Introduction

Learning occurs over multiple time scales—evolutionary, developmental and moment-to-moment—to support a diverse range of abilities: navigating complex sensory environments (Bregman, 1994; Lewicki et al., 2014; Smith & Lewicki, 2006), expressing intricate behaviours in social settings (Gariépy et al., 2014; van den Bos et al., 2013), or acquiring communication skills such as bird song (Brainard & Doupe, 2002; Lauay et al., 2004; Marler, 1970) or spoken language (Aslin et al., 1998; Saffran et al., 1996; Saffran, 2003). Although some learning requires active or explicit involvement in a task (Huyck & Wright, 2011; Mathews et al., 1989; Rebuschat, 2015), perhaps with a system of rewards and punishments to make the learning ‘stick’ (Barberis, 2013; Schultz, 2002; Wächter et al., 2009), other forms of learning seem automatic or implicit (Reber, 1967), acquired with individuals seemingly unaware it is taking place. This type of learning, referred to as ‘statistical learning’ (Ambrus et al., 2020; Saffran et al., 1996), ‘sequence learning’ (Nissen & Bullemer, 1987; Vékony et al., 2022) or ‘sequential learning’ (Conway & Christiansen, 2001; Vékony et al., 2022), is thought to entail automatic and incidental extraction of regularities or patterns within external stimuli or in the environment (Conway, 2020; Takács et al., 2021). Evident across sensory modalities to support automatic learning of tonal (Saffran et al., 1999) or linguistic (Saffran et al., 1996) sequences, strings of letters (Reber, 1967), visual scenes and shapes (Fiser & Aslin, 2001), visual-motor patterns (Nissen & Bullemer, 1987), and even tactile input (Conway & Christiansen, 2005), statistical learning appears to be a unitary, domain-general phenomenon, potentially governed by a single mechanism or neurocognitive principle (Conway, 2020; Kirkham et al., 2002).

Increasing evidence suggests that statistical learning is a critical part of how listeners deal with complex and cluttered acoustic scenes. Potential background sounds such as rain or insects—referred to as sound textures (Hicks & McDermott, 2024; McDermott et al., 2013; McWalter & McDermott, 2018) as well as changes in the regularity of sound patterns (Barascud et al., 2016; Bianco et al., 2020) are processed—seemingly unconsciously—in terms of their summary statistics. This form of statistical learning likely contributes to our ability to follow conversations in background noise (‘cocktail party listening’; Cherry, 1953) and to deal with reverberant spaces where multiple, delayed copies of the same sound reach a listener in the form of reflections from walls and other acoustically opaque surfaces (Blesser & Salter, 2009; Sabine, 1953; Schroeder, 1962). Though listeners rely on early-arriving sound energy to determine source location—suppressing potentially conflicting localization cues in later-arriving, often more intense, sound energy (Bradley et al., 1999; Culling et al., 2003; Houtgast & Steeneken, 1985; Nielsen & Dau, 2010)—the perception of reflected sound energy is informative of the listening environment more broadly (Bronkhorst & Houtgast, 1999; Shinn-Cunningham, 2000). This includes whether environments are real or synthetic i.e., deviated from natural reverberation characteristics (Traer & McDermott, 2016), room dimensions (Cabrera et al., 2005; Kolarik et al., 2021; Zahorik & Wightman, 2001) and source distance (Bronkhorst & Houtgast, 1999; Zahorik & Wightman, 2001).

Nevertheless, despite its potential utility for understanding background features of a sound environment, the accumulation over time of late-arriving, reverberant sound energy is thought to generate an additional burden on listening performance beyond that from sound energy direct from interfering sources (Houtgast & Steeneken, 1973; Knudsen, 1929; Lochner & Burger, 1961; Santon, 1976; Shinn-Cunningham & Kawakyu, 2003), smearing the acoustic waveform and occluding temporal gaps that might otherwise be helpful for ‘glimpsing’ speech in background noise (Cooke, 2006). However, if listeners are able to utilize late-arriving, reverberant energy of known acoustic environments to suppress disruptive spatial cues and enhance speech understanding (Brandewie & Zahorik, 2010, 2013; Vlahou et al., 2019; Watkins, 2005a, 2005b), then the acoustic characteristics of an environment can be learned and stored for later use in complex listening tasks.

Here, using an ecologically relevant listening task—understanding speech in background noise—we assessed the ability of human listeners to learn the statistical structure of different sound environments defined by their RT_60, the time it takes for reverberant energy to decay by 60 decibels (dB), and confirmed that speech understanding in background noise improved over time. Specifically, when asked to report words from unfamiliar and semantically uninformative spoken sentences of varying duration in noise convolved with the reverberant qualities of different acoustic environments, listeners’ performance improved with increasing sentence duration, and with repeated exposure to each environment (Brandewie & Zahorik, 2010, 2013).

Building upon this literature, we wondered, if the learning of acoustic environments i.e., improvements in performance with increasing sentence duration, followed a similar time-course to in vivo experimental evidence of increased adaptive capacity for neural learning of sound environments upon repeated exposure to those environments (Dean et al., 2005, 2008), and whether this adaptive capacity was modulated by cortical circuits (Robinson et al., 2016) such as the dorsolateral prefrontal cortex (dlPFC), a cortical locus hypothesised to contribute to statistical learning in human listeners (Ambrus et al., 2020; Vékony et al., 2022). Specifically, we asked whether knowledge of the acoustic environment accumulated over time—that is, whether performance during initial exposures was poorer than during later ones—and whether this accumulation was reduced when bilateral dlPFC activity was disrupted by TMS. We found that the capacity to learn the acoustic environment to aid speech understanding was diminished when repetitive transcranial magnetic stimulation (rTMS) was applied bilaterally to impair the function of dlPFC, suggesting that indeed human listeners have neural mechanisms supporting listening in noise and reverberant conditions.

Consistent with this, Francl and McDermott (2022) demonstrated that an auditory model trained under realistic listening conditions—incorporating both noise and reverberation—was able to reproduce several human-like spatial hearing characteristics. To this end, we wondered if the learning effects we observed relied on the combination of real-world environments we employed, i.e., the RT₆₀s in which listening performance was assessed. Specifically, we hypothesized that the ability to understand speech in noise depends on exposure to specific values of RT₆₀ across our experimental paradigm, including those in the range commonly experienced by human listeners in natural and built environments (Traer & McDermott, 2016). Counter to expectations that reverberant energy can only harm speech understanding, listeners’ abilities to leverage the knowledge of a sound environment (talker identity, speech corpus, noise characteristics, spatial configuration) to improve speech understanding was best when a ‘typical’ room with reverberant characteristics close to the average of a wide range of common (built) environments (Traer & McDermott, 2016) was included as one of the three environments presented in a single experimental run. Performance declined systematically when this room was switched for one with more, or less, reverberation, including a fully anechoic (i.e., non-reverberant or dry) environment in which listeners initially trained on the recall task. Importantly in the context of other potentially learnable acoustic features, talker identity (three male and three female) represented a random variable in our experimental design. Evidence for a reverberation ‘sweet spot’ suggests the existence of brain mechanisms adapted to the longer-term structure of common listening environments (Traer & McDermott, 2016).

Materials and Methods

Participants

A total of 74 participants (53 were females), aged between 19-26 years old (mean ± SD = 22 ± 2 years old) were recruited across all experiments. They were Australian native-English speakers, had normal pure tone thresholds (< 20 dB HL tested at octave intervals between 0.5-8 kHz (Hughson & Westlake, 1944); Interacoustics Hearing Aid Fitting Analyzer Affinity 2.0 Audiometry) and normal middle ear function (assessed using standard 226 Hz tympanometry; Titan, Interacoustics). To ensure normal outer hair cell function, all participants were screened for Distortion Product Otoacoustic Emissions (DPOAEs) between 0.5-10 kHz (stimulus parameters: f1/f2 =1.2, f1 =65 dB SPL; f2 =55 dB SPL; responses parameters: SNR > 6 dB, signal level > −10 dB SPL, and reliability > 98% (DPOAE440; Titan, Interacoustics). All participants had normal steady-state ipsilateral broadband noise middle ear muscle reflexes (MEMR) (Titan, Interacoustics). The audiological testing took 15-20 minutes to complete.

Acoustic stimuli

To assess listeners’ abilities to understand speech in background noise across different sound environments, they were asked to verbally report keywords spoken by a talker virtually located in front of the participant that were masked by white noise arriving from a virtual source 90° to the left (Figure 1A) (Brandewie & Zahorik, 2010, 2013). A binaural configuration was necessary as speech intelligibility improvements following exposure to room acoustics is significantly reduced under monaural listening conditions (Brandewie & Zahorik, 2010). The speech stimuli used in this study were from the Coordinate Response Measure (CRM) corpus (Bolia et al., 2000), and all combinations of Callsigns (‘Baron’, ‘Eagle’, ‘Charlie’, ‘Tiger’, ‘Arrow’, ‘Ringo’, ‘Laker’, ‘Hoper’), Colors (four monosyllabic choices, ‘red’, ‘white’, ‘blue’, or ‘green’), and Numbers (the English digits between ‘one’ and ‘eight’) were used in this experiment (Figure 1B) (Brandewie & Zahorik, 2010, 2013). The masking white noise was randomly generated on a PC using MATLAB (The MathWorks, Inc. of Natick, MA: https://au.mathworks.com/) and was presented with the speech. The masking noise preceded the speech stimuli by 150 ms. The masker’s amplitude was set to increase linearly from zero to the target level (70 dB SPL), and ended at the same time as the speech stimuli, without ramping (Brandewie & Zahorik, 2013).

Figure 1.

Table 1.

