Many voices are easier to ignore than one: Equivalent distractibility in autistic and nonautistic adults

Many voices are easier to ignore than one: Equivalent distractibility in autistic and nonautistic adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Many voices are easier to ignore than one: Equivalent distractibility in autistic and nonautistic adults Lejla Alikadic, Jan Philipp Röer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9447894/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract There is ample evidence that autistic adults respond differently to task-irrelevant background sound compared to nonautistic adults. Theories can be categorized according to the processing level at which this difference should occur. To test these theories, we contrasted two classic auditory distraction effects in autistic and nonautistic adults: (1) The irrelevant babble effect refers to the greater disruption by single- and dual-channel speech compared to multi-channel speech, (2) the intensity effect to greater disruption by high-intensity compared to low-intensity sound. Four prominent prediction-based autism theories predict a more pronounced irrelevant babble effect for autistic individuals due to weak priors (Hypo Priors Hypothesis), volatility fragmentation (Aberrant Precision Account of Autism), prediction delays (Predictive Impairment in Autism Hypothesis), and enhanced prediction error weighting (High, Inflexible Precision of Prediction Errors in Autism Hypothesis), respectively. However, there were no group differences when it comes to the irrelevant babble effect and the intensity effect, suggesting once again that autistic adults (with average or above-average intelligence and typical language skills) and nonautistic adults are not so different after all in how they process and respond to irrelevant auditory information. Social science/Psychology/Human behaviour Health sciences/Diseases/Neurological disorders/Neurodevelopmental disorders/Autism spectrum disorders Irrelevant sound effect irrelevant speech effect auditory distraction working memory selective attention Figures Figure 1 Introduction Our ears are open all the time meaning that we process and respond to auditory information 24 hours a day. Task-irrelevant background sound represents a serious challenge for the attentional system, because it has the potential to distract us from what we are doing. Many studies have found that autistic adults respond differently to task-irrelevant background sound than nonautistic adults (e.g. Remington & Fairnie, 2017; Hu et al., 2018; Dunlop et al., 2016; Schafer et al., 2020; Schelinski & von Kriegestein, 2020; Alcántara et al., 2004). This is not only of practical relevance (MacLennan et al., 2022; Poulsen et al., 2025; Werkman et al., 2020), but also of theoretical significance. Prediction-based accounts have been proposed to explain differences in the processing of auditory information in autistic and nonautistic individuals. In this preregistered study, we contrasted two classic auditory distraction effects to test four prominent prediction-based autism theories. The Hypo Prior Hypothesis (Pellicano and Burr, 2012) posits that autistic individuals exhibit a reduced reliance on prior knowledge (weak priors) in the perception of sensory input so that they perceive the sensory environment as what is typically described to as “too real” compared to nonautistic individuals. In the Aberrant Precision Account of Autism (Lawson et al., 2014), an imbalance in sensory precision relative to priors results in difficulties in attenuation and contextualization, leading autistic individuals to overestimating the volatility of the sensory environment (Lawson et al., 2017). According to the Predictive Impairment (PIA) hypothesis (Sinha et al., 2014) autistic individuals face challenges in forming predictions because of a reduced sensitivity to temporal or spatial relationships between events and objects. Within the High, Inflexible Precision of Prediction Errors in Autism (HIPPEA) hypothesis (Van de Cruys et al., 2014; Van de Cruys et al., 2019; Van de Cruys et al., 2017) autistic individuals form adequate predictions but show inflexibility in dealing with expectation violations resulting in a disproportionate weighting of random variations in the sensory environment, leading to difficulties in ignoring noisy sensory input. Although the four theories differ in their assumptions at which processing level differences in auditory processing between autistic and nonautistic individuals should occur, clear predictions can be derived when it comes to the complexity of task-irrelevant sound. And that is precisely what we examined in this preregistered study. The irrelevant sound effect is widely considered as the standard paradigm to measure auditory distraction in the laboratory (e.g., Colle & Welsh, 1976; Jones et al., 1992; Salamé & Baddeley, 1982). In this task, participants see a list of digits presented in sequence on a computer screen while they hear irrelevant sound through headphones. The participants are instructed to memorize the digits and pay no attention to the sound. Acoustically complex and unpredictable sounds such as natural speech produce a particularly large disruptive effect (e.g., Leist et al., 2025; Röer et al., 2019; Vachon et al., 2012; for review, see Ellermeier & Zimmer, 2014, Marois & Vachon, 2024; see also Schlittmeier et al., 2012; but see Viswanathan et al., 2014). Two classic auditory distraction effect are particularly relevant to the present study, because the four prediction-based autism theories all point in the direction of a difference in how autistic and nonautistic adults should respond to irrelevant speech. The first effect is the irrelevant babble effect which states that multi-channel speech, consisting of multiple superimposed voices, produces a smaller disruptive effect than single-channel and dual-channel speech (Jones & Macken, 1995). In another experiment of the series, each additional superimposed voice—up to six voices—led to fewer errors in the serial recall task. The second effect is the intensity effect. Although in early studies the irrelevant sound effect was largely independent of intensity (Colle, 1980; Ellermeier & Hellbrück, 1998; Schlittmeier et al., 2008; Tremblay & Jones, 1999), recent studies demonstrated that loud auditory distractors are more difficult to ignore after all. This is true both for classic steady-state and changing-state sequences (Alikadic & Röer, 2022) and for natural speech sequences (Kattner et al., 2024). Although a number of studies compared the effect of competing irrelevant speech in autistic and nonautistic individuals, the findings do not form a coherent picture. Some studies found higher auditory distractibility (i.e., enhanced distractor processing) in adults with autism spectrum disorder (ASD) (Remington & Fairnie, 2017; Hu et al., 2018; Dunlop et al., 2016; Schafer et al., 2020; Schelinski & von Kriegestein, 2020; Alcántara et al., 2004), others found no differences between autistic and nonautistic individuals (Smith & Bennetto, 2007; Tyndall et al., 2018; Tillmann et al. 2021). In an audiovisual dual-task paradigm (Hu et al., 2018), for example, prosodic voices were presented in form of an oddball task. The focal task was to classify pictures while ignoring the voices. Adolescent autistic individuals had significantly higher error rates in picture classification across all prosodic voice types, indicating higher impairment by speech distractors than nonautistic individuals. Further, while listening to a central scene conversation during an inattentional deafness task, a male voice saying "I am a gorilla" repeatedly was recognized more often by autistic than nonautistic adults (Remington & Fairnie, 2017). In contrast, adult and adolescent autistic individuals detected a similar amount of unexpected social sounds in a cross-modal selective attention task (Tyndall et al., 2018; Tillmann et al. 2021). Dunlop et al. (2016) showed that autistic adults performed poorer in a speech-in-noise discrimination task with a background noise comprising four-channel speech. Similarly, Schafer et al. (2020) reported lower discrimination performance for spoken sentences embedded in four-channel speech in autistic compared to nonautistic adults, and Schelinski and von Kriegstein (2020) found that autistic adults had greater problems than nonautistic adults to identify target stimuli embedded in speech-shaped noise that simulated real-live noise situations (see also Alcántara et al., 2004, but see Smith & Bennetto, 2007). The situation is not so different when it comes to the role of intensity. Although many studies seem to suggest that the intensity of auditory distractors has no substantial influence on the disruption of working memory performance in nonautistic participants (e.g., Colle, 1980; Ellermeier & Hellbrück, 1998; Tremblay & Jones, 1999; but see Alikadic & Röer, 2022; Kattner et al., 2024), this does not mean that these results can be generalized to autistic individuals. ASD is often associated with a reduced tolerance to loud sound (for reviews, see Poulsen et al., 2024; Rotschafer 2021), and restricted and repetitive behaviors (RRBs)—one of the hallmark features in ASD (American Psychiatric Association, 2013)— are related to noise sensitivity