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Passive exposure to speech sounds modifies change detection brain responses in adults - ScienceDirect
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## Article preview

- [Abstract](#preview-section-abstract)
- [Introduction](#preview-section-introduction)
- [Section snippets](#preview-section-snippets)
- [References (37)](#preview-section-references)
- [Cited by (9)](#preview-section-cited-by)

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# Passive exposure to speech sounds modifies change detection brain responses in adults

Author links open overlay panelJari L.O.Kurkelaa, Jarmo A.Hämäläinena, Paavo H.T.Leppänena, HuaShub, PiiaAstikainena

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## Highlights

- •
Passive exposure to speech sounds induces plastic changes in the adult human brain.

- •
Plasticity was indexed by the MMN, P3a and [P3b](/topics/neuroscience/p3b "Learn more about P3b from ScienceDirect's AI-generated Topic Pages") of event-related potentials.

- •
The effect of passive exposure transfers to some extend to non-speech sounds.

- •
Results encourage applying passive exposure to real-life learning situations.


## Abstract

In early life auditory discrimination ability can be enhanced by passive sound exposure. In contrast, in adulthood passive exposure seems to be insufficient to promote discrimination ability, but this has been tested only with a single short exposure session in humans. We tested whether passive exposure to unfamiliar auditory stimuli can result in enhanced cortical discrimination ability and change detection in adult humans, and whether the possible learning effect generalizes to different stimuli. To address these issues, we exposed adult Finnish participants to Chinese lexical tones passively for 2 h per day on 4 consecutive days. Behavioral responses and the brain's event-related potentials (ERPs) were measured before and after the exposure for the same stimuli applied in the exposure phase and to sinusoidal sounds roughly mimicking the frequency contour in speech sounds. Passive exposure modulated the ERPs to speech sound changes in both ignore (mismatch negativity latency, P3a amplitude and P3a latency) and attend (P3b amplitude) test conditions, but not the behavioral responses. Furthermore, effect of passive exposure transferred to the processing of the sinusoidal sounds as indexed by the latency of the [mismatch negativity](/topics/neuroscience/mismatch-negativity "Learn more about mismatch negativity from ScienceDirect's AI-generated Topic Pages"). No corresponding effects in the ERPs were found in a control group that participated to the test measurements, but received no exposure to the sounds. The results show that passive exposure to foreign speech sounds in adulthood can enhance cortical discrimination ability and attention orientation toward changes in speech sounds and that the learning effect can transfer to non-speech sounds.

## Introduction

In early infancy, cortical discrimination ability is enhanced even by passive sound exposure alone (e.g., Cheour et al., 1998; Cheour et al., 2002; Kuhl, 2004; Trainor et al., 2011). In contrast, in adulthood passive sound exposure in absence of training seems to be insufficient to affect the neural-level discrimination ability (Näätänen et al., 1993; Sheehan et al., 2005; Elmer et al., 2017) or behavioral discrimination performance (Wright et al., 2010, 2015). Instead, effects of active discrimination training have been shown in several studies by measuring the mismatch negativity (MMN) (Kraus et al., 1995; Tremblay et al., 1997, 1998; Tamminen et al., 2015), P3a (Atienza et al., 2004; Uther et al., 2006; Seppänen et al., 2012) and P2 (Atienza et al., 2002; Reinke et al., 2003; Sheehan et al., 2005) components of event-related potentials (ERPs). These components reflect pre-attentive change detection (MMN) and subsequent attention shifting (P3a) based on a memory trace formed by the learned sound feature (Näätänen et al., 2005; Polich, 2007) and sound feature encoding and stimulus classification (P2) (for a review see Crowley and Colrain, 2004).

Even though effects of passive exposure have been studied on brain responses related to pre-attentive change detection, possible effects of passive exposure on attentive change detection of sounds have not been investigated, i.e. effects on N2b and P3b components. 1-hour attentive identification training with speech sounds, however, showed learning-related changes in N2b and P3b (Alain et al., 2010). In another study, identification training resulted in only enhanced P3b responses and no changes in N2b (Ben-David et al., 2011). Similarly, attentive discrimination training with speech sounds resulted in enhanced P3b-like but not N2b-like microstates in electroencephalography (Giroud et al., 2017).

Even if previous studies have failed to demonstrate effect of passive exposure on auditory change detection in adults (Näätänen et al., 1993; Sheehan et al., 2005; Elmer et al., 2017; Wright et al., 2010, 2015), passive exposure to sounds seems not to be entirely ineffective either. Perceptual learning on an auditory discrimination task (Wright et al., 2010) or on an identification task (Wright et al., 2015) that is combined with sessions of passive exposure is more efficient than the active training alone as indexed by behavioral responses (Wright et al., 2010, 2015). Furthermore, passive exposure to sounds increases amplitude of the P2 component (Sheehan et al., 2005; Tremblay, 2007, 2010; Ross et al., 2013). Thus, passive exposure seems to have at least facilitating effect on auditory perceptual learning in adulthood.

