Learning-related improvements in auditory detection ...

Learning-related improvements in auditory detection sensitivities correlate with neural changes observable during active and passive sound processing Natalie J. Ball, Matthew G. Wisniewski, Alexandria C. Zakrzewski, Nandini Iyer, Brian D. Simpson, Amanda Seccia, Eric R. Thompson, and Nathan Spencer

Citation: Proc. Mtgs. Acoust. 30, 050012 (2017); doi: 10.1121/2.0000630 View online: https://doi.org/10.1121/2.0000630 View Table of Contents: http://asa.scitation.org/toc/pma/30/1 Published by the Acoustical Society of America

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Volume 30

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Acoustics `17 Boston 173rd Meeting of Acoustical Society of America and 8th Forum Acusticum Boston, Massachusetts 25-29 June 2017

Psychological and Physiological Acoustics: Paper 4pPPc28

Learning-related improvements in auditory detection sensitivities correlate with neural changes observable during active and passive sound processing Natalie J. Ball

Psychology, University at Buffalo, The State University of New York, Buffalo, NY, 14260; [email protected]

Matthew G. Wisniewski

U.S. Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH; [email protected]

Alexandria C. Zakrzewski

University of Richmond, Richmond, VA; [email protected]

Nandini Iyer and Brian D. Simpson

U.S. Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH; [email protected], [email protected]

Amanda Seccia

University at Buffalo, The State University of New York, Buffalo, NY, 14260; [email protected]

Eric R. Thompson and Nathan Spencer

U.S. Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH; [email protected], [email protected] Auditory detection can improve with training. Though the majority of theorists posit that such improvements relate to dynamics of selective attention, longer-term sensory plasticity may also play a role (e.g., increased cortical processing for trained sounds). Here, listeners were trained in a single session to detect either an 861-Hz or 1058-Hz tone (counterbalanced across participants) presented in a frozen white noise masker. On the following day, EEG was collected while listeners: 1) attempted to detect 861-Hz and 1058-Hz tones at an SNR of -21 dB, and 2) passively heard the same tones presented in quiet. Listeners were significantly better at detecting tones at their trained frequency. In addition, P3 amplitudes were larger for trained than for untrained tones during the active detection task. During passive exposure to the same tones, P2 amplitudes were similarly larger for trained than for untrained tones. The difference in P3 amplitudes suggests that training leads to more efficient post-sensory processing related to decision-making. Differences in P2 amplitudes may reflect training-induced sensory cortical plasticity. Further, since the P2 effect was obtained during passive exposure, training induced improvements in detection may not be solely related to attention.

Published by the Acoustical Society of America © 2017 Acoustical Society of America. https://doi.org/10.1121/2.0000630 Proceedings of Meetings on Acoustics, Vol. 30, 050012 (2017)

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Learning-related improvements in auditory detection sensitivities correlate with neural changes

Introduction Experience can alter performances in a number of different listening tasks (for review, see Wright & Zhang1). For instance, practice can improve detection thresholds,2,3 increase sensitivities to differences between simple4 and complex sounds5, and alter spatial acuities (for review, see Wright & Zhang6). Given the variety of tasks that show this perceptual learning, it is perhaps unsurprising that the processes behind experience-related changes in perception have been hotly debated (for review, see Wisniewski, Radell, Church, & Mercado7). Explanations vary between selective attention views (i.e., learners determine the correct features to attend),8 and views that posit relatively long-term changes to sensory representations and their read-out connections.9-11 Explanations of experience-related changes in auditory detection have been weighted heavily towards the selective attention position. For instance, Zwislocki, Marie, Feldman, and Rubin2 ascribed reduced attention as a “fairly straightforward” (p. 256) reason for decrements in detection threshold, and motivation as key to improving it. Tanner and Norman12 had listeners detect a 1000-Hz tone in noise in a 4I-4AFC task for hundreds of trials in which listeners were approximately 65% correct. When the frequency of the tone was changed to 1300 Hz, performance was brought down to chance (~25% correct). Performance promptly rose after subjects were informed of the frequency change, presumably because they recalibrated their attention. In another classic study, Greenberg and Larkin13 setup expectation of a tone at 1100-Hz by presenting initial trials using tones at 1100 Hz. In the following experimental blocks, probe stimuli at different frequencies were presented on 23% of trials. The detection probability of tones in noise at 1100Hz was relatively high compared to the detection of tones higher (>1200-Hz) or lower ( .20. In contrast, there appeared to be an effect of training on the P2 amplitude, with trained frequencies evoking larger P2 amplitude than untrained frequencies. The mean amplitudes for P2 for trained and untrained frequencies were M = 0.77 μV (SD = 0.77) and M = 0.53 μV (SD = 0.62) respectively. The difference in P2 amplitudes did reach significance, p = .03.

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Learning-related improvements in auditory detection sensitivities correlate with neural changes

Figure 3. Passive ERP Results. (a) ERPs at the central and parietal electrode clusters during the passive portion of the experiment. N1 and P2 components are labeled. The dotted vertical line depicts tone onset (b) Scalp maps showing mean voltage within the designated N1 time window. (c) Scalp maps showing mean voltage within the designated P2 time window.

