Processing load during listening: The influence of task ...

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Processing load during listening: The influence of task characteristics on the pupil response a

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Sophia E. Kramer , Artur Lorens , Frans Coninx , Adriana A. a

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Zekveld , Anna Piotrowska & Henryk Skarzynski

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Department of ENT/Audiology and the EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands b

Institute of Physiology and Pathology of Hearing, International Center of Hearing and Speech, Warsaw, Poland c

Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany Published online: 13 Mar 2012.

To cite this article: Sophia E. Kramer , Artur Lorens , Frans Coninx , Adriana A. Zekveld , Anna Piotrowska & Henryk Skarzynski (2013) Processing load during listening: The influence of task characteristics on the pupil response, Language and Cognitive Processes, 28:4, 426-442, DOI: 10.1080/01690965.2011.642267 To link to this article: http://dx.doi.org/10.1080/01690965.2011.642267

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LANGUAGE AND COGNITIVE PROCESSES, 2013 Vol. 28, No. 4, 426442, http://dx.doi.org/10.1080/01690965.2011.642267

Processing load during listening: The influence of task characteristics on the pupil response Sophia E. Kramer1, Artur Lorens2, Frans Coninx3, Adriana A. Zekveld1, Anna Piotrowska2, and Henryk Skarzynski2 Downloaded by [Vrije Universiteit Amsterdam] at 02:39 26 August 2013

1

Department of ENT/Audiology and the EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands 2 Institute of Physiology and Pathology of Hearing, International Center of Hearing and Speech, Warsaw, Poland 3 Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany

This study examined the magnitude of the pupillary response evoked by a number of tasks varying in the nature and complexity of the auditory and linguistic information provided. The tasks comprised passive listening, anticipation to verbally responding to a prompt signal, auditory detection, and the identification of meaningful words. Performance in the auditory detection and identification tasks was matched at 79% correct. In all, 42 normally hearing adults (aged 1844 years, mean age 25.5 years) from three different sites (Amsterdam, Cologne, and Warsaw) participated. During each condition, the pupil diameter was measured. A Repeated Measures ANOVA was conducted to examine within and between subject (site) differences in the pupil response over 8 time intervals during the four conditions. The maximum mean pupil dilation was largest in the words-in-noise identification task (0.13 mm) and differed significantly from the maximum mean dilation in the noise-in-noise-detection task. The latter did not differ significantly from the pupil response during passive listening to noise and an answer prompt. No significant differences between sites were observed. Task evoked pupillary responses to theory-based measures of linguistic processing are robust, reliable, and sensitive to differences in task demands. Word-in-noise identification requires more processing load than nonspeech detection. To obtain information about within-subject differences in auditory processing, examination of both processing load and behavioural performance is recommended. Methodological implications are discussed.

Keywords: Processing load; Pupil dilation; Task demands; Linguistic; Words; Background noise; Detection.

A complete account of speech perception requires an understanding of the interaction between basic auditory (bottom-up) and higher-level cognitive (top-down) processes Correspondence should be addressed to Sophia E. Kramer, Department of ENT/Audiology, EMGO Institute, VU University Medical Center, Amsterdam, The Netherlands. E-mail: [email protected] Part of the research reported herein was supported by Marie Curie Host Fellowships for Transfer of Knowledge; Remediation of Hearing Loss; No. 042387. We greatly acknowledge the assistance of Hans van Beek in the development of the data analysis software and testing equipment. # 2013 Taylor & Francis


PROCESSING LOAD DURING LISTENING

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(Akeroyd, 2008; Arlinger, Lunner, Lyxell, & Pichora-Fuller, 2009; Davis & Johnsrude, 2007; Plomp, 2002). The perceptual grouping of sequences of sounds that convey meaning is not only driven by primitive grouping cues, such as similarity of pitch and timbre but also by powerful knowledge-driven mechanisms sensitive to linguistic characteristics like lexicality, context, and expectations (Kalikow, Stevens, & Elliot, 1977; Neely, 1991; Sheldon, Pichora-Fuller, & Schneider, 2008). Recently, research into the interplay between task and stimulus characteristics and the relevance of cognitive abilities in speech perception has been rapidly evolving (Arlinger et al., 2009), and there is ample evidence that bottom-up peripheral and top-down cognitive processes interact to support the perception and comprehension of meaningful speech (Kramer, Zekveld, & Houtgast, 2009; Lunner & Sundewall-Thore´n, 2007; PichoraFuller, 2007; Ro¨nnberg, 2003; Ro¨nnberg, Rudner, Foo, & Lunner, 2008; Schneider, Daneman, & Pichora-Fuller, 2002; Wingfield, 1996).

