Intentional attention switching in dichotic listening: exploring the efficiency of nonspatial and spatial selection.

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Intentional attention switching in dichotic listening: Exploring the efficiency of nonspatial and spatial selection a

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Vera Lawo , Janina Fels , Josefa Oberem & Iring Koch a

Institute of Psychology, RWTH Aachen University, Aachen, Germany

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Institute of Technical Acoustics, RWTH Aachen University, Aachen, Germany Published online: 19 Mar 2014.

To cite this article: Vera Lawo, Janina Fels, Josefa Oberem & Iring Koch (2014) Intentional attention switching in dichotic listening: Exploring the efficiency of nonspatial and spatial selection, The Quarterly Journal of Experimental Psychology, 67:10, 2010-2024, DOI: 10.1080/17470218.2014.898079 To link to this article: http://dx.doi.org/10.1080/17470218.2014.898079

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THE QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY, 2014 Vol. 67, No. 10, 2010–2024, http://dx.doi.org/10.1080/17470218.2014.898079

Intentional attention switching in dichotic listening: Exploring the efficiency of nonspatial and spatial selection Vera Lawo1, Janina Fels2, Josefa Oberem2, and Iring Koch1 1

Downloaded by [The Aga Khan University] at 05:13 25 November 2014

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Institute of Psychology, RWTH Aachen University, Aachen, Germany Institute of Technical Acoustics, RWTH Aachen University, Aachen, Germany

Using an auditory variant of task switching, we examined the ability to intentionally switch attention in a dichotic-listening task. In our study, participants responded selectively to one of two simultaneously presented auditory number words (spoken by a female and a male, one for each ear) by categorizing its numerical magnitude. The mapping of gender (female vs. male) and ear (left vs. right) was unpredictable. The tobe-attended feature for gender or ear, respectively, was indicated by a visual selection cue prior to auditory stimulus onset. In Experiment 1, explicitly cued switches of the relevant feature dimension (e.g., from gender to ear) and switches of the relevant feature within a dimension (e.g., from male to female) occurred in an unpredictable manner. We found large performance costs when the relevant feature switched, but switches of the relevant feature dimension incurred only small additional costs. The feature-switch costs were larger in ear-relevant than in gender-relevant trials. In Experiment 2, we replicated these findings using a simplified design (i.e., only within-dimension switches with blocked dimensions). In Experiment 3, we examined preparation effects by manipulating the cueing interval and found a preparation benefit only when ear was cued. Together, our data suggest that the large part of attentional switch costs arises from reconfiguration at the level of relevant auditory features (e.g., left vs. right) rather than feature dimensions (ear vs. gender). Additionally, our findings suggest that ear-based target selection benefits more from preparation time (i.e., time to direct attention to one ear) than gender-based target selection. Keywords: Auditory task switching; Selective attention; Dichotic listening; Switch costs; Preparation.

In many situations of every-day life, a variety of acoustic information is simultaneously available for auditory processing. Since not all auditory information is relevant at a time, selective attention is needed to focus on the relevant information while less relevant information remain in the perceptual background (see e.g., Lachter, Forster, & Ruthruff, 2004; Schneider, Li, & Daneman,

2007; Shinn-Cunningham, 2008, for reviews). Listening to a single talker in a noisy environment, like in cocktail-party situations, is probably the “best-known real life example of selective attention” (Pashler, 1998, p. 37). Research on selective attention dates back to the 1950s (see Broadbent, 1958; Cherry, 1953). These early studies often used dichotic-listening

Correspondence should be addressed to Vera Lawo, Institute of Psychology, RWTH Aachen University, Jägerstr. 17–19, D-52066 Aachen, Germany. E-mail: [email protected] We would like to thank Frank Wefers from the Institute of Technical Acoustics for help in producing the stimulus material and Nadine Wahl for her assistance with data collection. We are also grateful for helpful comments by Frederick Verbruggen, James A. Grange, and two anonymous reviewers. This research was supported by the Deutsche Forschungsgemeinschaft (DFG) [grant number FE1168/1–1], [grant number KO2045/11–1].

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© 2014 The Experimental Psychology Society


AUDITORY ATTENTION SWITCHING

procedures (Hugdahl, 2011, for a review) that required participants to selectively attend to information (e.g., verbal messages) presented to one ear while ignoring information presented to the other ear (on the basis of, e.g., speaker gender). Participants were able to shadow (i.e., to repeat aloud) the relevant information and usually had no memory for the irrelevant information. Based on these findings, Broadbent (1958) suggested an early selection mechanism (i.e., “filter theory” of attention). In these studies, attention switches (to the irrelevant information) were not instructed, but they could occur spontaneously and nonintentionally (Lachter et al., 2004; see ShinnCunningham, 2008, for a discussion). To examine the intentional aspect of switching auditory attention, Koch, Lawo, Fels, and Vorländer (2011) introduced a version of the task-switching paradigm that combined the methodologies of dichotic listening (Cherry, 1953) and explicit task cueing (Meiran, 1996), one basic variant of task switching. In explicit task cueing, the sequence of tasks is unpredictable, and an instructional cue precedes the stimulus to indicate the next task (see, Jost, De Baene, Koch, & Brass, 2013, for a recent review). In this auditory task-switching paradigm (Koch et al., 2011), two auditory stimuli (i.e., spoken number words, always spoken by one female and one male speaker) were presented dichotically via headphones. A visual selection cue that preceded the auditory stimuli explicitly indicated whether the number word spoken by the female or male speaker was relevant in the upcoming trial. Participants categorized the relevant number word as smaller or larger than 5. The order of the tobe-attended gender switched unpredictably on a trial-by-trial basis. The main finding was that a cued switch of the relevant gender resulted in worse performance relative to repetitions: so called switch costs (Koch & Lawo, 2014; Koch et al., 2011; Lawo & Koch, 2014). To decompose switch costs in “pure” attention switch costs and perceptual cue-repetition priming effects, Koch et al. (2011; see also Koch & Lawo, 2014) used two cues per gender (Logan & Bundesen, 2003; Mayr & Kliegl, 2003; Monsell & Mizon, 2006)