in both autistic children (Courchesne et al., 2021; Kanakri, 2017) and adults (Kargas et al., 2015; Nwaordu & Charlton, 2024). Furthermore, the pattern of results with regard to similarities and differences in auditory processing of intensity between autistic and nonautistic individuals is mixed (e.g., Bonnel et al., 2010; Kargas et al., 2015; Kuiper et al., 2019; Takahashi et al., 2014). There are as many studies in which differences at the behavioral level between autistic and nonautistic individuals have been found (Takahashi et al., 2014; Kargas et al., 2015) as studies in which there were no differences (Kuiper et al., 2019; Bonnel et al., 2010). In summary, it appears that autistic individuals often (but not always) process irrelevant speech differently than nonautistic individuals and that both, the number of voices and the intensity may play an important role in this. This is highly relevant both theoretically and practically. Every day, we are almost constantly exposed to irrelevant auditory information, which we have to filter out in order to concentrate on the processing of relevant information. With the present study, we seek to provide a more comprehensive understanding of the stimulus features that guide the allocation of auditory attention in ASD. To do so, we examined the effect of single- and dual-channel speech compared to multi-channel speech and that of high-intensity and low-intensity sound in autistic and nonautistic adults. The irrelevant babble effect can be interpreted as based on the perceptual unification of multi-channel speech as an auditory uniform babble with few or no auditory deviations, leading to less disruption compared to single-channel and dual-channel speech that contains a larger number of abrupt changes in frequency and amplitude (Jones & Macken, 1995). However, prominent prediction-based autism theories (Pellicano & Burr, 2012; Lawson et al., 2014; Sinha et al., 2014; Van de Cruys et al., 2014) suggest that this advantage cannot be automatically assumed in autistic individuals. In the Hypo Priors Hypothesis (Pellicano & Burr, 2012), it is hypothesized that weak priors have less influence on the perception of sensory input. Therefore, the priors for multi-channel speech that leads nonautistic individuals to a consistent model of the irrelevant babble are less informative in autistic individuals. Conversely, the Aberrant Precision Account of Autism (Lawson et al., 2014), assumes that autistic individuals show an imbalanced sensory precision and that they tend to overestimate the volatility of the sensory environment (Lawson et al., 2017). Complex acoustic environments with multiple voices also create a high degree of uncertainty and a lack of predictability. Thus, for autistic individuals, multi-channel speech would be more disruptive than single- or two-channel speech. The PIA hypothesis (Sinha et al., 2014) posits that autistic individuals experience difficulties in generating accurate predictions about incoming auditory stimuli. Multi-talker speech represents a considerable challenge in this respect, because multiple streams of information with different auditory features overlap. These difficulties should become particularly apparent in a situation in which many different voices are presented at the same time and less so when only a single voice or two voices are presented. Therefore, PIA Hypothesis predicts a lower predictive advantage in multi-channel speech in autistic individuals compared to nonautistic individuals. According to the HIPPEA hypothesis (Van de Cruys et al., 2014; Van de Cruys et al., 2019; Van de Cruys et al., 2017), autistic individuals show a strong and inflexible reaction to prediction errors. Multi-channel speech in autistic individuals is associated with an enhanced number of simultaneously highly weighted errors compared to single- or dual-channel speech, leading to difficulties in ignoring this noisy sensory input. However, HIPPEA is theoretically ambiguous regarding the processing of irrelevant multi-channel speech. Autistic individuals also show intact prediction generation and may thus be capable to form a consistent model of the irrelevant babble resulting in fewer prediction errors than in single-and dual-channel speech and therefore show a comparable irrelevant babble effect to nonautistic individuals. We tested these predictions directly in a pre-registered study, using a straightforward approach, and established methodologies. We used a standard serial recall task and a within-subject manipulation of stimulus complexity, which is considered to represent an important role in speech processing in autism (e.g., Key & D'Ambrose Slaboch, 2021), and we measured self-reported autistic traits to account for possible group differences in the heterogeneity of the ASD sample (e.g., Hobson & Petty, 2021; Litman et al., 2025). Further, we presented the to-be-ignored sequences at two different sound levels (low intensity, high intensity). While no clear theoretical predictions about the intensity effect can be derived, this has not been studied in ASD. Moreover, there is evidence that seems to suggest that autistic and nonautistic individuals might respond differently to sound presented at low and high intensity (Takahashi et al., 2014; Kargas et al., 2015). Methods Data availability statement Stimulus materials, raw data, and analysis code are publicly available at the project page [details omitted for double-anonymized peer review]. Prior to the start of data collection, a time-stamped preregistration document was published outlining in detail the method and planned analysis. This preregistration document is also available at the project page. The experiment protocol is available under [details omitted for double-anonymized peer review]. The related data paper is [details omitted for double-anonymized peer review]. Deviations from the Preregistration The final sample size in the autistic group consisted of 49 participants. This is five participants fewer than our target sample size in the preregistered document. In all other aspects, we followed the preregistered method and analyses. Power Analysis To compare auditory distraction in autistic individuals and nonautistic individuals, a 2 × 2 × 3 design with group as the between-subjects variable (autistic individuals, nonautistic individuals) and stimulus complexity (multi-channel, dual-channel, single-channel) and distractor intensity (low, high) as within-subjects variables were used. The dependent variable was serial recall performance. Of primary interest was a possible interaction of the between-subjects variable group and the within-subjects variables stimulus complexity. Given the total sample of N = 141 α = .05 and an assumed correlation among the levels of the repeated measures variable of ρ = .5, an interaction of η p 2 = .018 could be detected with a probability of 1 - β > .28. Power calculations were conducted with G*Power software (Faul, Erdfelder, Lang & Buchner, 2007 ). Participants A total number of 142 participants took part in the study. One participant was excluded before data analysis because the demographic information they provided during the preliminary interview did not match the information provided during testing. Thus, the final sample size was 141. The ASD group consisted of 49 participants (23 female, 23 male, 2 persons that answered with "other/not specified" and one missing answer due to technical error) with a mean age of 29.6 years ( SD = 6.1). The nonautistic group consisted of 92 participants (57 female, 34 male, and 1 person that answered with "other/not specified“) with a mean age of 23.1 years ( SD = 3.5). Autistic participants were recruited from institutions that exclusively serve individuals with professional ASD diagnoses (i.e., a university outpatient clinic and autism therapy centers). Given the cognitive demands of the experimental paradigm, the ASD sample was restricted to young adults meeting specific diagnostic criteria. Eligible participants had received at least one formal diagnosis from a clinical professional, encompassing autistic disorder (previously termed "high-functioning"), Asperger's disorder, or ASD "without accompanying intellectual impairment/without accompanying language impairment" as defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR, DSM-5; American Psychiatric Association, 2000 , 2013 ), or equivalent diagnoses according to the International Classification of Diseases (ICD-10, ICD-11; World Health Organization, 2016, 2019). Participants were required to provide documentation of their diagnosis during the preliminary interview. The nonautistic group comprised undergraduate students who received course credit for their participation. Socioeconomic status data were not recorded. Ethical approval and informed consent statements Research was performed in accordance with the Declaration of Helsinki and received the approval of the institutional ethics committee of [details omitted for double-anonymized peer review] University (IRB number: 189/2021). Written informed consent was obtained from all participants prior to participation. Materials A standard serial recall task was used. For each trial, eight to-be-remembered digits were sampled randomly without replacement from the set {1, 2, ..., 9}. Digits were presented at a rate of 