Interaction between bottom-up and top-down processes Speech perception depends on both the detection and integration of sounds (Cooke, 2006). Bottom-up detection of a signal involves grouping of spectro-temporal regions, followed by transmission of the signal to the auditory cortex, via the central auditory system. Interpretation of a signal involves more than detection alone; the listener combines dissimilar acoustic elements within the signal or phoneme and interprets them as a single meaningful event (Barker & Cooke, 1999; Best, Studdert-Kennedy, Manuel, & Rubin-Spitz, 1989; Carrell & Opie, 1992; Remez & Rubin, 1990). Signal discrimination is computationally more complex than signal detection as it involves additional top-down cognitive processes. The dynamic interplay between bottom-up and top-down processes during the comprehension of speech has been described by several models, like the framework for Ease of Language Understanding (Ro¨nnberg, 2003; Ro¨nnberg et al. 2008; Stenfelt & Ro¨nnberg, 2009) and the TRACE model (McClelland & Elman, 1986).

Processing load It is generally assumed that the bottom-up transmission of an undistorted signal implies relatively fast and effortless decoding. The use of ‘‘top-down’’ cognitive support during speech comprehension requires concentration and attention and is thus associated with increased processing load and more effortful listening (e.g., Kramer et al., 2009; McCoy et al., 2005; Pichora-Fuller, Schneider, & Daneman, 1995; Rabbitt, 1991; Sarampalis, Kalluri, Edwards, & Hafter, 2009; Tun, Wingfield, Stine, & Mecsas, 1992). To gain more insight into the interplay between listening conditions and the importance of cognitive processing, the processing load during speech perception can be compared between different tasks and stimuli. One way to objectively measure processing load is by pupillometry, that is, the examination of the task evoked pupillary response. This is the continuous recording of the pupil diameter using infrared eyetracking technology. Presentation software is used to ensure that pupil size measurements are synchronised in time with the presentation of the stimuli. The diameter of the pupil indirectly mirrors the activity of the autonomous nervous system and reflects the ratio of sympathetic and parasympathetic innervation of the iris muscles during sustained processing (Steinhauer, Siegle, Condray, & Pless, 2004). Extensive research has shown that the variation of the pupil diameter is a sensitive indirect measure of task-evoked cognitive processing load in various fields of research, including language processing (Hyo¨na¨, Tommola, & Alaya, 1995), speech


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production (Hoeks, 1995) and visual perception (Verney, Granholm, & Dionisio, 2001; for reviews, see Beatty & Lucero-Wagoner, 2000; Janisse, 1977), and mental effort (Beatty, 1982; Just, Carpenter, & Miyake, 2003). As there is a reliable correlation between task-evoked pupil response and central processing load (Beatty & LuceroWagoner, 2000), pupillometry can be a useful method. Beatty and Lucero-Wagoner (2000) investigated the pupil response during a signal detection task in which participants had to rate their task performance. The pupil response was largest for hits (amplitude 0.10 mm) and smallest for correct rejections (0.02 mm), with intermediate values for hits and correct rejections that were rated as ‘‘uncertain’’ by the participants. Kahneman and Beatty (1966) compared pitch discrimination with signal detection using pupillometry. They demonstrated that pitch discrimination involved more processing load than signal detection. Respondents were asked to compare the frequency of a range of pure tones with a 850 Hz standard tone, using a stepsize of 6 Hz. The amplitude of the pupil response evoked by the target increased monotonically with the similarity between the target and the standard stimulus, up to 0.20 mm for the discrimination of two sequential tones differing only one stepsize (i.e. 6 Hz). This result indicated that the cognitive processing load evoked by the comparison or discrimination task resulted in systematically larger pupil responses than those evoked by Beattty and Lucero-Wagoner’s (2000) signal detection task.

Processing load during listening Kramer, Kapteyn, Festen and Kuik (1997) applied the method of pupillometry to the listening of sentences in noises by normal-hearing and hearing-impaired listeners. They demonstrated a relationship between pupil dilation and the speech-to-noise ratio (SNR) of sentences in noise. Their results were replicated by Zekveld, Kramer and Festen (2010). Piquado, Issaacowitz and Wingfield (2010) compared auditory sentence processing in young and older adults and their findings confirmed the applicability of pupillometry to assess the effects of syntactic complexity and sentence length on the processing load during listening. Both Kramer et al. (1997) and Zekveld, Kramer and Festen (2011) demonstrated that the pupil response during speech processing can reflect group-differences in the relation between speech intelligibility and processing load that are not reflected by speech perception performance differences. Thus, pupillometry offers the opportunity to measure the underlying mental effort demanded during hearing that is not easily assessed with traditional audiometric or psychoacoustical measures. Kramer et al. (1997), Zekveld et al. (2010, 2011) and Piquado et al. (2010) all used sentences as stimuli. They manipulated task demands by either varying the SNR or syntactic complexity. Rather than restricting the task manipulations to a relatively complex sentence-identification task, the current study examined the processing load evoked by a range of relatively basic auditory tasks, varying from auditory passive listening, through auditory detection of a meaningless target noise-burst in noise to the identification of meaningful words (c.f., Beatty & Lucero-Wagoner, 2000). The aim was to provide insight into the relation between task demands (detection versus identification), the linguistic complexity of the stimuli (listening to noise versus listening to words presented in background noise) and the pupil response. A thorough understanding of how the pupil behaves in a range of listening conditions and in a range of listener groups will aid the interpretation of future pupillometric studies. We hypothesised that the processing load reflected by the pupil response would be


