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Articles, Behavioral/Cognitive

Electrophysiological Correlates of Voice Learning and Recognition

Romi Zäske, Gregor Volberg, Gyula Kovács and Stefan Robert Schweinberger
Journal of Neuroscience 13 August 2014, 34 (33) 10821-10831; https://doi.org/10.1523/JNEUROSCI.0581-14.2014
Romi Zäske
1Department for General Psychology and Cognitive Neuroscience, Institute of Psychology,
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Gregor Volberg
2Department for Psychology, Institute of Psychology, and
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Gyula Kovács
3Institute of Psychology, Friedrich Schiller University of Jena, 07743 Jena, Germany
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Stefan Robert Schweinberger
1Department for General Psychology and Cognitive Neuroscience, Institute of Psychology,
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  • Figure 1.
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    Figure 1.

    Top, Trial procedure for one study-test cycle. The example shows an “old” test voice presented with a different sentence than at study. Bottom, The 12 study-test cycles for same and different sentence blocks, respectively.

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    Figure 2.

    Mean d′ for voice recognition in the same and different sentence conditions for both block halves (A) and collapsed over pairs of consecutive study-test cycles (B). Error bars indicate SEM.

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    Figure 3.

    Grand mean ERPs during study phases for subsequently remembered voices (hits) and forgotten voices (misses) when tested with the same or different sentences than during study.

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    Figure 4.

    Voltage maps of ERP differences for the Dm (study voices subsequently remembered − subsequently forgotten), reflecting subsequent voice recognition, are depicted for time windows of the Dm I (250–400 ms), Dm II (400–800 ms), and Dm III (800–1400 ms). Note that maps are based on spherical spline interpolation with 90° equidistant projection.

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    Figure 5.

    Grand mean ERPs at Pz during test phases for voices correctly recognized (hits) as old and correctly rejected as new.

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    Figure 6.

    Results of the time-frequency analyses. A, Significant time and frequency ranges (p-values) obtained from permutation tests. Only results with more than 10 adjacent significant bins were considered. The relevant time and frequency range is marked. B, Head topography of the mean signal change, old versus new voices, at the relevant time and frequency range. Significant electrodes are marked. C, Waveforms showing the mean signal chance (16–17 Hz) for old and new voices at the central electrode group. The waveforms show an amplitude drop after ∼300 ms that was stronger for old compared with new voices. D, Same as C, but for the right temporal electrode group. E, Head topography of the mean signal change, old versus new voices, for the “same sentence” condition. F, Same as E, but for the “different sentence” condition. G, Bar graph showing the mean signal change (± SEM) in each condition at the central electrode group. H, Same as G, but at right temporal electrodes.

Tables

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    Table 1.

    Accuracies for hits and CR, correct RT, d′, and C for block halves, sentence conditions, and voice conditions

    Block halvesSentence conditionOld voicesNew voicesd′C
    HitsRT (ms)CRRT (ms)
    FirstSame69.6% (1.7)1574 (72)63.1% (2.9)1608 (75)0.88 (0.08)0.09 (0.05)
    Different61.8% (2.6)1494 (84)62.8% (3.0)1562 (73)0.67 (0.09)−0.02 (0.06)
    SecondSame75.3% (2.0)1471 (71)70.1% (2.5)1447 (65)1.28 (0.10)0.07 (0.05)
    Different69.8% (2.9)1495 (79)65.5% (2.5)1509 (79)0.97 (0.10)0.07 (0.06)
    • SEMs are shown in parentheses.

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    Table 2.

    Statistical parameters for analyses of ERP effects in study phases

    ERPEffectdfFpηp2
    Dm I (250–400 ms)Anteriority × subsequent recognition2,465.540.0110.194
    Dm II (400–800 ms)Anteriority2,4644.24<0.0010.658
    Subsequent recognition1,234.450.0460.162
    Laterality × anteriority4,924.810.0010.173
    Laterality × subsequent recognition2,464.800.0130.173
    Anteriority × subsequent recognition2,4612.89<0.0010.359
    Dm III (800–1400 ms)Laterality2,464.030.0240.149
    Anteriority2,4623.59<0.0010.506
    Laterality × anteriority4,923.280.0210.125
    Laterality × subsequent recognition2,463.640.0340.137
    Anteriority × subsequent recognition2,464.010.0320.148
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The Journal of Neuroscience: 34 (33)
Journal of Neuroscience
Vol. 34, Issue 33
13 Aug 2014
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Electrophysiological Correlates of Voice Learning and Recognition
Romi Zäske, Gregor Volberg, Gyula Kovács, Stefan Robert Schweinberger
Journal of Neuroscience 13 August 2014, 34 (33) 10821-10831; DOI: 10.1523/JNEUROSCI.0581-14.2014

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Electrophysiological Correlates of Voice Learning and Recognition
Romi Zäske, Gregor Volberg, Gyula Kovács, Stefan Robert Schweinberger
Journal of Neuroscience 13 August 2014, 34 (33) 10821-10831; DOI: 10.1523/JNEUROSCI.0581-14.2014
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Keywords

  • ERPs
  • learning
  • memory
  • oscillations
  • speech
  • voice

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