Virtual Environments

Reverberant environments were reproduced by convolving the generated acoustic signals with Room Impulse Responses (RIRs) obtained using 62-channel microphone array recordings of real rooms that were subsequently decoded into 41 higher-order Ambisonic (HOA) channels (Badajoz-Davila et al., 2020; Weisser et al., 2019). These final decoded channels corresponded to the spherical array of 41 Tannoy V8 concentric loudspeakers (Tannoy) installed in the anechoic chamber at the Australian Hearing Hub where testing took place. To optimize the “directionality” of the acoustic stimuli, the speech and masking noise signals were separately convolved with RIRs that had enhanced direct sound components in loudspeakers associated with their respective virtual source locations (i.e., 0° azimuth loudspeaker for the target talker and 90° azimuth loudspeaker for the masking noise) (Badajoz-Davila et al., 2020; Weisser et al., 2019). In the anechoic condition, RIRs only incorporating these enhanced direct sound components were convolved with the speech and masking noise signals. The SNR for speech-in-noise during data collection was −15 dB to avoid ceiling performance and was manipulated after convolution with the RIR by adjusting the gain of the speech target relative to a fixed masker level of 70 dB SPL. All signal processing was performed on a PC using MATLAB (Mathworks) and the final spatialized signal were presented via the RME MADI sound card (RME Audio) to two RME 32-channel digital-to-analog converters (M-32, RME Audio). These, in turn, fed 11 Yamaha XM4180 power amplifiers (Yamaha) that drove the loudspeaker array.

Identity of sound environments

Three rooms were selected, with similar Reverberation Times (RT₆₀s) to those employed by Brandewie and Zahorik (2013) and assessed in 22 participants. As Bajadoz-Davila et al. (2020) and Brandewie and Zahorik (2013) calculated these RT₆₀s according to ISO-3382 guidelines (ISO, 2009), they are referred to in the text as RT_60iso: Lecture room (RT_60iso = 0.46 s), Open-Plan Office (RT_60iso = 0.96 s) and Underground Car Park (RT_60iso = 2.42 s) (Table 1). Additionally, we performed 3 followed up experiments in different groups of listeners and assessed performance across combinations of three rooms: 1) Anechoic Room/Open-Plan Office/Underground Car Park (10 naïve listeners); 2) Living Room/Open-Plan Office/Underground Car Park (11 naïve listeners) and 3) Highly Reflectant Room/Open-Plan Office/Underground Car Park (10 naïve listeners). These conditions were employed to determine whether a specific combination was required to observe improvements in speech performance as exposure to room acoustics increased, that is, with increasing ‘carrier phrase length’, assessed in the same way as (Brandewie & Zahorik, 2013). For each combination, Lecture Room was swapped with one of three virtual environments: Anechoic (RT_60iso = n/a), Living Room (RT_60iso = 0.33 s) and Highly Reflectant Room (RT_60iso of 1.55 s) (Table 1). Naïve participants were recruited for each room combination.

To compare our recordings to RIR data recorded and analyzed by Traer and McDermott (2016), we also calculated RT₆₀s for each room by integrating across the 41 HOA channels of each RIR and then calculating the median RT₆₀ value in 31 frequency sub-bands with centre frequencies ranging from 80 Hz to 10 kHz (Traer & McDermott, 2016). We refer to these measures of reverberation as RT_60traer. The range of sub-bands and their frequency selectivity was chosen to match that of the human ear (Glasberg & Moore, 1990; McDermott & Simoncelli, 2011). See SI Materials and Methods, (Traer & McDermott, 2016), for further information concerning how individual RT_60traers were extracted from each sub-band.

Stimulus Normalization and Binaural Rendering

Stimuli were generated and normalized within a 41-channel sound field prior to binaural rendering to preserve natural head-related acoustic effects (e.g., head shadow). Source speech and noise signals were first convolved with 41-channel anechoic or reverberant impulse responses. The overall energy of the 41-channel noise field was scaled to a target of 70 dB, and the 41-channel speech field was subsequently scaled to achieve the target SNR. Following sound field normalization, the 41-channel signals were rendered to two channels using a Higher Order Ambisonics to Binaural (hoa2bin) decoder. To verify the at-ear acoustic levels, simulated at-ear root-mean-square (RMS) energy was extracted for the speech and noise tokens post-rendering. These digital RMS values were mapped to approximate dB SPL, based on the 70 dB noise sound field anchor, to confirm the resulting at-ear acoustic SNRs across both anechoic and reverberant conditions (Table 2).

Table 2.

Continuous theta-burst stimulation

Applying repetitive Transcranial Magnetic Stimulation (rTMS)—specifically continuous theta burst stimulation (cTBS)—to the right and left dlPFC, elicits a period of depressed cortical excitability in the targeted area that lasts about 60 minutes post-stimulation (Gamboa et al., 2010; Hoogendam et al., 2010; Huang et al., 2005; Romero et al., 2022), the time during which speech recall in reverberant rooms was assessed. Participants were screened for contra-indications to rTMS (see Supplemental Information. Transcranial Magnetic Stimulation screening form). Two cTBS conditions (‘real’ and ‘sham’ TMS) were counterbalanced across normal-hearing participants, and participants were blinded to the type of manipulation they might receive.

cTBS intensity applied to dlPFC was adjusted for every participant (i.e., both ‘real’ and ‘sham’ TMS groups) as a function of their individual corticospinal excitability assessed with single-pulse TMS-motor thresholds. Single-pulse TMS was delivered using a 70 mm figure-of-eight coil (Magstim Rapid2 system, Magstim, Whitland, United Kingdom), orientated at 45° to the scalp with current flowing posterior-anterior across the primary motor cortex. For the determination of single pulse TMS-induced motor thresholds we used visual observation of the first dorsal interosseous (FDI) muscle twitch (Varnava et al., 2011). Coil position and angle was adjusted until the optimal site for consistent and larger elicitation of FDI muscle twitch. Once the optimal site was located, the minimum level of stimulator output that produces a visible twitch in either the left or right hand in each individual, following single pulse stimulation of the respective contralateral motor cortex, was determined. Subjects were instructed to relax their arm, and rest it on their lap, palm upwards, whilst gently squeezing their thumb and forefinger together – as if squeezing a pea, whilst simultaneously keeping their other fingers relaxed. We then monitored for 3/5 visible twitches in the FDI in response to single pulse TMS. Subjects were occasionally asked to shake their arm to relax everything and then to resume holding their hand as before (Coltheart et al., 2018; Dienes & Hutton, 2013). Participants’ motor thresholds ranged from 46-57% of the maximum stimulator output (mean = 51 ± 5). Participants’ individual motor threshold for left and right hemisphere were recorded to set the intensity for bilateral cTBS stimulation which was administered by placing the same figure-of-eight coil over the right and left dlPFC located over electrode positions F3 and F4 (10-20 system EEG) (Herwig et al., 2003; Jurcak et al., 2005). Bilateral TMS was performed serially i.e., first the right or the left dlFPC (order was alternated among subjects), was chosen to limit possible compensation effects of the non-stimulated hemisphere (Ambrus et al., 2020). Each cTBS burst consisted of three pulses at 50 Hz, with bursts repeated at a frequency of 5 Hz, applied continuously for 40s, and delivered at an intensity of 80% of the right or left resting motor threshold (Huang et al., 2005).

Both real (applied to 10 naïve listeners) and sham (applied to 11 naïve listeners) TMS stimulations were identical and delivered to two groups of listeners, with participants counterbalanced between the two conditions. The sole difference between the ‘real’ and ‘sham’ TMS was the figure-of-8 coil utilized. Real TMS was administered using an active figure-of-8 coil, while the sham stimulation employed a sham coil (Magstim Rapid2 system, Magstim, Whitland, United Kingdom). The sham coil is non-stimulating, meaning it does not induce a significant magnetic field, and is designed to be indistinguishable from the active coil in appearance, feel, and sound. The duration of the TMS procedure was 20–30 minutes, primarily due to the estimation of motor thresholds, with cTBS stimulation delivered in the final 2–5 minutes.

Procedure

Familiarization

All participants first completed (within 5 minutes) a familiarization task that consisted of 10 trials (i.e., different ‘carrier phrase lengths’) presented in anechoic conditions at 0 dB SNR where listeners were asked to report |Color| and |Number|. Participants exposed to ‘real’ or ‘sham’ TMS completed the familiarization and behavioural task right after cTBS procedures. All participants performed the familiarization phase with 80-100% accuracy, confirming that all participants including exposed to cTBS understood the task and procedural learning was achieved and not affected by either ‘sham’ or ‘real’ TMS exposure.

Behavioural task

The CRM sentences were spoken by six talkers (3 males and 3 females) and were of varying duration where all, some, or none of the preceding sentence (the ‘carrier phrase’ (CP, Figure 1B)) before ‘|Color| |Number|’ was included (Brandewie & Zahorik, 2010, 2013). Thus, for CP0, listeners heard ‘|Color| |Number| now’, CP1—‘go to |Color| |Number| now’, CP2—‘|Callsign| go to |Color| |Number| now’, CP3—‘Ready |Callsign| go to |Color| |Number| now’ (Figure 1B and C). After each phrase was presented, participants reported the |Color| and |Number| they heard to the experimenter and performance was assessed based on keywords correctly identified. The rationale for only reporting |Color| and |Number| was to ensure the same amount of keywords identifications across different phrases lengths. For the reverberant conditions SNR was set to −15 dB, which we found in pilot testing resulted in a level of performance similar to (Brandewie & Zahorik, 2010, 2011, 2013; Zahorik, 2009). The room environment was selected randomly for each trial except that the same room could not appear in two consecutive trials. The target |Color|, |Number| and talker were selected randomly for each trial. For each experimental session, three different listening environments were assessed, and listeners were presented 360 trials (30 repeats x ‘room = 3’ x ‘carrier phrase length =4’). Each listener participated in one session only. Throughout the session, listeners were required to verbally repeat the appropriate |Color| and |Number| combination they heard from corpus lists physically displayed on both sides of the 0° azimuth loudspeaker. The CRM is a closed-set corpus, therefore participants were provided with |Color| and |Number| choices to select from (Brandewie & Zahorik, 2010, 2011, 2013; Zahorik, 2009). The experimenter continuously monitored participants’ responses while scoring performance, but no feedback was provided. The behavioural total test time was 45 min.

