To assess working memory (WM) performance using novel tasks that included controlled-attention functions of updating of information, selective attention, task monitoring, and multiple task coordination as well as relating performance to perceived listening effort.
Sixteen adults with normal hearing participated in two tasks:1) the attention switching (AS) task that required participants to categorize digits rapidly and recall totals of each category and 2) the working memory span task (WMST) that involved storing and recalling digits while performing a secondary sentence verification task. Each experiment was conducted in quiet and in multi-talker babble.
In the AS task, performance was worse when noise was added and when digit categories changed. In the WMST, digit recall accuracy was worse in noise, but reaction times (RTs) were not affected.
The results of this experiment show that accuracy and RT performance on auditory cognitive tasks generally worsens in background noise, even when intelligibility remains high. This suggests that background noise increases WM load and the decline in performance can be interpreted as an increase in listening effort. The specific integration of attentional control in these tasks, especially the AS task, may 1) have increased the sensitivity over traditional WM tasks and 2) allow for the ability to differentiate between peripheral and central mechanisms.
Interfering sounds pose challenges to successful verbal interactions, especially if the listener has a hearing loss. For several decades, quantifying speech intelligibility in noise has been the preferred method to assess listener performance [
In a real-world listening environment, it is uncommon to encounter a situation in which a person is required to repeat back a sequence of words or sentences with no additional processing involved; virtually all verbal interactions require some level of language comprehension. Therefore, listening tests that engage higher levels of processing may provide a more realistic method to measure a person’s listening abilities. Since listening comprehension involves simultaneous bottom-up and top-down processing, it is generally believed that higher order cognitive systems, including attention and memory, are involved in these automatic and controlled processes [
Working memory (WM) describes a mechanism used to store and process auditory, verbal and linguistic information [
Supporting this notion, it has been shown that while cognitive factors have a role in speech intelligibility in noise, the extent of their contribution has been mixed across studies [
Baddeley et al. employed a verbal sentence recall task with concurrent WM tasks, thus involving executive (e.g., coordination, allocation, focus of attention) processes [
Rönnberg proposed the Ease of Language Understanding (ELU) model to account for the role of WM in language processing [
Allocation of attentional resources plays a key role in WM processes. Several models of WM incorporate a central processing unit that encumbers attentional resources in order to carry out executive functions [
In recent years, an increasing understanding of the modality-specific aspects of WM has been articulated. The idea of central storage has been maintained largely for categorical information. Added to this, however, is the notion of a peripheral store that holds certain types of modality-specific information. Given that sensory information decays quickly, the categorical information from central storage is required for recall. Thus, WMC is the aggregate capacities of these two stores. Attention, however, is a resource available to the central store only [
The difficulty of the task and/or the environment in which the information is presented, as well as the capacity inherent to the individual, modulates the amount of resources an individual must encumber; more difficult scenarios require the individual to utilize more resources (i.e., increase effort) to perform the task [
Several researchers have used a dual-task paradigm to measure listening effort along with speech recognition in noise. Dual-task paradigms are designed under the assumption that performing two concurrent tasks, generally of different modalities, will compete for common cognitive resources. A common protocol for dual-task procedures is speech recognition (primary task) followed by a memory or visual tracking task [
Dual-task experiments have demonstrated the dynamic relationship between cognitive load and speech understanding in noise. While dual-tasks have shown to be effective as objective measurements of effort using different modalities, this experiment was designed to assess the attentional demands placed on auditory WM when listening in an adverse environment. Accordingly, measuring WM and attentional processes can provide valuable information about listening effort. Considering recent evidence demonstrating the modality-specific mechanisms of WM, the tasks developed for this experiment allowed investigation of the auditory modality without interactions from others (e.g., visual). As such, performance on these tasks would directly assess the auditory domain. Tasks designed such that all stimuli are restricted to the auditory domain and include specific attentional control requirements may better inform the relative roles between the peripheral store and the central components of working memory and attention [