and found that cue-repetition priming effects contributed to switch costs but that substantial switch costs remained even if cue priming was excluded (see Koch & Lawo, 2014, for a discussion). In addition to the examination of effects of immediate cue repetitions, the cued auditory attention-switching paradigm also allows the manipulation of the temporal intervals within one trial (see Figure 1), so that potential effects of preparation and “decay” on switch costs could be examined independently (Kiesel et al., 2010; see also, e.g., Koch & Allport, 2006). To examine the preparation for an auditory attention switch, Koch et al. (2011) varied the time between the selection cue and the auditory stimuli (cue–stimulus interval, CSI) while varying the time between the response and the next selection cue (response–cue interval, RCI) inversely to the CSI to keep the intertrial interval (i.e., response–stimulus interval, RSI) constant. They found better performance with long CSI (i.e., preparation benefit). However, using two cues per gender revealed that primarily the cue-priming effect was reduced by preparation, whereas attention switch costs were less affected by preparation (Koch et al., 2011). To examine the decay of attentional settings, Koch and Lawo (2014) varied the RCI while keeping the CSI constant to effectively manipulate the RSI, which represents the overall time for “passive” processes to affect performance. However, they found no effect of RCI on auditory switch costs, suggesting that auditory attention settings do not dissipate quickly over time. However, these previous findings were obtained with cues that indicated the relevant gender prior to each trial. In contrast to exogenous cues, which are often used in detection tasks and lead to automatic (i.e., bottom up) target selection, we used endogenous cues (e.g., visual symbolic cue at screen centre) that need attention to “actively” select (i.e., top down) the target stimulus before the categorization task could be performed. Irrespective of whether exogenous or endogenous cues are used, these cues can be of either nonspatial (e.g., frequency, intensity) or spatial nature (Alain & Arnott, 2000; Cusack, Deeks, Aikman, & Carlyon, 2004; Kitterick, Bailey, & Summerfield, 2010; Maddox

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Figure 1. Time course of an experimental trial with the temporal intervals.

& Shinn-Cunningham, 2012; Mondor & Bregman, 1994; Mondor & Zatorre, 1995; Scharf, 1998; Woods, Alain, Diaz, Ogawa, & Rhodes, 2001; see Schneider et al., 2007, for a review). Importantly, these previous studies did not examine the specific processes underlying explicitly cued (i.e., intentional) attention switching. The aim of the present study was to explore the efficiency of different endogenous selection cues that indicate either the necessity of nonspatial (i.e., gender of the relevant speaker) or spatial target selection1 (i.e., relevant ear). To this end, we compared gender-based target selection with ear-based target selection in dichotic listening. Since there have been no studies comparing the efficiency of endogenous gender-based and earbased target selection, we could only speculate that a spatial selection criterion would be more beneficial than a nonspatial criterion in a dichotic-listening task. In three experiments, we cued either the relevant gender (i.e., nonspatial) or the relevant ear (i.e., spatial) to compare nonspatial and spatial selection in auditory attention switching. In Experiment 1, we examined whether cueing the relevant ear is beneficial compared to cueing the relevant gender by manipulating the relevant feature dimension (i.e., gender vs. ear) on a trialby-trial basis. In Experiment 2, we reexamined whether cueing the relevant ear is beneficial when reducing uncertainty by using a simplified design (i.e., feature-dimension switches did not occur by manipulating the relevant feature dimension blockwise). In Experiment 3, we manipulated the CSI to examine whether cueing by ear is more beneficial than cueing by gender if more time for preparation is available.

EXPERIMENT 1 In Experiment 1, we examined whether explicit endogenous selection is more efficient for spatial selection cues (i.e., ear) than for nonspatial selection cues (i.e., gender). The relevant feature dimension was either gender or ear, whereas the relevant features were either “female” or “male” for the feature dimension “gender”, and “left” or “right” for the feature dimension “ear”. The relevant feature dimension was manipulated on a trial-bytrial basis. A feature repetition was defined as a repetition of the relevant feature within the relevant feature dimension (e.g., from female speaker to female speaker, or from left ear to left ear) and a feature switch as a switch of the relevant feature within the same feature dimension (e.g., from female speaker to male speaker, or from left ear to right ear). A dimension switch implied always also a switch of the relevant feature across the relevant feature dimension (e.g., from relevant gender to relevant ear). We expected better performance when the relevant ear was cued for target selection than when the relevant gender was cued (Kitterick et al., 2010; Mondor, Zatorre, & Terrio, 1998). Please note that the response was always based on the numerical value of the spoken number word, so that target selection per se does not yet specify the response. Based on the idea that switches at a hierarchically higher level (i.e., feature dimension) are more costly than feature-based switches within a feature dimension, we expected higher switch costs for feature-dimension switches. Related findings from the visual domain (i.e., a visual search)

1 Please note that in this study spatial target selection does not refer to the nature of the cue but to the relevant (i.e., to-be-attended) dimension.

