56 · talker Mandarin, distractor signals, babble, music, English, scenes
[13] § Methods › Experiment 5: the precedence effect with concurrent speech signals › Model experiment ↔ src/eval_precedence.py, lines 107–174 · score 0.53 · anechoic room, lead channel, precedence, binaural, location, SNRs
[14] § Methods › Artificial neural network constituent operations › Weighted-average pooling ↔ src/custom_modules.py, lines 23–75 · score 0.52 · channel dimension, Hanning, windows, stride, Weighted, convolution
[15] § Methods › Experiment 6: effect of spatial separation in azimuth and elevation › Procedure ↔ src/get_swc_unfamiliar_distractor_stim_2024.py, lines 50–135 · score 0.51 · combined signal, cue signal, SPL, dB, excerpt, stimuli
[16] § Methods › Analysis of model locus of attention ↔ src/eval_symmetric_distractors.py, lines 99–175 · score 0.51 · RMS normalized, single distractor, composed, excerpts, signals, stimuli
[17] § Methods › Experiment 4: spatial tuning for masked speech › Model experiment ↔ notebooks/Paper_Figs_Raw_Analysis/Byrne_et_al_model_simulation_all_arch.ipynb, lines 368–466 · score 0.50 · model simulation, Model thresholds, fitting, 18 dB, sex, azimuth

Paper

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The authors' code

Python · 92 lines · 3.7 KB · MIT · 2 matches

# write torch dataloader for test set
import torch
import pandas as pd
import librosa
import pickle
from pathlib import Path
class SaddlerSWCWordRecTest(torch.utils.data.Dataset):
"""
Dataset for the Saddler word recognition experiment using
foreground excerpts from Spoken Wikipedia. Backgrounds are
either:
- music from musdb
- 8 talkerbabble from common voice
- spectrally matched noise (SSN; match each foreground)
- Festen and Plomp style modulated maskers
- audioset
- natural scenes from IEEE AASP CASA challenge
- clean (no background)
"""
def __init__(self, manifest_path, bg_stim_path, condition, label_type="WSN", sr=20_000):
"""
Args:
manifest_path (str): path to pandas manifest with fg and cue excerpts
bg_stim_path (str): path to directory with background stimuli
condition (str): background condition. Either "music", "babble", "stationary", "modulated", "audioset", "ieee_scenes", or "clean"
label_type (str): Set of word class labels to use. Either "WSN" (JSIN) or "CV" common voice
sr (int): sampling rate to load audio at
"""
self.manifest = pd.read_pickle(manifest_path)
self.condition = condition
self.label_type = label_type
self.sr = sr
self.condition_dict = {'music':"background_musdb18hq",
"babble":"background_cv08talkerbabble",
"stationary": "background_issnstationary",
"modulated": "background_issnfestenplomp",
"audioset": "background_audioset",
"ieee_scenes": "background_ieeeaaspcasa",
}
if condition == "clean":
self.bg_stim = None
self.test_cond_dir = None
else:
self.test_cond_dir = self.condition_dict[self.condition]
self.bg_stim = list((bg_stim_path / self.test_cond_dir).glob("*.wav"))
self.class_map, self.word_2_class = self.class_map()
self.dataset_len = len(self.manifest)
def class_map(self):
"""
Loads the mapping between the word IDX and human readable word map.
"""
if self.label_type == "WSN":
## load WSN vocab mapping
word_and_speaker_encodings = pickle.load( open( "/om2/user/imgriff/projects/Auditory-Attention/word_and_speaker_encodings_jsinv3.pckl", "rb" ))
class_map = word_and_speaker_encodings['word_idx_to_word']
elif self.label_type == "CV":
class_map = pickle.load( open("/om2/user/imgriff/datasets/commonvoice_9/en/cv_800_word_label_to_int_dict.pkl", "rb" ))
word_2_class = {v:k for k,v in class_map.items()}
return class_map, word_2_class
def __getitem__(self, index):
"""
Gets components of the hdf5 file that are used for training
Args:
index (int): index into the hdf5 file
Returns:
[signal, target] : the training audio (signal) containing the preprocessing
which may combine the foreground and background speech, and the target idx
specified by target_keys.
"""
foreground, _ = librosa.load(self.manifest['src_fn'][index], sr=self.sr)
cue, _ = librosa.load(self.manifest['cue_src_fn'][index], sr=self.sr)
if self.condition == "clean":
background = None
else:
background, _ = librosa.load(self.bg_stim[index], sr=self.sr)
word = self.manifest['word'][index]
word_label = self.word_2_class[word]
return cue, foreground, background, word_label
def __len__(self):
return self.dataset_len

