Interaction of Streaming and Attention in Human Auditory Cortex

<div><p>Serially presented tones are sometimes segregated into two perceptually distinct streams. An ongoing debate is whether this basic streaming phenomenon reflects automatic processes or requires attention focused to the stimuli. Here, we examined the influence of focused attention on streaming-related activity in human auditory cortex using magnetoencephalography (MEG). Listeners were presented with a dichotic paradigm in which left-ear stimuli consisted of canonical streaming stimuli (<i>ABA_</i> or <i>ABAA</i>) and right-ear stimuli consisted of a classical oddball paradigm. In phase one, listeners were instructed to attend the right-ear oddball sequence and detect rare deviants. In phase two, they were instructed to attend the left ear streaming stimulus and report whether they heard one or two streams. The frequency difference (ΔF) of the sequences was set such that the smallest and largest ΔF conditions generally induced one- and two-stream percepts, respectively. Two intermediate ΔF conditions were chosen to elicit bistable percepts (i.e., either one or two streams). Attention enhanced the peak-to-peak amplitude of the P1-N1 complex, but only for ambiguous ΔF conditions, consistent with the notion that automatic mechanisms for streaming tightly interact with attention and that the latter is of particular importance for ambiguous sound sequences.</p></div

Attention effect on right-ear target detection task.

<p>Grand average source waveforms averaged across subjects (N = 19) and hemispheres. The responses are shown separately for the different left ear patterns (upper = <i>ABA_</i>; lower = <i>ABAA</i>). Responses evoked in the attended phase one are plotted in gray, responses evoked in the unattended phase two are plotted in black. The attended responses are significantly larger for standards (a) as well as deviants (b).</p

Experimental setup and behavioral results.

<p>(a) In phase one of the experiments, listeners were instructed to listen to their right ear and detect rare deviants with a 18 dB less amplitude modulation. The carrier frequency of the tones was randomized and not relevant for the task. In phase two, listeners were instructed to listen to their left ear and indicate if the alternating tone sequence presented there was perceived as one or two streams. (b) Behavioral results of the streaming task for the <i>ABA_</i> (left) and <i>ABAA</i> pattern (right) obtained in phase 2. The data plots the average percentage of two-stream ratings averaged over trials, time and subjects (N = 19; mean ± standard error).</p

Source waveforms evoked by the left-ear streaming stimuli.

<p>(a) <i>ABA_</i> pattern (b) <i>ABAA</i> pattern. The waveforms are an average across subjects (N = 19) and hemispheres. Responses evoked in phase one are plotted in gray, those evoked in phase two of the experiment are plotted in black.</p

Schematic diagram of the Auditory Image Model and the Top-down Modulated Hierarchical Model of Pitch.

<p>a) Schematic view of the Auditory Image Model (AIM) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153947#pone.0153947.ref014" target="_blank">14</a>]. In the first stage, peripheral auditory filters transform the input waveform into a multi-channel representation of basilar membrane motion. The next stage applies a hair cell model and converts this motion into a neural activity pattern in the auditory nerve (NAP). In the final stage, this signal is used to produce a stabilised representation of the stimuli by means of strobed temporal integration. The output of this process is termed the stabilised auditory image (SAI) of the input stimulus. b) Schematic view of the top-down modulated Hierarchical Generative Model of pitch perception (GPM) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153947#pone.0153947.ref017" target="_blank">17</a>]. The peripheral processing is similar to the one in AIM (bottom). The next step consists of a coincidence detection process of auditory nerve activity patterns for different cochlear delay lines <i>l</i>, <i>A</i>₁(<i>t</i>, <i>l</i>). Further processing is carried out by two consecutive ensemble models <i>A</i>₂ and <i>A</i>₃ performing leaky integrations of input activity using time-varying integration windows. Such ensembles correspond putatively to pre-thalamic and central auditory areas. A top-down, stimulus-dependent mechanism modulates the size of the effective integration windows of bottom-up information.</p

Summary of the statistics of the fit between the N100m transient and the output of GPM.

<p>Left panel: Example of the model response derivative, normalized to the amplitude of the recording, for a ramped stimulus (<i>T</i>_1/2 = 0.5ms) and the corresponding recordings, averaged across right and left hemispheres and participants. Transparent shadows represent standard deviations. Right panel: Histograms of the Pearsons’s correlation coefficient and root-mean-square errors corresponding to the fittings between the GMP prediction and MEG recordings in an interval of 50 ms around the N100m peak. Each value corresponds to a single cross-validation instance for ramped and damped stimuli.</p

Auditory fields evoked by the ramped and damped sinusoids.

<p>Grand mean source waveforms for the five different conditions of ramped and damped sinusoids. Average was taken over subjects (<i>n</i> = 27) for both hemispheres. The magnitude of the N100m increases for rising <i>T</i>_1/2 values of the stimuli. Note the maximal difference between ramped and damped sinusoids in the right hemisphere for the <i>T</i>_1/2 = 4ms condition.</p

Grand-average source waveforms for specific stimuli.

<p><b>(a)</b> Responses pooled across hemispheres to a French horn tone (b-flat 117 Hz) and <b>(b)</b> to the spoken stimulus presented in experiment 2 to the Chinese and German listeners. Note the similarity of the AEF between groups for the horn tone in contrast to the significant larger response evoked by the syllable for Chinese listeners. <b>(c)</b> and <b>(d)</b> depict the grand-average source waveforms elicited by the vowels and <ö> (averaged across all four tone conditions, T1-T4). While the vowel occurs in both languages, <ö> is only part of the German language. <b>(e)</b> Responses to the syllables and of the Chinese group and <b>(f)</b> the German group. has no meaning in Chinese, in contrast to ; in German, both syllables are meaningless. <b>(g)</b> Time-variant difference of the Chinese responses to the stimuli meaningful and meaningless phonemes (red curve), and the difference of the average responses to the all spoken stimuli between Chinese and Germans (blue curve). <b>(h)</b> N100m signals of the Chinese vs the German listeners for the meaningful syllables (black boxes: -, -), and the meaningless signals (gray triangles: all vowels and ). The Pearson correlation coefficient corresponding to the mean values of the meaningless signals (blue triangles) between groups is <i>r</i> = 0.96, (<i>P</i><0.0001); however, no significant correlation was found for the meaningful syllables (black squares).</p

Output of the auditory image model for the <i>T</i>_1/2 = 4ms ramped and damped sinusoids.

<p>Auditory Image Models’ output for damped (a–c) and ramped (d–f) trains (<i>T</i>_1/2 = 4ms) at the time point of the same envelope height. Panels a) and d) show the stabilized auditory image (SAI) over time in each cochlear frequency channel. Panels c) and f) represent the spike probability averaged over time. Panels b) and e) show the summarised activity of all channels in the auditory image. The integration interval is the inverse of the carrier frequency applied [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153947#pone.0153947.ref011" target="_blank">11</a>], thus it shows a peak at <i>τ</i> = −1ms in the figure. The height of this peak predicts the perceived carrier salience.</p

Autocorrelation model’s predictions for the amplitude of the N100m peak.

<p>Predictions were computed following the same procedure as in the analysis of the top-down modulated model (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153947#pone.0153947.g004" target="_blank">Fig 4d</a>). Predictions of the autocorrelation model do not show statistically significant correlations with the N100m values or the perceptual predictions. Moreover, the predicted amplitudes elicited by ramped and damped sinusoids with <i>T</i>_1/2 = 4,ms are not significantly different in this analysis.</p