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also suggested larger switch costs across different feature dimensions than within one feature dimension (e.g., Müller, Reimann, & Krummenacher, 2003; Weidner & Müller, 2013), but we could not specify a strong prediction for the present auditory attention switching paradigm.


Method Participants Thirty-six German-speaking participants (24 women, 12 men) in the age range of 18–28 years (M = 20.89, SD = 2.11) took part and received partial course credit or 6 euros. Participants were recruited through a participant pool at the Institute of Psychology at RWTH Aachen University and reported no presence of hearing problems. Five participants were left-handed. Stimuli and task The explicit selection cue was a visually presented letter at the centre of a 17-in. monitor indicating either the gender of the relevant speaker or the ear to which the relevant number word was presented. The letter “f” was presented to indicate the relevance of the female speaker, whereas the relevance of the male speaker was indicated by “m”. To indicate that the relevant number word was presented to the left ear, “l” was presented, and “r” if the relevant number word was presented to the right ear. The selection cues were about 2–2.5 cm high and were presented in white colour on a black screen. The auditory stimuli were spoken number words 1–9 (without 5), which were presented via headphones (AKG K530 LTD). Three different female speakers (mean frequency: 212 Hz) and three male speakers (mean frequency: 135 Hz) were recorded in an anechoic chamber at the Institute of Technical Acoustics of RWTH Aachen University (sampling rate: 44.1 kHz, quantization: 24 bit). A subjective loudness calibration was carried out for each individual number word and for all different speakers. Furthermore, the

duration for all different speakers was adjusted across the set of the eight number words to be the same. A time-stretching algorithm was used to shorten longer samples while the frequency was maintained. If the relevant number word was smaller than 5, the participants were instructed to press the left response key with the left index finger, and if the relevant number word was larger than 5 the right response key with the right index finger (left and right key of the space bar), respectively. The assignment to the categories (,5 and .5) was held constant according to the “mental number line” (i.e., small is associated with left). Procedure Before the actual experiment started, there was an online instruction followed by a practice block with 32 trials. The experiment consisted of four blocks with 160 trials each, separated by short breaks. In each trial, the visual selection cue was presented first. After a CSI of 550 ms, the two auditory stimuli (i.e., number words) were presented dichotically via headphones. The two number words presented to the left and right ear were always different within each trial. The selection cue remained on the screen until participants responded by pressing the associated key. The interval between response and next selection cue (i.e., RCI) was also 550 ms, resulting in a constant RSI of 1100 ms. In the case of an error, a visual feedback (“Fehler!”, German for “error”) was displayed for 500 ms, delaying the onset of the next selection cue. Each selection cue occurred equally often (i.e., 25%); therefore the number of trials in which gender was relevant, and ear was relevant was also equal (i.e., 50%). In about half of the trials, the relevant feature dimension changed from trial to trial (49.7%). Half of the feature-dimension repetitions (50.3%) were also feature repetitions (24.5%), and half were feature switches (25.8%). In half of the trials (50%), both number words led to the same response (i.e., “congruent”)2. The mapping of

2 Previous results indicated worse performance on response-incongruent trials (Koch et al., 2011). However, congruency was not the focus of the present study, and including congruency in the data analyses did not affect the interpretation of the present data.


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gender to ear, the identity of speaker within each gender, and the identity of the relevant and irrelevant number word varied randomly from trial to trial. Design The independent variables were feature dimension (gender vs. ear) and transition (feature repetition vs. feature switch vs. dimension switch). As transition (i.e., switch of the selection criterion) was manipulated on feature level within one feature dimension and across feature dimensions, we planned two nonorthogonal contrasts and compared feature repetitions with feature switches ( feature-switch contrast) within a given dimension and feature switches with feature switches across dimensions (dimension-switch contrast). The dependent variables were reaction times (RTs) and error rates.

Results The practice block and the first trial in each block (“warm-up”) were excluded from the analyses. In the RT analysis, we additionally excluded errors and trials after an error. As outliers, we excluded RTs below 100 ms (0.2%) and RTs exceeding 3 standard deviations from the participant’s mean (1.9%)3. In the analysis of the error rates, trials after an error and outliers were excluded. RTs and error rates were submitted to separate analyses of variance (ANOVAs) using feature dimension and transition as independent variables (within participants). The data were analysed with two preplanned nonorthogonal contrasts. We start with the feature-switch contrast (feature repetition vs. feature switch) and continue with the dimension-switch contrast (feature switch vs. dimension switch). For each contrast, the RTs are reported first, followed by the error rates. Mean RTs of both contrasts as a function of feature dimension and transition are shown in Figure 2. Mean error rates of both contrasts as a function of feature dimension and transition are shown in Table 1.