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smallest for the condition in which listeners ‘‘just listened to noise’’ without task constraints, somewhat higher in the condition in which participants verbally responded after the response prompt, higher for the simple auditory detection task, and highest for the condition in which listeners had to identify meaningful words presented in noise. The secondary aim of this study was to investigate whether replication of the experiment in three different languages and in three different laboratories in different countries would yield comparable results. Such knowledge is indispensable to demonstrate the generalisability of the method.


MATERIALS AND METHODS Centres Three sites in three different countries participated in the data collection: VU University Medical Center in Amsterdam, The Netherlands, the Institute of Physiology and Pathology of Hearing in Warsaw, Poland, and the University of Cologne in Cologne, Germany. The sites obtained ethical approval from their local ethical review committees.

Participants Each site recruited young adults to participate in the study. All participants were students or employees with a high educational level. The groups were matched on age and gender and, hence, did not differ significantly (p.05) on these variables. In Amsterdam, 15 subjects (3 males, 12 females) were included. Their ages ranged from 18 to 44 years, with a mean of 26.7 years (SD6.9 years). In Cologne, 14 subjects (3 males, 11 females) participated. Their ages ranged from 23 to 25 years, with a mean of 24.4 years (SD0.6 years). In Warsaw, 14 subjects (3 males, 11 females) were included. Their ages ranged from 21 to 35 years, mean 25.4 years (SD4.5 years). None of the participants reported any hearing problems and all had normal hearing according to their word-perception performance as examined with the Adaptive Auditory Speech Test (AAST) test (see below).

Pupillometry The pupillometric data of the left eye were measured with a SensoMotoric Instruments (Berlin, Germany) recording system (2D Video-Oculography, version 4). The system uses infrared video-based tracking technology to measure the pupil diameter. The spatial resolution of the pupillometer was 0.03 mm. The location and size of the pupil was automatically recorded at 50 Hz and a PC connected to the pupillometer stored the data together with time stamps indicating the start of the trials and the stimuli, the prompt signal, and the response of the experimenter. During testing, the experimenter inspected the raw pupil data to check for a valid recording of the pupil diameter; if necessary, corrective action was taken. The pupillometric equipment was calibrated using printed pupils of known size positioned on a head model. Three identical sets of hardware and software were used, one at each location.

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Stimuli In each condition, an answer prompt was presented. The prompt consisted of a 0.22 second Dual-Tone Multi-Frequency signal that corresponds to the numbers 5 and 6 on a telephone keypad. The same prompt was used in all conditions, as it clearly differed from the target stimuli in the various conditions. The level of the answer prompt was 65 dB sound pressure level (SPL). In the noise-in-background-noise detection condition, the target noise burst was a 1-octave wide noise band with a centre frequency of 2 kHz and a duration of 930 msec. This duration was matched to the average duration of the words presented in the words-in-background-noise identification condition described below. In the words-in-noise identification condition, the AAST was applied. This test was developed in Germany by Coninx (2005). The stimuli are six spondee words: Eisba¨ r, Flugzeug, Fussball, Handschuh, Lenkrad, and Schneemann (ice bear, airplane, football, hand shoe, bicycle, and snow man). The average duration of the German words was 0.90 s (range 0.801.0, SD 0.08 s). Validated Polish and Dutch versions of the AAST were available at the start of this study. The Dutch version used the following spondees: glijbaan, handschoen, ijsbeer, vliegtuig, voetbal, and vuurwerk. The mean duration of the Dutch words ranged from 0.79 to 0.87 s, with a mean of 0.84 s (SD 0.04 s). Spondees do not exist in the Polish language. Therefore, tri-syllable words were used in this language version: cukierek, dziewczynka, mikolaj, nozyczki, pudelko, and zjezdzalnia. The average duration of the Polish words varied from 0.89 to 1.18 s (M1.0 s; SD 0.12 s). The overall mean duration of the words was 0.93 s (SD 0.11 s). The results of validation studies demonstrated that the three language-versions are equivalent, each with a different norm score in dB SNR required to reach the performance level of 79% correct. This level was selected as the criterion performance level to make sure that the test would not be too easy to perform (i.e. around 100% correct, likely resulting in effortless speech perception) or too difficult to avoid any risk of resource overload resulting in a decline of the pupil response (Granholm, Asarnow, Sarkin, & Dykes, 1996). Stationary noise with a long-term-average speech spectrum matched to that of the target speech served as background noise in all conditions. The average long-term spectrum was determined based on a 2 min recording of the speaker reading text. The noise stimulus will be referred to as ‘‘background noise’’.

