Atten Percept Psychophys (2014) 76:73–80 DOI 10.3758/s13414-013-0571-5

Exploring temporal dissipation of attention settings in auditory task switching Iring Koch & Vera Lawo

Published online: 26 October 2013 # Psychonomic Society, Inc. 2013

Abstract Using a cued auditory task-switching variant of dichotic listening, we varied the response–cue interval (RCI) to examine temporal dissipation effects. On each trial, participants were presented with two different number words, one spoken by a female speaker and another by a male speaker (dichotic listening), that served as competing targets for a numerical judgment. The gender of the task-relevant speaker was indicated by a visual task cue prior to each trial. Experiment 1A used two different cues for each task (i.e., gender) and showed only small cue repetition benefits (same cue vs. alternate cue) but large auditory switch costs (alternate cue vs. task switch). A replication without immediate cue repetitions (Experiment 1B) showed very similar switch costs, suggesting that immediate cue repetitions play a negligible role for the size of auditory task switch costs. Moreover, switch costs were reduced when the (entirely task-irrelevant) location of the task-relevant speaker changed, relative to when it was unchanged, suggesting an episodic feature-binding component in our dichotic-listening task. Importantly, both experiments showed no effect of RCI on auditory switch costs. Because statistical power for this null effect was reasonably high across experiments (n =50), this finding suggests that auditory attention settings do not dissipate quickly over time. Keywords Auditory task switching . Dichotic listening . Temporal dissipation Listening to a speaker in a noisy environment is one of the best real-life examples of selective attention (Pashler, 1998). On I. Koch (*) : V. Lawo Institute of Psychology, RWTH Aachen University, Jägerstr. 17-19, D-52066 Aachen, Germany e-mail: [email protected]

the basis of this example, we define “selective attention” behaviorally as context-sensitive preferential stimulus selection when there are competing stimuli, whereas the underlying theoretical concept refers to the cognitive mechanisms of biasing stimulus perception and response selection (attentional set or task set; see, e.g., Logan & Gordon, 2001). Examining selective auditory attention has a long tradition in experimental psychology. An experimental paradigm for examining selective auditory attention is dichotic listening (Broadbent, 1958; Cherry, 1953). Dichotic listening describes the situation in which different information is simultaneously presented to each ear (via headphones), and the instruction typically specifies information in one ear as being task relevant. Numerous studies have shown that auditory selection is quite good, so that the task-relevant information is processed, whereas processing of the task-irrelevant information displayed to the other ear is suppressed (for reviews, see, e.g., Hugdahl, 2011; Pashler, 1998). A long-standing research issue is whether evidence for residual processing of information in the to-be-ignored, taskirrelevant ear indicates automatic processing of nonattended information. Alternatively, such evidence might reflect the effect of involuntary switches of attention to the other ear (Pashler, 1998). For example, when instructed to respond to the stimulus presented to the left ear, participants might inadvertently process the stimulus at the irrelevant location (i.e., switch attention to the other ear). This issue of involuntary attention switching has led to a research strategy to experimentally minimize the role of noninstructed attention switches (e.g., Rivenez, Guillaume, Bourgeon, & Darwin, 2008; Wood & Cowan, 1995), but this has been shown to be very difficult (Lachter, Forster, & Ruthruff, 2004). In fact, attentional selection is, by definition, a flexible process. Unlike previous studies, the present study was aimed at examining this flexibility by studying explicitly

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instructed (i.e., cued ) attention switching in dichotic listening. Specifically, we were interested in the temporal dynamics of attentional control settings. To this end, we used a recently developed dichotic-listening paradigm (Koch, Lawo, Fels, & Vorländer, 2011) that was based on the cuing version of task switching (see, e.g., Kiesel et al., 2010, for a review). In the cued auditory task-switching variant of the dichoticlistening paradigm, two different auditory stimuli (number words) are presented simultaneously to the participants’ left and right ears, one spoken by a female speaker and the other by a male speaker. Participants receive a visual task cue prior to the auditory stimuli that indicates whether the number spoken by the female or male has to be categorized as being smaller or larger than five. Using this paradigm, Koch et al. (2011) found robust performance costs of cued task switches (i.e., switches of task-relevant speaker gender), suggesting that intentional changes in auditory selection criteria take some time. Moreover, auditory switch costs were reduced with increased cuing interval (i.e., cue–stimulus interval [CSI]; see Fig. 1 for a depiction of the timing parameters in the cuing paradigm), which is consistent with findings reported by Logan (2005) for similar visual attention switches. This reduction of auditory switch costs suggests an active process that utilizes cue information to prepare for an upcoming change in attentional control settings (see also Lawo & Koch, 2012). In the present study, we were interested in the stability of auditory attention settings over time. Therefore, in contrast to studying active preparation by manipulating the cuing interval, we examined whether auditory attentional control settings change passively as a function of temporal variations of the interval between the response and the next cue (response–cue interval [RCI]), which can be taken to represent the dissipation interval (see, e.g., Meiran, Chorev, & Sapir, 2000). In fact, previous studies on task switching using visual categorization tasks examined temporal dissipation of switch