Feature-switch contrast The 2 (feature dimension: gender, ear) × 2 (transition: feature repetition, feature switch) ANOVA of the RTs yielded no significant main effect of feature dimension, F , 1. The main effect of transition was significant, F(1, 35) = 84.098, MSE = 213,607, p , .001, η2p = .71, indicating higher RTs in feature switches than in feature repetitions (1086 ms vs. 966 ms) and thus switch costs of 120 ms. The interaction of feature dimension and transition was significant, F(1, 35) = 8.347, MSE = 4030, p , .05, η2p = .19, indicating larger switch costs for ear-relevant conditions than for gender-relevant conditions within one feature dimension (150 ms vs. 89 ms). Post hoc tests revealed that RT was higher for gender-relevant conditions than for earrelevant conditions with feature repetitions (986 ms vs. 946 ms), F(1, 35) = 4.265, MSE = 6772, p , .05, η2p = .11, but not with feature switches (1075 ms vs. 1096 ms), F , 1. The error rates yielded a significant main effect of feature dimension, F(1, 35) = 4.711, MSE = 0.001, p , .05, η2p = .12, indicating higher error rates for ear-relevant conditions than for gender-relevant conditions (6.6% vs. 5.3%). The main effect of transition was not significant, F(1, 35) = 3.978, MSE = 0.001, p = .054, η2p = .10, but indicated a trend for higher error rates in feature switches than in feature repetitions (6.5% vs. 5.5%) and thus switch costs of 1.0%. The interaction of feature dimension and transition was not significant, F(1, 35) = 2.247, MSE = 0.001, p . .05, η2p = .06. Dimension-switch contrast The 2 (feature dimension: gender, location) × 2 (transition: feature switch, dimension switch) ANOVA of the RTs yielded no significant main effect of feature dimension, F(1, 35) = 1.082, MSE = 16,020, p . .05, η2p = .03. The main effect of transition was significant, F(1, 35) = 5.666, MSE = 2136, p , .05, η2p = .14, indicating higher RTs in dimension switches than in feature switches (1104 ms vs. 1086 ms) and thus “dimension switch costs” (i.e., from relevant ear to relevant gender, or vice versa) of 18 ms. The interaction of

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Trimming RT for each participant additionally for each experimental condition did not change the interpretation of the results.

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Figure 2. Mean reaction times (RTs, in ms) in Experiment 1 as a function of feature dimension (gender vs. ear) and transition (feature repetition vs. feature switch vs. dimension switch). Error bars indicate the standard error of the mean.

Table 1. Mean error rates in Experiment 1 as a function of feature dimension and transition Feature dimension Transition Dimension switch Feature switch Feature repetition

Gender

Ear

4.8 (0.009) 5.4 (0.011) 5.2 (0.009)

8.1 (0.012) 7.5 (0.009) 5.7 (0.009)

Note: Error rates in percentages; standard errors in parentheses. Feature dimension: gender versus ear; transition: feature repetition versus feature switch versus dimension switch.

feature dimension and transition was not significant, F , 1. The error rates yielded a significant main effect of feature dimension, F(1, 35) = 16.142, MSE = 0.002, p , .001, η2p = .32, indicating higher error rates for ear-relevant than for gender-relevant conditions (7.8% vs. 5.1%). The main effect of transition was not significant, F , 1. The interaction of feature dimension and transition was also not significant, F(1, 35) = 1.468, MSE = 0.001, p . .05, η2p = .04.

Discussion In Experiment 1, we examined the role of nonspatial and spatial selection by using gender and ear as

selection cues and focused specifically on attention switching on feature level within one relevant feature dimension and on attention switching across both relevant feature dimensions. We hypothesized better performance when cueing by ear than when cueing by gender and hypothesized better performance for attention switches within one feature dimension (i.e., feature level) than for attention switches across different feature dimensions (i.e., dimension level). Overall, we observed comparable latencies for both relevant feature dimensions, but, unexpectedly, worse accuracy when ear was cued. Also, switch costs within one relevant dimension were larger when the relevant ear was cued than when the relevant gender was cued. Put differently, there is a larger feature-repetition benefit when the relevant ear was cued, meaning that this benefit only occurs when the relevant stimulus is presented to the same ear. The smaller feature-repetition benefit in gender-relevant conditions could be explained by the uncertainty of the relevant ear because the to-be-attended ear could have switched even though the relevant gender repeated. Moreover, we observed slightly but significantly worse performance when attention switched across relevant feature dimensions than when it only switched on feature level. That is, a switch of the


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relevant feature dimension (i.e., gender vs. ear) incurred additional costs relative to feature switches within one relevant feature dimension. However, these additional costs were quite small (18 ms) in comparison to switches of the relevant feature (120 ms), so that we focused on the latter in the subsequent experiments.


EXPERIMENT 2 In Experiment 2, we reexamined whether and under which conditions ear-based selection is beneficial. Therefore, we eliminated the dimensional uncertainty and used a simplified experimental design. We focused on attention switching when the relevant feature dimension remained constant throughout the block by manipulating the relevant feature dimension blockwise instead of randomly (i. e., no switches from one relevant feature dimension to the other). This resulted in attention switches only on feature level, so that participants had a priori knowledge of the relevant feature dimension within each block of trials. Based on the results of Experiment 1, we assumed that the uncertainty of the relevant feature dimension in the previous experiment might have prevented a general benefit for cueing by ear. Therefore, we hypothesized that the blockwise manipulation of the relevant feature dimension leads to a performance benefit when the relevant ear is cued compared to when the relevant gender is cued.

Method Participants Twenty new participants (10 women, 10 men) in the age range of 17–27 years (M = 23.05, SD = 2.50) took part and received partial course credit or 6 euros. Participants were recruited through a participant pool at the Institute of Psychology at RWTH Aachen University and reported no presence of hearing problems. Two participants were left-handed. Stimuli, task, and procedure The only difference to Experiment 1 was that the relevant feature dimension was blocked (first vs.