One possible reason for the failure of the previous studies in demonstrating the effect of passive exposure on discrimination ability can be the short, 1–2 hour, exposure time that has been used in previous studies (Näätänen et al., 1993; Sheehan et al., 2005; Elmer et al., 2017). Active training studies have provided training over several days, and this has led to better discrimination ability as indexed by the enhancement of the MMN, P3a and P3b responses (Kraus et al., 1995; Tremblay et al., 1997; Giroud et al., 2017). Furthermore, it has been shown that sleep deprivation hinders the learning-related increase in the MMN amplitude and prevents the appearance of the P3a component (Atienza et al., 2004). Thus, the learning-related changes in cortical responses seem to be sleep-dependent, probably requiring memory consolidation during nocturnal sleep (Alain et al., 2015). Based on this assumption, it could be possible that the effects of mere passive exposure emerge if the exposure is expanded on several days, allowing memory consolidation. This has not yet been tested explicitly, however.

The evidence on generalization of the auditory learning to stimulus features not encountered during training is scarce. There are some studies showing that frequency or syllable discrimination training generalizes to closely similar untrained stimuli (for a review see Wright and Zhang, 2009). One study applied MMN to study the generalization, and showed that categorization training of labial stop consonant generalizes also to alveolar stop consonant as indicated by the shortened latency and increased amplitude of the MMN to non-trained stimuli (Tremblay et al., 1997).

In the present study, we tested two highly novel aspects of auditory perceptual learning: i) Effect of passive speech sound exposure on change detection and attention orienting in ignore and attend test conditions, and ii) if the effect of passive exposure is observed, whether it generalizes to ignored non-speech stimuli. Adult native Finnish participants were exposed to speech sounds (changes in Chinese lexical tones) for a total of 8 hours over 4 days. ERPs were recorded before and after the exposure to the same speech sounds and also to sinusoidal sounds roughly mimicking the pitch contours of the speech sounds. A control group received no exposure but participated only in the ERP recordings at the same time intervals as the experimental group.

We expect that the passive exposure would result in modulations in the ERPs, reflecting changes in both pre-attentive and attentive change detection and attention orienting toward changes (MMN, P3a, N2b, and P3b), as the exposure time is longer than in the previous studies (Näätänen et al., 1993; Sheehan et al., 2005; Elmer et al., 2017) and allows memory trace consolidation during the nights between the exposure periods (Stickgold, 2005; Alain et al., 2015). Changes in these ERP components are assumed to occur due to the formation of long-term memory representations of the sounds, making change detection and attention orienting to them more efficient (as in Näätänen et al., 1997; Winkler et al., 1999). We also hypothesized, based on the findings on sound frequency training (Wright and Zhang, 2009), that the effect of passive exposure transfers to the non-speech sounds.

## Section snippets

## Participants

A total of 39 monolingual Finnish-speaking participants (mean age = 23.0 years, standard deviation \[SD\] = 3.3 years; 32 females and 7 males) volunteered for the study. They were recruited with announcements in the notice boards and e-mail lists of the University of Jyväskylä. The inclusion criteria for the study were an age of 18–30 years, right-handedness, normal hearing measured using audiometry, and self-reported normal vision (or corrected to normal vision). The exclusion criteria for the

## Behavioral results

There were no interactions including session x group for the reaction times or for the accuracy of the behavioral responses. Detailed results for the behavioral responses are reported in the supplementary materials S1.1.

## N2b component

There were no interactions including session x group for the N2b amplitude or latency (Table 1). The responses to deviant and standard sounds in N2b time window are reported in supplementary Fig. 2.

## P3b component

For the P3b amplitude, an interaction effect of deviant type x stimulus type x

## Discussion

Here we show in adult humans that passive exposure to foreign speech sounds for 4 consecutive days, 2 h per day, enhanced the neural discrimination ability and attention orientation toward changes in the speech sounds as indexed by ERPs recorded in ignore and attend test conditions. The effect of passive exposure to auditory change detection mechanism has earlier been found only in infants (Cheour et al., 1998, 2002; Kuhl, 2004; Trainor et al., 2011). In the attend test condition, effect of

## Disclosure statement

The authors report no conflicts of interest.

## Acknowledgements

The study was supported by the Academy of Finland (project number 273134 for Piia Astikainen) and by the Natural Science Foundation of China (grant number 81461130018 for Hua Shu).

