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Research ArticleNew Research, Cognition and Behavior

Evidence Integration in Natural Acoustic Textures during Active and Passive Listening

Urszula Górska, Andre Rupp, Yves Boubenec, Tansu Celikel and Bernhard Englitz
eNeuro 9 April 2018, 5 (2) ENEURO.0090-18.2018; DOI: https://doi.org/10.1523/ENEURO.0090-18.2018
Urszula Górska
1Department of Neurophysiology, Donders Institute, Radboud University Nijmegen, The Netherlands
2Psychophysiology Laboratory, Institute of Psychology, Jagiellonian University, Krakow, Poland
3Smoluchowski Institute of Physics, Jagiellonian University, Krakow, Poland
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Andre Rupp
4Section of Biomagnetism, Department of Neurology, University of Heidelberg, Heidelberg, Germany
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Yves Boubenec
5Laboratoire des Systèmes Perceptifs, CNRS UMR 8248, Paris, France
6Département d’Études Cognitives, École Normale Supérieure, PSL Research University, Paris, France
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Tansu Celikel
1Department of Neurophysiology, Donders Institute, Radboud University Nijmegen, The Netherlands
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Bernhard Englitz
1Department of Neurophysiology, Donders Institute, Radboud University Nijmegen, The Netherlands
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  • Extended Data
  • Figure 1.
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    Figure 1.

    Experimental paradigm for the detection of changed statistics of natural acoustic textures. A, We presented natural textures which change their statistics at random times to a variable degree (depicted: 3 s). The change in statistics leads to a distributed change in spectro-temporal properties. The present example shows a transition from bubbling to a linear mixture between bubbling and rain, with an intermediate mixing coefficient of 0.3. Level is relative to the maximal overall level. B, Texture sounds were provided via headphones while simultaneously recording whole-head EEG signals from 64 channels (10–20 system, see inset in C). In the active paradigm, listeners were instructed to report whether they heard a change by pressing a button after the termination of the sound and otherwise not to respond. Instead, in the passive variant they were just asked to listen to the stimuli. C, Basic design of the change detection paradigm. The trial was started by the appearance of a fixation cross in the center of the screen (prompting subjects to refrain from blinking), followed by the first texture stimulus. After a duration randomly drawn from the three possible change times, the sound continued uninterrupted and at the same level, but with changed statistics (middle, different colors indicate the linear mixing coefficients between the first and the second texture). The choice of first and second texture was always randomized, i.e., it could also stay the same (25% = catch trials). 2 s after the second statistic was presented, the sound was terminated, and the subjects responded (change) or remained silent (no change) within 2 s. The potentials on top show samples from the central (light blue, location on the scalp shown in inset) and the PO/posterior (dark blue) location. The central data shows a prominent N1/P2 complex after stimulus onset, a sustained suppression due to the continuous sound presentation, followed by barely a response after the change, and a final, clear stimulus offset. Conversely, the PO potential shows a response to the fixation cross, but a large response after the change in statistics.

  • Figure 2.
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    Figure 2.

    Reaction times and response rates suggest improved performance for stimulus exposure and larger changes in stimulus statistics. A, Reaction times decreased significantly as a function of change time and change size. Within each change size, reaction times did not reach statistical significance for individual change sizes. Reaction times decreased significantly as a function of change size (mixing coefficient, across colours). B, Response rates increased significantly with change time for all change sizes >0. As expected, the response rates did not increase in catch trials (light blue) with change time, indicating that subjects closely listened for changes, rather than responding as a function of time within the trial. Response rates also increased significantly as a function of change size (different colors). In addition, a two-way ANOVA was performed, which also indicated a significant effect of both factors. Error bars represent 1 SEM. Details on statistical testing given in the main text.

  • Figure 3.
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    Figure 3.

    Onset responses are similar across levels of task engagement. A–C, The scalp distribution of the P2 component (200 ms after stimulus onset) is centered with a slight asymmetry toward the front. Since the different stimulus conditions are all matched at the onset, the data were averaged over all stimuli. The scalp topography in the passive–aware conditions mirrors the active potential (B). In the passive-naive conditions, the topography was still centered, but slightly broadened toward frontal locations, probably due to uncontrolled blinks (C). Overall, the topographies were not statistically different (see text for details). Subject involvement was supported by their relative level of α-band activity (Extended Data Fig. 3-2). D, The central potential, typically associated with auditory-cortex activity (Nie et al., 2014), exhibited a classical response sequence after stimulus onset, with a negative component (N1, 150 ms) followed by a positive component (P2, 242 ms), both slightly delayed compared to classical latencies for pure tones (left, average over electrodes Cz, C1 and C2). The P2 peak size decays slightly but nonsignificantly from active to passive-aware and passive-naive (right, height averaged in [0.2-0.3]s). E, The onset-elicited response in the PO region also displayed a negative deviation around [0.2,0.3]s (left, electrodes Pz, POz), which likely reflects the negative part of the auditory cortex dipole (Extended Data Fig. 3-1) This activity did not differ significantly across conditions. Error bars in all plots indicate ±1 SEM. Effect size is given in parentheses after the p value.