ERP Data – Active Figure 4a depicts ERPs in the active portion of the experiment. Components N1, P2, and P3 are labeled. Mean N1 amplitudes for the trained and untrained frequencies were M = -0.92 μV (SD = 1.12) and M = -0.83 μV (SD = 1.45) respectively. As in the passive portion of the experiment, the N1 showed no significant difference in amplitude between the trained and untrained frequencies, p > .20. The P2 was larger for the trained (M = 1.58 μV, SD = 1.49) than the untrained (M = 0.48 μV, SD = 1.15) frequency. This difference was significant, p = .01. In addition, the P3 was larger for the trained (M = 3.22 μV, SD = 2.23) compared to the untrained (M = 2.02 μV, SD = 2.13) frequency. This difference also reached significance, p < .001.

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Figure 4. Active ERP Results. (a) ERPs from the active part of the experiment at central and parietal electrode clusters for both the trained (solid lines) and untrained (dashed lines) frequencies. The dotted vertical line depicts tone onset. Scalp maps of N1 (b), P2 (c), and P3 (d) components are also shown.

Discussion In the present study, we compared ERPs for trained and untrained tone frequencies under active and passive listening protocols. During active listening, participants were tasked with detecting tones in noise at a previously trained or untrained frequency. These same tones were presented in silence under a passive protocol with no associated task other than to ignore sounds. Debate over why humans show frequency-specific learning effects for detection, spurred the use of these separate protocols. Selective attention explanations predict differences between trained and untrained tones during active listening, but not during passive exposure to the same sounds (i.e., when top-down selective attention is not being utilized). In contrast, explanations that are based in relatively long-term changes in stimulus processing, predict differences between trained and untrained frequencies under both active and passive listening protocols. Essentially, by comparing ERPs to trained and untrained tone frequencies in these two tasks, we were able to look at processing-related differences during attentive and non-attentive listening states. Proceedings of Meetings on Acoustics, Vol. 30, 050012 (2017)

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Learning-related improvements in auditory detection sensitivities correlate with neural changes

Participants showed the expected detection advantage for the tone frequency they were trained with. There were paralleling effects in ERPs obtained during both active and passive portions of the experiment. For passive ERPs, the trained frequency elicited a larger P2 amplitude than the untrained frequency. For active ERPs, larger amplitude P2 and P3 components were observed for the trained frequency. The ERPs indicate effects of learning at multiple processing stages. Specifically, an exogenous (sensorial) component of the auditory ERP (the P2) and a later endogenous (cognitively generated) P3, are both enhanced for a trained relative to an untrained frequency. The effect observed for P2 further suggests that learning may not be exclusively related to selective attention, because effects of learning are observable even under a passive protocol. Studies of discrimination learning have similarly shown increased P2 amplitudes for trained stimuli under passive protocols.15,24 These learning effects found in P2 have been taken to indicate that P2 amplitude changes reflect changes in sensorial processing or the neural processes associated with acquisition.29 Tong and colleagues15 also suggest that P2 may regulate access to representations that would elicit P3 working memory processes during active tasks. For example, repeated exposure to target stimuli during training may increase their salience in working memory, increasing awareness to them in subsequent active tasks. While P2 may reflect perceptual discrimination accuracy, it has also been suggested to be an indicator of familiarity.30 Familiarity is essentially induced by learning, so this does not contradict our present findings. Though much of the P2 literature has been derived from discrimination work, to our knowledge we are the first to show parallel P2 effects with detection learning. As in the active detection portion of our study, increases in P3 have paralleled learning in discrimination studies.15,24 The P3 seems to be prominent in actively attending auditory tasks, going beyond initial sensory processing. With the detection of a change in stimuli (not restricted to audition), P3 may reflect context updating involving comparison of new stimuli with those previously experienced and stored in working memory.31 The P3 may represent the efficiency of post-perceptual processing related to decision-making,32 categorization,33,34 and certainty regarding the decision.35 Regardless, it reflects some higher than sensory cognitive process. Increases in P3 amplitude for trained tones demonstrate changes associated with learning outside the auditory system itself. That is to say, the trained tones elicit further processing, and while the specific kind of processing cannot be determined from this experiment, it is notable that the trained stimulus surpasses sensory processing alone. One important caveat to this work has to do with our assumption that selective attention was not being utilized in the passive protocol. Though participants were instructed to “ignore the sounds”, we cannot definitively say that participants avoided paying attention to the trained frequency. It is noteworthy that we observed no differences between trained and untrained frequencies for the N1. This is a component often associated with auditory selective attention.36,37 Even so, it may be more convincing if the same result holds when attention is diverted from the sounds. This could be accomplished by giving participants a distracting task of sufficient difficulty in future work (e.g., an attention-demanding visual search task). Conclusion This study suggests that there are ERP correlates of learning in auditory detection at several stages of processing. Effects on the P2 component suggests that learning produces stimulus processing changes that are not related to active selective attention. Learning correlates also exist in post-sensory processing (as indexed by the P3). Detection learning may not be solely attributed to selective attention, as some have suggested. Instead, learning likely relies on both long-term sensory processing changes and attentional biases. Future work should examine both mechanisms in order to understand the improvements in auditory detection.

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