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## Cited by (9)

- ### [Native language advantage in electrical brain responses to speech sound changes in passive and active listening condition](/science/article/pii/S0028393224001519)



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It is not clear whether the brain can detect changes in native and non-native speech sounds in both unattended and attended conditions, but this information would be important to understand the nature of potential native language advantage in speech perception. We recorded event-related potentials (ERPs) for changes in duration and in Chinese lexical tone in a repeated vowel /a/ in native speakers of Finnish and Chinese in passive and active listening conditions. ERP amplitudes reflecting deviance detection (mismatch negativity; MMN and N2b) and attentional shifts towards changes in speech sounds (P3a and P3b) were investigated. In the passive listening condition, duration changes elicited increased amplitude in the MMN latency window for both standard and deviant sounds in the Finnish speakers compared to the Chinese speakers, but no group differences were observed for P3a. In passive listening to lexical tones, P3a was increased in amplitude for both standard and deviant stimuli in Chinese speakers compared to Finnish speakers, but the groups did not differ in MMN. In active listening, both tone and duration changes elicited N2b and P3b, but the groups differed only in pattern of results for the deviant type. The results thus suggest an overall increased sensitivity to native speech sounds, especially in passive listening, while the mechanisms of change detection and attentional shifting seem to work well for both native and non-native speech sounds in the attentive mode.

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Second, deepening theoretical understanding also requires that we understand the totality of performance for which we need to consider data from a number of modalities – some of which may be more sensitive to the effects of interest than others. For instance, brain measures may better capture some aspects of early L2 learning than overt behavioral responses (e.g., Bice & Kroll, 2015, 2019; Kurkela, Hämäläinen, Leppänen, Shu, & Astikainen, 2019; McLaughlin, Osterhout, & Kim, 2004), although in other cases, both brain and behavior converge (see Li, Legault, & Litcofsky, 2014 for a review on imaging studies). Multi-lab collaborations can be an effective way to explore the boundary conditions of such issues for a given task and sample.







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An important aim of research on bilingualism is to understand how the brain adapts to the demands of using more than one language.In this paper, we argue that pursuing such an aim entails valuing our research as a discovery process that acts on variety.Prescriptions about sample size and methodology, rightly aimed at establishing a sound basis for generalization, should be understood as being in the service of science as a discovery process. We propose and illustrate by drawing from previous and contemporary examples within brain and cognitive sciences, that this necessitates exploring the neural bases of bilingual phenotypes:the adaptive variety induced through the interplay of biology and culture. We identify the conceptual and methodological prerequisites for such exploration and briefly allude to the publication practices that afford it as a community practice and to the risk of allowing methodological prescriptions, rather than discovery, to dominate the research endeavor.

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Contrastively, other studies showed that while different training tasks can modulate the amplitude of MMN (Kraus et al., 1995; Tremblay, Kraus, Carrell, & McGee, 1997), passive exposure alone is not sufficient to do so (Elmer, Hausheer, Albrecht, & Kühnis, 2017; Sheehan, McArthur, & Bishop, 2005). As described in Kurkela et al. (2019), the role of passive exposure in the modulation of the electrophysiological activity related to auditory change detection is still unclear. Therefore, the interpretation offered here only represents a speculative proposal that needs to be adequately addressed with further empirical inquiries.







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Linguistic and vocal information are thought to be differentially processed since the early stages of speech perception, but it remains unclear if this differentiation also concerns automatic processes of memory retrieval. The aim of this ERP study was to compare the automatic retrieval processes for newly learned voices vs phonemes. In a longitudinal experiment, two groups of participants were trained in learning either a new phoneme or a new voice. The MMN elicited by the presentation of the two was measured before and after the training. An enhanced MMN was elicited by the presentation of the learned phoneme, reflecting the activation of an automatic memory retrieval process. Instead, a reduced MMN was elicited by the learned voice, indicating that the voice was perceived as a typical member of the learned voice identity. This suggests that the automatic processes that retrieve linguistic and vocal information are differently affected by experience.

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Additionally, the differences emerged in the neural measures but not behavioral measures. While it is not clear why the effect would present in brain activity but not in behavior, a growing number of studies have reported such patterns (e.g., Bice & Kroll, 2015; Kurkela et al., 2019; McLaughlin et al., 2004), suggesting that the two measures are capturing different aspects of the learning process. The research on childhood overhearers who later attempt to learn the language as an adult reveals quite specific effects, with advantages in learning phonological, but not grammatical, aspects of the language (Au, Knightly, Jun, & Oh, 2002; Au et al., 2008).







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