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second half of experiment), and, therefore, attention switches from one relevant feature dimension to the other did not occur (i.e., from gender to ear, or vice versa), and all attention switches occurred exclusively on the feature level. In contrast to Experiment 1, there was one practice block for each relevant feature dimension. As participants were not informed about both relevant feature dimensions prior to the experiment, the “second” relevant feature dimension was introduced before the second half of the experiment, which was prior to the third block. The relevant feature dimension varied blockwise (first vs. second half of the experiment), and the order of the relevant feature dimension was counterbalanced across participants. Design The independent variables were feature dimension (gender vs. ear) and transition (feature repetition vs. feature switch). The dependent variables were reaction times (RTs) and error rates.

Results As in Experiment 1, the practice blocks (32 trials each) and the first trial of each block were excluded from the analyses. In the RT analysis, we excluded errors and trials after an error. As outliers, we excluded RTs below 100 ms (0.2%) and RTs exceeding 3 standard deviations from the participant’s mean (1.7%). Mean RTs as a function of feature dimension and transition are shown in Figure 3. The 2 (feature dimension: gender, ear) × 2 (transition: feature repetition, feature switch) ANOVA of the RTs yielded no significant main effect of feature dimension, F(1, 19) = 3.011, MSE = 30,645, p . .05, η2p = .14, but the main effect of transition was significant, F(1, 19) = 43.332, MSE = 7600, p , .001, η2p = .70, indicating higher RTs in feature switches than in feature repetitions (1058 ms vs. 930 ms) and thus switch costs of 128 ms. The interaction of transition and feature dimension was significant, too, F(1, 19) = 11.706, MSE = 2932, p , .05, η2p = .38, indicating



Table 2. Mean error rates in Experiment 2 as a function of feature dimension and transition Feature dimension Transition Feature switch Feature repetition

Gender

Ear

6.4 (0.009) 4.7 (0.008)

6.3 (0.009) 4.2 (0.006)


Note: Error rates in percentages; standard errors in parentheses. Feature dimension: gender versus ear; transition: feature repetition versus feature switch.

Figure 3. Mean reaction times (RTs, in ms) in Experiment 2 as a function of feature dimension (gender vs. ear) and transition (feature repetition vs. feature switch). Error bars indicate the standard error of the mean.

larger switch costs when ear was cued than when gender was cued (170 ms vs. 87 ms).4 The same analysis of the error rates (see Table 2) revealed no significant main effect of feature dimension, F , 1. The main effect of transition was significant, F(1, 19) = 26.616, MSE = 0.000, p , .001, η2p = .58, indicating higher error rates in feature switches than in feature repetitions (6.4% vs. 4.4%) and thus switch costs of 2.0%. The interaction was not significant, F , 1.

Discussion In Experiment 2, we replicated the substantial feature-switch costs from Experiment 1. Moreover, the finding of larger switch costs in ear-based selection than in gender-based selection is also in line with our findings from Experiment 1. However, results from Experiment 2, in which we also did not find a general benefit for earbased selection, could not rule out potential

differences of gender-based and ear-based selection. Therefore, we examined in Experiment 3 whether the preparation time of 550 ms was not long enough to bring about the expected benefit of ear-based selection. Specifically, in Experiment 3 we maintained the simplified design of Experiment 2 and examined whether ear-based selection is beneficial compared to gender-based selection with prolonged CSI.

EXPERIMENT 3 In Experiment 3, we manipulated the CSI to examine whether the switching performance of ear-based selection benefits more from preparation time than that of gender-based selection. To this end, the CSI was either short (100 ms) or long (1000 ms), whereas in Experiment 1 and Experiment 2 it was constant (550 ms) throughout. We expected a larger preparation benefit for earbased selection than for gender-based selection.

Method Participants Twenty-four new participants (14 women, 10 men) in the age range of 19–32 years (M = 23.29, SD = 3.03) took part and received partial course credit or

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Notably, when comparing overall RT across Experiments 1 and 2, ear-based selection seemed to have benefited more from reducing uncertainty (i.e., no dimension switches) than did gender-based selection (62 ms vs. 3 ms). However, a post hoc analysis taking experiment as between-subject variable and dimension and transition as within-subject variables revealed that this benefit (i.e., the twoway interaction of dimension and experiment) was statistically not significant, F(1, 54) = 2.240, p , .14, so that we refrain from giving an interpretation of the potential role of dimensional uncertainty on auditory attention switch costs. THE QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY, 2014, 67 (10)

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Figure 4. Mean reaction times (RTs, in ms) in Experiment 3 as a function of feature dimension (gender vs. ear), transition (feature repetition vs. feature switch), and cue–stimulus interval (CSI; 100 ms vs. 1000 ms). Error bars indicate the standard error of the mean.

6 euros. Participants were recruited through the participant pool at the Institute of Psychology at RWTH Aachen University and reported no presence of hearing problems. Four participants were left-handed. Stimuli, task, and procedure The only difference to Experiment 2 was that the CSI was either short (i.e., 100 ms) or long (i.e., 1000 ms) and varied blockwise. The RCI varied inversely (i.e., 1000 ms or 100 ms), so that the RSI remained constant at 1100 ms throughout. The experiment consisted of two blocks with short CSI (in one of these blocks ear was cued, and in the other one gender was cued) and two blocks with long CSI (in one of these blocks ear was cued, and in the other one gender was cued), with counterbalanced order. Design The independent variables were feature dimension (gender vs. ear), transition (feature repetition vs. feature switch), and CSI (100 ms vs. 1000 ms).