1 Hz (800 ms on, 200 ms off) in black font on a white background in the center of the computer screen. During the presentation of the to-be-remembered digits, auditory distractor sequences were played binaurally over closed headphones with high-insulation hearing protection covers (Beyerdynamic DT 100). The distractor sequences comprised sentences read aloud from a physiology textbook. These were recorded digitally at 44.1 kHz and 16-bit encoding with six text-to-speech computer voices. The single-channel speech condition contained one female voice, the dual-channel speech condition contained one female and one male voice played simultaneously, and the multi-channel speech condition contained three female and three male voices played simultaneously. In the low-intensity condition, distractors were presented at 45 dB(A), in the high-intensity condition at 75 dB(A), representing an approximately eight-fold increase in loudness. In total, six conditions of distractor sequences were presented in randomized order. The intensity of the auditory distractors was measured using a professional hand-held sound level meter that was inserted through the opening of a polystyrene ear. Procedure Before the experiment, we conducted a preliminary interview to verify the diagnosis. During this interview, we used the German version of the AQ-10 (Allison et al., 2012 ) to assess self-reported autistic traits and IST Screening (Liepmann et al., 2012 ) to assess intellectual and language abilities. Only participants with an IST score of 85 or above were included in the analyses. Each dataset was assigned a unique identifier code which were deleted prior to data analysis to ensure anonymity. No data were excluded based on outliers. In the university laboratory, participants were individually seated in a sound-attenuated booth. On-screen instructions informed the participants that all auditory stimuli were task-irrelevant and should be ignored. The experiment comprised 76 trials, divided into two blocks. The training block consisted of four trials without auditory distractors. The experimental block consisted of 72 trials (12 trials per condition). The experiment lasted 27 minutes on average. Results Confirmatory Analyses Participants’ responses were scored according to a strict serial-recall criterion. Only digits recalled at the correct serial position were scored as correct. A multivariate approach was used for all within-subjects comparisons. All multivariate test criteria correspond to the same exact F statistics, which is reported. The level of α is set to .05 for all analyses. Partial eta squared is reported as the effect size measure. The most important question was whether there are differences between autistic individuals and nonautistic individuals regarding the susceptibility to the irrelevant babble effect. The following analyses were performed to test this. First, we ran a global 2 × 2 × 3 repeated measures analysis of variance (ANOVA) with group (autistic individuals, nonautistic individuals) as between-subjects variable, intensity (low, high), and complexity (multi-channel, dual-channel, single-channel) as within-subjects variables and recall performance as dependent variable. There was a main effect of complexity, F (2,138) = 63.87, p < .001, η p 2 = .48. There was no main effect of group, F (1,139) = .06, p = .814, η p 2 < .01, and no interaction with complexity, F (2,138) = 1.30, p = .276, η p 2 = .02. Orthogonal contrasts revealed a significant irrelevant babble effect in that serial recall performance in the multi-channel speech condition was better than in the single-channel and dual-channel speech conditions combined, F = 127.38, p < .001, η p 2 = .48, but no interaction with group, F = .029, p = .866, η p 2 < .01. There was a main effect of intensity, F (1,139) = 101.45, p < .001, η p 2 = .42, but no interaction with group, F (1,139) = .34, p = .564, η p 2 < .01. There was a significant interaction of complexity and intensity, F (2,138) = 13.78, p < .001, η p 2 = .17, but no three-way interaction F (2,138) = 1.06, p = .349, η p 2 = .02. Orthogonal contrasts revealed a significant intensity effect in that performance in the low-intensity condition was better than in the high-intensity condition, F = 101.45, p < .001, η p 2 = .42, but no interaction with group, F = .335, p = .564, η p 2 = .00. Exploratory Analyses Following the standard procedure for testing habituation effects (e.g., Ellermeier & Zimmer, 1997 ; Röer et al., 2011 ), we conducted a 2 × 3 × 12 repeated-measures ANOVA with group as between-subjects variable (autistic individuals, nonautistic individuals) and complexity (multi-channel, dual-channel, single-channel) and ordinal trial position (1–12) as within-subjects variables and a 2 × 2 × 12 repeated-measure ANOVA with group as between-subjects variable (autistic individuals, nonautistic individuals) and intensity (low, high) and ordinal trial position (1–12) as within-subjects variables. To make it short, no habituation effects were observed. The interaction between the ordinal trial position and the complexity variable contrasting the multi-channel, dual-channel and single-channel conditions, F (22,118) = .59, p = .925, η p 2 = .10, was not significant, nor was the interaction between the ordinal trial position and the auditory condition variable contrasting the low-intensity and high-intensity conditions, F (11,129) = .72, p = .719, η p 2 = .06. There were also no three-way interactions with group, [ F (22,118) = .79, p = .731, η p 2 = .13, and F (11,129) = 1.13, p = .341, η p 2 = .08]. Discussion In this preregistered study, we contrasted two classic auditory distraction effects to learn more about the processing of irrelevant auditory information in autistic and nonautistic young adults and to test the predictions derived from four prediction-based autism theories (Pellicano & Burr, 2012; Lawson et al., 2014; Sinha et al., 2014; Van de Cruys et al., 2014). First of all, we successfully replicated both the irrelevant babble effect referring to the greater disruption by single- and dual-channel speech compared to multi-channel speech (Jones & Macken, 1995) and the intensity effect referring to greater disruption by high-intensity compared to low-intensity sound (Alikadic & Röer, 2022). Interestingly, we found no group differences for both effects. This is inconsistent with what prediction-based autism theories predict. According to these theories, autistic individuals should be more distracted by irrelevant babble due to weak priors (Hypo Priors Hypothesis), volatility fragmentation (Aberrant Precision Account of Autism), prediction delays (Predictive Impairment in Autism Hypothesis), and enhanced prediction error weighting (High, Inflexible Precision of Prediction Errors in Autism Hypothesis). One possible explanation is that the assumed difference in the predictive advantage of multi-channel speech is particularly pronounced in the case of moderately complex auditory stimuli. If the acoustic environment is highly predictable, then this difference might be more difficult to observe, because even a less accurate model allows for a sufficiently precise prediction. The same is true for a highly unpredictable environment—such as multiple sound streams presented at the same time—because even a very accurate model can reduce the unpredictability of the incoming auditory stimulation to a limited extent. Note that while this inverse U-shaped function seems to fit the descriptively larger difference between the single-channel and dual-channel conditions in the autistic group compared to the nonautistic group in the present study, it is a mere post-hoc interpretation that would require critical empirical testing in future studies. However, this does not necessarily mean that the inflexibility in handling prediction errors does not have an effect on the processing of irrelevant auditory information. The formation of a neural model and the detection of prediction errors operate at different stages of the information processing cycle, but there is constant cross-talk between these stages. If the attentional system detects a mismatch between the neural model and the incoming auditory stimulation, the neural model will be updated accordingly (e.g., Cowan, 1995; Näätänen, 1990). This model contains bottom-up information such as the physical characteristics of the auditory sequence, but also top-down information such as prior knowledge and experience. In the present study, we only focused on bottom-up prediction errors. For a comprehensive understanding, it would be worth investigating whether the same results can be observed with top-down prediction errors, for example in statistical learning or semantic processing. This is relevant to the ongoing debate in predictive processing about how the disruptive potential of changing-state distractors as speech are determined by both, the consistency of bottom-up stimulation and top-down expectations (Hughes & Marsh, 2020; Vachon et al., 2012).The finding that the intensity of the distractors had a similar effect in both groups is consistent with the prediction-based autism theories (Pellicano & Burr, 2012; Lawson et al., 2014; Sinha et al., 2014; Van de Cruys et al., 2014). A priori, however, it was unclear whether autistic individuals react differently to loud sounds than