Conditions Four conditions were presented. These are described below. A schematic illustration of the timing of the interval in which the baseline pupil diameter was determined, stimuli and the answer prompt in the four conditions are given in Figure 1. 1) Background noise alone answer prompt, no response (BN-notask) In this condition, the background noise was presented to the listener for 25 trials of 7 s each at a level of 60 dB SPL. The answer prompt was presented at the end of each trial, 7 s after the onset of the noise. The next trial was started by the test leader directly after the answer prompt. The listener was instructed to just listen and say nothing, despite the answer prompt.


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Baseline interval pupil diameter Measurement interval of the main data

Answer prompt

Condition 1: BN-notask

Condition 2:


BN-response

Condition 3: Noise burst

NiBN-detect

Condition 4: AAST word

WiBN-ident

0

1

2

3

4 Time (sec) T1 T2 T3

5 T4 T5 Interval

6 T6

7 T7

T8

Figure 1. A schematic illustration showing the timing of the interval in which the baseline pupil diameter was determined, the stimuli, and the eight intervals used in the statistical analyses.

2) Background noise alone prompt response (BN-response) The background noise was presented to the listener for 25 trials. The noise was presented for 7 s. The answer prompt was presented at the end of each trial, 7 s after the onset of the noise. The listener was instructed to verbally respond with ‘‘yes’’ (in their native language) after each prompt. The next trial was started by the test leader directly after the participant’s response. This condition enabled us to examine whether anticipation to hearing a prompt signal and responding would elicit a pupillary reaction. 3) Noise-in-background-noise detection (NiBN-detect) During this condition, the background noise (60 dB SPL) was presented for 7 s. Three seconds after the onset of the background noise, the target noise burst was presented. Three seconds after the offset of the target noise burst, the answer prompt was presented. The listener had to respond ‘‘yes’’ if the target noise burst was heard and ‘‘no’’ if it was not detected. The first target noise burst was presented at a SNR of 10 dB. Using an adaptive procedure, the level of the target noise burst presented in the remaining trials was varied to estimate the SNR corresponding to 79% correct detection performance. After three subsequent correct answers, the next target noise burst was presented at a 2 dB lower level than the previous one. After each incorrect answer, the presentation level of the next target noise burst was increased by 2 dB.


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This 3-up-1-down staircase adaptive method targets 79% performance (Levitt, 1971). The test stopped after the seventh incorrect answer, resulting in at least 30 trials. The test started with five practice trials of which the data were not included in the analyses. The detection threshold was the average SNR of all reversal points in the staircase adaptive procedure. 4) Words-in-background-noise identification (WiBN-ident) In this condition, participants performed the AAST test using a 6-alternative forced choice (AFC) closed set procedure (Coninx, 2006). Prior to each word, the background noise was presented at 60 dB SPL for 3 s. Three seconds after the end of the word, the answer prompt was presented after which the participant repeated the word aloud or said ‘‘not understood’’. The experimenter scored the response after each word on a personal computer and then the next word was presented. The first word was presented at a SNR of 10 dB. The test started with five practice trials of which the data were not included in the analyses. After each word, the participant repeated the word or said ‘‘not understood’’ if they had not heard or identified the word. The same 3-up-1-down adaptive procedure and stop rule as used in the NiBN-detect condition was applied. The participant’s words-in-noise identification threshold was the average SNR of all reversal points in the adaptive procedure. At least 30 words were presented in this condition.

General procedure Test administration took place in a sound-treated room. The size of the rooms at the different locations were as follows: Amsterdam, L 4.2 m, W 3.1 m, H 2.2 m; Cologne, L 4.5 m, W 2.2 m, H 2.8 m; and Warsaw, L 4.5 m, W 3.5 m, H 2.3 m. Each participant sat in a comfortable chair. During the test conditions, they were instructed to maintain focus at a fixation dot to reduce the possibility that the light reflex affected the task evoked pupil response. The fixation dot was positioned at a white wall at 4 m distance from the participant. All auditory stimuli were presented through a loudspeaker, placed in front of the participant, positioned immediately under the fixation dot at about 3.8 m distance from the participant. First, participants performed the AAST using words in quiet and in noise to confirm normal hearing of the participants and to make them acquainted with the material. Then, the pupillometric equipment was positioned on the head of the participants and the camera focus was optimised. Participants were not allowed to wear corrective glasses during the tests, but corrective lenses were allowed, as they do not affect the recording of the pupil by reflecting the infrared illumination. The room illumination was adjusted to prevent ceiling and floor effects in the pupil response (Chapman, Oka, Bradshaw, Jacobson, & Donaldson, 1999; Zekveld et al., 2010, 2011). Therefore, first the pupil diameter was measured in maximum illumination (around 230 lx) and subsequently in complete darkness by placing the front cover plate of the pupillometric equipment in front of the eyes. The room illumination was adapted such that the pupil diameter was around the middle of its dynamic range. This makes the task-evoked pupil response independent of the baseline pupil diameter (Beatty & Lucero-Wagoner, 2000; Hyo¨ na¨ et al., 1995; Janisse, 1977). Participants were asked to inhibit eye blinks. Subsequently, participants performed the four conditions as described above. In each condition, the pupil diameter was measured in each trial. Every test session started with the BN-notask and BN-response conditions. The order of the NiBN-detect and