Speech performance analysis and time course-fittings of mean cumulative hit rates

d’ was first calculated individually for both |Color| and |Number| in different room/carrier combinations using Equation 1, where z(H) and z(F) were the z transforms of the hit rate and false alarm, respectively. Hit rates consisted of the correct |Color| or |Number| being selected for each trial, whereas false alarms were considered when a |Color| or |Number| was reported incorrectly to the presentation of any other |Color| or |Number|. To prevent infinite values of d’, hit rates/false alarms of 1 were set to 0.99, and corresponding false alarms/hit rates of 0 were set to 0.01 leading to maximal/minimal d’ values of ± 4.65. The total d’ for a carrier phrase length in a particular room was calculated by averaging |Color| and |Number| d’ values for that combination of room and ‘carrier phrase length’ across individuals.

Because d’ is calculated independently for |Color| and |Number|, it lacked the temporal resolution necessary to describe performance over time due to data paucity. Consequently, we analysed the development of individual and average performance in each acoustic environment using cumulative hit rates. We applied a 5-point moving average (∼7 seconds) to each trace and plotted performance as a function of mean cumulative exposure time.

To quantify performance trajectories, we fitted the time-series data with both single (Eq. 2) and double (Eq. 3) exponential functions:

where, f(t) represents the fitted performance at time t, and C denotes the constant y-intercept. The terms mag and τ represent the magnitude and time constant (in seconds), respectively, for the single, fast, and slow components.

Constrained optimization was performed using MATLAB’s fmincon function to minimize the mean squared error. To avoid local minima, fits were initialized using a gridded search. For single exponential models, we tested a 25 × 25 grid with τ_single ranging from 0–100 s and mag_single from −50 to 50. For double exponential models, we utilized a grid of 5 linearly spaced values for τ_slow (50–500 s), τ_fast (0–100 s), and their corresponding magnitudes (−10 to 10), resulting in 625 initialization points per model type. Upper bounds were set at 2000 s for τ_single and τ_slow, and 500 s for τ_fast. The optimal fit for a listening environment was selected from the resulting collection of single/double exponential curves by identifying fits with the highest adjusted R². During optimization, fits were weighted by their variance; therefore, greater importance was attributed to performance at later exposure times. Furthermore, we forced all final fitted curves to pass through the final hit rate i.e. last data point, by penalizing those that did not during fitting.

Double exponential models were fitted exclusively to the full dataset, as they are capable of accounting for early data reversals. However, to account for inconsistent initial results, single exponential models were calculated for both the full dataset and a truncated dataset (excluding the first 10 points, (∼14 seconds). To determine the optimal model, we calculated the Akaike Information Criterion (AIC). AIC estimates prediction error by balancing goodness-of-fit against model complexity, defined as:

Where k is the number of estimated parameters and L̂ is the maximum likelihood of the model. In the context of least-squares fitting, this is derived from the sum of squared errors. To ensure valid comparisons, AIC values were computed using identical data ranges for both candidate models (comparing fits on the full dataset against one another or fits on the truncated dataset against one another).

For analysis of ‘Learning Settling Time”, we calculated the absolute percentage deviation of cumulative hit rate curves relative to the final time point in each curve i.e., their Final Hit Rate (FHR), we then calculated back in time to determine where fitted curves first deviated from the FHR by ±10%. Note that FHR is the final data point of the cumulative hit rate i.e., after all responses have been accumulated in that listening environment.

Statistical analysis

Repeated measures ANOVAs (rANOVA) were performed to assess speech understanding (d’) within subjects with factors: Rooms (levels: Lecture Room, Open-Plan Office and Underground Car park); carrier phrase length (levels: CP0, CP1, CP2 and CP3) and target word (levels: |Color| and |Number|). A mixed ANOVA was employed to assess whether speech understanding was affected by the between factor: TMS exposure (levels ‘sham’ and ‘real’ TMS), and within-subject’s factors: Rooms (levels: Lecture Room, Open-Plan Office and Underground Car park); carrier phrase length (levels: CP0, CP1, CP2 and CP3) and target word (levels: |Color| and |Number|). Additionally, three follow up experiments were performed and a one-way ANOVA was used to assess whether speech understanding was affected by the room-context (i.e., the 3^rd room in which Open-Plan Office and Underground Car Park were learned). Only one between-subject’s factor was assessed: room context.

Effect sizes were calculated for all post hoc statistical analyses (Partial Eta-squared (), Cohen’s d (parametric analysis) and Person’s r (for nonparametric Wilcoxon Signed-Rank test) (Pallant, 2011). Further two-tailed t-tests (original alpha=0.05, with post hoc Bonferroni corrections for multiple comparisons) were performed. Corrected alpha value is not reported as all statistical analyses were performed in SPSS: RRID:SCR_002865 (IBM Corp. Released 2023. IBM SPSS Statistics for Windows, Version 29.0.2.0 Armonk, NY), and SPSS output adjusts the p-value instead of the alpha value itself.

Power Analysis

Initial sample size estimation for individual ANOVA analysis (18) was computed using G*Power: RRID:SCR_013726 (Faul et al., 2007) (Effect size f = 0.25; α err prob = 0.05; Power (1-β err prob) = 0.95). However, given the large effect size observed in previous studies (Brandewie & Zahorik, 2010, 2013; Srinivasan & Zahorik, 2012; Pavel Zahorik & Brandewie, 2016) using sample sizes = 9-16 listeners, we expected relatively large effect sizes with a sample ≥ 10. The variance of all variables was tested for normal distribution (Shapiro-Wilk test, Shapiro & Wilk, 1965).

Results

Human listeners can incorporate knowledge of a listening environment, specifically its reverberant characteristics, to improve speech understanding in noise (Brandewie & Zahorik, 2010, 2013; Srinivasan & Zahorik, 2012; Zahorik & Brandewie, 2016). To understand how this learning of sound environments is achieved, we recreated acoustic characteristics of real rooms using an array of loudspeakers located in an anechoic chamber (Figure 1A) and assessed listeners’ performance in a speech-in-noise task using sentences from the Coordinate Response Measure (CRM) corpus—“Ready |Callsign| go to |Color| |Number| now” (Figure 1B). We employed six environments, Figure 1A, that varied from fully anechoic to a highly reverberant underground car park with RT_60iso = 2.42 s (RT₆₀s according to ISO-3382 guidelines (ISO, 2009).

The CRM corpus is commonly used to quantify listening performance in noisy or cluttered environments (Bolia et al., 2000). Listeners reported the |Color| (one of four monosyllabic choices, ‘red’, ‘white’, ‘blue’, or ‘green’) and the |Number| (the English digits between ‘one’ and ‘eight’) they heard for sentences of varying duration where all, some, or none of the preceding sentence (the ‘carrier phrase length’) before ‘|Color| |Number|’ was included (Figure 1B). Importantly, words in the CRM phrase preceding ‘|Color| |Number|’ are uninformative as to the color and number (Brandewie & Zahorik, 2010, 2013). CRM sentences—complete or partial (see Methods and Figure 1B)—were presented as if originating from in front of a participant, whilst a randomly generated white noise was presented from a source 90° to their left. Listeners’ abilities to recognize |Color| and |Number| from whole or partial CRM sentences were assessed. Although sentences varied in duration from trial to trial, they always contained the element ‘|Color| |Number| now’ embedded in noise and convolved with the impulse response of the real rooms (Figure 1C). Sentence length varied by adjusting the duration of the preceding ‘carrier phrase’ (CP; Figure 1B-C), which was always constructed from the same CRM sentence embedded in noise and convolved with the same impulse response as the remainder of the phrase. Thus, for CP0, listeners heard only ‘|Color| |Number| now’, CP1—‘go to |Color| |Number| now’, CP2—‘|Callsign| go to |Color| |Number| now’, CP3—‘Ready |Callsign| go to |Color| |Number| now’.

After each phrase was presented, participants verbally reported the |Color| and |Number| they heard to the experimenter and performance was assessed based on keywords correctly identified. CRM phrases of varying length were spoken by one of 6 talkers (3 female, 3 male) and the different environments were presented in a pseudorandom order to avoid the same environment being presented in consecutive trials (e.g., Figure 1C). Our initial assessment, Figure 1D, examined listening performance in three environments: a Lecture Room (RT_60iso = 0.45 s), an Open-Plan Office (RT_60iso = 0.96 s), and an Underground Car Park (RT_60iso = 2.42 s). We confirmed, in 22 listeners, that understanding speech in background noise depends on the reverberant characteristics of the listening spaces from which impulse responses were obtained (Figure 1D (expanded datasets) and Figure 2A (specific comparisons between environments)). Overall, listeners performed better (quantified in terms of hit rate for reporting the correct |Color| and |Number|) in the Lecture Room—the least reverberant of the 3 environments (Figure 1D and 2A)—with a significant main effect of room [rANOVA: F (2,42) = 76.75, p<0.001, = 0.79]. Bonferroni-corrected post-hoc pairwise comparisons demonstrated that performance in the Lecture Room was significantly better than in the Open-Plan Office [mean difference = 0.41, p < 0.001] and Underground Car Park [mean difference = 1.05, p < 0.001], (Figure 2A). Performance was also significantly better in the Open-Plan Office when compared to the Underground Car Park [mean difference = 0.65, p < 0.001], Figure 1D and Figure 2A.