saddler_word_rec.py at commit bb61769, under MIT · at the source

Overview

Authors: Ian M. Griffith^1,2,3, R. Preston Hess^1,2, Josh H. McDermott^1,2,3,4

ORCID iDs: Ian M. Griffith, R. Preston Hess, Josh H. McDermott

Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology,Cambridge, MA USA
McGovern Institute for Brain Research, Massachusetts Institute of Technology,Cambridge, MA USA
Program in Speech and Hearing Biosciences and Technology, Harvard University,Cambridge, MA USA
Center for Brains, Minds, and Machines, Massachusetts Institute of Technology,Cambridge, MA USA

Institutions: Massachusetts Institute of Technology (United States); Harvard University (United States)

Journal: Nature human behaviour, volume 10, issue 5, pages 937-959

Dates: received 3 July 2025; accepted 20 January 2026; published online 13 March 2026; in print 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41562-026-02414-7 · PMID 41826525 · PMCID PMC13192276 · OpenAlex W7135192550

Open access: hybrid, a free copy (OpenAlex)

Status: code verified

Categories: human (organism), cognitive (subfield)

Methods: Spectral & time-frequency, Connectivity, Statistics, Graphs, Machine learning

Keywords: Human behaviour, Auditory system, Computational neuroscience

MeSH: Attention*, Auditory Perception*, Neural Networks, Computer*, Speech Perception*, Acoustic Stimulation, Adult, Cues, Female, Humans, Male (* major topic)

Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: NIH (R01DC017970)

Citations: cited by 5 papers (Europe PMC); 94 references in the paper

Abstract

Attention facilitates communication by enabling selective listening to sound sources of interest. However, little is known about why attentional selection succeeds in some conditions but fails in others. While neurophysiology implicates multiplicative feature gains in selective attention, it is unclear whether such gains can explain real-world attention-driven behaviour. Here we optimized an artificial neural network with stimulus-computable feature gains to recognize a cued talker’s speech from binaural audio in ‘cocktail party’ scenarios. Though not trained to mimic humans, the model produced human-like performance across diverse real-world conditions, exhibiting selection based both on voice qualities and on spatial location as well as selection failures in conditions where humans tended to fail. It also predicted novel attentional effects that we confirmed in human experiments, and exhibited signatures of ‘late selection’ like those seen in human auditory cortex. The results suggest that human-like attentional strategies naturally arise from the optimization of feature gains for selective listening.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 17 matches between paragraphs and lines of code.

mcdermottLab/auditory_attention

License: MIT

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: bb617690c0e2f7713721650e8c1e164122874b39, 31 March 2026

Languages: Python (61), Jupyter (49), Shell (32)

Size: 166 files, 142 scripts

Software Heritage: not archived

Found in: “Code availability”

Holds: README, license file, environment (requirements.txt), 49 notebooks

Not found: CITATION.cff, tests, continuous integration, documentation

Tools: NumPy (99 files), pandas (91 files), Matplotlib (64 files), seaborn (63 files), SciPy (39 files), PyTorch (28 files), h5py (21 files), Pingouin (15 files), statsmodels (8 files), scikit-learn (7 files), PyTorch Lightning (6 files), TensorFlow (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