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WiBN-ident conditions was balanced across the subjects. The reason for this was that the pupil response varies during task performance (see e.g. Zekveld et al., 2010), and only in the NiBN-detect and WiBN-ident conditions, participants were asked to perform a reasonably difficult task rather than just saying ‘‘yes’’ after a response prompt. The duration of the test session was half an hour.


Data selection, cleaning, and reduction Down sampling of the data was performed by averaging each five subsequent samples which reduced the sample frequency to 10 Hz. Eye blink artifacts*defined as a brief drop of the pupil diameter to 0 mm*were removed by linear extrapolation across a 100 ms interval containing the blink. The traces were furthermore visually inspected for artifacts due to eye-movements by examining the recorded diameter, and the x- and y-coordinates of the pupil centre. For each participant, at least 75% of the traces per condition were included in the data analysis.

Calculation of pupil dilation indices A 5-point moving average smoothing filter was passed over the data of the selected and de-blinked traces after which pupil traces were averaged separately for each participant per condition. In accordance with the calculation of the scores, the pupil data (i.e. the pupil traces) of the first five trials in each condition were omitted from the analyses. The pupillometry data of all valid trials in each condition were included in the analyses. In each of the four conditions, the average pupil diameter in a 1.5 s interval during the presentation of the background noise alone was used as the baseline. In the NiBN-detect and WiBN-ident conditions, this baseline interval preceded the speech (AAST word) or the target noise burst. We determined the mean pupil dilation relative to the baseline pupil diameter in eight subsequent time intervals of 0.5 s between the end of the baseline interval and the response-prompt (see Figure 1). The mean dilation provides greater reliability and stability than peak measures (Ahern & Beatty, 1979; Verney et al., 2001). We divided the signal into 8 time intervals because we wanted to study the course of the pupil response and differences between the conditions in this relevant interval between the start of the baseline and the answer prompt.

Statistical analyses The purpose of this study was two-fold. First, we aimed to examine the effect of condition on the pupillary response. The second objective was to investigate whether replication of the experiment in another language and laboratory would yield similar results. Hence, we conducted a within-subjects repeated measures analysis of variance (ANOVA) with the eight intervals and four conditions as within-subject factors and centre as the between-subject factor. Descriptive statistics of the NiBN-detect, WiBNident, and pupil data were calculated. All analyses were conducted using SPSS 15.0.

RESULTS Table 1 reports the mean detection and identification thresholds (including standard deviations) per centre. The mean NiBN-detect thresholds in the three groups were similar. Note that the WiBN-ident thresholds using the AAST words slightly differed

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TABLE 1 Means and standard deviations (between parentheses) of the NiBN-detect and the WiBN-ident thresholds (dB SNR) per centre Centre

NiBN-detect, dB SNR

WiBN-ident, dB SNR

9.9 (1.7) 9.9 (1.5) 10.0 (1.7)

9.8 (1.2) 8.2 (1.3) 7.2 (2.1)

per language version (Coninx, 2006). Hence, different thresholds for the 79% performance level were observed. The descriptive statistics of the pupil data for each of the centres separately are graphically presented in Figure 2. The repeated measures ANOVA revealed a significant main effect of interval, F(7, 245) 10.1, pB.001, on the mean pupil dilation. We also observed a significant main effect of condition, F(3, 105)24.1, pB.001, and a significant interval condition interaction effect, F(21, 725)13.0, pB.001. The main effect of centre was not statistically significant (p.9), nor was the interaction effect between interval and centre and the interaction between condition and centre. To examine the origin of the interaction effect between condition and interval on the pupil response, we applied Tukey’s test (honest significant difference) to make all pairwise comparisons between conditions within each time interval. In this analysis, we averaged the results over the three centres, as no statistically significant effect of centre was observed. We additionally applied the Bonferroni correction as the comparisons between conditions were made 8 times, once for each interval. The mean dilation per interval for each condition is provided in Table 2. Table 3 indicates

Pupil dilation relative to baseline (mm)


Amsterdam (Dutch) Warsaw (Polish) Cologne (German)

Amsterdam (Dutch)

Warsaw (Polish)

Cologne (German)

0,2 0,15 0,1 0,05 0 -0,05 -0,1 1 2 3 4 5 6 7 8 0

1

2

3

1 2 3 4 5 6 7 8 Interval 0 1 2 3

1 2 3 4 5 6 7 8 0

1

2

3

sec WiBN-ident

NiBN-detect

BN-response

BN-notask

Figure 2. The mean pupil dilation relative to baseline (mm) during each of the four conditions separately displayed for each of the three centres (Amsterdam Dutch, Warsaw Polish, Cologne German). The pupil response is shown during 8 subsequent time intervals of 0.5 sec between the end of the baseline interval (start of the word or noise-burst in the WiBN-ident and NiBN-detect conditions respectively) and the response-prompt.