  • Figure 4.
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    Figure 4.

    PO potential depends on stimulus evidence and integration time during task engagement. A, Scalp topography of change-elicited activity at 0.7 s post-change exhibits a single peaked positivity over the PO regions for correct detection (hit) trials. Only trials with the lowest difficulty, i.e., MC = 0.6 were included for clarity of display. B, In the central electrodes (auditory cortex associated, as in Fig. 3), the change in statistics leads to a delayed ERP only for the longest change times and the largest mixing coefficients. Data are aligned to change time and averaged over hit trials only. Black lines at the bottom indicate window selected for the analysis of height (300–350 ms) and slope (200–330 ms). The responses for different change times are shifted vertically to avoid crowding. Gray trials indicate conditions with <10% of response percentage and correspondingly more variability. If all trials are included, variability is comparable between conditions (Extended Data Fig. 4-1). C, The PO potential (electrodes as in Fig. 3) first turns slightly negative and then becomes strongly positive, lasting until the end of the sampling period of the second texture. The potential's height (600- to 1000-ms window) and slope (400- to 600-ms window) depended significantly on change time and size. Colors as in B. The subsequent panels show the dependence of the slope and height of the respective potentials for all stimulus parameters (left column, color bar represents slope or height) or in relation to change size (middle) and change time (right) only. D–F, Height of the central potential did not depend significantly on change size or change time (see B for measurement range). G-I, The slope of the central potential depended significantly on change size, while it did not depend on change time (see B for measurement range). J, The PO potential's height also depends systematically on change time and size (see C for measurement range). K, The height of PO potential varied significantly with change size both if catch trials are included or not. L, The PO potential's height dependence on change time was also significant. M, The slope of the PO potential increased with both change time and change size. N, Change-related build-up in PO potential is significantly faster with increasing change size. O, The PO slope dependence on change time was highly significant. All error bars indicate ±1 SEM, and p values of the two-way ANOVA for the factor on the x-axis are denoted in the figure, all tests are described in the main text. p+ is the significance when leaving out the no-change condition. Effect size is given in parentheses after the p value.

  • Figure 5.
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    Figure 5.

    Same analysis as in Figure 4 but for misses. Nomenclature identical to Figure 4. No dependencies were found, except for a central potential, slight, but inverted dependency for height (with a borderline p value). Gray curves indicate conditions containing <10% of possible trials.

  • Figure 6.
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    Figure 6.

    The PO potential is reduced but stays stimulus dependent for passive-aware subjects. A, The post-change scalp topography (600–700 ms) is dominated by posterior components, but the overall potential size is much smaller (only trials for MC = 0.6 included for display); however, note the scale difference in comparison to Figure 4. B, In the central electrodes, the change in statistics elicited a small ERP complex (positive peak ∼320 ms) for the longest change times and largest change sizes (colors as in Fig. 3, see legend). C, In the PO electrodes, the change in statistics also led to a slow negative-positive potential, reaching a plateau around 700 ms. D–F, The height of the central potential neither depended on change size nor time. G–I, The slope of the central potential showed a borderline significant dependence on change size, but not change time (measurement range: 200–330 ms). J, The PO potential's height varied systematically with change time and size. K, The PO potential was significantly larger for bigger change sizes; however, the size of the potential was much smaller than in the active condition, corresponding to the change in topography. L, The PO potential also increased slightly but significantly in height with change time (see Results for interpretation). M, The slope of the PO potential exhibited a similar dependence on change size and time as in the active condition (compare to Fig. 4G). N, The PO slope is significantly steeper as a for larger change sizes. O, The PO slope displayed a nonsignificant tendency to be faster for longer change times. Conventions as in Figure 4.

  • Figure 7.
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    Figure 7.

    In the passive-naive group the PO potential exhibits much weaker but partially significant dependence on change size and time. A, The post-change scalp topography (600–700 ms) exhibits a more diverse shape than in the active or passive-aware conditions (only trials for mixing coefficient equal 0.6 included for display). The scale bar was kept the same as in Figure 5, to emphasize the difference in potential size. B, In the central electrodes, the change in statistics again elicited a small ERP complex (positive peak around 320 ms), as in the other conditions (colors as in Fig. 3, see legend). C, In the PO electrodes, the change in statistics lead to residual potentials with much more diversity between stimulus properties than in the other conditions. D–F, The height of the central potential depended significantly on change size but not on change time. Measurement range (300–350 ms post-change) is shown in B. G–I, The slope of the central potential also depended significantly on change size but not on change time. J–L, The PO potential's height depends significantly on change time, however, with a nonmonotonic progression. M–O, The PO potential's slope depends significantly on change size; however, this effect is driven by the last condition with MC = 0.6. Conventions as in Figure 4.