144 files

corpus/binaural_attention_h5.py, Python, 515 lines
corpus/binaural_swc_currated_pd.py, Python, 183 lines
corpus/saddler_word_rec.py, Python, 92 lines, 2 matches
corpus/speaker_room_dataset.py, Python, 235 lines
corpus/speech_and_texture_test.py, Python, 56 lines
corpus/swc_mono_test.py, Python, 295 lines
corpus/swc_popham_test_h5.py, Python, 90 lines
notebooks/Final_Figures/final_figures_paths.py, Python, 18 lines
notebooks/Final_Figures/individual_participants_fig_2.ipynb, Jupyter, 408 lines
notebooks/Final_Figures/individual_participants_fig_2.py, Python, 467 lines
notebooks/Final_Figures/interaction_test_for_experiment_6.ipynb, Jupyter, 200 lines
notebooks/Final_Figures/interaction_test_for_experiment_6.py, Python, 246 lines
notebooks/Final_Figures/plot_experiment_1_and_ext_data_fig_1.ipynb, Jupyter, 808 lines
notebooks/Final_Figures/plot_experiment_1_and_ext_data_fig_1.py, Python, 899 lines
notebooks/Final_Figures/plot_experiment_2.ipynb, Jupyter, 448 lines
notebooks/Final_Figures/plot_experiment_2.py, Python, 426 lines
notebooks/Final_Figures/plot_experiment_3.ipynb, Jupyter, 118 lines
notebooks/Final_Figures/plot_experiment_3.py, Python, 163 lines
notebooks/Final_Figures/plot_experiment_4.ipynb, Jupyter, 150 lines
notebooks/Final_Figures/plot_experiment_4.py, Python, 193 lines
notebooks/Final_Figures/plot_experiment_5.ipynb, Jupyter, 141 lines
notebooks/Final_Figures/plot_experiment_5.py, Python, 181 lines
notebooks/Final_Figures/plot_experiment_6.ipynb, Jupyter, 162 lines
notebooks/Final_Figures/plot_experiment_6.py, Python, 174 lines
notebooks/Final_Figures/plot_experiment_7.ipynb, Jupyter, 188 lines
notebooks/Final_Figures/plot_experiment_7.py, Python, 234 lines
notebooks/Final_Figures/plot_extended_data_fig_2_experiment_1b_cue_duration.ipynb, Jupyter, 456 lines
notebooks/Final_Figures/plot_extended_data_fig_2_experiment_1b_cue_duration.py, Python, 86 lines
notebooks/Final_Figures/plot_extended_data_fig_5.ipynb, Jupyter, 190 lines
notebooks/Final_Figures/plot_extended_data_fig_5.py, Python, 248 lines
notebooks/Final_Figures/plot_extended_data_figure_3_abd.ipynb, Jupyter, 172 lines
notebooks/Final_Figures/plot_extended_data_figure_3_abd.py, Python, 237 lines
notebooks/Final_Figures/plot_extended_data_figure_4.ipynb, Jupyter, 156 lines
notebooks/Final_Figures/plot_extended_data_figure_4.py, Python, 227 lines
notebooks/Final_Figures/plot_extended_data_figure_6.ipynb, Jupyter, 86 lines
notebooks/Final_Figures/plot_extended_data_figure_6.py, Python, 114 lines
notebooks/Final_Figures/plot_figure_4a_and_supplementary_model_spatial_sim.ipynb, Jupyter, 192 lines
notebooks/Final_Figures/plot_figure_4a_and_supplementary_model_spatial_sim.py, Python, 241 lines
notebooks/Final_Figures/plot_figure_5_b-c.ipynb, Jupyter, 164 lines
notebooks/Final_Figures/plot_figure_5_b-c.py, Python, 167 lines
notebooks/Final_Figures/plot_figure_6_a-c.py, Python, 1,202 lines
notebooks/Final_Figures/plot_figure_6_a-c_extended_data_figs_7_8_9.ipynb, Jupyter, 1,091 lines
notebooks/Final_Figures/plot_figure_6_a-c_extended_data_figs_7_8_9.py, Python, 585 lines
notebooks/Final_Figures/plot_figure_6_d.ipynb, Jupyter, 279 lines
notebooks/Final_Figures/plot_figure_6_d.py, Python, 334 lines
notebooks/Final_Figures/run_all_figure_gen.ipynb, Jupyter, 73 lines
notebooks/Final_Figures/run_all_figure_gen.py, Python, 83 lines
notebooks/Gen_Model_Arch_Search_Configs/CNN_Arch_Search_v10.ipynb, Jupyter, 322 lines