Seconds Interval 1. 2. 3. 4.

BN-notask BN-response NiBN-detect WiBN-ident

1

2

1 0.00 0.00 0.00 0.00

2 (0.01) (0.01) (0.01) (0.00)

0.01 0.01 0.01 0.00

3

3 (0.2) (0.02) (0.04) (0.02)

0.02 0.02 0.01 0.00

4 (0.04) (0.04) (0.07) (0.05)

0.01 0.03 0.03 0.07

4

5 (0.04) (0.06) (0.09) (0.07)

0.00 0.05 0.06 0.13

6 (0.04) (0.08) (0.10) (0.08)

0.00 0.05 0.05 0.11

7 (0.05) (0.10) (0.10) (0.09)

0.00 0.05 0.02 0.07

TABLE 3 Statistically significant results of all pairwise comparisons between conditions, corrected for multiple comparisons, for each of the 8 intervals Interval Pair 1 vs 2 vs 3 vs 1 vs 1 vs 2 vs

2 3 4 3 4 4

1

2

3

*

4

5

6

**

** *

**

** **

** **

** **

8

** **

*

(0.04) (0.10) (0.10) (0.09)

0.00 0.05 0.00 0.05

(0.04) (0.10) (0.09) (0.09)

435

Note: 1 BN-notask, 2 BN-response, 3 NiBN-detect, 4WiBN-ident. *p B.05, **p B.01.

7

8



TABLE 2 Mean pupil dilation (mm, relative to the baseline) per time interval for each condition (averaged over the three centres). Standard deviations are presented between parentheses


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which of the pairwise comparisons between conditions was statistically significantly different when corrected for multiple comparisons. No significant difference in pupil dilation between the BN-notask the BN-response conditions was observed during any of the eight time intervals nor between the BNnotask and NiBN-detect conditions. The pupil response in the BN-response condition was significantly smaller than in the NiBN-detect condition during the intervals T4 (Tukey’s q6.9, pB.01), T5 (q 9.3, p B.01), and T6 (q 8.0, p B.01). The pupil response in the BN-notask condition was significantly smaller than in WiBNident during T4 (q 7.1, p B.01), T5 (q11.5, pB.01), T6 (q 10.3, p B.01), and T7 (q8.8, pB.01). Similarly, the pupil responses in the BN-response condition were significantly smaller than in the WiBN-ident condition during the intervals T3 to T8: T3 (q5.1, p B.05), T4 (q11.7, p B.01), T5 (q11.1, pB.01), T6 (q8.8, p B.01), T7 (q6.7, pB.01), and T8 (q5.5, p B.05). Note that the mean pupil dilation in the NiBN-detect condition and in the WiBNident condition reached their maximum during T5 (Table 2). The difference in pupil response between these two conditions during this interval was significant (q5.1, pB.01) with a smaller pupil response in the NiBN-detect condition. In summary, the WiBN-ident condition evoked the largest pupillary response. This response was significantly larger than the response in BN-notask and BN-response conditions during at least four intervals. The response in the WiBN-ident condition was also significantly larger than that in the NiBN-detect condition during the interval in which the pupil response in these two conditions reached maximum dilation (i.e. T5). There were no significant differences between the BN-notask and BN-response conditions, or between the BN-notask and NiBN-detect conditions.

DISCUSSION This experiment was designed to examine the magnitude of the pupillary response evoked by a number of tasks varying in the nature and complexity of the auditory information provided. The tasks varied from auditory passive listening, anticipation to hearing a prompt signal, auditory detection of meaningless target noise-bursts in noise, and the identification of meaningful words. We hypothesised that the processing load reflected by the pupil response would be smallest for the condition in which listeners ‘‘just listened to background noise’’ or anticipated hearing a prompt signal without task constraints, somewhat higher for the simple auditory detection task, and highest for the condition in which listeners had to identify meaningful words presented in noise. The results of this study demonstrate that our hypotheses were only partly confirmed. The mean pupil dilation was largest in the WiBN-ident task, followed by the NiBN-detect condition that subsequently imposed a larger processing demand than the BN-response task in which subjects listened to the background noise and verbally responded after the prompt signal. However, there were no differences observed between the BN-notask and the BN-response conditions or between BNnotask and NiBN-detect conditions. Thus, a hierarchical order of the four conditions with increasing processing load was not observed. Although we hypothesised that the detection of a target noise-burst in noise would elicit more processing load than just listening to noise alone and a prompt signal, this was not supported by our data. The results of this study thus demonstrated that auditory signal detection is a relatively easy task not demanding high levels of processing load.



