DOI: 10.5061/dryad.hmgqnk9w8
title: Data from: Humans can use positive and negative spectrotemporal correlations to detect rising and falling pitch

description: To discern speech or appreciate music, the human auditory system detects
 how pitch changes over time. However, the algorithms used to detect
 changes in pitch, or pitch motion, are incompletely understood. Here,
 using psychophysics, computational modeling, functional neuroimaging, and
 analysis of recorded speech, we ask if humans can detect pitch motion
 using computations analogous to those used by the visual system. We
 adapted stimuli from studies of vision to create novel auditory correlated
 noise stimuli that elicited robust pitch motion percepts. Crucially, these
 stimuli are inharmonic and possess no long-range features across frequency
 or time, but do possess positive or negative local spectrotemporal
 correlations in intensity. In psychophysical experiments, we discovered
 that humans can judge pitch direction based only on positive or negative
 spectrotemporal intensity correlations. The key behavioral result—robust
 sensitivity to the negative spectrotemporal correlations—is a direct
 analogue of illusory “reverse-phi” motion in vision, and thus constitutes
 a new auditory illusion. Our behavioral results and computational modeling
 led us to hypothesize that human auditory processing may employ pitch
 direction opponency. fMRI measurements in auditory cortex supported this
 hypothesis. To link our psychophysical findings to real-world pitch
 perception, we analyzed recordings of English and Mandarin speech and
 found that pitch direction was robustly signaled by both positive and
 negative spectrotemporal correlations, suggesting that sensitivity to both
 types of correlations confers ecological benefits. Overall, this work
 reveals how motion detection algorithms sensitive to local correlations
 are deployed by the central nervous system across disparate modalities
 (vision and audition) and dimensions (space and frequency).
description: # Data from: Humans can use positive and negative spectrotemporal
 correlations to detect rising and falling pitch Code and data for
 generating stimuli and conducting analyses related to human auditory
 correlation sensitivity. ## Each folder corresponds to an experiment
 displayed in corresponding figures: **ternaryExperiment.zip**: contains
 the psychophysical script (`ternary.m`), tone generating function
 (`ternaryFunction.m`), and analysis code (`analyzeTernaryExperiment.m`) to
 generate **Figure 1c**. **coherenceExperiment.zip**: contains the
 psychophysical script (`coherence.m`), tone generating function
 (`coherenceFunction.m`), and analysis code
 (`analyzeCoherenceExperiment.m`) to generate **Figure 1d**.
 **binauralExperiment.zip**: contains the psychophysical script
 (`binaural.m`), tone generating function (`binauralFunction.m`), and
 analysis code (`analyzeBinauralExperiment.m`) to generate **Figure 1f**.
 **pipDeltaTimeExperiment.zip**: contains the psychophysical script
 (`pipDeltaTime.m`), tone generating function (`pipDeltaTimeFunction.m`),
 and analysis code (`analyzePipDeltaTimeExperiment.m`) to generate **Figure
 2c**. **pipDeltaTimeSuppExperiment.zip**: contains the psychophysical
 script (`pipDeltaTimeSupp.m`), tone generating function
 (`pipDeltaTimeSuppFunction.m`), and analysis code
 (`analyzePipDeltaTimeSuppExperiment.m`) to generate **Figure S2a, b**.
 **pipDeltaNoteExperiment.zip**: contains the psychophysical script
 (`pipDeltaNote.m`), tone generating function (`pipDeltaNoteFunction.m`),
 and analysis code (`analyzePipDeltaNoteExperiment.m`) to generate **Figure
 2d**. **correlatedPipsExperiment.zip**: contains the psychophysical script
 (`correlatedPips.m`), tone generating function
 (`correlatedPipsFunction.m`), and analysis code
 (`analyzeCorrelatedPipsExperiment.m`) to generate **Figure 2b, c**.
 **glidersExperiment.zip**: contains the psychophysical script
 (`gliders.m`), tone generating function (`glidersFunction.m`), and
 analysis code (`analyzeGlidersExperiment.m`) to generate **Figure S3c,
 d**. **soundAnalysisCode.zip**: contains the script
 `analyzeCorrelationsFunctionsFigure_final.m` and audio clip
 `cummings_trimmed.m4a` to generate **Figures 5** and **S5**.
 **generalToneGeneration.zip**: contains the tone generating function
 `generalToneGenerationFunction.m` for creating and listening to the tone
 types that were used in these experiments. **fmriExperiment.zip**:
 contains the code and NIFTI files to generate **Figures 4h, i, j** and
 **S4g, h, i**. ## Variables: Each `.mat` file outputs several variables
 that were saved during the corresponding experiments, with little
 variation between experiments. Most importantly, `[participant ID].mat`
 gives a struct with a given participant's responses, and
 `[participant ID]_toneData.mat` gives a struct with sufficient information
 to recreate each tone that the participant was presented with. Within a
 given participant's response struct, the variables of particular
 interest across experiments are the tone direction (`displacement`) and
 correlation (`corrparity`). ## Specifications: All psychophysical task
 code was run on a MacBook Pro with an Intel chip using Matlab 2021b and
 Psychtoolbox 3.0.18. All fMRI analyses were conducted in JupyterLab. ##
 Directions: 1. Ensure that the above specifications are met. 2. To
 participate in a task, download the psychophysical script and
 corresponding tone generating function. Make sure that `iso226.m` and the
 corresponding data folder are also in the working folder. Run the
 psychophysical script, and enter a subject name of choice. Follow the task
 instructions until it concludes. 3. To analyze psychophysical data from
 the original experiments, download the corresponding data folder located
 inside of each experiment folder, which will contain the `.mat` files for
 all the participants for a particular experiment. Make sure that the
 analysis script is in the same folder as the data, and run the script. ##
 Code access: The code used to produce stimuli, analyze data, and generate
 figures is available on the **humanAuditoryCorrelations** GitHub
 repository. ## Human subjects data All participant data was anonymized
 prior to sharing. Prior to the study, all participants provided informed
 consent for the sharing of fully anonymized data.