Elsevier

Cortex

Volume 134, January 2021, Pages 181-194
Cortex

Research Report
Distractor-induced deafness: The effect of multiple auditory distractors on conscious target processing

https://doi.org/10.1016/j.cortex.2020.10.015Get rights and content

Abstract

Conscious access to a target stimulus embedded in a rapid serial visual presentation can be impaired by the preceding presentation of multiple task-irrelevant distractors. While this phenomenon – labeled distractor-induced blindness (DIB) – is established in vision, it is unknown whether a similar effect can be observed in the auditory modality. Considering the differences in the processing of visual and auditory stimuli, modality-specific effects in the inhibitory mechanisms triggered by distractors can be expected. First, we aimed to find evidence for a distractor-induced deafness (DID) for auditory targets in a behavioral experiment. The target was defined by a transient increase in amplitude in a continuous sinusoidal tone, which was to be detected if accompanied or preceded by a deviant tone (cue). Both events were embedded in separate streams in a binaural rapid serial auditory presentation. Distractors preceded the cue and shared the target's features. As previously observed for DIB, a failure to detect the auditory target critically relied on the presentation of multiple distractor episodes. This DID effect was followed up in a subsequent event-related brain potentials (ERP) study to identify the signature of target detection. In contrast to missed targets, hits were characterized by a larger frontal negativity and by a more pronounced centro-parietal P3b wave. Whereas the latter process was also observed in the visual domain, indicating a post-perceptual updating process, the frontal negativity was exclusively observed for auditory DID. This modality-specific process might signal that early attentional control processes support conscious access to relevant auditory events.

Introduction

In contrast to our rich environment, the verbally accessible contents of consciousness at a given time are limited (Baars, 1997; Block, 2007; Dehaene, Changeux, & Naccache, 2011). Although theoretical accounts agree that stimuli compete for access to consciousness, the implementation and mechanism of the selection process is still debated.

Top-down models of attentional control emphasize that a pre-defined task defines the settings of a central filter (attentional set). The attentional set prioritizes stimuli based on simple feature characteristics, such as color or orientation (Leber & Egeth, 2006). Positive attentional sets enhance processing of stimuli with pre-defined features (Nieuwenstein, 2006; Raymond, Shapiro, & Arnell, 1995), whereas negative attentional sets lead to the inhibition of these stimuli (Olivers & Watson, 2006; Zhang, Zhou, & Martens, 2009). A negative attentional set might also be engaged in the suppression of task-irrelevant distractors in a rapid serial visual presentation (RSVP) task (Olivers & Meeter, 2008; Zhang et al., 2009), which can affect the probability for access to the target.

To assess the characteristics of inhibitory processes, several experimental paradigms have been developed. In this experiment, we use the distractor-induced blindness (DIB (Michael, Hesselmann, Kiefer, & Niedeggen, 2011; Sahraie, Milders, & Niedeggen, 2001)), which shares some characteristics with the established Contingent Attentional Capture (CAC (Folk & Remington, 1998; Folk, Remington, & Johnston, 1992)) and the Attentional Blink (AB (Raymond, Shapiro, & Arnell, 1992)). In CAC, a single distractor preceding the target affects response time. In AB, two targets are embedded in a RSVP stream and conscious access to the second target is restricted depending on the temporal distance to the first target. The distractor-induced blindness combines the properties of these paradigms: Two relevant events occur in two RSVP streams presented concurrently. A “local stream” (e.g. change of the color of fixation at 10 Hz) contains a cue (e.g. single onset of a red fixation). In a “global stream” (e.g. random dot kinematogram surrounding the fixation), the target event is defined (e.g. short coherent motion). Participants have to decide whether simultaneously with or shortly after cue onset, the target stimulus was presented. Target detection is affected significantly by the presentation of target-like stimuli appearing before the cue, labeled as distractors, which ought to be ignored. Importantly, the DIB effect is solely observed if distractors share the target features (Michael et al., 2011; Sahraie et al., 2001; Winther & Niedeggen, 2018) and if multiple distractors and not only one are preceding the target (Winther & Niedeggen, 2017b). Probability of target detection gradually recovers with increasing the cue-target stimulus onset asynchrony (SOA). These characteristics were consistently observed for the target features motion, orientation, and color (Michael et al., 2011; Winther & Niedeggen, 2017a).