nonautistic individuals, because ASD is often associated with a reduced tolerance to loud sound (Poulsen et al., 2024; Rotschafer 2021) and in some studies, autistic individuals were more distracted by loud sounds than nonautistic individuals (Takahashi et al., 2014; Kargas et al., 2015; but see Kuiper et al., 2019; Bonnel et al., 2010). To verify whether our results are evidence for the absence of a group effect, we performed a post-hoc equivalence test against zero (Campbell & Lakens, 2021), which could not be confirmed as the confidence intervals of both effect sizes for the non-significant group-interaction effects for the irrelevant babble effect and the intensity effect [(0.00, 0.074); (0.00, 0.043)] exceed the predefined equivalence bounds (η p 2 = 0.03), and both tests were not significant [( p = 0.223); ( p = 0.062)] [1]. Importantly, the absence of a significant group-interaction does not seem to be simply due to a particularly heterogeneous sample that would obscure possible group effects, making it challenging to detect differences between autistic and nonautistic individuals. ASD is characterized by significant heterogeneity, encompassing a wide range of symptoms and manifestations, also concerning sensory features (Uljarević et al., 2017). Statistically, this would have been reflected in a greater variance within the autistic group compared to the nonautistic group. However, Levene's tests revealed homoscedasticity for both the irrelevant babble effect ( p = .123) and the intensity effect ( p = .131). Nevertheless, we would like to draw attention to the fact that the autistic participants in this study represent only a subgroup of the autism population (i.e., individuals with average or above-average intelligence and typical language skills). Furthermore, groups were not matched for age nor gender. On average, the autistic group was 6.5 years older than the neurotypical group and there were relatively more female participants in the nonautistic group. Importantly, post-hoc analyses suggested that neither age nor gender contributed significantly to the pattern of results [2] . Moreover, speech stimuli may possibly reflect general language problems rather than specific predictive processing deficits. We circumvented this possible confounding factor by controlling for language skills in the autistic sample. See O’Shea & Engelhardt, (2025), for a similar argument. Also, autistic people were reported to show a comparable impression of humanness for human and synthetic voices (Kuriki et al., 2016). Lastly, one consideration that applies to many empirical studies is that more data would allow for an even better estimate of the true effect. We counter this argument by having preregistered an effect size of minimal interest that we deemed important enough to be theoretically and practically meaningful. Of course, we cannot rule out the possibility that a larger sample size would have resulted in a statistically significant group difference. However, such a small effect, if it exists, would not change the overall interpretation that the similarities in the processing of irrelevant auditory information between autistic and nonautistic individuals outweigh the differences. This result is in line with accumulating evidence that autistic individuals do not always respond to irrelevant auditory information differently than nonautistic individuals, at least when it comes to relatively early stages in the processing cycle (Alikadic & Röer, 2024; Alikadic & Röer, 2025; Haigh et al., 2016). However, this similarity does not mean that there are no practical implications for the autistic community. The opposite is true. It is precisely because we know that differences can emerge at later stages in the processing cycle that autistic people must take special precautions to protect themselves from irrelevant sound (Alho et al., 2021; Patterson et al., 2021; Li et al., 2025, but see Alispahic et al., 2024). Complex stimuli in particular should be avoided (see also Gonçalves & Monteiro, 2023; Key & D'Ambrose Slaboch, 2021) and if that is not possible, adding white noise can help to minimize the disruptive effect (Beaman, 2005). All sounds processed by our system have the potential to distract us. It is therefore important to develop a better understanding of which sounds in particular are subjectively and objectively harmful to derive tailored, autism-specific recommendations in order to improve the circumstances of autistic people’s lives. Declarations Statements and Declarations The first author of the study is autistic. The recruitment procedure and study were co-designed with autistic consultants. Disclosure Statement The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The research reported in this article was supported by [details omitted for double-anonymized peer review]. References Alcántara, José I., Weisblatt, E. J. L., Moore, B. C. J., & Bolton, P. F. (2004). Speech-in-noise perception in high-functioning individuals with autism or Asperger’s syndrome. 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International statistical classification of diseases and related health problems (11th ed.). https://icd.who.int Footnotes The analysis can be found on the project page [details omitted for double-anonymized peer review] The analysis can be found on the project page [details omitted for double-anonymized peer review]. Additional Declarations There is NO Competing Interest. Supplementary Files PosthocAnalysesEquivalenceTests.pdf Post-hoc Analyses, Equivalence Tests Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9447894","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":626089309,"identity":"79b7894d-2f74-4b35-8837-be9362f25fc5","order_by":0,"name":"Lejla Alikadic","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIie3Rv0oDQRAG8A8Ct83EtAfG3CucHIiQoK8yi5BrBZsUgRwISXMP4GPESuw2LFgF0yrXXHrrEFDQufinECZYWuzHssUsPz6GBUJC/mFSwDog7kYkN2MAtL6PTljIgL7I8E8EQoaEhgB+N91PTFG7LTwdkF8/1eOVvTOIDi/R76mEHC9KIVF7mp3yQ2Xvr4XcIM9UEjM7akgHJ7EtKjv3nU0lE1toJKl58YZ3IWYj5FEIooZMVBKD/a6lXTYt7oewvguz76ZCaHklu1xkQswrpfmx1nI+W9r1y8j3kjK/fd6Oz47mK9fKaNRPtBaA+PN3frXrADBu32tISEhICPABMN1SbQC+R1IAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-6970-8344","institution":"Witten/Herdecke University","correspondingAuthor":true,"prefix":"","firstName":"Lejla","middleName":"","lastName":"Alikadic","suffix":""},{"id":626089310,"identity":"f9795a5f-4e2a-4f7d-bfe5-0b04a61d6eba","order_by":1,"name":"Jan Philipp Röer","email":"","orcid":"https://orcid.org/0000-0001-7774-3433","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"Philipp","lastName":"Röer","suffix":""}],"badges":[],"createdAt":"2026-04-17 10:35:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9447894/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9447894/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107531421,"identity":"b31f1359-5898-466d-bc8e-ea9ad8daa494","added_by":"auto","created_at":"2026-04-22 10:27:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":74417,"visible":true,"origin":"","legend":"\u003cp\u003eSerial recall performance as a function of complexity (multi-channel, dual-channel, single-channel; left panel) and intensity (low, high; right panel) for nonautistic and autistic individuals. The error bars represent the standard errors of the means.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9447894/v1/e634ddf3b22108b76741807f.png"},{"id":108803242,"identity":"1af9d588-56ea-469d-a6b7-e023c0f3595b","added_by":"auto","created_at":"2026-05-08 14:44:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":409483,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9447894/v1/fed396d2-9ef7-4b0b-8789-03de04fe5778.pdf"},{"id":107531440,"identity":"1072b14a-f6ed-4895-bf2d-89a65cebab06","added_by":"auto","created_at":"2026-04-22 10:27:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":66127,"visible":true,"origin":"","legend":"Post-hoc Analyses, Equivalence Tests","description":"","filename":"PosthocAnalysesEquivalenceTests.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9447894/v1/c1aed9e6bafe230dc9c60ff1.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Many voices are easier to ignore than one: Equivalent distractibility in autistic and nonautistic adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOur ears are open all the time meaning that we process and respond to auditory information 24 hours a day. Task-irrelevant background sound represents a serious challenge for the attentional system, because it has the potential to distract us from what we are doing. Many studies have found that autistic adults respond differently to task-irrelevant background sound than nonautistic adults (e.g. Remington \u0026amp; Fairnie, 2017; Hu et al., 2018; Dunlop et al., 2016; Schafer et al., 2020; Schelinski \u0026amp; von Kriegestein, 2020; Alc\u0026aacute;ntara et al., 2004). This is not only of practical relevance (MacLennan et al., 2022; Poulsen et al., 2025; Werkman et al., 2020), but also of theoretical significance. Prediction-based accounts have been proposed to explain differences in the processing of auditory information in autistic and nonautistic individuals. In this preregistered study, we contrasted two classic auditory distraction effects to test four prominent prediction-based autism theories. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Hypo Prior Hypothesis (Pellicano and Burr, 2012) posits that autistic individuals exhibit a reduced reliance on prior knowledge (weak priors) in the perception of sensory input so that they perceive the sensory environment as what is typically described to as \u0026ldquo;too real\u0026rdquo; compared to nonautistic individuals. In the Aberrant Precision Account of Autism (Lawson et al., 2014), an imbalance in sensory precision relative to priors results in difficulties in attenuation and contextualization, leading autistic individuals to overestimating the volatility of the sensory environment (Lawson et al., 2017). According to the Predictive Impairment (PIA) hypothesis (Sinha et al., 2014) autistic individuals face challenges in forming predictions because of a reduced sensitivity to temporal or spatial relationships between events and objects. Within the High, Inflexible Precision of Prediction Errors in Autism (HIPPEA) hypothesis (Van de Cruys et al., 2014; Van de Cruys et al., 2019; Van de Cruys et al., 2017) autistic individuals form adequate predictions but show inflexibility in dealing with expectation violations resulting in a disproportionate weighting of random variations in the sensory environment, leading to difficulties in ignoring noisy sensory input. Although the four theories differ in their assumptions at which processing level differences in auditory processing between autistic and nonautistic individuals should occur, clear predictions can be derived when it comes to the complexity of task-irrelevant sound. And that is precisely what we examined in this preregistered study.\u003c/p\u003e\n\u003cp\u003eThe irrelevant sound effect is widely considered as the standard paradigm to measure auditory distraction in the laboratory (e.g., Colle \u0026amp; Welsh, 1976; Jones et al., 1992; Salam\u0026eacute; \u0026amp; Baddeley, 1982). In this task, participants see a list of digits presented in sequence on a computer screen while they hear irrelevant sound through headphones. The participants are instructed to memorize the digits and pay no attention to the sound. Acoustically complex and unpredictable sounds such as natural speech produce a particularly large disruptive effect (e.g., Leist et al., 2025; R\u0026ouml;er et al., 2019; Vachon et al., 2012;\u0026nbsp;for review, see Ellermeier \u0026amp; Zimmer, 2014, Marois \u0026amp; Vachon, 2024; see also Schlittmeier et al., 2012; but see Viswanathan et al., 2014).\u0026nbsp;Two classic auditory distraction effect are particularly relevant to the present study, because the four prediction-based autism theories all point in the direction of a difference in how autistic and nonautistic adults should respond to irrelevant speech.\u003c/p\u003e\n\u003cp\u003eThe first effect is the irrelevant babble effect which states that multi-channel speech, consisting of multiple superimposed voices, produces a smaller disruptive effect than single-channel and dual-channel speech (Jones \u0026amp; Macken, 1995). In another experiment of the series, each additional superimposed voice\u0026mdash;up to six voices\u0026mdash;led to fewer errors in the serial recall task.\u003c/p\u003e\n\u003cp\u003eThe second effect is the intensity effect. Although in early studies the irrelevant sound effect was largely independent of intensity (Colle, 1980; Ellermeier \u0026amp; Hellbr\u0026uuml;ck, 1998; Schlittmeier et al., 2008; Tremblay \u0026amp; Jones, 1999), recent studies demonstrated that loud auditory distractors are more difficult to ignore after all. This is true both for classic steady-state and changing-state sequences (Alikadic \u0026amp; R\u0026ouml;er, 2022) and for natural speech sequences (Kattner et al., 2024). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough a number of studies compared the effect of competing irrelevant speech in autistic and nonautistic individuals, the findings do not form a coherent picture. Some studies found higher\u0026nbsp;auditory distractibility\u0026nbsp;(i.e., enhanced distractor processing) in adults with autism spectrum disorder (ASD) (Remington \u0026amp; Fairnie, 2017; Hu et al., 2018; Dunlop et al., 2016; Schafer et al., 2020; Schelinski \u0026amp; von Kriegestein, 2020; Alc\u0026aacute;ntara et al., 2004), others found no differences between autistic and nonautistic individuals (Smith \u0026amp; Bennetto, 2007;\u0026nbsp;Tyndall et al., 2018; Tillmann et al. 2021). In an audiovisual dual-task paradigm (Hu et al., 2018), for example, prosodic voices were presented in form of an oddball task. The focal task was to classify pictures while ignoring the voices. Adolescent autistic individuals had significantly higher error rates in picture classification across all prosodic voice types, indicating higher impairment by speech distractors than nonautistic individuals. Further, while listening to a central scene conversation during an inattentional deafness task, a male voice saying \u0026quot;I am a gorilla\u0026quot; repeatedly was recognized more often by autistic than nonautistic adults (Remington \u0026amp; Fairnie, 2017).\u0026nbsp;In contrast, adult and adolescent autistic individuals detected a similar amount of unexpected social sounds in a cross-modal selective attention task (Tyndall et al., 2018; Tillmann et al. 2021). Dunlop et al. (2016) showed that autistic adults performed poorer in a speech-in-noise discrimination task with a background noise comprising four-channel speech. Similarly, Schafer et al. (2020) reported lower discrimination performance for spoken sentences embedded in four-channel speech in autistic compared to nonautistic adults, and Schelinski and von Kriegstein (2020) found that autistic adults had greater problems than nonautistic adults to identify target stimuli embedded in speech-shaped noise that simulated real-live noise situations (see also Alc\u0026aacute;ntara et al., 2004, but see Smith \u0026amp; Bennetto, 2007).\u003c/p\u003e\n\u003cp\u003eThe situation is not so different when it comes to the role of intensity. Although many studies seem to suggest that the intensity of auditory distractors has no substantial influence on the disruption of working memory performance in nonautistic participants (e.g., Colle, 1980; Ellermeier \u0026amp; Hellbr\u0026uuml;ck, 1998; Tremblay \u0026amp; Jones, 1999; but see Alikadic \u0026amp; R\u0026ouml;er, 2022; Kattner et al., 2024), this does not mean that these results can be generalized to autistic individuals. ASD is often associated with a reduced tolerance to loud sound (for reviews, see Poulsen et al., 2024; Rotschafer 2021), and restricted and repetitive behaviors (RRBs)\u0026mdash;one of the hallmark features in ASD (American Psychiatric Association, 2013)\u0026mdash; are related to noise sensitivity in both autistic children (Courchesne et al., 2021; Kanakri, 2017) and adults (Kargas et al., 2015; Nwaordu \u0026amp; Charlton, 2024). Furthermore, the pattern of results with regard to similarities and differences in auditory processing of intensity between autistic and nonautistic individuals is mixed (e.g., Bonnel et al., 2010; Kargas et al., 2015; Kuiper et al., 2019; Takahashi et al., 2014). There are as many studies in which differences at the behavioral level between autistic and nonautistic individuals have been found (Takahashi et al., 2014; Kargas et al., 2015) as studies in which there were no differences (Kuiper et al., 2019; Bonnel et al., 2010).\u003c/p\u003e\n\u003cp\u003eIn summary, it appears that autistic individuals often (but not always) process irrelevant speech differently than nonautistic individuals and that both, the number of voices and the intensity may play an important role in this. This is highly relevant both theoretically and practically. Every day, we are almost constantly exposed to irrelevant auditory information, which we have to filter out in order to concentrate on the processing of relevant information. With the present study, we seek to provide a more comprehensive understanding of the stimulus features that guide the allocation of auditory attention in ASD. To do so, we examined the effect of single- and dual-channel speech compared to multi-channel speech and that of high-intensity and low-intensity sound in autistic and nonautistic adults.\u003c/p\u003e\n\u003cp\u003eThe irrelevant babble effect can be interpreted as based on the perceptual unification of multi-channel speech as an auditory uniform babble with few or no auditory deviations,\u0026nbsp;leading to less disruption compared to\u0026nbsp;single-channel and dual-channel speech that contains a larger number of abrupt changes in frequency and amplitude (Jones \u0026amp; Macken, 1995). However, prominent prediction-based autism theories (Pellicano \u0026amp; Burr, 2012; Lawson et al., 2014; Sinha et al., 2014; Van de Cruys et al., 2014) suggest that this advantage cannot be automatically assumed in autistic individuals.