The aim of this study was to further examine the role that cognitive load plays in regards to listening effort using WM- and attention-related tasks. By collecting objective data on how listeners’ WM processes and attention are affected by background noise, it is possible to develop an understanding of how the presence of noise impacts speech understanding at a cognitive level. A recent study by Lunner et al. showed that when hearing-impaired participants performed a word identification and retrieval task in the presence of low levels of background noise (i.e., 95% identification), performance on the task was significantly improved when digital noise reduction algorithms were implemented [
To better understand the cognitive mechanisms involved in listening in difficult environments, an auditory-based complex working memory span task (WMST) and an auditory attention switching (AS) task were designed. The WMST designed for this study was a complex auditory WM span task that involved simultaneous processing and storage of auditory information, rather than the traditional dual-task paradigm involving two concurrent tasks of different modalities. This task was adapted from the reading span task [
In this experiment, the WMST incorporated attentional control by requiring a decision on the truth value of the sentences while retaining unrelated information. Considering the importance of attentional control in complex WM tasks, an auditory attention-switching (AS) task was designed to tax this aspect of WM. The AS task explicitly examined attentional control by measuring a participant’s ability to update and switch between two separate “bins” of information. This task was adapted from Magimairaj and Montgomery [
Demands on explicit processing in the WMST and AS tasks were controlled by the independent variables of listening condition (both tasks), stimuli category (AS), and sentence complexity (WMST). To manipulate the difficulty of the listening condition, participants completed the tasks in quiet and in the presence of multi-talker babble (MTB). For the AS task, several hypotheses were made concerning listening condition: 1) recall accuracy at the end of each string of digit presentations would be poorer in the noise condition, which served as the primary task outcome, 2) reaction times (RTs) would be longer in the noise condition, and 3) subjective effort ratings were expected to be higher (more effortful) in the noise condition. The AS task was designed so that the digit presented fell into one of two categories which could either remain the same or switch to the other category for each subsequent presentation. The final hypothesis was that the presentations that switched categories would have longer reaction times (RTs) than those that stayed in the same category.
For the WMST, several main hypotheses were also made concerning noise condition: 1) digit recall accuracy at the end of each block of sentences would be poorer in the noise condition, which served as the primary task outcome, 2) RTs would be longer in the noise condition, and 3) subjective effort ratings would be higher (more effortful) in the noise condition. The WMST was designed to have simple “subject-verb-object” (SVO) and complex “objective-relative” (OR) sentences to measure speech comprehension. This independent variable of sentence complexity led to several further hypotheses: sentence verification accuracy would likely be decreased for the more-difficult OR sentences. Accordingly, it was hypothesized that RTs would be higher for the OR sentences. The addition of MTB however, was not expected to affect sentence verification accuracy since intelligibility for both conditions was intended to be high (SNR=90%).
Sixteen adults (mean age, 22.9 years; range: 21 to 29 years) with normal hearing participated in the study. All participants passed hearing screenings for air conduction thresholds at 20 dB HL for octave frequencies from 500–4,000 Hz. Participants also completed an informed consent form and the Six-Item Cognitive Screener [
Digits and MTB were used as stimuli for Experiment 1 (attention-switching task, AS) and Experiment 2 (working memory span task, WMST). Digits one through nine were recorded by a male speaker in a sound attenuating booth using a digital recorder (Marantz PMD661, Mahwah, NJ), a microphone (Sennheiser 845 S, Old Lyme, CT), and digital recording software (Avid Audio Pro Tools 8.1, Burlington, MA). All digits were edited to normalize duration and level using Adobe Audition 3.0 and Pro Tools 8.1. The average duration of all raw recorded digits was 392 ms; this duration was chosen as the target length for each digit and all digits were time compressed or lengthened to make them equal in duration with no audible distortion. All stimuli were normalized to have equal RMS. The MTB was taken from the QuickSIN test [
The digits, MTB, and sentences were calibrated to 60 dB SPL using a Brüel and Kjær (Nærum, Denmark) sound level meter Type 2,250 with Brüel and Kjær half inch, style 4,189, free field microphone, C-weighting network. For calibration, the microphone was placed directly facing the speaker at 0° azimuth with no person or objects positioned around the microphone. The distance from the microphone to the speaker was 3 feet; the height of the speaker and microphone was 3 feet off the ground.
All stimuli were output by a computer soundcard (Creative Labs Soundblaster X-Fi, Milpitas, CA) and routed through a programmable attenuator (Tucker-Davis Technologies PA5, Alachua, FL). The stimuli were then amplified through an audio amplifier (Crown CTs-4200, Elkhart, IN) and presented over a loudspeaker (B&W DM601 S3, West Sussex, UK). Participants were instructed to keep their heads positioned directly in front of the speaker; the placement of the chair in front of the computer facilitated this. All stimuli were presented from a loudspeaker located at 0° azimuth and at a distance of 3 feet from the participant’s head.