The DIB has been related to the activation of a negative attentional set by the repeated presentation of target-like, but task-irrelevant distractors (Niedeggen, Busch, & Winther, 2015). The effect demonstrates that conscious access to a relevant target can be prevented if distractors have cumulatively activated a negative attentional set. Comparable distractor effects have been assumed for the Attentional Blink (Zhang et al., 2009; Zhang, Zhou, & Martens, 2011).

ERP studies provided evidence that the DIB relies on a post-perceptual process. Comparing the ERP signatures of detected (hits) and non-detected (misses) targets, no differences were observed regarding early sensory components (Niedeggen et al., 2015; Niedeggen, Hesselmann, Sahraie, Milders, & Blakemore, 2004). However, in all studies a late centro-parietal positivity (further labeled as P3b) was significantly more pronounced if the target was detected (Niedeggen et al., 2004, 2015; Niedeggen, Michael, & Hesselmann, 2012; Winther & Niedeggen, 2017a). This indicates that the activation of the negative attentional set does not induce suppression on a sensory level of processing, but activates a central gating system preventing the update of target-like information into working memory. These results parallel findings from the attentional blink (Kranczioch, Debener, & Engel, 2003; Sergent, Baillet, & Dehaene, 2005; Vogel, Luck, & Shapiro, 1998) and support the notion that working memory operations – related to the expression of the P3b – serve as an indicator of conscious access (Dehaene et al., 2011).

In this study, we raise the question whether the reliable DIB effect in the visual modality can be extended to the auditory modality. Although overarching top-down filtering models apply to visual (e.g. (Di Lollo, Kawahara, Shahab Ghorashi, & Enns, 2005; Moore & Zirnsak, 2017; Olivers & Meeter, 2008)) and auditory stimuli (Sussman, Winkler, Huotilainen, Ritter, & Näätänen, 2002), we have to consider that visual and auditory processing differ in several respects. In comparison with the visual system, the auditory system exhibits a higher temporal integration of incoming information, as well as an extensive preprocessing of sensory input by subcortical structures (VanRullen, Zoefel, & Ilhan, 2014). Most importantly, perceptual load has an impact on the processing of irrelevant distractors in the visual modality (Lavie, 2010; Lavie & Tsal, 1994), whereas a corresponding effect cannot be consistently observed in the auditory modality (for a review (Murphy, Spence, & Dalton, 2017)). Murphy, Fraenkel, and Dalton (2013) interpreted the missing influence of perceptual load on auditory distractor detection as a result of additional processing capacity for not attended information exclusive to this modality.

The impact of modality-specific differences has already been demonstrated in studies on the auditory Attentional Blink. Even though several studies suggest an AB-like effect in the auditory modality (Duncan, Martens, & Ward, 1997; Horváth & Burgyán, 2011; Shen & Alain, 2010; Shen, Vuvan, & Alain, 2018; Tremblay, Vachon, & Jones, 2005; Vachon & Tremblay, 2006), it was absent (Potter, Chun, Banks, & Muckenhoupt, 1998) or reduced (Arnell & Jenkins, 2004; Soto-Faraco & Spence, 2002) in other studies. If an auditory AB was reported, its time course (T2 detection as a function of the T1/T2 lag) was characterized by a rather linear increase with increasing T1/T2 lag (Shen & Alain, 2010), and the typical lag-1 sparing (Shapiro, Raymond, & Arnell, 1997) was missing (e.g. (Horváth & Burgyán, 2011; Vachon & Tremblay, 2005)). These differences in behavioral effects were also confirmed in ERP studies: Although the reduction of the P3b amplitude observed for the processing of T2 at short T1-T2-SOA is in line with a post-perceptual process (Shen & Alain, 2010; Shen et al., 2018), a reduced N1 wave suggests an effect on sensory processing (Shen & Alain, 2010).