\u003c/p\u003e\n\u003cp\u003eIn the Hypo Priors Hypothesis (Pellicano \u0026amp; Burr, 2012), it is hypothesized that weak priors have less influence on the perception of sensory input. Therefore, the priors for multi-channel speech that leads nonautistic individuals to a consistent model of the irrelevant babble are less informative in autistic individuals. Conversely, the Aberrant Precision Account of Autism (Lawson et al., 2014), assumes that autistic individuals show an imbalanced sensory precision and that they tend to overestimate the volatility of the sensory environment (Lawson et al., 2017). Complex acoustic environments with multiple voices also create a high degree of uncertainty and a lack of predictability. Thus, for autistic individuals, multi-channel speech would be more disruptive than single- or two-channel speech. The PIA hypothesis (Sinha et al., 2014) posits that autistic individuals experience difficulties in generating accurate predictions about incoming auditory stimuli. Multi-talker speech represents a considerable challenge in this respect, because multiple streams of information with different auditory features overlap. These difficulties should become particularly apparent in a situation in which many different voices are presented at the same time and less so when only a single voice or two voices are presented. Therefore, PIA Hypothesis predicts a lower predictive advantage in multi-channel speech in autistic individuals compared to nonautistic individuals. According to the HIPPEA hypothesis (Van de Cruys et al., 2014; Van de Cruys et al., 2019; Van de Cruys et al., 2017), autistic individuals show a strong and inflexible reaction to prediction errors. Multi-channel speech in autistic individuals is associated with an enhanced number of simultaneously highly weighted errors compared to single- or dual-channel speech, leading to difficulties in ignoring this noisy sensory input. However, HIPPEA is theoretically ambiguous regarding the processing of irrelevant multi-channel speech. Autistic individuals also show intact prediction generation and may thus be capable to form a consistent model of the irrelevant babble resulting in fewer prediction errors than in single-and dual-channel speech and therefore show a comparable irrelevant babble effect to nonautistic individuals.\u003c/p\u003e\n\u003cp\u003eWe tested these predictions directly in a pre-registered study, using a straightforward approach, and established methodologies. We used a standard serial recall task and a within-subject manipulation of stimulus complexity, which is considered to represent an important role in speech processing in autism (e.g., Key \u0026amp; D\u0026apos;Ambrose Slaboch, 2021), and we measured self-reported autistic traits to account for possible group differences in the heterogeneity of the ASD sample (e.g., Hobson \u0026amp; Petty, 2021; Litman et al., 2025). Further, we presented the to-be-ignored sequences at two different sound levels (low intensity, high intensity). While no clear theoretical predictions about the intensity effect can be derived, this has not been studied in ASD. Moreover, there is evidence that seems to suggest that autistic and nonautistic individuals might respond differently to sound presented at low and high intensity (Takahashi et al., 2014; Kargas et al., 2015).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eStimulus materials, raw data, and analysis code are publicly available at the project page [details omitted for double-anonymized peer review]. Prior to the start of data collection, a time-stamped preregistration document was published outlining in detail the method and planned analysis. This preregistration document is also available at the project page. The experiment protocol is available under [details omitted for double-anonymized peer review]. The related data paper is [details omitted for double-anonymized peer review].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDeviations from the Preregistration\u003c/h2\u003e \u003cp\u003eThe final sample size in the autistic group consisted of 49 participants. This is five participants fewer than our target sample size in the preregistered document. In all other aspects, we followed the preregistered method and analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePower Analysis\u003c/h3\u003e\n\u003cp\u003eTo compare auditory distraction in autistic individuals and nonautistic individuals, a 2 \u0026times; 2 \u0026times; 3 design with group as the between-subjects variable (autistic individuals, nonautistic individuals) and stimulus complexity (multi-channel, dual-channel, single-channel) and distractor intensity (low, high) as within-subjects variables were used. The dependent variable was serial recall performance. Of primary interest was a possible interaction of the between-subjects variable group and the within-subjects variables stimulus complexity. Given the total sample of \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;141 α\u0026thinsp;=\u0026thinsp;.05 and an assumed correlation among the levels of the repeated measures variable of ρ\u0026thinsp;=\u0026thinsp;.5, an interaction of η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.018 could be detected with a probability of 1 - β\u0026thinsp;\u0026gt;\u0026thinsp;.28. Power calculations were conducted with G*Power software (Faul, Erdfelder, Lang \u0026amp; Buchner, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eA total number of 142 participants took part in the study. One participant was excluded before data analysis because the demographic information they provided during the preliminary interview did not match the information provided during testing. Thus, the final sample size was 141. The ASD group consisted of 49 participants (23 female, 23 male, 2 persons that answered with \"other/not specified\" and one missing answer due to technical error) with a mean age of 29.6 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.1). The nonautistic group consisted of 92 participants (57 female, 34 male, and 1 person that answered with \"other/not specified\u0026ldquo;) with a mean age of 23.1 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.5). Autistic participants were recruited from institutions that exclusively serve individuals with professional ASD diagnoses (i.e., a university outpatient clinic and autism therapy centers). Given the cognitive demands of the experimental paradigm, the ASD sample was restricted to young adults meeting specific diagnostic criteria. Eligible participants had received at least one formal diagnosis from a clinical professional, encompassing autistic disorder (previously termed \"high-functioning\"), Asperger's disorder, or ASD \"without accompanying intellectual impairment/without accompanying language impairment\" as defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR, DSM-5; American Psychiatric Association, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), or equivalent diagnoses according to the International Classification of Diseases (ICD-10, ICD-11; World Health Organization, 2016, 2019). Participants were required to provide documentation of their diagnosis during the preliminary interview. The nonautistic group comprised undergraduate students who received course credit for their participation. Socioeconomic status data were not recorded.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e \u003cb\u003eand informed consent statements\u003c/b\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eResearch was performed in accordance with the Declaration of Helsinki and received the approval of the institutional ethics committee of [details omitted for double-anonymized peer review] University (IRB number: 189/2021). Written informed consent was obtained from all participants prior to participation.\u003c/p\u003e"},{"header":"Materials","content":"\u003cp\u003eA standard serial recall task was used. For each trial, eight to-be-remembered digits were sampled randomly without replacement from the set {1, 2, ..., 9}. Digits were presented at a rate of 1 Hz (800 ms on, 200 ms off) in black font on a white background in the center of the computer screen. During the presentation of the to-be-remembered digits, auditory distractor sequences were played binaurally over closed headphones with high-insulation hearing protection covers (Beyerdynamic DT 100). The distractor sequences comprised sentences read aloud from a physiology textbook. These were recorded digitally at 44.1 kHz and 16-bit encoding with six text-to-speech computer voices. The single-channel speech condition contained one female voice, the dual-channel speech condition contained one female and one male voice played simultaneously, and the multi-channel speech condition contained three female and three male voices played simultaneously. In the low-intensity condition, distractors were presented at 45 dB(A), in the high-intensity condition at 75 dB(A), representing an approximately eight-fold increase in loudness. In total, six conditions of distractor sequences were presented in randomized order. The intensity of the auditory distractors was measured using a professional hand-held sound level meter that was inserted through the opening of a polystyrene ear.