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The dependent variables were reaction times (RTs) and error rates.

Results Data analyses proceeded as in Experiment 1 and Experiment 2. Data from one participant were excluded because poor overall performance throughout the experiment (mean error rate of 19.6%) suggested that the task was not understood correctly. As outliers, we excluded RTs below 100 ms (0.5%) and RTs exceeding 3 standard deviations from the participant’s mean (1.7%). Mean RTs as a function of feature dimension, transition, and CSI are shown in Figure 4. The 2 (feature dimension: gender, ear) × 2 (transition: feature repetition, feature switch) × 2 (CSI: 100 ms, 1000 ms) ANOVA of the RTs yielded no significant main effect of feature dimension, F , 1, but the main effect of transition was significant, F(1, 22) = 70.610, MSE = 14,480, p , .001, η2p = .41, indicating higher RT in feature switches than in feature repetitions (1183



Table 3. Mean error rates in Experiment 3 as a function of feature dimension, transition, and cue–stimulus interval Feature dimension Transition Feature switch


Feature repetition

CSI (ms) 100 1000 100 1000

Gender 8.9 7.5 6.1 7.0

(0.012) (0.010) (0.008) (0.010)

Ear 11.2 (0.014) 8.4 (0.012) 7.7 (0.012) 5.5 (0.010)

Note: Error rates in percentages; standard errors in parentheses. Feature dimension: gender versus ear; transition: feature repetition versus feature switch; cue–stimulus interval (CSI): 100 ms versus 1000 ms.

ms vs. 1034 ms) and thus switch costs of 149 ms. The interaction of transition and feature dimension was not significant, F(1, 22) = 1.027, MSE = 6544, p . .05, η2p = .05. The main effect of CSI was significant, F(1, 22) = 10.475, MSE = 53,261, p , .05, η2p = .32, indicating that RT was higher at the short CSI than at the long CSI (1164 ms vs. 1054 ms) and thus a general preparation benefit of 110 ms. The interaction of CSI and feature dimension was also significant, F(1, 22) = 15.375, MSE = 17,006, p = .001, η2p = .41, indicating a substantially larger preparation benefit for ear-based selection than for gender-based selection (185 ms vs. 35 ms). Post hoc tests revealed that this preparation benefit was significant only for ear-based selection, F(1, 22) = 26.333, MSE = 30,061, p , .001, η2p = .55, but not for gender-based selection, F , 1. RT was significantly higher for gender-based selection than for ear-based selection with long CSI (1109 ms vs. 998 ms), confirmed by post hoc test, F(1, 22) = 7.246, MSE = 287,617, p , .05, η2p = .25, but not with short CSI (1144 ms vs. 1183 ms), F , 1. The interaction of CSI and transition and the three-way interaction were not significant, Fs , 1. The same analysis of the error rates (see Table 3) revealed no significant main effect of feature dimension, F , 1. The main effect of transition was significant, F(1, 22) = 16.430, MSE = 0.002, p = .001, η2p = .43, indicating higher error rates in feature switches than in feature repetitions (9.0%

vs. 6.6%) and thus switch costs of 2.4%. These switch costs did not differ across relevant feature dimensions, F(1, 22) = 2.151, MSE = 0.001, p . .05, η2p = .09. The main effect of CSI was significant, F(1, 22) = 6.273, MSE = 0.001, p , .05, η2p = .22, indicating that error rates were higher at the short CSI than at the long CSI (8.5% vs. 7.1%) and thus a preparation benefit of 1.4%. The interaction of CSI and feature dimension was significant, F(1, 22) = 12.981, MSE = 0.000, p , .05, η2p = .37, indicating a larger preparation benefit for ear-based selection than for genderbased selection (2.5% vs. 0.2%). As for the RT data, we conducted post hoc analysis for the relevant feature dimensions separately, indicating that the preparation benefit was significant only for ear-based selection, F(1, 22) = 13.838, MSE = 0.001, p = .001, η2p = .39, but not for gender-based selection, F , 1. Neither was the interaction of transition and CSI significant, F(1, 22) = 2.701, MSE = 0.002, p . .05, η2p = .11, nor the three-way interaction, F(1, 22) = 1.236, MSE = 0.001, p . .05, η2p = .05.

Discussion In Experiment 3, we examined whether ear-based selection is beneficial compared to gender-based selection with prolonged preparation time. In line with our hypothesis, we observed a performance benefit for ear-based selection compared to gender-based selection (1109 ms vs. 998 ms) with long preparation time, whereas there was no substantial difference with short preparation time. Importantly, the preparation benefit was very substantial for ear-based selection but not for genderbased selection. Moreover, we note that Experiment 3 again revealed numerically larger switch costs for ear-based selection, at least at the short CSI, but unlike in Experiments 1 and 2 this effect was not significant (see General Discussion).

GENERAL DISCUSSION The aim of this study was to explore whether gender-based and ear-based selection lead to


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differential effects in auditory attention switching. We hypothesized that endogenous cueing is more effective for ear-based selection than for genderbased selection when switching attention in dichotic listening. In three experiments, participants responded selectively to one of two simultaneously presented number words, always spoken by one female and one male speaker. The relevant feature (i.e., female vs. male speaker or left vs. right ear) was indicated by an explicit visual selection cue prior to auditory stimulus onset, and either a nonspatial attribute (i.e., gender) or a spatial attribute (i.e., ear) had to be attended to select the relevant number word and judge its magnitude as smaller or larger than 5.