Figure 2.

Statistical learning of reverberant environments occurs over long and short-time courses

Statistical learning likely occurs over different time courses subject to a range of possible brain mechanisms controlling or modulating the different cadences over which learning emerges (Robinson et al., 2016; Simpson et al., 2014). We sought to distinguish short-term from longer-term learning by assessing how prior exposure to the listening environment benefits word recall over multiple time courses. The design of our paradigm—with talker, length of carrier phrase, target words |Color| & |Number| and reverberation time (listening environment) constituting random variables—means that the initial phase of learning may also be highly variable; listeners, idiosyncratically, likely experience very different parameters over the first few trials, making it difficult to assess the rate at which learning accumulates over these early epochs, and we indeed found this to be the case (see Supplemental Figure 1).

To characterize behavioural response trajectories, we fitted data with single and double exponential functions. Double exponentials were fitted to the full time course (Supp. Fig 1 and 2). Single exponentials were fitted to both the full time course (Supp. Fig. 1) and a truncated version excluding the first 10 points (∼14 s) to mitigate initial variability (Supp. Fig. 2). AIC (Akaike Information Criterion) comparisons heavily favoured the double exponential model for the full time course (mean ΔAIC Double Vs Single (fitted to full time course) = −61.41 ± 7.13), providing a better fit for ≥ 20/22 subjects in all three environments. When compared against the truncated single-exponential fit, the double exponential model still produced smaller AIC values on average (mean ΔAIC Double Vs Single (fitted to truncated timecourse) = −21.55 ± 5.90), and remained the preferred model for approximately half the subjects. Crucially, the double exponential model was preferred not only for its automated nature (requiring no manual truncation) but also for the magnitude of improvement: when the single model was superior, the advantage was marginal (ΔAIC = 9.7 ± 1.4), whereas the double model’s advantage was substantial (ΔAIC = 52.8 ± 8.9). We therefore used the double exponential fit for further analysis.

Using these fittings we assessed the ‘learning settling time (LST)’—defined here as the time at which participants achieved stable performance in each environment and quantified as the time point at which performance stabilised to ± 10% of the Final Hit Rate (FHR). For the 22 listeners, LST (Figure 2B; Supplemental Figure 2A-C) was similar across all rooms (a total of 180 trials per room and average trial length of 1.5s): Lecture Room: [45.45 ± 41.70 s]; Open-Plan Office: [58.46 ± 32.34 s] and Car Park: [64.52 ± 34.47 s], with a one-way ANOVA revealing no statistical differences in LST for the three reverberant environments [F(2,42)=2.33, p=0.11, =0.10] suggesting listeners learned/adapted to these environments at the same rate.

We next wondered whether the FHR achieved by a participant within an environment was related to the time point at which LST was reached. To this end, a Pearson’s correlation analysis revealed FHR and the timepoint at which stable performance was achieved were negatively correlated for the Lecture Room: r₍₂₂₎ = −0.66, p < 0.001, 95% CI [−0.86, −0.31] and Open Plan Office r₍₂₂₎ =-0.50, p = 0.02, 95% CI [−0.74, −0.20], but not for the more-highly reverberant Car Park r₍₂₂₎ = −0.08, p = 0.73, 95% CI [−0.44, 0.35], Figure 2C. This analysis suggests that the earlier in time a listener achieves asymptotic performance (i.e., the time point defined as global learning)—presumably by accumulating knowledge about these environments over time—the higher their performance (defined by FHR) in the task, at least in the two less-reverberant environments assessed here.

We next analysed whether speech understanding—quantified in terms of hit rates—improves as the length of the ‘carrier phrase’ was increased (Figure 2D). Consistent with Brandewie and Zahorik (2010; 2013), we found a significant main effect of length of ‘carrier phrase’: [rANOVA: F (3,63) =26.59, p<0.001, =0.56]. Bonferroni-corrected post-hoc pairwise comparisons revealed a significant effect for most carrier phrase lengths (Supplemental Table 1) other than between the two longest, CP2 vs. CP3. This plateau in performance between CP2 and CP3 suggests an upper limit in the ability to exploit/accumulate information in reverberant listening environments with increasing sound duration, consistent with Brandewie and Zahorik’s (Brandewie & Zahorik, 2013) observation of a plateau (and a decline in some reverberant environments) in listening performance with increasing length of carrier phrase, and the existence of an upper limit of listeners to exploit prior exposure to sound environments to benefit listening (Hicks & McDermott, 2024; McDermott et al., 2013; McWalter & McDermott, 2018).

We hypothesized that if prior exposure to the statistics of reverberant rooms arises from an increase in the length of the carrier phrase, then performance to |Number| will always be better than to |Color| for the shortest carrier phrase (CP0: ‘|Color| |Number| now’; Figure 2E) as |Number| is subject to a longer preceding phrase than |Color|. Assessed in terms of reporting |Color| and |Number| alone, listeners performed significantly better (i.e., showed greater sensitivity) for |Number| compared to |Color|: main effect of target word: [rANOVA F (1,21) =48.79, p<0.001, =0.70], [mean difference = 0.38, p < 0.001] despite there being twice as many (eight compared to four) possibilities. Moreover, a significant interaction ‘carrier phrase length’ x ‘target word’ was observed: [rANOVA F (1,21) =4.33, p=0.008, =0.17]. Post-hoc pairwise comparisons (see Supplemental Table 2) indicate that for all carrier phrases except CP3, performance to |Number| was significantly higher than for |Color|. This result also suggests that a roll-over effect i.e., a lack or limit to the improvement in performance is evident for carrier phrases longer than CP2 across environments (Brandewie & Zahorik, 2013; McWalter & McDermott, 2018).

A potential explanation for poorer performance in reporting |Color| relative to |Number| for the shortest carrier phrase (CP0) is the impact of the utterance of |Color|, namely its immediate appearance at the start of the phrase i.e., the impact of “order/certainty or predictability” in statistical learning (Conway et al., 2010; Daikoku & Yumoto, 2023). One way to determine whether |Color| presented in the context of the CP0 is at all subject to statistical learning (and is therefore not solely reliant on the short-term accumulation of information within a CP0 trial; see Figure 3G) is to assess whether performance for |Color| for the shortest carrier phrase (CP0) improves from the longer-term accumulation of knowledge i.e., the process of meta-adaptation (Robinson et al., 2016) in which adaptation to short-term statistics improves with repeated exposure to those statistics (after 8 exposures). Specifically, we tested the hypothesis that, independent of the environment, performance for |Color| in later steady trials (here, trials 9-10) of the shortest carrier phrase, CP0, are better compared to initial (1-2) trials (Figure 2F). Across the total of 360 trials (across carrier phrase lengths and environments), CP0 initial trials occurred early (trial 6±4), while steady CP0 trials (9th–10th occurrences) did not emerge until trial 38±7. If this hypothesis is supported, it suggests performance for |Color| for CP0 benefits from meta-adaptive information conveyed in later trials as knowledge about the global structure of the environment accumulates over time.

Figure 3.

Consistent with this hypothesis, a Wilcoxon signed rank test revealed significant better performance (n=22, Z= −2.16, p=0.03, medium effect size (r=0.46)) for |Color| for steady trials (9-10; assumed to reflect a meta-adaptive state; Robinson et al., 2016) compared to initial trials (1-2; i.e., short-term adaptation). Interestingly, in anechoic conditions i.e., in the absence of reverberation, this meta-adaptive process—the expected improvement in performance between initial and steady trials—was not observed: Wilcoxon signed rank test [n=10, Z= −0.71, p=0.48, small effect size (r=0.23)], with Initial trials appearing also early in the task: 6±3, whereas steady trials did not appear later until trial 36±8. This suggests that performance for |Color| conveyed in the shortest carrier phrase, CP0, improves over time even in the absence of immediate information in the form of any preceding carrier phrase, with knowledge of the statistical/acoustical properties of environments accumulating over the course of the experimental task.

Improvements in performance are explained by exposure to the environment not talker idiosyncrasies

Normal-hearing listeners are reported to understand speech slightly better when listening to male talkers in a mixture of male and female talkers (Larsby et al., 2015), and quickly adapt to talkers’ idiosyncrasies such as non-native accented speech (Idemaru & Holt, 2011, 2014; Liu & Holt, 2015). Here, we wondered if exposure to the different talkers (3 female and 3 male) could explain the improvement in speech as the length of the carrier phrase was increased in the 22 listeners (Figure 2D). We performed a rANOVA with the rooms, carrier length and talker identity as within-subject’s factor. Despite listening performance to some talkers (Supplemental Figure 3A) appearing better than for others [main effect of talkers: [rANOVA: F (5,105) = 27.19, p<0.001, =0.56], i.e., significantly worse performance for Talker 1 (male) and Talker 6 (female), see Supplemental Table 3 for details, overall differences in performance due to a specific talker did not explain the benefit to speech understanding as the length of the carrier phrase was increased in each environment (i.e., lack of significant interaction: or ‘talker’ x ‘carrier’ x ‘room’: F (30,630) = 0.73, p=0.85, =0.034]), Supplemental Figure 3 for details.