  • Figure 8.
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    Figure 8.

    Task involvement scales change-related potentials A–C, Change-time aligned, central potentials exhibited different profiles across task involvement. The initial P2-like response (peak at 313 ms) did not differ significantly across involvement levels [height (B), slope (C)]. However, the potentials diverged strongly thereafter, with increasingly negative slopes for more active involvement. Data averaged over all change times and the two largest mixing coefficients (0.3,0.6). D–F, The PO potentials exhibited clear differences in slope (E) and height (F) across different levels of task involvement. For the passive-naive condition, the potential appears to be a combination between a lasting negative potential in combination with a more transient positive potential added. All error bars indicate ±1 SEM, and p values are denoted in the figure, all tests are described in the main text. Effect size is given in parentheses after the p value. Same electrodes as in Figure 3.

  • Figure 9.
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    Figure 9.

    Contribution of the different sources to the change related response. A, The scalp potential was well approximated [residual variance (0–2000 ms) = 0.64%] by a mixture of six sources in each hemisphere, activating at different times after the change. The sources are located in the auditory cortex (blue, light blue, B, C), the temporo-parietal junction (dark blue, D), the visual cortex (red, E; provided for completeness), the medial-parietal cortex (orange, F), and finally the frontal cortex (green, G). All sources were estimated as bilateral pairs; however, potentials in B–G show averages across pairs. B, The first source activation was located close to the auditory cortex (blue in a A). It exhibited a significant dependence on change size only around 750 ms (bottom, gray, analysis window: 700–850 ms), while the P2-like potential (peak: 350 ms, vertical blue line, correspondingly in the following panels) was borderline insignificant (bottom, black, window: 250–450 ms). C, As before, we estimate a radial component of the AEP, which exhibited a borderline significant dependence on mixing coefficient (bottom, window: 350–550 ms). D, The activation of the temporo-parietal junction was very small and did not contribute significantly (window: 250–500 ms). E, The contribution from the visual source was nonsignificant (window: 500–700 ms). F, The activation in the parietal cortex exhibited a clear and significant dependence on the mixing coefficient. While the onset time remained similar across change sizes, the peak time was slightly delayed for lower change sizes, similar to potentials the scalp level (window: 600–800 ms). G, A further pair of sources was localized in the frontal cortex, which, however, did not show a significant dependence in their size on change size (window: 600–1000 ms). Error hulls and bars indicate ±1 SEM over n = 17 subjects, and only 3-s change time is shown. Effect size is given in parentheses after the p value.

Extended Data

  • Figures
  • Extended Data Figure 3-1

    The parietal potential at and before stimulus onset has a visual origin (A, C). The auditory response can be well explained by two orthogonal dipole sources located in the auditory cortex (A, B). The parietal dipole (D) remains quiet at the onset of the stimulus, indicating that the negative potential around 240 ms can indeed be accounted for by a mixture with the auditory cortex source(s) (see B). The residual variance of this estimate was 1.48%. Download Figure 3-1, TIF file.

  • Extended Data Figure 3-2

    Distribution of spectral power in different levels of task involvement. Active subjects show barely elevated power in the α-band (red, n = 18). The passive-aware subjects showed elevated α-band activity around 10–15 Hz (maroon, n = 8). The passive-naive subjects showed elevated α-band activity around 9–14 Hz (black, n = 10). Spectra were computed from channel Oz. The difference between the three conditions was significant (p < 0.001, two-way ANOVA, on frequency (df = 5, range 9–15 Hz, vertical lines) and condition, df = 2). The elevated α-band can be taken as an indication for a reduced level of task engagement in both passive groups compared to the active group. The power spectra were computed in the 1.5 s preceding the start of the stimulus to avoid contributions of the task-related ERPs. Outside the depicted frequency range, the spectra were quite similar. Error hulls represent ±1 SEM. Download Figure 3-2, TIF file.

  • Extended Data Figure 4-1

    Same analysis as in Figure 4 but for all trials. Nomenclature identical to Figure 4. Demonstrates that the variability was very similar across conditions; however, the number of trials per condition differed across hits and misses. Note that the strong dependence of slope and height on change size and change height is due to the larger number of miss trials for small change sizes and change times. Download Figure 4-1, TIF file.

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Evidence Integration in Natural Acoustic Textures during Active and Passive Listening
Urszula Górska, Andre Rupp, Yves Boubenec, Tansu Celikel, Bernhard Englitz
eNeuro 9 April 2018, 5 (2) ENEURO.0090-18.2018; DOI: 10.1523/ENEURO.0090-18.2018

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Evidence Integration in Natural Acoustic Textures during Active and Passive Listening
Urszula Górska, Andre Rupp, Yves Boubenec, Tansu Celikel, Bernhard Englitz
eNeuro 9 April 2018, 5 (2) ENEURO.0090-18.2018; DOI: 10.1523/ENEURO.0090-18.2018
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