notebooks/Gen_Model_Arch_Search_Configs/CNN_Make_50Hz_alternates.ipynb, Jupyter, 95 lines
notebooks/Gen_Model_Arch_Search_Configs/Dev_CNN_Arch_Search.ipynb, Jupyter, 268 lines
notebooks/Paper_Figs_Raw_Analysis/Byrne_et_al_model_simulation_all_arch.ipynb, Jupyter, 482 lines, 1 match
notebooks/Paper_Figs_Raw_Analysis/Model_v_Human_Similarity_Summary.ipynb, Jupyter, 1,783 lines
notebooks/Paper_Figs_Raw_Analysis/final_Popham_SWC_Tests_all_stim_arch_search_summary_v10.ipynb, Jupyter, 1,170 lines
notebooks/Paper_Figs_Raw_Analysis/final_diotic_human_v_model_2024_arch_summary_v10_figs.ipynb, Jupyter, 1,645 lines
notebooks/Paper_Figs_Raw_Analysis/final_diotic_human_v_model_2024_summary_v10.ipynb, Jupyter, 1,515 lines
notebooks/Paper_Figs_Raw_Analysis/final_human_threshold_experiment_stats.ipynb, Jupyter, 469 lines
notebooks/Paper_Figs_Raw_Analysis/final_precedence_results_all_arch_summary_v10.ipynb, Jupyter, 392 lines
notebooks/Paper_Figs_Raw_Analysis/final_spotlight_human_v_model_pink_noise_model.ipynb, Jupyter, 1,049 lines
notebooks/Paper_Figs_Raw_Analysis/final_stage_of_selection_v10_all_architectures_cued_targets_full_rep_pool_alts.ipynb, Jupyter, 1,411 lines
notebooks/Paper_Figs_Raw_Analysis/final_texture_background_tests.ipynb, Jupyter, 270 lines
notebooks/Paper_Figs_Raw_Analysis/final_thresholds_2024_pink_noise_summary.ipynb, Jupyter, 896 lines
notebooks/Paper_Figs_Raw_Analysis/interaction_for_experiment_6.ipynb, Jupyter, 200 lines
notebooks/Paper_Figs_Raw_Analysis/model_on_all_spatial_configurations.ipynb, Jupyter, 410 lines
notebooks/Paper_Figs_Raw_Analysis/model_on_core_spatial_configurations_speech_v_ssn.ipynb, Jupyter, 226 lines
notebooks/Paper_Figs_Raw_Analysis/plot_experiment_2.ipynb, Jupyter, 342 lines
notebooks/Paper_Figs_Raw_Analysis/plot_experiment_3.ipynb, Jupyter, 118 lines
notebooks/Paper_Figs_Raw_Analysis/plot_experiment_4_and_sup_fig_7.ipynb, Jupyter, 223 lines
notebooks/Paper_Figs_Raw_Analysis/plot_experiment_5.ipynb, Jupyter, 136 lines
notebooks/Paper_Figs_Raw_Analysis/plot_experiment_6.ipynb, Jupyter, 160 lines
notebooks/Paper_Figs_Raw_Analysis/plot_experiment_7.ipynb, Jupyter, 173 lines
notebooks/Paper_Figs_Raw_Analysis/plot_extended_data_figure_3_abd.ipynb, Jupyter, 438 lines
notebooks/Paper_Figs_Raw_Analysis/plot_figure_4a_and_supplementary_figures_2_and_3.ipynb, Jupyter, 190 lines
notebooks/Paper_Figs_Raw_Analysis/plot_figure_5_and_sup_figure_5.ipynb, Jupyter, 391 lines
notebooks/Paper_Figs_Raw_Analysis/plot_figure_5_b-c.ipynb, Jupyter, 295 lines
notebooks/Paper_Figs_Raw_Analysis/plot_figure_6.ipynb, Jupyter, 998 lines
notebooks/Paper_Figs_Raw_Analysis/plot_supplementary_figure_4.ipynb, Jupyter, 160 lines
notebooks/Paper_Figs_Raw_Analysis/plot_thresholds_2024_trials_human_conds_pink_noise.ipynb, Jupyter, 719 lines
scripts/get_binaural_stim_transcripts.sh, Shell, 31 lines
scripts/get_human_thresholds.sh, Shell, 23 lines
scripts/get_swc_prolific_transcripts.sh, Shell, 23 lines
scripts/jupyter.sh, Shell, 24 lines
scripts/jupyter_torch2.sh, Shell, 25 lines
scripts/make_swc_binaural_azim_spotlight_stim_2024.sh, Shell, 19 lines
scripts/make_swc_mono_expt_stim_2024.sh, Shell, 19 lines
scripts/make_swc_popham_expt_stim_2024.sh, Shell, 19 lines
scripts/make_swc_popham_expt_stim_2024_for_model.sh, Shell, 23 lines
scripts/make_swc_unfamiliar_distractor_stim_2024.sh, Shell, 21 lines