\u003c/p\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eBefore the experiment, we conducted a preliminary interview to verify the diagnosis. During this interview, we used the German version of the AQ-10 (Allison et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) to assess self-reported autistic traits and IST Screening (Liepmann et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) to assess intellectual and language abilities. Only participants with an IST score of 85 or above were included in the analyses. Each dataset was assigned a unique identifier code which were deleted prior to data analysis to ensure anonymity. No data were excluded based on outliers. In the university laboratory, participants were individually seated in a sound-attenuated booth. On-screen instructions informed the participants that all auditory stimuli were task-irrelevant and should be ignored. The experiment comprised 76 trials, divided into two blocks. The training block consisted of four trials without auditory distractors. The experimental block consisted of 72 trials (12 trials per condition). The experiment lasted 27 minutes on average.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eConfirmatory Analyses\u003c/h2\u003e \u003cp\u003eParticipants\u0026rsquo; responses were scored according to a strict serial-recall criterion. Only digits recalled at the correct serial position were scored as correct. A multivariate approach was used for all within-subjects comparisons. All multivariate test criteria correspond to the same exact \u003cem\u003eF\u003c/em\u003e statistics, which is reported. The level of α is set to .05 for all analyses. Partial eta squared is reported as the effect size measure. The most important question was whether there are differences between autistic individuals and nonautistic individuals regarding the susceptibility to the irrelevant babble effect. The following analyses were performed to test this.\u003c/p\u003e \u003cp\u003eFirst, we ran a global 2 \u0026times; 2 \u0026times; 3 repeated measures analysis of variance (ANOVA) with group (autistic individuals, nonautistic individuals) as between-subjects variable, intensity (low, high), and complexity (multi-channel, dual-channel, single-channel) as within-subjects variables and recall performance as dependent variable. There was a main effect of complexity, \u003cem\u003eF\u003c/em\u003e(2,138)\u0026thinsp;=\u0026thinsp;63.87, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.48. There was no main effect of group, \u003cem\u003eF\u003c/em\u003e(1,139) = .06, \u003cem\u003ep\u003c/em\u003e = .814, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, and no interaction with complexity, \u003cem\u003eF\u003c/em\u003e(2,138)\u0026thinsp;=\u0026thinsp;1.30, \u003cem\u003ep\u003c/em\u003e = .276, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.02. Orthogonal contrasts revealed a significant irrelevant babble effect in that serial recall performance in the multi-channel speech condition was better than in the single-channel and dual-channel speech conditions combined, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;127.38, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.48, but no interaction with group, \u003cem\u003eF\u003c/em\u003e = .029, \u003cem\u003ep\u003c/em\u003e = .866, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01. There was a main effect of intensity, \u003cem\u003eF\u003c/em\u003e(1,139)\u0026thinsp;=\u0026thinsp;101.45, \u003cem\u003ep \u0026lt;\u003c/em\u003e .001, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.42, but no interaction with group, \u003cem\u003eF\u003c/em\u003e(1,139) = .34, \u003cem\u003ep\u003c/em\u003e = .564, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01. There was a significant interaction of complexity and intensity, \u003cem\u003eF\u003c/em\u003e(2,138)\u0026thinsp;=\u0026thinsp;13.78, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.17, but no three-way interaction \u003cem\u003eF\u003c/em\u003e(2,138)\u0026thinsp;=\u0026thinsp;1.06, \u003cem\u003ep\u003c/em\u003e = .349, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.02. Orthogonal contrasts revealed a significant intensity effect in that performance in the low-intensity condition was better than in the high-intensity condition, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;101.45, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.42, but no interaction with group, \u003cem\u003eF\u003c/em\u003e = .335, p = .564, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.00.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExploratory Analyses\u003c/h3\u003e\n\u003cp\u003eFollowing the standard procedure for testing habituation effects (e.g., Ellermeier \u0026amp; Zimmer, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; R\u0026ouml;er et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), we conducted a 2 \u0026times; 3 \u0026times; 12 repeated-measures ANOVA with group as between-subjects variable (autistic individuals, nonautistic individuals) and complexity (multi-channel, dual-channel, single-channel) and ordinal trial position (1\u0026ndash;12) as within-subjects variables and a 2 \u0026times; 2 \u0026times; 12 repeated-measure ANOVA with group as between-subjects variable (autistic individuals, nonautistic individuals) and intensity (low, high) and ordinal trial position (1\u0026ndash;12) as within-subjects variables.\u003c/p\u003e \u003cp\u003eTo make it short, no habituation effects were observed. The interaction between the ordinal trial position and the complexity variable contrasting the multi-channel, dual-channel and single-channel conditions, \u003cem\u003eF\u003c/em\u003e(22,118) = .59, \u003cem\u003ep\u003c/em\u003e = .925, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.10, was not significant, nor was the interaction between the ordinal trial position and the auditory condition variable contrasting the low-intensity and high-intensity conditions, \u003cem\u003eF\u003c/em\u003e(11,129) = .72, \u003cem\u003ep\u003c/em\u003e = .719, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.06. There were also no three-way interactions with group, [\u003cem\u003eF\u003c/em\u003e(22,118) = .79, \u003cem\u003ep\u003c/em\u003e = .731, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.13, and \u003cem\u003eF\u003c/em\u003e(11,129)\u0026thinsp;=\u0026thinsp;1.13, \u003cem\u003ep\u003c/em\u003e = .341, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.08].\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this preregistered study, we contrasted two classic auditory distraction effects to learn more about the processing of irrelevant auditory information in autistic and nonautistic young adults and to test the predictions derived from four prediction-based autism theories (Pellicano \u0026amp; Burr, 2012; Lawson et al., 2014; Sinha et al., 2014; Van de Cruys et al., 2014). First of all, we successfully replicated both the irrelevant babble effect referring to the greater disruption by single- and dual-channel speech compared to multi-channel speech (Jones \u0026amp; Macken, 1995) and the intensity effect referring to greater disruption by high-intensity compared to low-intensity sound (Alikadic \u0026amp; R\u0026ouml;er, 2022). Interestingly, we found no group differences for both effects.\u003c/p\u003e\n\u003cp\u003eThis is inconsistent with what prediction-based autism theories predict. According to these theories, autistic individuals should be more distracted by irrelevant babble due to weak priors (Hypo Priors Hypothesis), volatility fragmentation (Aberrant Precision Account of Autism), prediction delays (Predictive Impairment in Autism Hypothesis), and enhanced prediction error weighting (High, Inflexible Precision of Prediction Errors in Autism Hypothesis).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne possible explanation is that the assumed difference in the predictive advantage of multi-channel speech is particularly pronounced in the case of moderately complex auditory stimuli. If the acoustic environment is highly predictable, then this difference might be more difficult to observe, because even a less accurate model allows for a sufficiently precise prediction. The same is true for a highly unpredictable environment\u0026mdash;such as multiple sound streams presented at the same time\u0026mdash;because even a very accurate model can reduce the unpredictability of the incoming auditory stimulation to a limited extent. Note that while this inverse U-shaped function seems to fit the descriptively larger difference between the single-channel and dual-channel conditions in the autistic group compared to the nonautistic group in the present study, it is a mere post-hoc interpretation that would require critical empirical testing in future studies.\u003c/p\u003e\n\u003cp\u003eHowever, this does not necessarily mean that the inflexibility in handling prediction errors does not have an effect on the processing of irrelevant auditory information. The formation of a neural model and the detection of prediction errors operate at different stages of the information processing cycle, but there is constant cross-talk between these stages. If the attentional system detects a mismatch between the neural model and the incoming auditory stimulation, the neural model will be updated accordingly (e.g., Cowan, 1995; N\u0026auml;\u0026auml;t\u0026auml;nen, 1990). This model contains bottom-up information such as the physical characteristics of the auditory sequence, but also top-down information such as prior knowledge and experience. In the present study, we only focused on bottom-up prediction errors. For a comprehensive understanding, it would be worth investigating whether the same results can be observed with top-down prediction errors, for example in statistical learning or semantic processing. This is relevant to the ongoing debate in predictive processing about how the disruptive potential of changing-state distractors as speech are determined by both, the consistency of bottom-up stimulation and top-down expectations (Hughes \u0026amp; Marsh, 2020; Vachon et al., 2012).The finding that the intensity of the distractors had a similar effect in both groups is consistent with the prediction-based autism theories (Pellicano \u0026amp; Burr, 2012; Lawson et al., 2014; Sinha et al., 2014; Van de Cruys et al., 2014). A priori, however, it was unclear whether autistic individuals react differently to loud sounds than nonautistic individuals, because ASD is often associated with a reduced tolerance to loud sound (Poulsen et al., 2024; Rotschafer 2021) and in some studies, autistic individuals were more distracted by loud sounds than nonautistic individuals (Takahashi et al., 2014; Kargas et al., 2015; but see Kuiper et al., 2019; Bonnel et al., 2010).