To explore further the possibility of any talker-specific effects on performance, we assessed the potential benefit of experiencing the same talker on consecutive trials across an experiment, hypothesizing that, independent of room characteristics or length of carrier phrase, experiencing the same talker on consecutive trials would provide a benefit to listening performance. Across all our participants, a total of 1262, 217, and 40 trials were identified as having two, three, or four consecutive trials in which the same talker appeared (see Supplemental Figure 4). Despite the possibility that sustained experience of the same talker might lead to improved listening performance, a Wilcoxon signed rank test revealed no significant differences in performance between trial 1 vs. 2 consecutive/same talker trials (n=1262, Z= −0.42, p=0.68, small effect size (r= 0.01)), trial 1 vs. 3 consecutive/same talker trials (n=217, Z= −0.17, p=0.87, small effect size (r= 0.01)) or trial 1 vs. 4 consecutive/same talker trials (n=40, Z= −0.159, p=0.11, small effect size (r= 0.02)). Our data suggest that listeners do not use talker identity, at least in the task reported here, to benefit their speech-in-noise understanding.

We also explored the extent to which a talker and/or the task itself could be learned by analysing improvements in performance with increasing exposure to carrier phrases and talkers in the absence of reverberation i.e., under anechoic conditions. In a follow up experiment we assessed performance (similarly to our 3 initial rooms (Brandewie & Zahorik, 2013)) but swapping the Lecture Room with an Anechoic room in 10 naïve listeners. Here, we isolated only the Hit Rate in the Anechoic room and performed an rANOVA with only carrier phrase length as the within-subject’s factor. Although a significant rANOVA was observed: F (3,27) = 9.10, p=0.007, =0.57; post-hoc pairwise comparisons, with Bonferroni corrections, revealed that only a significant improvement in performance was observed between CP0 and CP2 [mean difference = 15.67, p = 0.02; see Supplemental Table 4], suggesting that in anechoic conditions very little improvement in performance is achieved by learning the talker/noise/location/task as length of carrier phrase increases. These data support us employing a listening task with high ecological relevance—comprehending speech—to demonstrate the potential benefits of learning background acoustic features to leverage listening performance without requiring listeners to attend to or report features related to the statistics of presumed background sounds per se (e.g., Agus et al., 2014, McWalter & McDermott 2018, Bianco et al., 2020).

TMS disrupts long- but not short-term adaptation to an environment’s reverberation profile

The capacity for learning the statistical structure of acoustic environments and use this knowledge to better understand speech embedded in background noise, suggests a real-world benefit to this form of learned listening. To determine possible brain mechanisms underlying this ability, we reversibly impaired dorsolateral prefrontal cortex (dlPFC, Figure 3A) —a brain region implicated in listening performance in noise—using repetitive transcranial magnetic stimulation (rTMS) and then assessed the ability of listeners to recall |Color| and |Number| in our modified CRM sentences. The procedure began with TMS manipulation, and although the behavioural task lasted 45 minutes, the inhibitory effects of TMS extended for at least 60 minutes post-stimulation (Gamboa et al., 2010; Hoogendam et al., 2010; Huang et al., 2005; Romero et al., 2022). Specifically, 10 naïve, normal-hearing listeners were subjected to ‘real’ TMS—continuous theta burst on dlPFC for 40s on each side—and then transferred to the anechoic chamber, where they were presented sequences of CRM phrases of varying phrase length in background noise convolved with one of the three listening environments (Lecture Room, Open-Plan Office, and Underground Car Park) as before. A second, control, group of 11 naïve participants underwent ‘sham’ TMS stimulation in which an otherwise-identical TMS procedure was performed with a ‘sham’ TMS coil. All participants were naïve to differences in the TMS procedure as well as to the listening task; indeed, as with the experimental group, participants in the ‘sham’ group experienced a short period of single-pulse TMS stimulation to obtain motor thresholds, followed by the ‘sham’ TMS with the stimulator positioned over dlPFC. This process familiarised all participants with the procedure and the influence of TMS in generating involuntary finger movements through direct stimulation of motor cortex. The ‘sham’ procedure was designed to maintain participant blinding, such that participants in the ‘sham’ group would not know whether the subsequent speech-recall task followed active or ‘sham’ stimulation.

Given the potential placebo effects of a ‘sham’ TMS stimulation, we first tested whether our sample of 11 ‘sham’ TMS participants exhibited similar behavioural performance to the larger sample of 22 participants who had not been exposed to any TMS manipulation. A mixed ANOVA with a between-group factor of Condition and within-group factors of room and CP length confirmed that performance in these two populations was comparable, with no significant main effect of TMS conditions observed (‘sham’ vs. no exposure to TMS): [F (1,31) =0.01, p=0.91, = 0.00], and no significant interaction Condition x CP length was observed: [F (3,93) =0.48, p=0.69, = 0.01]; confirming that participants experiencing ‘sham’ TMS did not perform significantly differently from the ‘no exposure to TMS’ population.

We first assessed learning over the time-course of the task itself—‘Learning Settling Time—as the cumulative mean hit rate for listeners reporting |Color| and |Number|, fitted for each TMS condition and environment with a two-phase association model (Figure 3B and Supplemental Figure 5 and 6). Our metric for ‘Learning Settling Time’ was achieved for Lecture room at: ‘sham’ TMS [40.22 ± 34.17 s] and ‘real’ TMS [64.86 ± 17.08 s]; for Open-Plan Office: ‘sham’ [51.50 ± 33.26 s] and ‘real’ TMS [67.32 ± 37.04 s]; and for Car Park: ‘sham’ [64 ± 39.02 s] and ‘real’ TMS [69.80 ± 37.31 s]. A Mixed ANOVA between ‘sham’ TMS and ‘real’ TMS participants revealed a between-subject’s effect of TMS Conditions: [F (1,19) = 184.42, p<0.001, = 0.91], suggesting participants always achieved an early LST under ‘sham’ stimulation [51.91 ± 36.11s] compared to longer LST under ‘real TMS’ [71.76 ± 36.09 s]. No significant Rooms or Rooms x TMS condition effects were observed.

Pearson’s correlation analysis (collapsed across all environments) revealed a negative correlation between FHR and ‘Learning Settling Time’ for ‘sham’ TMS listeners (see Figure 3C): [r₍₃₃₎ =-0.45, p = 0.01, 95% CI [−0.70, −0.11] but not for ‘real’ TMS listeners: [r₍₃₀₎ = −0.21, p = 0.27, 95% CI [−0.53, 0.16]. Specifically, participants undergoing ‘sham’ TMS who achieved stable performance relatively early in the task and maintained this level of performance throughout. Relative delay in attaining stable performance was associated with a lower FHR, with the time at which early LST is achieved predicting overall task performance. This relationship was not evident in listeners subject to ‘real’ TMS, where the time to reach a stable performance was not predictive of the magnitude of Final Hit Rate. Under TMS manipulation of dlPFC, LST had no predictive value, indicating that task performance was less stable and reliable for participants exposed to TMS.

We next employed a mixed ANOVA to compare performance (d’), (see Figure 3D)—of ‘sham’ participants with those who received ‘real’ TMS, and observed a significant between-subject’s effect of TMS condition: ANOVA [F (1,17) = 6.18, p=0.02, = 0.28], where ‘sham’ participants showed significantly better abilities to recall |Color| and |Number| compared to participants subjected to ‘real’ TMS [post-hoc pairwise comparisons with Bonferroni corrections: [mean difference = 0.46]. In addition, a significant interaction ‘TMS condition’ x ‘carrier phrase length’ was observed, Figure 3E: [F (3,51) = 6.72, p<0.001, = 0.28]. A post-hoc pairwise comparison with Bonferroni corrections showed that performance in the CP0 condition was not statistically different between ‘sham’ and ‘real’ TMS conditions (mean difference = 0.14, p = 0.49), however performance was better for ‘sham’ compared to ‘real’ TMS for CP1 (mean difference = 0.47, p = 0.04), CP2 (mean difference = 0.51, p = 0.02), and CP3 (mean difference = 0.71, p < 0.001). Listeners exposed to ‘sham’ TMS retained the improvements of performance as the length of carrier phrase was increased whereas those listeners receiving ‘real’ TMS—in which activity in dlFPC is presumably impaired—appeared unable to accumulate over time knowledge of the sound environment.

The different time courses over which statistical learning might occur suggests the involvement of multiple neural mechanisms and brain circuits (Anderson & Malmierca, 2013; Antunes & Malmierca, 2011; Robinson et al., 2016), with different cadences of learning potentially controlled, or at least modulated, by different brain centres. The time-course of midbrain neurons to adapt to different sound environments, for example, is slowed, and the capacity for retaining a ‘memory’ of those sound environments disappears, when cortex is inactivated through cooling (Robinson et al., 2016). This suggests feed-forward and feed-back mechanisms, with their own time-courses or time-constants, contribute to performance, including the rate at which sound environments are learned. We specifically wondered if ‘real’ TMS had a detrimental effect on short-term adaptation i.e., the observed advantage in performance for |Number| compared to |Color|, for the shortest carrier phrase CP0 (Figure 2E and 3F). Mixed ANOVA revealed a main effect of ‘target word’, where performance for |Number| was always significantly better than for |Color| for all the environments explored and across TMS conditions [F (1,17) = 31.99, p<0.001, = 0.65]. However, no interaction between ‘TMS conditions’ and ‘target word’ was observed, indicating that in both ‘real’ and ‘sham’ TMS conditions, the same trend of better performance to |Number| compared to |Color| was observed (Figure 3F).

To dissociate specifically short-term adaptation from longer-term meta-adaptation, we compared performance (Hit Rate) to |Number| for initial trials (1-2), for CP0 only, as performance for this length of carrier phrase should be influenced only by short-term accumulation of information (i.e., within trial; see Figure 3G). Note that for both groups, CP0 ‘initial’ trials occurred early in the task (360 total trials): trial 5±4 for ‘sham’ TMS and trial 10±9 for ‘real’ TMS. However, ‘steady’ trials (9th–10th instances) did not emerge until trial 34± 6 and 38± 12, respectively.