Communication between the participant and examiner occurred through an intercom system. Instructions were provided via a touch-screen monitor (3M Microtouch, St. Paul, MN). The subject used a keyboard (Dell, Round Rock, TX) to respond to the stimulus. Responses were recorded through specialized software (Psychology Software Tools E-Prime 2.0 Professional Edition, Sharpsburg, PA).
In this task, the ability of participants to process and update incoming speech information appropriately was assessed in quiet and in the presence of MTB. Participants were first asked to determine whether a digit belonged in one of two categories, and after an unspecified number of digits had been presented, they were asked to express how many digits were presented for each category. Measuring RT after each presentation and accuracy after each string of digits allowed the assessment of how one can simultaneously categorize and store new information in the presence of background noise. Based on the discussed models of WM, adding low levels of noise to the task increased the cognitive load by drawing some cognitive resources away from the processing task in order to suppress the background noise.
Prior to the AS task, an SNR assessment was employed to determine the level that the MTB interferer must be presented in order to load WM and attention capacities without reducing intelligibility. This assessment found the level of the MTB (or rather, the ratio between the speech and babble) for which ≥ 90% recognition was achieved for digit stimuli; this was done to ensure that each participant performed the task under the same relative load. In this task, the participants were asked to repeat back digits in the presence of MTB starting at 0 dB SNR. Twenty-seven digits were randomly presented in each SNR condition. The level of the digits was kept constant at 60 dB SPL while the level of MTB was adjusted in 2.5 dB steps. The lowest SNR at which each participant was able to achieve ≥ 90% correct recognition was the SNR used for that participant in the noise condition of Experiment 1 (see
The AS task was performed in quiet and in the presence of MTB by all participants; the order was counterbalanced. The MTB level was determined by the participant’s SNR assessment results, as described above. The level of digits was kept constant at 60 dB SPL for both quiet and noise conditions. For any given presentation, the participants were instructed to keep track of the category in which the digit was associated, e.g. “Low” (i.e., 1–4) or “High” (i.e., 6–9). One-hundred milli-seconds after the participant pressed a button, a subsequent digit was presented. Participants were instructed to keep their hand on the designated area marked in front of the keyboard between presses of the space bar; they were told not to leave their hand on or above the space bar. Participants were also instructed to respond as quickly as possible on the task while still maintaining accuracy; there was a 2.5 second maximum RT allowed. The time between the end of the digit presentation and the button press was recorded as the RT. The participant pressed a button to initiate the next trial. This response indicated the participant’s readiness for the next trial. As such, RTs were recorded after each digit trial, but accuracy data were not. Accuracy was tallied at the end of each block when the participant was instructed to recall verbally how many digits were presented from each category (e.g., “5 low, 7 high”). Therefore, accuracy calculations were performed at the string level (i.e., accuracy results were only recorded after an entire string of digits had been presented). The total number of digits in each string ranged randomly from 12–15. See
The presentations of the digits were categorized as either
Effort Rating: Following each block for both the quiet and MTB condition, participants were asked to rate their perceived effort on a visual scale ranging from 0 (no effort) to 10 (most effort) by typing the corresponding value on the keyboard.
Results for the AS experiment were analyzed using a paired-samples t-test to examine accuracy results and a within-subjects repeated-measures 2 (condition: quiet vs. noise)×2 (task condition: switch vs. non-switch) factorial ANOVA to examine RT results.
A paired-samples t-test was conducted to examine the effect of noise condition on digit recall accuracy. Digit recall accuracy scores (proportion correct) were significantly higher for the quiet condition (M=0.88, SE=0.024) compared to the noise condition (M=0.64, SE=0.037.),
A repeated-measures factorial ANOVA conducted on RT revealed a significant main effect of noise condition; the mean RTs for the quiet condition (M=1,671 ms, SE=101.6) were significantly faster than the noise condition (M=1,997 ms, SE= 124.8),
After each block, listeners rated their perceived effort using a rating scale from 0–10. A Mann-Whitney U test was used to analyze subjective effort rating in quiet and noise conditions. For the AS task, the test results revealed significantly less effort in quiet (median=4.00) than in noise (median=5.94), U= 54.5, z=−2.77,
The results of Experiment 1 demonstrate that adding MTB to the AS task significantly impacted both accuracy and RT. Performance accuracy was significantly poorer for conditions with MTB present, and the RT data show that responses were also significantly slower in the noise condition. These findings show that the addition of noise, even at relatively low levels (90% SNR), significantly affects attention-related processing abilities, presumably as a result of requiring more cognitive resources to perform the tasks in an adverse listening environment. A reasonable interpretation of this decrease in performance is that it is due to an increase in listening effort [
During the task, the participants’ attentional focus was likely directed toward the memory bank of the previous trial [
The ability to assess the semantic plausibility, or truth value, of sentences as well as to remember digits spoken both in quiet or MTB was tested using a complex working memory span task. Listeners were asked to determine whether a sentence was true or false while simultaneously remembering digits presented prior to each sentence. By assessing two different sentence types as well as the ability to store digit information while simultaneously recalling the truth value of each sentence, the task assesses how a significant cognitive load can affect one’s accuracy and RT.