The mixed evidence from research on the auditory AB triggered the question whether the characteristics of the DIB can be observed in the auditory modality. To this end, we designed an auditory setup containing two streams in which we defined cue, target, and distractor events (see Fig. 1). This setup allows us to tackle the following experimental questions:

  • 1.

    Can we identify the behavioral characteristics of a DIB in the auditory domain?

  • If a Distractor-induced deafness (DID) shares the characteristics of a visual DIB, it is expected to depend on the presence of multiple distractors (Winther & Niedeggen, 2017b) and the temporal proximity of cue and target (Sahraie et al., 2001; Winther & Niedeggen, 2017a). Therefore, we hypothesized that an auditory DIB is elicited by multiple, but not by a single distractor. Moreover, we hypothesized that an auditory DIB is more expressed when cue and target are presented simultaneously as compared to a successive presentation.

  • 2.

    Does the DID effect rely on a post-perceptual process?

  • Following previous ERP-studies on the DIB (Niedeggen et al., 2004, 2012, 2015), we hypothesized that the P3b amplitude is a reliable signature of conscious target detection and that this component is enhanced for detected as compared to non-detected trials. If earlier ERP components are affected by the accessibility of the target – as suggested by Shen & Alain (2010) in an auditory AB study – an effect of distractors on perceptual processing must be considered.

Section snippets

Participants

The experimental procedure was approved by the local ethics committee at the FU Berlin (027/2019).

Sample sizes were computed a priori using G∗Power (Faul, Erdfelder, Lang, & Buchner, 2007). In both studies, we aimed to detect effects in an F-test with a power of 80% and an α of .05. For the behavioral study (dependent variable: target detection rate) we assumed a medium sized effect (f = .30) for the within-subject factor ‘number of distractors’ (one vs multiple distractors), leading to a

Behavioral study

Mean detection rates are presented in Table 1.

Data analysis revealed that target detection was significantly affected by the number of distractors preceding the target (Factor ‘number of distractors’: F (1.471, 35.312) = 12.99, p < .001, ηp2 = .351). To decide whether this main effect was driven by the multiple distractor condition, we performed pairwise post-hoc comparisons between the three conditions. Targets were less likely to be detected, if multiple distractors were presented (0 vs 1

Summary of results

The current study aimed to test whether the prerequisites of distractor-induced blindness identified in the visual modality also apply to the auditory modality. Moreover, we examined whether an ERP signature comparable to the one observed for visual stimuli likewise characterizes explicit auditory target detection. The behavioral data confirmed that a distractor-induced deafness (DID) can be elicited, if multiple task-irrelevant distractors that share the target's features precede the

Credit author statement

Lea Kern: Conceptualization, Methodology, Formal Analysis, Investigation, Data Curation, Writing – Original Draft, Review & Editing, Visualization; Michael Niedeggen: Conceptualization, Methodology, Formal Analysis, Supervision, Resources, Writing – Review & Editing, Visualization.

Author notes

We report how we determined our sample size, all data exclusions, all inclusion/exclusion criteria, whether inclusion/exclusion criteria were established prior to data analysis, all manipulations, and all measures in the study. No part of the study procedure and analysis was pre-registered prior to the research being conducted.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Open practices

The study in this article earned Open Materials and Open Data badges for transparent practices. Materials and data for the study are available at Mendeley Data, https://dx.doi.org/10.17632/b5gwh2y65d.5.

Declaration of competing interest

None.

Acknowledgements

We thank two anonymous reviewers for their constructive and helpful comments on this manuscript.

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      For the behavioral study, we conservatively assumed a medium sized effect (f = 0.30) for the within-subject factor ‘number of distractors’ (one vs. multiple distractors), leading to a calculated sample size of N = 24. For the ERP study, we expected a medium to large effect of f = 0.35 for the frontal negativity and P3, based on the effects observed in the auditory DID study (Kern and Niedeggen, 2021a). This resulted in a calculated sample size of N = 19 for the difference between hits and misses (within-subject factor ‘condition’).

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