costs by manipulating the RCI. Because the task sequence is usually unpredictable in the cuing paradigm (see Jost, De Baene, Koch, & Brass, 2013, for a recent review), the RCI cannot be used for active preparation, because the cue has not yet been presented. Several studies found that switch costs were reduced with increased RCI (e.g., Altmann, 2005; Meiran et al., 2000). This dissipation of switch costs might indicate passive decay of task set activation, reducing both positive priming in task repetitions and competition (based on activation carryover) in switches (Meiran et al., 2000). In addition to this decay account, it has been suggested that episodic retrieval processes contribute to RCI effects on switch costs (Horoufchin, Philipp, & Koch, 2011). Yet the existing empirical findings refer to costs in visual task switching. In the present study, we examined temporal dissipation of auditory switch costs. We report two experiments (Experiment 1A and Experiment 1B). In both experiments, we used a short cuing interval throughout but varied the dissipation interval (i.e., RCI) in random sequence. As RCI variation, we chose the levels of 100 and 1,000 ms because previous studies on visual task switching that varied RCI values with more levels and across larger RCI ranges generally showed no obvious discontinuities in the effect of RCI and, particularly, not beyond 1,000 ms (e.g., Horoufchin et al., 2011, using four RCI values ranging from 50 to 2,000 ms in Experiment 1; Meiran et al., 2000, using four RCI values from 132 to 3,032 ms in Experiment 1 and from 132 to 2,032 ms in Experiment 2). Also, in both experiments, we used two cues for each gender. Verbal cues (e.g., the German word “Frau” for “woman” [vs. “man”]) and figurative cues (e.g., a pictogram depicting a woman) varied unpredictably, so that we could assess auditory switch costs independently of whether the identical cue repeated (see Fig. 1). It has been shown that repetition priming on the level of cue encoding itself contributes to switch costs (Logan & Bundesen, 2003). To isolate the “pure” auditory switch costs, we contrasted cue switches that indicated a task switch (e.g.,

Cue Transitions FRAU MANN

Task Switch Task Repetition Cue Repetition

time Response-Cue Interval (RCI) Manual Response Visual Cue (Key Press)

Fig. 1 Time course of an experimental trial and definition of temporal intervals. In each trial, the cue was either a word written in capital letters (MANN vs. FRAU, meaning “man” vs. “woman” in English) or a pictogram (depicting a male or a female). The size of the cues is not up

Cue-Stimulus Interval (CSI) Auditory Stimuli (Number Words)

to scale in this figure. The presented cue transitions are examples; all possible transitions occurred in Experiment 1A, whereas immediate cue repetitions were disallowed in Experiment 1B

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from female to male) with those cue switches that indicated a task repetition, and we examined whether these costs are influenced by RCI. Note that both experiments constitute independent replications with respect to exploring temporal dissipation of auditory switch costs, but they differ with respect to whether perceptual priming based on immediate cue repetition was allowed (Experiment 1A) or disallowed (Experiment 1B). In Experiment 1A, we allowed immediate cue repetitions. Consequently, we could decompose the “total” switch costs (i.e., task switch indicated by changed cue vs. task repetition indicated by repeated identical cue) into the “true” auditory switch costs (see above) and a cue priming component (task repetition with changed vs. repeated cue). On the basis of this decomposition, we could explore whether the influence of RCI would actually refer to the abstract attentional settings (affecting “true” switch costs), to perceptual encoding of the cue itself (affecting cue priming), or to both. In Experiment 1B, we disallowed cue repetitions and, thus, had the cue change on every trial. This variation, relative to Experiment 1A, reflects the debate in the literature on task cuing as to whether the use of two cues per task contaminates the estimate of switch costs (see Jost et al., 2013, for a review). For example, Altmann (2006) found greatly inflated switch costs when using two cues instead of only one cue per task, whereas Schneider and Logan (2011) found only small differences as a function of number of cues. In Experiment 1B, instead of comparing situations with two cues per task with one-cue situations, we addressed the issue of a potential contamination of the auditory switch cost measure by avoiding immediate cue repetitions altogether, following a suggestion by Monsell and Mizon (2006). By comparing the data of Experiment 1A with those of Experiment 1B, we then assessed whether the presence of immediate cue repetitions has an influence on cue processing. Such an influence seemed possible because cue repetitions, which are necessarily associated with task repetitions, might induce an expectation that a changed cue would go along with a changed task, too. Hence, Experiment 1B allowed us to check whether the changed-cue/repeated-task condition can be used as an uncontaminated baseline for establishing auditory switch costs and, therefore, served to replicate but also to extend Experiment 1A.