Each participant’s digit span was measured in quiet to determine the number of sentences to be presented in the WMST experiment. The digit span task is a classic measure of short term memory with well-established validity and reliability [
The method for determining each participant’s 90% SNR was identical to Experiment 1 except sentences were used instead of digits. This level was then used as the setting for the noise condition in Experiment 2 (see
In this task, participants were presented with a sequence of digits and SVO and OR true/false sentences. First, a digit was presented followed by a 100 ms gap and then a sentence. After each sentence, the participant responded by pressing a key indicating whether the sentence was true or false. This response was measured as the participant’s sentence accuracy. The time between the end of the sentence and the response was recorded as the RT. One hundred ms after the participant’s response, the next digit was presented. After the sequence of digits and sentences was complete, the participant was instructed to recite all of the digits presented in serial order; the participant needed to recall each digit in its correct serial position in order for the sequence to be counted as correct. This was recorded as the participant’s digit accuracy.
To maximize the probability that participants were listening until the end of every sentence, dummy sentences were also included in each block. The dummy sentences were created in such a way that their truth value could not be determined until the last word in the sentence was presented (e.g., SVO Dummy: “
Effort Rating: Following each condition, participants were asked to rate their perceived effort. This effort scale ranged from 0 (no effort) to 10 (most effort).
Results for the WMST experiment were analyzed using a paired-samples t-test and two within-subjects repeated-measures 2 (condition: quiet vs. noise)×2 (complexity: SVO vs. OR) factorial ANOVAs. The dependent variables in this experiment were digit recall accuracy, sentence verification accuracy, and sentence response time. The independent variables were noise condition (quiet vs. noise) and sentence complexity (SVO vs. OR). A paired-samples t-test was conducted to examine the effect of noise condition on digit recall accuracy. Digit recall accuracy scores (proportion correct) were significantly higher for the quiet condition (M=0.67, SE=0.067) compared to the noise condition (M=0.51, SE=0.058),
After each trial, listeners rated their perceived effort using a rating scale from 0–10. A Mann-Whitney U test was used to analyze subjective effort rating in quiet and noise conditions. For the WMST, there was no significant difference in subjective effort between quiet (median =5.31) and noise (median=5.19), U=121.5,
Like Experiment 1, the results of Experiment 2 also showed that the primary experimental measurement, in this case digit recall accuracy, was significantly poorer when MTB noise was present, indicating that background noise affected the participants’ ability to store, process, and maintain information in adverse listening conditions. The complexity of the sentences affected the participants’ ability to judge the truth value of the sentences, with the more complex OR sentences having significantly lower accuracy scores than the simpler SVO sentences. This finding likely reflects the increased language processing required for the OR sentences. The addition of noise had no effect on RT in Experiment 2. It is possible that in terms of sentence complexity, the participants were trading off accuracy performance with speed performance; accuracy for the more difficult OR sentences was significantly lower than SVO, but RTs were the same. Therefore, participants were taking the same amount of time to process the easier SVOs and more difficult ORs, which resulted in lower accuracy for the OR sentences. This performance tradeoff could also provide a possible explanation as to why the digit recall accuracy was better in quiet than in noise, but no difference in RTs between the quiet and noise conditions. Participants spent the same amount of time processing sentences in both conditions, but had better digit recall performance in quiet. With the noise condition designed to be the more difficult listening condition, the participants could have been trading off better digit recall performance with faster RT performance. Similarly, there was no significant difference in subjective effort ratings between the quiet and noise conditions. The participants taking the same amount of time to respond in both the quiet and the noise conditions could also contribute to the subjective effort ratings being the same for both conditions in the WMST.
