Effects of Exogenous Auditory Attention on Temporal and Spectral Resolution

Previous research in the visual domain suggests that exogenous attention in form of peripheral cueing increases spatial but lowers temporal resolution. It is unclear whether this effect transfers to other sensory modalities. Here, we tested the effects of exogenous attention on temporal and spectral resolution in the auditory domain. Eighteen young, normal-hearing adults were tested in both gap and frequency change detection tasks with exogenous cuing. Benefits of valid cuing were only present in the gap detection task while costs of invalid cuing were observed in both tasks. Our results suggest that exogenous attention in the auditory system improves temporal resolution without compromising spectral resolution

A Neural Mechanism for Time-Window Separation Resolves Ambiguity of Adaptive Coding

<div><p>The senses of animals are confronted with changing environments and different contexts. Neural adaptation is one important tool to adjust sensitivity to varying intensity ranges. For instance, in a quiet night outdoors, our hearing is more sensitive than when we are confronted with the plurality of sounds in a large city during the day. However, adaptation also removes available information on absolute sound levels and may thus cause ambiguity. Experimental data on the trade-off between benefits and loss through adaptation is scarce and very few mechanisms have been proposed to resolve it. We present an example where adaptation is beneficial for one task—namely, the reliable encoding of the pattern of an acoustic signal—but detrimental for another—the localization of the same acoustic stimulus. With a combination of neurophysiological data, modeling, and behavioral tests, we show that adaptation in the periphery of the auditory pathway of grasshoppers enables intensity-invariant coding of amplitude modulations, but at the same time, degrades information available for sound localization. We demonstrate how focusing the response of localization neurons to the onset of relevant signals separates processing of localization and pattern information temporally. In this way, the ambiguity of adaptive coding can be circumvented and both absolute and relative levels can be processed using the same set of peripheral neurons.</p></div

Summary of the adaptation (<i>τ_a</i>) and recovery (<i>τ_r</i>) time constants for the <i>T. oceanicus</i> and <i>T. leo</i> AN2 cells.

<p>See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000182#pcbi-1000182-g003" target="_blank">Figure 3</a> for the adaptation protocols. SD is the standard deviation across the <i>n</i> cells.</p

ILD coding improves with additional intrinsic adaptation in the central, ascending neurons.

<p>A: Response of a pair of modeled direction coding central neurons to an artificial grasshopper song played back from three directions (indicated by line colors) and the difference between the responses of the ipsi- and contralateral ascending neuron (bottom panel). B: Same simulation, but with intrinsic adaptation added to the model of the ascending/central neurons. C: Decoding of the ILD from the responses as pictured in (A). Responses of the AN pair to combinations of ten directions (ILDs) and 33 mean levels were used and classified for decoding performance of ILDs. Left panel: all syllables of the song taken into account, right panel: only responses to the first syllable of the song were used for classification. D: Classification as in C but with adaptation added to the central neurons in the model. See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002096#pbio.1002096.s002" target="_blank">S2 Code</a> for the code ran to model the network responses and classification of these responses.</p

Typical recording trace from a cricket AN2 neuron (<i>T. oceanicus</i>).

<p>The figure shows the voltage trace during constant stimulation (duration 1 s) with a sinusoidal tone of 16 kHz frequency. The shaded area depicts the spike detection window, bounded by the lower and upper threshold.</p

Intrinsic adaptation of the central ILD coding neuron.

<p>A: Response adaptation of an AN2 neuron to 500 ms current injection. B: The same neuron stimulated with a 500 ms sound stimulus of constant intensity level (56 dB SPL). The dotted line depicts responses of a model of AN2 that does not include the intrinsic adaptation seen in (A) but receives adapting inputs from the periphery (inset). C: Incorporation of an intrinsic adaptation current into the AN2 model reproduces the strong adaptation in response to acoustic stimuli. See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002096#pbio.1002096.s004" target="_blank">S2 Data</a> for experimental data underlying panels A–C and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002096#pbio.1002096.s001" target="_blank">S1 Code</a> for the code used to generate the modeling results in B and C. w/o: without.</p

Optimal response curves for the bimodal (circles) and trimodal (squares) stimulus distribution predicted by the infomax principle (A) and the selective coding hypothesis (B).

<p>The figures show the predicted relationship between the response variable (spike rate) and the stimulus intensity. The Gaussian curves depict the probability distributions of stimulus intensity, where the dark shaded areas under the curve denote the bimodal stimulus distribution and the light shaded area under the curve the additional peak of the trimodal stimulus distribution (cf. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000182#pcbi-1000182-g001" target="_blank">Figure 1</a>).</p