Synopsis of results In Experiment 1, the selection criterion (to-beattended feature) switched unpredictably on a trial-by-trial basis, which resulted in transitions within one relevant feature dimension (i.e., gender vs. ear) and transitions across feature dimensions. We observed worse performance when attention switched across relevant feature dimensions than when it switched within one feature dimension. That is, a switch of the relevant feature dimension incurred additional costs relative to feature switches within one relevant feature dimension. Moreover, we found larger switch costs (and generally increased error rates) when using ear-based selection (i.e., spatial selection criterion). In Experiment 2, we removed dimensional uncertainty by manipulating the feature dimension (ear vs. gender) in a blocked manner and replicated the increased attentional switch costs (on the feature level) with ear-based selection relative to gender-based selection. Finally, in Experiment 3 we manipulated the cueing interval (CSI) and found that cueing was primarily beneficial for earbased selection (even though there was no preparatory reduction of attention switch costs, see below), suggesting that the expected benefit of spatial selection over nonspatial selection requires sufficient preparation time to become effective. In the following subsections, we first discuss the finding of attentional switch costs on the level of auditory

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dimensions and features. Then we discuss the specific role of the type of selection criterion.

Attentional switch costs on the level of auditory dimensions and features The present experiments demonstrate robust auditory attention switch costs for both gender-based and ear-based target selection. In Experiment 1, we found that attentional switch costs are primarily driven by switches at the level of auditory features (e.g., male vs. female or left vs. right). Notably, switches across feature dimensions (i.e., from gender-based to ear-based selection, or vice versa), which always imply a concomitant switch at the level of the relevant feature, incur some additional costs, but these dimension switch costs are comparatively small, and they are not specific to the feature dimension to which attention switched (i.e., ear vs. gender; see next subsection). Similar attention switch costs have also been found in visual attention studies (see, e.g., Logan, 2005; Longman, Lavric, & Monsell, 2013; Mayr, Kuhns, & Rieter, 2012). More specifically, dimension-specific and feature-specific repetition effects have been found in studies on visual search (Mortier, Theeuwes, & Starreveld, 2005; Rangelov, Müller, & Zehetleitner, 2011). Based on such findings, Müller and colleagues (e.g., Rangelov et al., 2011) developed a multipleweighting system account, suggesting that processing weights can be adjusted to prioritize processing of prespecified features and/or feature dimensions. We believe that the present auditory attention switch costs could also be explained by similar mechanisms of attentional weighting or biasing (see also Meiran, Kessler, & Adi-Japha, 2008). Switch costs could then be accounted for by assuming that it reflects the time to actively change parameters of the attentional set (i.e., as part of taskset “reconfiguration”, see, e.g., Logan & Gordon, 2001; Rogers & Monsell, 1995), or it could also reflect proactive interference on the level of attentional processing biases, which would then give rise to attentional “inertia” effects (e.g., Allport, Styles, & Hsieh, 1994; Koch & Lawo, 2014). The absence of clear preparation effects on switch




costs, which we discuss below, suggests a strong contribution of proactive interference to auditory attention switch costs. At this stage, however, it must remain an open question as to whether this interference is due to carryover of persisting processing biases favouring the previously relevant feature (or feature dimension) or whether it represents persisting suppression, or inhibition, of processing the now relevant features (see e.g., Koch, Gade, Schuch, & Philipp, 2010, for a discussion).

Role of the type of selection criterion in auditory attention switching The present study specifically examined whether the assumed perceptual biasing processes underlying attentional switching depend on the type of criteria for target selection. The present experiments extend our previous findings (Koch & Lawo, 2014; Koch et al., 2011; Lawo & Koch, 2014) by showing that ear-based selection in attentional switching situations is, somewhat unexpectedly, not generally better, even though the literature on auditory selective attention suggests that spatial selection should be superior to nonspatial selection (e.g., Kidd, Arbogast, Mason, & Gallun, 2005; Kitterick et al., 2010; Mondor et al., 1998; Shinn-Cunningham, 2008). Specifically, we found that the benefit for earbased selection became apparent only with a sufficiently long preparation time. In line with existing theories in the task-switching literature (for reviews see, e.g., Kiesel et al., 2010; Vandierendonck, Liefooghe, & Verbruggen, 2010), we assume that some aspect of attentional reconfiguration, in this case referring to spatial processing biases, can indeed be completed, at least partially, in advance. When interpreting the differential preparation effect for spatial versus nonspatial selection in Experiment 3, it is important to keep in mind that the present experiments required explicitly cued attention switches (unlike in exogenous attentional capture; see, e.g., Pashler, Johnston, & Ruthruff, 2001) and that spatial target selection is completely decoupled from (spatial) response selection, which was based on the magnitude of the relevant target number word. Therefore, we assume