The goal of this study was to assess WM performance and the effects of a MTB interferer while completing novel attention-controlled, auditory-based cognitive tasks. The results of both experiments demonstrated that the presence of MTB significantly reduced the listeners’ ability to perform both auditory cognitive tasks. In Experiment 1, listeners’ ability to rapidly switch their attention was measured using an updating task paradigm. In order for participants to achieve high levels of accuracy with short RTs, careful control of attention was required. Adding noise significantly decreased accuracy and increased RTs. In addition, updating during switch trials was significantly longer than non-switch trials for both quiet and noise conditions. The switch cost, which represents the average RT difference between the switch and non-switch conditions, is the purest measure in this study of the time required to shift attention from one item to the next. As such, the significant cost measured in this experiment demonstrates the attentional load in these presentations. These results suggest that the switching of attention is significantly affected in the presence of noise due to an increase in cognitive load.
In Experiment 2, a complex WMST was constructed to measure serial digit recall along with basic language processing. The results indicated that listeners’ overall performance was poorer in recalling digits in the adverse listening condition; listeners were able to recall fewer digits in the presence of MTB. As hypothesized, not only was sentence verification accuracy significantly higher for SVO sentences than for OR sentences, but overall sentence verification accuracy was not different when MTB was added. Sentence verification depends heavily on intelligibility; in order to be able to determine the truth value of the sentences, participants must first be able to correctly identify all words in the sentence. Since the level of the background noise was intended to keep intelligibility levels high (90%), the finding that the addition of MTB had no effect on sentence verification demonstrates that intelligibility could be maintained throughout the experiment.
At its core, the WMST is a span task. Additional loads were added to allow a more comprehensive assessment of the WM system, including attentional control. A high accuracy score for sentence verification helps assure that the language processing served as a sufficient processing load during the task. Making decisions on the semantic plausibility of sentences requires controlled attention and language processing, which served to limit active digit rehearsal. Therefore, sentence verification accuracy can be thought of as a quality-control measure for the WMST digit recall accuracy, positioned to sufficiently tax WM cognitive resources. Thus, lower digit recall accuracy performance in the presence of background noise is likely attributed to an increase in cognitive processing load during the sentence verification portion of the task, not decreased intelligibility of the digits.
When MTB was added, overall performance dropped. Two possibilities have merit. First, the presence of an interfering sound that has many characteristics of speech posed a distractive load to attentional maintenance. This argument might explain the greater sensitivity of the AS task given its heightened attentional requirements. Second, even though intelligibility was impacted minimally, the redundant cues in speech may have been reduced, requiring greater top-down resources to achieve comprehension. In the ELU model [
The addition of MTB in Experiment 1 had a detrimental effect on both accuracy performance and RTs. This suggests that the switching of attention is negatively impacted by background noise, a notion that is corroborated by subjective effort ratings being significantly higher in the MTB condition compared to the quiet condition. The low level of background noise was chosen so as not to interfere with intelligibility of the stimuli, but instead was meant to tax cognitive resources. Participants rated the task as requiring significantly more effort in the noise condition. Consistently switching attention back and forth between two memory banks while also ignoring background noise requires more cognitive resources than switching attention in quiet. Decreased accuracy and increased RTs combined with the finding that subjective effort ratings were also affected by MTB shows that the addition of noise to cognitive tasks that require the consistent switching of attention are more effortful in the presence of modulated background noise. The participants’ perception of increased effort was substantiated by a drop in performance.
The addition of MTB in Experiment 2 had no effect on sentence verification accuracy, nor did participants subjectively rate the noise condition as being more difficult. The WMST consists of two concurrent sub-tasks: a span and language comprehension interdigitated within the span. Given that the sentences were readily intelligible even in noise, and that there was no carry-over (i.e., once a decision was made about a sentence, all information related to the sentence could be forgotten), this component of the WMST likely did not require substantial perceived effort. However, the span component of the WMST was likely perceived as challenging. First, there was significant time between presentations of the span elements in which rehearsal was largely prevented. Second, the block length was set to one element greater than the listener’s digit span. The instructions to the listener did not differentiate between the subtasks within the WMST. As such, a listener may have responded to the effort he or she expended for only the span component, the language processing, or a holistic impression of total effort. Accordingly, the likely outcome over a group of participants is one that would have an increased variability with an unpredictable distribution.