A non-parametric Wilcoxon signed rank test revealed no significant differences in |Number| performance for initial trials between ‘sham’ and ‘real’ TMS exposed listeners (n=10, Z= −0.28, p=0.78, with a small effect size (r= 0.01); Figure 3H), suggesting that rapid learning of the statistical structure of the reverberant environment—in the order of a few hundreds of milliseconds—is resistant to the effects of ‘real’ TMS applied to dlPFC. To determine how much meta-adaptative improvement in performance could be disrupted by applying ‘real’ TMS to dlPFC, we then compared initial trials (1-2) and steady trials (9-10) for CP0—our proxy for meta-adaptation (Figure 3G). We tested the hypothesis that disruption of dlPFC would affect late (meta-) but not early adaptation. To this end, we expected no differences between performance in initial trials between ‘real’ and ‘sham’ TMS (as initial trials are influenced only by short-term adaptation), and a relative lack of improvement in performance in later, steady trials (meta-adaptation influenced) in ‘real’ compared to ‘sham’ TMS. Consistent with our hypothesis, a Wilcoxon signed rank test revealed no significant differences in performance for initial trials between ‘sham’ and ‘real’ TMS exposed listeners (n=10, Z= −0.21, p=0.83, with a small effect size (r = 0.01), but improved performance for steady compared to initial trials (n=10, Z=-2.06, p=0.04, large effect size (r=0.65)) for ‘sham’ TMS only (Figure 3I). These data suggest that performance in |Color| for the shortest carrier phrase, CP0, was disrupted due to impaired accumulation of knowledge about the environments encountered. In turn, performance under ‘TMS’ in steady trials (presumed to be influenced by meta-adaptation) was reminiscent of performance observed in initial trials where only immediate knowledge could be used to improve performance. This is reminiscent also of adaptation to sound environments reported in vivo (Dean et al., 2005, 2008; Robinson et al., 2016; Wen et al., 2009), where interrupting efferent feedback by cortical cooling impairs the capacity of midbrain neurons to learn the statistical structure of sound environments; though neurons adapt to the short-term statistical structure of a sound environment each time they are exposed to it, they fail to demonstrate the acceleration of adaptation and improvement in (neural) discrimination performance as the same environment is re-encountered, adapting to it only as if exposed to it the first time.

Statistical learning of room acoustics is tuned to universally experienced reverberation times

Reverberation is a common feature of many acoustic environments—natural and built (Traer & McDermott, 2016). Interestingly, Zahorik and Brandewie (2013) reported that improvements in speech understanding when a ‘simulated room’ is repeatedly encountered were best when listeners experienced moderately reverberant environments. Specifically, we hypothesized that the ability to understand speech in noise depends on exposure to specific values of RT₆₀ across our experimental paradigm, including those in the range commonly experienced by human listeners in natural and built environments (Traer & McDermott, 2016). To test this hypothesis, we recruited a new population of naïve participants with no previous exposure to our experimental paradigm and assessed their ability to understand speech in noise using the same CRM corpus as before, but in different combinations of acoustic environments i.e., room contexts defined by their RT₆₀s.

We first tested the ability of a new sample of 10 naïve participants to recall |Color| and |Number| in Open-Plan Office and Underground Car Park as before, but with the Lecture Room (RT_60iso = 0.42 s) swapped out for a more Highly Reflectant elevator lobby (Figure 4) with an RT_60iso of 1.55 s—i.e., between that of the Open-Plan Office (0.96s) and the Underground Car Park (2.42s). We therefore changed the ‘room-context’ in which Open-Plan Office and Underground Car Park were learned. When contrasting the performance (Final Hit Rate) in an ANOVA considering performance only in the common rooms (Open-Plan Office and Underground Car Park) of these 10 naïve participants vs. the 11 subjects randomly selected from the initial 22 participants sample, we found that speech understanding in Open-Plan Office and Underground Car Park was significantly better when the room acoustics of these environments were learned in the context of the Lecture Room than in the context of the Highly Reflectant Room [mean difference = 14.8, F (1,41) = 30.72, p<0.001, = 0.45].

Figure 4.

Wondering whether the ‘room-context’ effect was determined by task difficulty i.e., the more-highly reverberant lobby being intrinsically more difficult for listening than the Lecture Room [mean difference = 35.75, t ₍₉₎ =18.72, p < 0.001, Cohen’s d = 5.92]. We recruited a further new sample of 11 naïve participants and performed the same task in which they were exposed to another combination of three rooms, here with the Lecture Room swapped for an Anechoic Room. Anechoic spaces—rooms whose walls are treated to completely absorb reflected sound energy—have been extensively reported to aid speech understanding, especially in background noise. To test this hypothesis directly, we recruited a further sample of 10 naïve participants and compared their speech-in-noise performance in a combination of an Anechoic Room, Open-Plan Office and Underground Car Park, with the performance of 11 subjects randomly selected from the initial 22 participants’ sample. Although task difficulty might be expected to be at its lowest i.e., easiest, in the less-reverberant Anechoic Room, this was not the case, performance in the Anechoic room was poorer compared to Lecture Room [mean difference = 11.08, t ₍₉₎ =2.66, p = 0.013, Cohen’s d = 0.84] (Figure 4 A-B). Additionally, we found that performance in the common rooms: Open-Plan Office and Underground Car Park, was significantly better when these environments were again learned in the context of the Lecture Room: [mean difference = 9.7, F (1,41) = 13.24, p<0.001, = 0.26].

Although reverberation might be considered detrimental to speech-in-noise performance, short-term benefits of more, compared to less, room reverberation for speech understanding have, in fact, been reported, including for the task we report here (Brandewie & Zahorik, 2010, 2013). Furthermore, it is likely that listening performance in noise is indeed better when listeners have access to more commonly experienced, or ethologically realistic, levels of reverberation. We tested this hypothesis by recruiting a new sample of naïve listeners and swapping again the Lecture room with a Living Room with a shorter RT_60iso (0.33 s). When comparing performance between Lecture and Living Room no significant differences were observed: [mean difference = 7.04, t ₍₁₀₎ =1.69, p = 0.061, Cohen’s d = 0.51] (Figure 4 A-B). However, significantly better performance was observed in the common rooms: Open-Plan Office and Underground Car Park when these environments are learned in the context of the Lecture room: [mean difference = 9.7, F (1,43) = 5.79, p=0.21, = 0.13]. These results suggest that when certain reverberant characteristics are encountered e.g., Lecture Room, they might unlock a perceptual advantage in understanding speech in harder/more reverberant environments such as Open-Plan Office and Indoor Car Park.

Seeking to explain why some rooms might be better than others in aiding speech understanding, we recalculated reverberation times for our 5 reverberant rooms according to the method of Traer and McDermott (2016) where RT_60traer equals the median RT₆₀ measured in 31 sub-bands with centre frequencies between 80 Hz and 10 kHz (see Methods). We then compared these with the values collected for a wide range (n= 271) of built and natural environments by the same authors (Figure 4C-D. RT_60traers for 5 rooms conditions and Traer’s data, plotted as log₁₀ RT_60traer for visualisation). In particular, the RT_60traer of the Lecture Room, at 0.49 s, lies extremely close to the median and mean RT_60traer values of the built environments [0.42 s and 0.50 s respectively generated by the skewed distribution (n= 199), Figure 4C]. This suggests that superior performance in understanding speech understanding in noise in the Lecture Room—and the positive impact on speech understanding when the Lecture Room is included in the three-room listening task—is related to its reverberant characteristics being commonly encountered in everyday listening situations. Notably, the mean and median RT_60traers of the recorded natural environments were much lower (0.12 s and 0.16 s, respectively [n = 72], Figure 4C) than the RT_60traers of the Lecture Room. This suggests that brain mechanisms contributing to effective speech understanding in reverberant environments might have adapted to the range of environments experienced over some longer time course than we assessed here.

Discussion

We assessed the ability of listeners to understand speech embedded in background noise and convolved with the room impulse responses (RIRs) of different indoor environments defined by their reverberation decay time, or RT₆₀—the time taken for reverberant sound energy to decay 60 decibels (dB). We found speech understanding improved with repeated exposure to an environment, a form of learning that was impaired following continuous, bilateral theta-burst transcranial magnetic stimulation (TMS) of dorsolateral prefrontal cortex (dlPFC). Specifically, we observed rapid learning i.e., an improvement in speech understanding over the first few seconds of room exposure, likely due to listeners learning the acoustic reverberation—the only variable held constant across trials for a given environment. This learning, on the timescale of several seconds, was impaired by TMS, whilst learning at shorter or longer timescales was not, suggesting that TMS applied to dlPFC specifically disrupted learning of the reverberant characteristics of an environment. Listeners also showed better listening performance in moderately reverberant environments, and this performance was transferable between different acoustic environments. Specifically, the ability correctly to report keywords spoken in environments with the more extreme—higher—RIRs was best when these environments were encountered in experimental blocks also containing the moderately reverberant environments. This transference suggests an ethological tuning to more commonly encountered environments when learning acoustic backgrounds.

A role for dlPFC in statistical learning of room acoustics

Inactivation of dlPFC using theta-burst TMS reduced performance in our speech-in-noise task, assessed in terms of the hit rate for |Color| and |Number| in CRM phrases as a function of duration of exposure to a sound environment. Notably, however, TMS did not impair listening performance for the shortest-duration phrase, ‘|Color| |Number| now’. For this duration of carrier phrase, performance i.e., the tendency to report more accurately |Number| compared to |Color|—despite |Number| having twice as many options as |Color|—was unaffected by TMS stimulation of dlPFC. The robustness of listening performance to the shortest carrier phrase suggests that the ability to hear out speech in background noise and potentially to learn this over time in complex acoustic environments does not rely solely on cortical feedback from dlPFC. The ability to leverage performance on |Number| by exposure to |Color| presented in background noise convolved with the room impulse response is evident even when the function of dlPFC is (presumably) impaired. It is the case, however, that the rate of learning—i.e., how rapidly performance improved with increasing time of exposure to an acoustic environment—was influenced when dlPFC was impaired. Under the influence of prior TMS stimulation, listeners were less able, and less reliable, when learning the acoustic background features of an environment to enhance their speech understanding, particularly in the more challenging environments of the Underground Car Park and Open-Plan Office.