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At its peak level during T5, the mean pupil dilation in the NiBN-detect condition was 0.06 mm. This is similar to the results of Beatty and Lucero-Wagoner (2000). They employed a signal detection task and observed peak dilation amplitudes varying from 0.02 to 0.10 mm. In addition, the mean dilation at T5 was significantly smaller than that in the WiBN-ident condition. This finding indicates that the identification of linguistic information (i.e. identification of a meaningful word presented in noise) requires more processing demands than just detecting a meaningless target noise burst in background noise or passively listening to background noise with or without anticipating a verbal response. Note that the actual performance in the WiBN-ident and NiBN-detect conditions was fixed at 79% correct by means of the adaptive procedure applied in both conditions. The differences in the pupil size therefore reflect differences in processing load that are independent of performance level. This implies that when alternative performance assessments such as accuracy or response time are similar across conditions, pupillometry may reveal actual differences in cognitive effort. This finding underlines the value of examining both processing load and behavioural performance as both supplementary measures provide information about within-subject differences in listening conditions. It is interesting to notice that the current findings agree with previous research demonstrating that the processing of orally presented linguistic information involves more cognitive load than just listening to noise alone (e.g. Kramer et al., 1997; Zekveld et al., 2010). However, in those studies, sentences were used as stimuli and these are likely more complex and thus impose more cognitive load than words. To our knowledge, no study has directly compared the pupil response evoked by listening to words versus sentences. The majority of studies investigating the pupillary response during linguistic processing used sentences as stimuli and manipulated the complexity of the sentence material (e.g. Just & Carpenter, 1993; Piquado et al., 2010, Stanners, Headley, & Clark, 1972). An exception is a study by Hyo¨ na¨ et al. (1995), who auditorily presented words in quiet and modified the task demands. In their so-called ‘‘listening’’ condition, participants listened to words and just responded ‘‘yes’’ after having recognised the stimulus word. In their second ‘‘shadowing’’ condition, subjects were asked to repeat each word. Changing the task demands from word recognition to word repetition produced an orderly increase in the task evoked pupillary response. The mean pupil dilation relative to the baseline diameter in quiet was 0.12 mm in the listening and 0.16 mm in the shadowing condition. In the current study, words were presented in noise, but the closed set of six words made the task relatively easy. In our WiBN-ident task, pupil dilation relative to baseline was 0.13 mm. Zekveld et al. (2010) used sentences in noise and their subjects were asked to repeat the sentence. They observed a peak dilation relative to the noise-alone baseline of 0.17 mm when 50% of the sentences were reproduced entirely correctly. This corresponds to around 75% word perception, which is similar to the current task. Kramer et al. (1997) observed a peak dilation amplitude of 0.15 mm in a comparable condition. Thus, fairly consistent findings have been reported so far, with comparable or smaller mean pupil dilation for word identification (current and Hyo¨ na¨ ’s study), compared to sentence identification and smaller mean pupil dilation for detection tasks than for word or sentence identification. In all eight intervals, the pupillary response in the NiBN-detect condition did not differ significantly from the BN-notask condition. The mean pupil dilation in the BNnotask condition (listening to noise and prompt alone, no response) was zero (i.e. equal to the baseline pupil size) during all intervals (see Table 2), despite the