Method Participants In Experiment 1A, 20 participants (15 women, 5 men) in the age range of 18–31 years (M = 23.45, SD = 3.38) took part and received partial course credit or 6 Euro. The participants were right-handed and reported no presence of hearing problems. In Experiment 1B, 30 new participants (22 women,

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8 men; age range of 18–32 years; M = 22.40, SD = 3.68) took part and received partial course credit. They reported no presence of hearing problems. Three participants were lefthanded. All participants were recruited through a participant pool at the Institute of Psychology at RWTH Aachen University. Stimuli and task The visual cues were presented in white at the center of a black 17-in. screen. The participant’s distance to the screen was about 60 cm. Two verbal cues, in Arial font, and two figurative cues (pictograms) were used (see Fig. 1). The German word “FRAU” (2.5 × 8.5 cm; angle of vision: 2.4° × 8.1°) or a pictogram of a female (6.5 × 3.0 cm; 6.2° × 2.9°) was presented to indicate the female speaker as being task relevant. The German word “MANN” (2.5 × 9.5 cm; 2.4° × 9.1°) or a pictogram of a male (6.5 × 2.5 cm; 6.2° × 2.4°) was presented to indicate the male speaker as being task relevant. The auditory stimuli were the spoken number words one to nine (without five), presented dichotically via headphones (AKG K530 LTD). Three different female speakers and three male speakers were recorded with standard parameters (sampling rate, 44,1 kHz; quantization, 24 bit) in an anechoic chamber at the Institute of Technical Acoustics of RWTH Aachen University. The mean frequency of the number words spoken by the female speakers was 212 Hz, whereas the mean frequency of the number words spoken by the male speakers was 135 Hz. 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 (see Koch et al., 2011). Participants were instructed to press the key left versus right of the space key of the computer keyboard (QWERTZ) with their index fingers. The assignment to the categories ( < 5 and > 5) was held constant according to the “mental number line” (i.e., smaller associated with left, larger with right). Procedure Each trial started with the visual cue. After a CSI of 100 ms, the two auditory stimuli were presented simultaneously. The cue remained on screen until manual response execution. The RCI varied randomly (100 vs. 1,000 ms). In case of an error, a visual feedback (“Fehler!”; German for “error”) was displayed for 500 ms, delaying the onset of the next cue. Prior to the experiment, there was an online instruction, followed by a practice block of 32 trials. The experiment consisted of four blocks with 128 trials each, separated by short breaks. The gender of the task-relevant speaker, the

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mapping of gender to location (left vs. right ear), identity of speaker within each gender, and the identity of the relevant and irrelevant number words (which always differed from each other) were independently randomized. The only difference between Experiment 1A and Experiment 1B was that the cues varied randomly in Experiment 1A (allowing direct repetitions), whereas direct cue repetitions were disallowed in Experiment 1B. Design The independent variables were RCI (100 vs. 1,000 ms) and transition (task switch vs. task repetition vs. cue repetition in Experiment 1A and task switch vs. task repetition in Experiment 1B).

Results and discussion We report the results in three subsections. First, for Experiment 1A, we performed two nonorthogonal contrasts, one for auditory switch costs (task switch vs. task repetition) and the other for cue priming (task repetition vs. cue repetition). Second, we report Experiment 1B to focus specifically on auditory switch costs. Third, we compare directly the auditory switch costs in Experiment 1A with those in Experiment 1B to examine the potential influence of immediate cue repetition on auditory switch costs. Experiment 1A We excluded the practice block, the first trial in each block (“warm-up”), and trials subsequent to an error from all analyses. Moreover, for reaction time (RT) analysis, for each participant we excluded RTs below 100 ms (0.2 %) and RTs exceeding 3 standard deviations from the participant’s mean (1.8 %) as RT outliers. Outliers were removed prior to error analysis. RTs and error rates were submitted to separate analyses of variance (ANOVAs) using RCI and transition as independent variables (within subjects). Mean RTs as a function of RCI and transition are shown in Fig. 2 (left panel; see Table 1 for error rates). For the auditory switch contrast, the ANOVA on RT with RCI (100 vs. 1,000 ms) and transition (task switch vs. task repetition) as independent variables yielded an effect both of transition, F (1, 19) = 13.22, MSE = 78,188, p < .01, η 2p = .41, and of RCI, F(1, 19) = 4.62, MSE = 11,981, p < .05, η 2p = .196, indicating higher RTs in task switches than in task repetitions (1,113 vs. 1,051 ms) and, thus, auditory switch costs of 62 ms, and higher RTs for the long versus short RCI (1,094 vs. 1,070 ms). The interaction was clearly nonsignificant, F < 1. Switch costs were 55 ms for the short