scripts/run_get_acts_for_tuning_and_selection_analysis.sh, Shell, 34 lines
scripts/run_get_acts_for_tuning_anova_jsin.sh, Shell, 34 lines
scripts/run_unit_tuning_anova_parallel.sh, Shell, 27 lines
scripts/test_binaural_popham_swc_stim.sh, Shell, 31 lines
scripts/test_binaural_popham_swc_stim_alt_archs.sh, Shell, 30 lines
scripts/test_binaural_popham_swc_stim_control_archs.sh, Shell, 32 lines
scripts/test_binaural_saddler_tetxture_bg.sh, Shell, 31 lines
scripts/test_binaural_swc_stim_2024.sh, Shell, 34 lines
scripts/test_binaural_swc_stim_2024_alt_archs.sh, Shell, 32 lines
scripts/test_binaural_swc_stim_2024_control_archs.sh, Shell, 52 lines
scripts/test_cue_duration.sh, Shell, 30 lines
scripts/test_model_precedence_effect_distractor.sh, Shell, 108 lines
scripts/test_simulate_2024_human_azim_spotlight_experiment_v02_alt_archs.sh, Shell, 105 lines
scripts/test_simulate_2024_human_azim_spotlight_experiment_v02_alt_archs_ssn.sh, Shell, 31 lines
scripts/test_simulate_2024_human_azim_spotlight_experiment_v02_control_archs.sh, Shell, 49 lines
scripts/test_simulate_2024_human_threshold_experiment_v02.sh, Shell, 35 lines
scripts/test_simulate_2024_human_threshold_experiment_v02_alt_archs.sh, Shell, 105 lines
scripts/test_simulate_2024_human_threshold_experiment_v02_control_archs.sh, Shell, 48 lines
scripts/test_simulate_all_target_distractor_positions.sh, Shell, 31 lines
scripts/test_simulate_byrne_et_al_2023_all_archs.sh, Shell, 130 lines
scripts/test_simulate_core_distractor_positions_ssn.sh, Shell, 31 lines
scripts/train_binaural_single_model.sh, Shell, 43 lines
src/__init__.py, Python, 1 line
src/audio_attention_transforms.py, Python, 609 lines, 2 matches
src/audio_transforms.py, Python, 900 lines, 1 match
src/custom_modules.py, Python, 355 lines, 1 match
src/eval_cue_duration.py, Python, 258 lines
src/eval_precedence.py, Python, 356 lines, 1 match
src/eval_sim_array_spotlight_experiment_v02.py, Python, 426 lines
src/eval_sim_array_threshold_experiment_v02.py, Python, 467 lines
src/eval_swc_mono_stim.py, Python, 395 lines
src/eval_swc_popham_2024.py, Python, 216 lines
src/eval_symmetric_distractors.py, Python, 415 lines, 1 match
src/eval_texture_backgrounds.py, Python, 182 lines
src/get_acts_for_tuning_and_selection_analysis.py, Python, 574 lines
src/get_acts_for_tuning_anova_jsin.py, Python, 436 lines
src/get_human_thresholds.py, Python, 258 lines
src/get_swc_binaural_azim_spotlight_stim_2024.py, Python, 139 lines
src/get_swc_binaural_manifest_transcripts.py, Python, 124 lines
src/get_swc_mono_expmt_stim_2024.py, Python, 155 lines, 1 match
src/get_swc_popham_expmt_stim_2024.py, Python, 274 lines, 2 matches
src/get_swc_popham_expmt_stim_2024_for_model.py, Python, 262 lines, 2 matches
src/get_swc_prolific_manifest_transcripts.py, Python, 114 lines
src/get_swc_unfamiliar_distractor_stim_2024.py, Python, 166 lines, 2 matches
src/layers/conv2d_same.py, Python, 39 lines
src/layers/padding.py, Python, 75 lines
src/make_swc_test_stimuli.py, Python, 193 lines
src/spatial_attn_architecture.py, Python, 369 lines
src/spatial_attn_lightning.py, Python, 377 lines
src/spatialtrain.py, Python, 206 lines
src/time_domain_cochleagram.py, Python, 288 lines, 1 match
src/unit_tuning_anova_parallel_jsin.py, Python, 278 lines
src/util_analysis.py, Python, 595 lines
src/util_process_prolific.py, Python, 186 lines
src/util_tfrecord.py, Python, 323 lines
LICENSE, License, 21 lines
README.md, Text, 132 lines