Dense connections between dlPFC and the sensory cortices provide the conduit for prediction-based, top-down regulation (Morrone, 2010). These model-based predictions rely on learned templates of sensory environments (Alexander & Brown, 2018) that are critical for making inferences under situations of sensory uncertainty, such as those encountered in noisy and challenging listening environments (Bartolo & Averbeck, 2021). Dorsolateral prefrontal cortex can influence auditory processing by means of direct connections to auditory cortex (Hackett et al., 1999; Plakke & Romanski, 2014)—see Figure 3Ai—and to the auditory thalamic reticular nucleus, a key modulator of auditory thalamo-cortical loops (Zikopoulos & Barbas, 2006). In return, dlPFC receives projections from primary and secondary auditory cortex (Barbas & Pandya, 1987; Barbas & Pandya, 1991; Goldman-Rakic & Schwartz, 1982; Pandya & Barnes, 2019; Petrides & Pandya, 2002), indicating that a reciprocal auditory-to-prefrontal ‘listening loop’ exists to support hierarchical predictions and prediction-error feedback. These ‘listening loops’ could potentially extend to cortical efferent feedback (Blackwell et al., 2020; McAlpine & de Hoz, 2023) and modulate the function of the inner-ear sensitivity in attended speech-in-noise tasks (de Boer & Thornton, 2008; Garinis et al., 2011; Giraud et al., 1997; Mishra & Lutman, 2014; Hernández-Pérez et al., 2021).

The specific mechanism by which TMS-induced modulation of dlPFC impairs listening performance remains unknown. Neurostimulation studies targeting dlPFC during learning paradigms have generated conflicting directions of effect i.e., stimulation of dlPFC is reported to impair implicit learning (Nydam et al., 2018; Pascual-Leone et al., 1996) or enhance it (Ambrus et al., 2020). Ambrus and colleagues argued that impairment of dlPFC during implicit/statistical learning allows for increased engagement of a learning mechanism that is ‘model-free’ i.e., does not rely on inherent sensory representations and therefore takes longer to consolidate. In the present study, although the TMS protocol was designed to target dlPFC using the 10–20 EEG cap system, this approach does not permit precise stimulation of dlPFC (Jiang et al., 2025; Nikolin et al., 2019; Trapp et al., 2020) or its direct projections to the auditory cortex. Instead, stimulation likely influenced prefrontal regions producing diffuse downstream effects including the auditory cortex and other brain regions such as hippocampus (Bennett et al., 2011; Stillman et al., 2013), striatum (Albouy et al., 2015) and basal ganglia (Packard & Knowlton, 2002). We therefore speculate that the short periods over which learning is required in our task are subserved by a pre-frontally mediated, ‘model-based’ learning able to construct predictions ‘on the fly’ (Daw et al., 2005). We suggest, in our study, that disrupting a rapidly acting model-based form of learning supported by prefrontal regions, as part of the auditory-prefrontal ‘listening loop’, may lead to impaired learning of reverberant environments.

Dorsolateral prefrontal cortex is also implicated in several, high-level cognitive functions including multi-sensory integration (Fuster et al., 2000), executive functions such as inhibition and working memory (Castro-Meneses et al., 2016; Coltheart et al., 2018; Wang et al., 2015) and listening in noisy environments (Du et al., 2016). The impact of TMS on performance of our speech-in-noise task may be linked to the involvement of dlPFC in more generalized cognitive functions such as executive, memory, and attention processes. Dissociating the potential influence of TMS on such processes relative to a specific role in speech-in-noise understanding in noisy, reverberant environments would be an ideal next step. Specifically frontal regions have been linked to sensorimotor integration during speech-in-noise perception in young and older human participants (Du et al., 2016), suggesting that TMS might disrupt global functioning required for the analysis and understanding of speech.

Whilst not entirely unrelated, all participants completed a familiarization task preceding the main experiment, which consisted of 10 trials presented in anechoic conditions at 0 dB Signal-to-Noise Ratio (SNR). Participants exposed to either ‘real’ or ‘sham’ TMS underwent this task following cTBS procedures. Notably, all participants, including those subjected to cTBS, achieved 75-100% accuracy during the familiarization phase, with no statistical differences between TMS groups [mean difference=1.25, t_(1,9)=0.43, p=0.68, d=0.14], confirming their comprehension of the task and successful procedural learning (i.e. learning the task per se). In addition, speech understanding in the most challenging condition (CP0) was not disrupted following TMS manipulations, suggesting that the effect of TMS was specifically in terms of influencing the way these environments were learned over time. Nevertheless, we acknowledge that the primary task may have imposed greater cognitive demands, potentially requiring a more significant involvement of the dlPFC to meet such task requirements.

Moreover, we cannot rule out individual variability in the duration of TMS effects, nor differences in effect duration across anatomical sites—such as the motor cortex (Gamboa et al., 2010; Hoogendam et al., 2010; Huang et al., 2005; Romero et al., 2022) versus the dLPFC examined in our study. Continuous theta burst stimulation (cTBS), as used in this study, typically induces aftereffects lasting 20–50 minutes (Huang et al., 2005; Wischnewski & Schutter, 2015). While these effects are well established in the motor cortex, where motor-evoked potential changes can persist for up to one-hour, comparable durations of cortical modulation have also been reported in the prefrontal cortex (Taylor et al., 2024; Wagner et al., 2006). Specifically, inhibitory rTMS applied to the dorsolateral prefrontal cortex (dlPFC) has been shown to reduce cortical oxygenation levels—indicative of decreased neural activity—for at least 45 minutes (Tupak et al., 2013). Given that changes in cerebral haemoglobin concentration closely correspond to neuronal activation (Liao et al., 2013), these findings support the use of functional Near Infrared Spectroscopy (fNIRS) as an indirect but reliable method to infer TMS-induced neural modulation within this timescale. Although no objective post-stimulation measures were collected in the present study, we acknowledge this as a limitation and plan to incorporate such measures in future work to confirm the duration and magnitude of TMS effects more directly.

What is being learned in statistical learning of acoustic features?

The need to communicate in reverberant environments is common to daily life. It is well known that long reverberation times have detrimental effects on speech quality and intelligibility (Brandewie & Zahorik, 2010, 2013; Srinivasan & Zahorik, 2012; Pavel Zahorik & Brandewie, 2016) and that, specifically, reverberation blurs phoneme boundaries, increasing the extent to which similar words might be confused, e.g., ‘sir’/’stir’ (Watkins, 2005b; Watkins & Makin, 2007). However, with sufficient exposure to longer reverberation times in the form of a carrier phrase, similar levels of word identification to those achieved in minimal reverberation are observed (Watkins, 2005b; Watkins & Makin, 2007). Based on the premise that providing sufficient contextual information enables the auditory system to adapt and compensate for the detrimental effects of reverberation, Zahorik and colleagues (Brandewie & Zahorik, 2010, 2013; Srinivasan & Zahorik, 2012; Zahorik & Brandewie, 2016) and, here, ourselves, demonstrate that prior exposure to the reverberant characteristics of a room can enhance sentence understanding when that environment is re-encountered. Moreover, Zahorik and colleagues, and our own data (see Figure 4A), demonstrate that improvements in speech understanding are always greater in reverberant compared to anechoic, environments (Brandewie & Zahorik, 2010, 2013; Srinivasan & Zahorik, 2012), i.e., improved performance arises only through exposure to a reverberant environment rather than to the speech material per se.

Adaptation to continuous noise is a well-known phenomenon in the auditory system (Costalupes et al., 1984; Gibson et al., 1985; Phillips, 1985; Rees & Palmer, 1988) that could potentially also explain the speech improvements observed among carrier lengths. This is particularly relevant in our experiments because the masking noise preceded the onset of the speech material, potentially providing a window for noise-adaptation to occur (Ainsworth & Meyer, 1994; Ben-David et al., 2016). However, Zahorik and Brandewie reported similar effect sizes in speech enhancements when 1 s (Brandewie & Zahorik, 2010) or 150 ms (Brandewie & Zahorik, 2013) of white noise was presented prior to the carrier phrase (i.e., CP0—|Color| |Number|). From this, the authors concluded that noise alone did not convey sufficient information about the acoustic environment to elicit improvements in speech understanding.

Adult listeners can also acclimatise to speech acoustics that deviate from the norm or from long-term language regularities such as dialects and foreign accents, with sufficient exposure (Idemaru & Holt, 2011, 2014; Liu & Holt, 2015). Even if our participants were all Australian-English native speakers exposed to an American-English speech corpus—the CRM—some perceptual learning of individual talker’s idiosyncratic speech patterns might arise over the course of the task (Choi & Perrachione, 2019; Liu & Holt, 2015; Stilp, 2020). In addition, it has been reported that ‘target voice continuity’ i.e., the same talker within trials—similar to our design—can enhance the build-up of selective attention and improve listeners abilities to report correctly digits sequences in the presence of other talkers (Best et al., 2008). However, when the room build-up effect—the effect assessed here—is explored in an isolated anechoic space i.e., not intermingled with room acoustic of other reverberant spaces, there is little or no improvement in speech understanding with increasing exposure to the talker per se; i.e., in the form of an increasing length of carrier phrase (Brandewie & Zahorik, 2010, 2013). Interrupting the continuity of the room—its reverberation profile—rather than the continuity of the target talker, is the factor reported to disrupt significantly the improvement in speech understanding with increasing exposure (Brandewie & Zahorik, 2018). If listeners adapted to a talker in the course of our experiment, this occurred rapidly, within a few speech tokens (Cooke et al., 2022; Kakehi, 1992; Kato & Kakehi, 1988) and contributed little to the enhancement of speech understanding we observed in reverberant rooms.