that a gender-based (i.e., nonspatial) selection criterion actually leads to somewhat better performance in our experimental conditions when the preparation time is short, possibly because it is either easier to discriminate male from female voices than left versus right ear, or because it is somewhat more difficult to disengage attention from a previously attended location (see e.g., Posner & Petersen, 1990). However, with enough preparation time, it is possible to selectively “tune” attention to the cued location, which increases the signal-to-noise ratio relative to unattended locations, whereas preparing for genderbased selection is more difficult because the location of the relevant gender cannot be predicted. However, Experiment 3 also revealed three other notable findings. First, compared to our previous studies (Koch et al., 2011; Lawo & Koch, 2014), the preparation effect was very small for gender-based selection. We attribute this diminished size of the preparation effect to the fact that we used more “transparent” letter cues in the present study, whereas our previous studies used abstract colour cues for the relevant gender. Hence, it is likely that previous CSI effects included a large component of translating the perceptual cue into an internal verbal feature label. However, it should be noted that the cue transparency was similar for nonspatial versus spatial selection in the present study, so that the differences in the preparation effects cannot be due to this factor. Second, we found that the preparation effect enhanced performance generally, but preparation did not significantly reduce the attention switch costs specifically. Notably, in a previous study (Koch et al., 2011, Experiment 3), we disentangled the influence of perceptual cue encoding from more “abstract” attention-control processes by using two cues for each gender (see also Logan & Bundesen, 2003) and found that preparation indeed affected primarily the cue encoding component but less so the attentional control component. Therefore, the existing data with respect to active advance preparation of attentional control settings are actually somewhat mixed, and the present data add to this mixed evidence by showing that there is no switch-specific preparation when using transparent


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cues. In addition, Koch and Lawo (2014) examined passive dissipation effects with two cues for each selection-relevant feature and found that attentional switch costs were also not affected by variations of RCIs (with CSI held constant), suggesting that auditory attention settings do not dissipate quickly, at least over time ranges that have been found to result in strong RCI effects in visual task switching (e.g., Horoufchin, Philipp, & Koch, 2011; Meiran, Chorev, & Sapir, 2000). That is, the present data, together with previous data, suggest that auditory attention is characterized by comparatively strong “inertia”. It seems to be an interesting avenue for future research to examine whether this attentional inertia represents a true modality-specific difference to visual attention or is rather due to methodological differences of experimental paradigms used to study auditory and visual attention and task switching, respectively. Finally, Experiment 3 apparently failed to replicate the increased switch costs (or the increased repetition benefit, for that matter) when using ear-based selection. However, Experiment 3 differed from Experiments 1 and 2 in the duration of the preparation time, and we found that the expected benefit of spatial selection became apparent only with long preparation time (even though it was not switch-specific; see above). Therefore, with respect to replication, the condition with short CSI is probably more diagnostic. In fact, here we see again the relative benefit of gender-based selection, but this difference was numerically rather small (switch costs of 134 ms vs. 174 ms for genderbased selection vs. ear-based selection) and not significant (p = .19 for this specific post-hoc contrast; i.e., 2 × 2 interaction). We can only speculate that the absence of a significant effect relative to Experiments 1 and 2 is due either to a random sampling error or to reduced statistical power based on a smaller sample size and, because CSI was included in the full-factorial design, the reduced number of observations in each of the cells of the experimental design. However, we would also like to note that the increased switch costs for ear-based selection at short CSI were not theoretically expected in the first place, so

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that, instead of overemphasizing the absence of an unexpected effect, we should rather focus on the relative benefit of increased preparation time for spatial selection. When evaluating the present pattern of findings, though, it should be kept in mind that these findings were obtained with dichotic stimulus presentation. It is possible that the relative benefit of using spatial stimulus separation as selection criterion would be larger if a more natural binaural stimulus presentation is used. For future studies it would be interesting to examine performance as a function of different auditory stimulus presentation conditions.

Conclusion The present study compared performance with spatial and nonspatial criteria for target selection in auditory attention switching using a dichotic-listening paradigm. With relatively short cueing intervals, we found larger switch costs for earbased (spatial) selection than for gender-based (nonspatial) selection, but with long cueing intervals performance was generally much better with spatial than with nonspatial selection. Together, these data suggest that spatial selection cues are more efficient than nonspatial selection cues only when there is sufficient preparation time for reconfiguration of spatial parameters in the auditory attention setting. Original manuscript received 2 April 2013 Accepted revision received 9 February 2014 First published online 19 March 2014

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Dichotic listening and allusive thinking.

Effects of Auditory Attention Training with the Dichotic Listening Task: Behavioural and Neurophysiological Evidence.

Dichotic listening, forced attention, and brain asymmetry in righthanded and lefthanded children.

Directed attention dichotic listening in reading disabled children: a test of four models of maladaptive lateralization.

Measuring laterality effects in dichotic listening.

Dichotic listening during forced-attention in a patient with left hemispherectomy.

Stuttering, dichotic listening, and cerebral dominance.

Dichotic listening and complex partial seizures.

Semantic processing in dichotic listening? A replication.

The reliability of ear advantage in dichotic listening.

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Dichotic listening in monozygotic twins discordant and concordant for schizophrenia.

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Dichotic listening studies of hemispheric asymmetry in brain damaged patients.

Lateral eye movements during verbal and nonverbal dichotic listening.

Differences in dichotic listening asymmetries in depression according to symptomatology.

Dichotic listening pattern in relation to interhemispheric disconnexion.

Interpretation of outcome on dichotic listening tests following stroke.

Influence of encoding and acoustic similarity on the ear advantage and lag effect in dichotic listening.

Strategy selection in a task with spatial and nonspatial components: effects of fimbria-fornix lesions in rats.

Intentional Switching Between Bimanual Coordination Patterns.