The results from this study support models of WM that describe attention as a separate control mechanism for WM. Participants were required to either maintain fixed attentional focus or shift their attention according to the presentation. Both tasks used in this study demand specific attentional control; the WMST involves shifting between the span element and the language processing, whereas the AS task has a relatively greater attentional requirement of shifting between bins that need updating on a more frequent basis as well as having a minimal processing and memory load. According to Cowan’s theoretical framework, the maintenance of the items (the memory span in the WMST and the size of each bin in the AS task) in short term memory is a result of activation, which is understood to be the mechanism of attention [
As described, the AS task has an augmented attentional load which distinguishes it from many other tasks used in published literature. Attentional resources have been described as a central bottleneck limiting retrieval to a single item at a time [
The results of the AS task are robust, more so than many tasks that include concurrent tasks such as dual-tasks or our WMST. A power analysis indicated that the minimum number of subjects is just a single subject for the AS task, and 12 for the WMST for a power of 0.8. The primary differentiator between the AS and WMST is that the direct measure of attention switching as a substrate of attentional control. A reasonable conclusion is that a task that has high attentional control requirements, such as the AS task, relative to storage and processing may allow a greater sensitivity than other traditional tasks. Indeed, this has been the case with a similar task in other modalities [
Current thinking on the WM system describes a modality-specific peripheral store and a general central mechanism [
Overall, the present study builds on the previous findings in auditory WM [
While the findings from this study are demonstrative, they were limited in scope. For example, the study allowed elucidation of the importance of attentional mechanisms and the relative impact of MTB, but not in a manner that is applicable to clinical populations at this time. Given the degraded signal output from impaired peripheral auditory systems, we suspect that equivalent conditions would result in a greater tax to WM and/or attentional mechanisms reducing overall performance for the hearing impaired. Stated in terms of the ELU model, the degraded cochlear output would increase the number of instances the explicit loop would be required for comprehension. Another limitation is that the study design limited the degree to which associations could be made with listening effort. Admittedly, this association was a secondary goal, but one that could prove interesting in how attention relates to perceived effort. Moreover, the perceived effort measurements in the WMST did not allow differentiation of which component of that task accounted for the perceived effort rating, thus limiting interpretation. Finally, while the use of MTB was based on pilot data, the impacts of other types of distractors and/or noises may be useful to understand, especially in how they relate to real-world situations.
Both of these tasks were designed to tax cognitive resources thought to be crucial for speech understanding. The complex WMST and AS tasks used in the current study required listeners to store and process information concurrently under attentional control; this is a skill highly utilized in conversations and general communication. In this study, adding MTB increased the load on central resources resulting in significantly increased time needed to process information and also reducing the accuracy of temporarily stored speech information. Since speech intelligibility remained unaffected during the tasks, the resulting decrease in performance can be attributed to an increase in cognitive load. The findings of this study demonstrate that increasing the processing load in WM by adding MTB negatively impacts listening effort but not speech understanding, and that a high-attentional load provides a sensitive measure of WM as well as offering a potential method to delineate between peripheral and central WM mechanisms.
A schematic representation of the AS task is depicted. A digit, from either a low (1–4) or high category (6–9), was presented at 60 dB SPL through a speaker to participants in quiet and in the presence of MTB. The RT (ms) was a measure of how quickly the participant selected which category (i.e. high or low) a digit belonged to. Following this keystroke, there was a 100 ms gap before the cycle repeated.
Results from the AS task are shown in box-plot form. In the upper panel, digit recall accuracy per noise condition is shown. In the lower left panel response time per noise condition is shown, and in the lower right panel, response time per trial condition (switch/non-switch) and noise condition is shown.
Perceived effort ratings for both AS and WMST per noise condition are shown.
A schematic representation of the WMST experimental set up is depicted. A digit and a sentence, separated by a 100 ms gap, were presented through a speaker in both quiet and in the presence of MTB. The RT (ms) was a measure of how quickly the participant determined the truth value of the sentence. Following this keystroke, there was a 100 ms gap before the cycle repeated.
Results of the WMST are displayed in box-plot form. Digit recall accuracy per noise condition is shown in the left panel and sentence judgment accuracy per trial condition (SVO/OR) and noise condition is shown in the right panel.
Descriptive statistics for participant settings in experiment 1 and experiment 2
AS SNR (dB) | 16 | −5 | 0 | −3.28 | 1.62 |
WMST SNR (dB) | 16 | 0 | 5 | 1.91 | 1.95 |
Digit Span | 16 | 5 | 9 | 6.06 | 1.06 |