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo verify whether our results are evidence for the absence of a group effect, we performed a post-hoc equivalence test against zero (Campbell \u0026amp; Lakens, 2021), which could not be confirmed as the confidence intervals of both effect sizes for the non-significant group-interaction effects for the irrelevant babble effect and the intensity effect [(0.00, 0.074); (0.00, 0.043)] exceed the predefined equivalence bounds (\u0026eta;\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e\u0026nbsp; = 0.03), and both tests were not significant [(\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.223); (\u003cem\u003ep\u003c/em\u003e = 0.062)]\u003csup\u003e[1].\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImportantly, the absence of a significant group-interaction does not seem to be simply due\u0026nbsp;to a particularly heterogeneous sample that would obscure possible group effects, making it challenging to detect differences between autistic and nonautistic individuals. ASD is characterized by significant heterogeneity, encompassing a wide range of symptoms and manifestations, also concerning sensory features (Uljarević et al., 2017). Statistically, this would have been reflected in a greater variance within the autistic group compared to the nonautistic group. However, Levene\u0026apos;s tests revealed homoscedasticity for both the irrelevant babble effect (\u003cem\u003ep\u003c/em\u003e = .123) and the intensity effect (\u003cem\u003ep\u003c/em\u003e = .131).\u003c/p\u003e\n\u003cp\u003eNevertheless, we would like to draw attention to the fact that the autistic participants in this study represent only a subgroup of the autism population (i.e., individuals with average or above-average intelligence and typical language skills). Furthermore, groups were not matched for age nor gender. On average, the autistic group was 6.5 years older than the neurotypical group and there were relatively more female participants in the nonautistic group. Importantly, post-hoc analyses suggested that neither age nor gender contributed significantly to the pattern of results\u003csup\u003e[2]\u003c/sup\u003e. Moreover, speech stimuli may possibly reflect general language problems rather than specific predictive processing deficits. We circumvented this possible confounding factor by controlling for language skills in the autistic sample. See O\u0026rsquo;Shea \u0026amp; Engelhardt, (2025), for a similar argument. Also, autistic people were reported to show a comparable impression of humanness for human and synthetic voices (Kuriki et al., 2016). Lastly, one consideration that applies to many empirical studies is that more data would allow for an even better estimate of the true effect. We counter this argument by having preregistered an effect size of minimal interest that we deemed important enough to be theoretically and practically meaningful. Of course, we cannot rule out the possibility that a larger sample size would have resulted in a statistically significant group difference. However, such a small effect, if it exists, would not change the overall interpretation that the similarities in the processing of irrelevant auditory information between autistic and nonautistic individuals outweigh the differences.\u003c/p\u003e\n\u003cp\u003eThis result is in line with accumulating evidence that autistic individuals do not always respond to irrelevant auditory information differently than nonautistic individuals, at least when it comes to relatively early stages in the processing cycle (Alikadic \u0026amp; R\u0026ouml;er, 2024; Alikadic \u0026amp; R\u0026ouml;er, 2025; Haigh et al., 2016). However, this similarity does not mean that there are no practical implications for the autistic community. The opposite is true. It is precisely because we know that differences can emerge at later stages in the processing cycle that autistic people must take special precautions to protect themselves from irrelevant sound (Alho et al., 2021; Patterson et al., 2021; Li et al., 2025, but see Alispahic et al., 2024). Complex stimuli in particular should be avoided (see also Gon\u0026ccedil;alves \u0026amp; Monteiro, 2023; Key \u0026amp; D\u0026apos;Ambrose Slaboch, 2021) and if that is not possible, adding white noise can help to minimize the disruptive effect (Beaman, 2005). All sounds processed by our system have the potential to distract us. It is therefore important to develop a better understanding of which sounds in particular are subjectively and objectively harmful to derive tailored, autism-specific recommendations in order to improve the circumstances of autistic people\u0026rsquo;s lives.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatements and Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first author of the study is autistic. The recruitment procedure and study were co-designed with autistic consultants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research reported in this article was supported by [details omitted for double-anonymized peer review].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlc\u0026aacute;ntara, Jos\u0026eacute; I., Weisblatt, E. J. L., Moore, B. C. J., \u0026amp; Bolton, P. F. (2004). 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International statistical classification of diseases and related health problems (11th ed.). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://icd.who.int\u003c/span\u003e\u003cspan address=\"https://icd.who.int\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e The analysis can be found on the project page [details omitted for double-anonymized peer review]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The analysis can be found on the project page [details omitted for double-anonymized peer review].\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Irrelevant sound effect, irrelevant speech effect, auditory distraction, working memory, selective attention","lastPublishedDoi":"10.21203/rs.3.rs-9447894/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9447894/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"There is ample evidence that autistic adults respond differently to task-irrelevant background sound compared to nonautistic adults. Theories can be categorized according to the processing level at which this difference should occur. To test these theories, we contrasted two classic auditory distraction effects in autistic and nonautistic adults: (1) The irrelevant babble effect refers to the greater disruption by single- and dual-channel speech compared to multi-channel speech, (2) the intensity effect to greater disruption by high-intensity compared to low-intensity sound. Four prominent prediction-based autism theories predict a more pronounced irrelevant babble effect for autistic individuals due to weak priors (Hypo Priors Hypothesis), volatility fragmentation (Aberrant Precision Account of Autism), prediction delays (Predictive Impairment in Autism Hypothesis), and enhanced prediction error weighting (High, Inflexible Precision of Prediction Errors in Autism Hypothesis), respectively. However, there were no group differences when it comes to the irrelevant babble effect and the intensity effect, suggesting once again that autistic adults (with average or above-average intelligence and typical language skills) and nonautistic adults are not so different after all in how they process and respond to irrelevant auditory information.","manuscriptTitle":"Many voices are easier to ignore than one: Equivalent distractibility in autistic and nonautistic adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 10:26:52","doi":"10.21203/rs.3.rs-9447894/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"262c1b2c-3bac-45fb-8571-42bc6ead61af","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66630576,"name":"Social science/Psychology/Human behaviour"},{"id":66630577,"name":"Health sciences/Diseases/Neurological disorders/Neurodevelopmental disorders/Autism spectrum disorders"}],"tags":[],"updatedAt":"2026-04-24T10:18:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 10:26:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9447894","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9447894","identity":"rs-9447894","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}