At the source: github.com/mcdermottLab/auditory_attention

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The code used for modelling and data analysis in this study is available via GitHub at https://github.com/mcdermottLab/auditory_attention and is linked from the project OSF repository93.

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

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This paper

Griffith, I. M., Hess, R. P., & McDermott, J. H. (2026). Optimized feature gains explain and predict successes and failures of human selective listening. Nature human behaviour, 10(5), 937-959. https://doi.org/10.1038/s41562-026-02414-7

BibTeX

@article{griffith2026optimized, author = {Griffith, Ian M. and Hess, R. Preston and McDermott, Josh H.}, title = {{Optimized feature gains explain and predict successes and failures of human selective listening}}, journal = {Nature human behaviour}, year = {2026}, month = mar, volume = {10}, number = {5}, pages = {937--959}, publisher = {Nature Portfolio}, issn = {2397-3374}, doi = {10.1038/s41562-026-02414-7}, url = {https://doi.org/10.1038/s41562-026-02414-7}, pmid = {41826525}, pmcid = {PMC13192276} }

RIS

TY - JOUR AU - Griffith, Ian M. AU - Hess, R. Preston AU - McDermott, Josh H. TI - Optimized feature gains explain and predict successes and failures of human selective listening T2 - Nature human behaviour J2 - Nat Hum Behav PY - 2026 DA - 2026/03/13 VL - 10 IS - 5 SP - 937 EP - 959 SN - 2397-3374 PB - Nature Portfolio DO - 10.1038/s41562-026-02414-7 UR - https://doi.org/10.1038/s41562-026-02414-7 LA - en ER -

CSL-JSON

{ "id": "10.1038/s41562-026-02414-7", "type": "article-journal", "title": "Optimized feature gains explain and predict successes and failures of human selective listening", "container-title": "Nature human behaviour", "author": [ { "family": "Griffith", "given": "Ian M." }, { "family": "Hess", "given": "R. Preston" }, { "family": "McDermott", "given": "Josh H." } ], "container-title-short": "Nat Hum Behav", "volume": "10", "issue": "5", "page": "937-959", "DOI": "10.1038/s41562-026-02414-7", "PMID": "41826525", "PMCID": "PMC13192276", "ISSN": "2397-3374", "publisher": "Nature Portfolio", "URL": "https://doi.org/10.1038/s41562-026-02414-7", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 13 ] ] } }

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Optimized feature gains explain and predict successes and failures of human selective listening.

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