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presentation of the prompt signal at the end of the trial. The absence of the requirement to respond to the prompt signal apparently resulted in the absence of an anticipatory pupil response to the response prompt. The lack of an effect may be due to the fact that the mental effort involved in this condition was not of sufficient magnitude to elicit a pupil dilation (Moresi et al., 2008). This finding indicates that anticipation towards a prompt signal of 65 dB SPL does not affect the pupil if the sound can be ignored. Hence, such effects can be disregarded in studies applying pupillometry. In the BN-notask and BN-response conditions, exactly the same stimuli were presented but the instructional set differed. Even though a significant influence of the instructional set on the pupil response has been reported before (Stanners & Headley, 1972), we did not find such an effect. The pupil response in the BN-response condition in which subjects anticipated hearing a prompt signal and gave a response was below baseline during all intervals. Although the pupil response in this condition did not differ significantly from the BN-notask condition, a negative response was unexpected. We had hypothesised that having to respond to the prompt would increase the pupil response relative to the condition in which no response was required. A possible explanation for the negative response is that the response was affected by possible cross-over effects of the pupil response to responding to the prompt on the previous trial. It is known that a verbal reaction results in a positive pupil response with a maximum amplitude around 0.5 s after the start of responding (e.g. Richer & Beatty, 1985), which decreases again after the response has been given. Therefore, the pupillary reaction to responding may have affected the baseline pupil diameter of the subsequent trial that was presented immediately after the response. Inspection of the raw data indicated that baseline values were slightly positively biased and the further decreasing pupil size during listening to the next noise-alone signal subsequently resulted in a negative response. We suspect that the pupil response evoked by the verbal response was relatively large in this condition, as responding was the only task participants had to perform. The 1.5 s time interval preceding the baseline interval may have been too short for the pupil to get back to normal. Nevertheless, it should be noted that the (negative) pupil response in this condition did not differ significantly from that in the BN-notask. Inspection of the raw data of the other conditions did not indicate that the data in these conditions were biased (see also Figure 2). This is likely due to the presence of clearly defined tasks in these conditions that each obviously elicited a task evoked pupillary response. The present results show that future studies applying pupillometry should consider the duration of the time interval between trials to avoid cross-over effects between trials, in particular in conditions without a task (i.e. passive listening), combined with a verbal response. Both the tasks (detection versus identification) and the stimuli (target noise burst versus AAST words) differed between the NiBN-detect versus WiBN-ident conditions. Therefore, we cannot dissociate which aspect (task or stimuli) affected the processing load differences between these conditions. Based on the results of Kahneman and Beatty (1966) on pitch discrimination, we suggest that it is plausible to assume that both differences in the task and differences in stimulus characteristics contributed to the differences in pupil response between conditions. It would be interesting to employ a word in noise detection task to further investigate this issue and explore the dynamic interplay between bottom-up and top-down processes during the processing of words. Such a study would require the use of words from an unfamiliar language to ensure that identification of the words does not occur, or sine-wave replicas to remove traditional acoustic characteristics of the word (see for example Nittrouer &



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Lowenstein, 2010). In addition, the WiBN-ident condition with the largest pupillary response was the only language task applied. One may wonder whether this condition differed significantly from the other ones simply because of noncognitive factors such as anxiety. We argue that this was not the case. Firstly, in both the NiNdetec and the WiBN-ident condition, performance was fixed at 79% correct, thus we do not expect any differences in anxiety levels between conditions based on the task difficulty. Secondly, we used simple neutral words in the WiNindetif test that are unlikely to trigger an emotional response. Besides this, the pupillometric findings reported by Beatty and Lucero-Wagoner (2000) argue against any interpretation of the pupillary dilation as a reflection of test anxiety or similar emotional reaction to the possibility of error, because the task evoked pupillary response in their study did not vary directly with subjective uncertainty about the stimulus presentation. In addition, there is evidence suggesting that human anxiety is reflected in the inhibited pupillary light response rather than in the increased pupil diameter (Bitsios, Szabadi, & Bradshaw, 1996). Bitsios et al. argued that the observed dissociation between the light reflex amplitude (dip) and the initial pupil diameter in their study reflect separate physiological regulatory pathways of these two pupillary measures. Inhibition of the parasympathetic innervation of the pupil during a state of anxiety may occur independently of any increase in the sympathetic influence. This issue deserves further attention in future research. The second aim of this study was to investigate whether replication of the experiment in three different languages and in three different countries would yield comparable results. The repeated measures ANOVA revealed that the main effect of centre was not statistically significant (p .9). Also, no significant interactions between interval and centre and between condition and centre were observed. Thus, although slightly divergent patterns across the three countries were observed (Figure 2), there is no statistical basis for concluding that the experiment yielded different findings in the different countries. The nonsignificant divergence in patterns could be associated with differences in subject characteristics. For example, although the groups were matched on age and educational level, the German group of subjects was somewhat more homogenous regarding age than the other two groups. The current data support the applicability of the method across countries and languages. This demonstrates the validity and robustness of the pupil response during listening tasks. Currently, there is a worldwide interest in the concept of listening effort and the possibility to quantify this (e.g. Edwards, 2007; Kramer, Kapteyn, & Houtgast, 2006; Nachtegaal et al., 2009). Pupillometry imposes no additional task requirements on the process under study and does not interfere with stimulus presentation and task performance. As such, pupillometry is a promising tool with considerable practical advantages compared to more expensive and more complex physiological measures like functional magnetic resonance imaging (MRI). An example of a potential valuable application is the evaluation of the influence of listening devices such as hearing aids or cochlear implants on the processing load required by listeners with hearing impairment.

CONCLUSION The current data demonstrate that the task-evoked pupillary response to theory-based measures of linguistic processing is a remarkably robust, reliable, and sensitive indirect measure of cognitive processing load. Together with performance measures, the task-

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evoked pupillary response reflects differences in task demands and differences in stimulus characteristics in language processing tasks. Processing linguistic information (i.e. identification of a meaningful word presented in noise) requires more processing demands than just detecting a meaningless target noise-burst in background noise. The data underline the value of examining both processing load and behavioural performance, as both supplementary measures provide information about withinsubject differences in listening conditions.


Manuscript received 30 November 2010 Revised manuscript received 14 November 2011 First published online 13 March 2012

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