A key feature of this study, one that distinguishes it from previous assessments of statistical learning of acoustic features in human listeners, is our use of an ethologically valid listening task—understanding speech in background noise while listeners implicitly learned background features. Given the perceptual biases inherent in sensory processing, it is notable that investigators have previously reported different noise tokens to be differently learnable (Agus et al., 2014; Daikhin et al., 2017), for example, and that textures must be carefully controlled to ensure listeners are not exploiting subtle spectro-temporal features in their judgments of statistical similarity (McDermott et al., 2013). The potential for listeners to hear out specific—potentially unique to them—spectro-temporal features of otherwise statistically identical sound tokens, or to make judgments on the regularity or similarity of tone sequences based on unique listening experiences (Barascud et al., 2016; Bianco et al., 2020), presents a potential confound for these studies. Our study countermands this problem by actively exploiting the propensity for human listeners to ascribe meaning to spectro-temporal fluctuations (here, speech) in acoustic waveforms (Brandewie & Zahorik, 2010, 2013). Though still targeting the learning of background acoustic features, it engages listeners in a highly relevant listening task, one for which humans have rapidly evolved—i.e., understanding speech in background noise. Listeners’ attention, therefore, was called away from the background acoustic features to the ethologically relevant foreground task of attending to human speech, void of semantics; additionally, length of phrase, talker, and |Callsign| were uninformative to |Color| and |Number| in our utterances, as were |Color| and |Number| to each other. This misdirection allows us to exploit speech as a ‘reporter’ or biomarker for statistical learning of background, more-abstract, acoustic features, and without the potential confounding factor of (individualised) perceptual bias. Further, we have demonstrated that prior exposure to ethologically relevant environments matters when learning less-commonly encountered acoustic scenes.

The impact on speech understanding of more extreme (longer RT₆₀s) environments is reminiscent of the detrimental effect of TMS, with lower listening performance. This suggests that learning the statistical structure of sound environments, to aid speech understanding in reverberant background noise, requires some longer form of memory (experience, developmental, or evolutionary). Brain circuits, therefore, might act to improve listening performance, operating best when at least some of the exposure to listening environments includes plausible, and commonly experienced, reverberation times (Traer & McDermott, 2016). When the acoustics of commonly encountered rooms were replaced with those of other environments having higher or lower amounts of reverberation, performance declined. Intriguingly, the most effective reverberation time we employed, at least in terms of its capacity to be learned and potentially from which learning could be transferred—the RT_60traer of 0.49 s of the small Lecture Room is very close to the median and mean RT_60traer of 0.42 and 0.50 s previously recorded for a wide range of built environments (indoors and outdoors). This suggests that statistical learning of acoustic background features might be tuned to ethologically relevant environments. Alternatively, built environments might be constructed to generate reverberation subjectively most suited to maximising speech-in-noise understanding, though we are unaware as to whether such a constraint is consciously applied in the design of listening spaces given contingencies such as the range and relative consistency and constancy of potential contents of any given space that influence its reverberant characteristics. Compared to built environments, however, the RT_60traers of natural environments were much lower than all the rooms we assessed, save for that of the anechoic room, which was suboptimal in terms of listening performance.

Neural adaptation as a contributing mechanism to the learning of room acoustics

Our data are consistent with improved performance in a relevant ‘foreground’ task emerging as the brain adapts to the statistics of background features of the listening environment. Adaptation to stimulus statistics has been proposed as a neurophysiological mechanism underlying selective suppression of background noise in auditory cortex (Fuglsang et al., 2017; Kell & McDermott, 2019; Khalighinejad et al., 2019; Mesgarani et al., 2014). More recently, it has been shown that spectro-temporal receptive fields of auditory-cortical neurons are sensitive to the RT₆₀s of reverberant noise i.e., a form of adaptation specific to reverberation and consistent with a de-reverberation of the environment encountered (Ivanov et al., 2022). Cortical adaptation to reverberation has been also reported in awake listeners (Fuglsang et al., 2017; Mesgarani et al., 2014) and to this end, our data suggest a role for top-down mechanisms in improving over time an attended task—recalling spoken words—in reverberant environments. It is therefore possible that goal-directed behaviours and the feedback the auditory cortex receives from areas such as dlPFC may contribute to fine-tuning the adaptation to reverberant environments.

Our data are also consistent with reports of midbrain auditory neurons recorded in vivo adapting to the statistical structure of evolving sound environments over the course of several hundred milliseconds (Dean et al., 2005, 2008) and this capacity for adaptation increases with repeated exposures to the same statistically structured environment. Importantly, whilst rapid adaptation is retained when descending cortical influences are disrupted (through cortical cooling), the longer-term learning effect—a speeding up of adaptation referred to as meta-adaptation—is abolished (Robinson et al., 2016). As with auditory neurons recorded in vivo (Bakay et al., 2018; Dean et al., 2005, 2008; Ivanov et al., 2022), it seems listeners might adapt to the statistical structure of a sound environment within a few hundred milliseconds of exposure to enhance performance in a listening task, an initial, rapid, learning phase impervious to inactivation of the dorsolateral prefrontal cortex by theta-burst TMS. The nested set of temporal sensitivities to room acoustics we observe is consistent with features of statistical learning that emerges from the level of the auditory nerve to primary cortex in vivo (Dean et al., 2005, 2008; Watkins & Barbour, 2008; Wen et al., 2009), and likely modulated by efferent influences that span the entire auditory pathway all the way to the sensory receptors of the inner ear (Hernández-Pérez et al., 2021; Perrot et al., 2006; Terreros & Delano, 2015) and even the mechanical sensitivity to sound of the eardrum and ossicles (middle-ear bones) (Gruters et al., 2018).

Dissociating auditory cortical feedback circuits to the midbrain impairs the capacity of midbrain neurons to ‘recall’ previous experience of that environment (Bajo et al., 2019; Robinson et al., 2016). Like the performance in our listening tasks, these neurons were still able to adapt rapidly (within hundreds of milliseconds) each time an environment was encountered as if for the first time, but showed no capacity to exploit a memory of that environment to improve neural coding (i.e., meta-adaptation) (Robinson et al., 2016). Meta-adaptation was consistent with neurons in lower brain centres adapting to the current sound environment, and those in higher brain centres learning the longer-term statistical structure of changing environments. Once higher brain centres learned the experienced environment, this information, conveyed to early brain centres, ensures they were ‘primed’ for coding an environment when it is re-encountered.

Inclusion and Ethics statement

This study was approved by the Human Research Ethics Committee of Macquarie University (ref: 5201833344874). Each participant signed a written informed consent form and was given a small financial remuneration for their time.

Supplementary material

Supplemental Table 1.

Supplemental Table 2.

Supplemental Table 3.

Supplemental Table 4.

Supplemental Table 5.

Supplemental Figure 1.

Supplemental Figure 2.

Supplemental Figure 3.

Supplemental Figure 4.

Supplemental Figure 5.

Supplemental Figure 6.

Supplemental Information.

Data availability

All data and related metadata were deposited in Figshare, an appropriate public repository (https://doi.org/10.25949/24295342). All custom code used for data collection and analysis will be available upon request.

Acknowledgements

The study was supported by the Australian Research Council (DP180102524 awarded to D.M, J.J.M.M, and P.F.S, and FL 160100108 awarded to D.M.). The authors would like to thank Jörg Bulcholz and Javier Badajoz-Davila for their assistance with the spatialization and sound field simulation of the acoustic stimuli. We thank Adam Weisser for his assistance in obtaining the room acoustic calculations for the actual rooms used in this study. We would also like to thank Kurt Shulver for his assistance during rTMS manipulations. We sincerely thank Yuranny Cabral-Calderin for her valuable insights and constructive feedback on improving this manuscript.

Additional information

Authors’ contributions (CReditT taxonomy: (McNutt et al., 2018))

Conceptualization: H.H-P., J.J.M.M., J.M-H., J.T., P.F.S. and D.M.

Data Curation: H.H-P., J.J.M.M., J.M-H., and D.M.

Formal Analysis: H.H-P., J.M-H. and J.T.

Funding Acquisition: H.H-P., J.J.M.M., P.F.S. and D.M.

Investigation: H.H-P and J.T.

Methodology: H.H-P., J.J.M.M., J.M-H., J.T., P.F.S. and D.M.

Project Administration: H.H-P. and P.F.S

Resources: H.H-P., J.J.M.M., J.M-H., J.T. and P.F.S.

Software: H.H-P., J.J.M.M., J.M-H. and J.T.

Supervision: H.H-P., J.J.M.M., P.F.S. and D.M.

Validation: H.H-P., J.J.M.M., J.M-H., J.T., and D.M.

Visualization, H.H-P., J.M-H.,J.T. and D.M

Writing – Original Draft Preparation: H.H-P., J.M-H., J.T., P.F.S. and D.M.

Writing – Review & Editing: H.H-P., J.J.M.M., J.M-H., J.T., P.F.S and D.M.

Funding

Department of Education and Training | Australian Research Council (ARC) (DP180102524)

• David McAlpine

• Paul Sowman

• Jessica JM Monaghan

Department of Education and Training | Australian Research Council (ARC) (FL 160100108)

• David McAlpine