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RCI and 70 ms for the long RCI, with a numerical trend against dissipation with a long RCI. The same analysis of the error rates (see Table 1) revealed an effect of transition, F(1, 19) = 5.83, MSE = .003, p < .05, η 2p = .235, with higher error rates on switches than on repetitions (5.3 % vs. 4.0 %), but both the effect of RCI and the interaction were nonsignificant, Fs < 1. For the cue repetition priming contrast (task repetition vs. cue repetition), the ANOVA revealed no significant main effects (both F s < 1), and also the interaction was not significant, F (1, 19) = 1.06, MSE = 2,680, p > .3, η 2p = .053. The cue repetition priming effect was numerically very small at the short RCI (6 ms) and increased somewhat (but not significantly) at the long RCI (29 ms; p = .072, if tested separately with a one-tailed t-test). The same ANOVA on error rates did not yield significant effects, all Fs < 1. Together, the data indicate substantial auditory switch costs. Because also a task repetition was indicated by a changed cue, the influence of cue repetition priming is removed for this particular contrast. This demonstration of “pure” auditory switch costs is in line with the finding of our previous study that focused on CSI (Koch et al., 2011, Experiment 3). However, despite a significant effect of RCI on RT in general, there was no evidence for a temporal dissipation of auditory switch costs. The purpose of Experiment 1B was to replicate the RCI effect (or the absence thereof) on switch costs in a situation in which immediate cue repetitions were disallowed. Experiment 1B The data were analyzed as in Experiment 1A. As outliers, we excluded for each individual participant RTs below 100 ms (0 %) and RTs exceeding 3 standard deviations from the mean (1.7 %). Experiment 1B focused on the auditory switch contrast. The ANOVA on RT (see Fig. 2, right panel) obtained an effect of transition, F (1, 29) = 53.34, MSE = 206,255, p < .01, η 2p = .648, showing higher RTs in task switches than in task

Table 1 Mean error rates (in percentages, with standard errors in parentheses) in Experiment 1A and Experiment 1B and as a function of response–cue interval (RCI; 100 vs. 1,000 ms) and transition (task switch vs. task repetition vs. cue repetition) Experiment 1A RCI (ms) Transition

100

Task switch 5.3 (0.8) Task repetition 4.0 (1.0) Cue repetition 4.7 (1.0)

Experiment 1B RCI (ms) 1,000

100

5.3 (0.8) 6.0 (0.6) 4.0 (0.6) 3.8 (0.6) 4.0 (0.7) –

1,000 6.3 (0.7) 2.9 (0.4) –

Atten Percept Psychophys (2014) 76:73–80 1400

77 Experiment 1A (With Cue Repetitions)

Experiment 1B (Without Cue Repetitions)

1300 Task Switch Task Repetition Cue Repetition

RT (ms)

1200 Switch Costs = 62 ms Cue Repetition Benefit = 17 ms

1100

1000

Switch Costs = 82 ms

900

800 100

1000

100

1000

Response-Cue Interval (ms) Fig. 2 Mean reaction times (RTs, in milliseconds) in Experiment 1A and Experiment 1B as a function of response–cue interval (100 vs. 1,000 ms) and transition (task switch vs. task repetition vs. cue repetition). Error bars indicate the standard error of the mean

repetitions (1,064 vs. 982 ms) and, thus, an auditory switch cost of 82 ms. The main effect of RCI was not significant, F (1, 29) = 3.06, MSE = 5,267, p = .091, η 2p = .096, but there was a trend for higher RTs with long RCIs (1,030 vs. 1,016 ms), which is in line with the significant effect observed in Experiment 1A. The interaction was clearly nonsignificant, F(1, 29) = 1.23, MSE = 1,327, p > .27, η 2p = .041, and again, switch costs even increased somewhat with the long RCI (89 vs. 76 ms). For the error rates (see Table 1), the ANOVA revealed an effect of transition, F (1, 29) = 48.41, MSE = .024, p < .01, η 2p = .625, with higher error rates on switches than on repetitions (6.2 % vs. 3.3 %), but both the effect of RCI, F < 1, and the interaction, F (1, 29) = 3.00, MSE = .001, p = .094, η 2p = .094, were nonsignificant. The nonsignificant interaction suggests a trend toward larger switch costs with long RCIs than with short RCIs (3.4 % vs. 2.2 %), which is descriptively consistent with the RT data. For Experiment 1B, the sample size was larger than in Experiment 1A (n = 30 vs. n = 20), which encouraged us to conduct a supplemental analysis of the data with respect to whether the location of the task-relevant speaker (i.e., ear of dichotic presentation) remained the same or changed (“location change”). Please note that the mapping of gender to location (i.e., left vs. right ear) varied randomly from trial to trial, and location was not related to the required response. Still, even though location was an entirely task-irrelevant feature in this paradigm, auditory location changes might nevertheless interact with auditory task switching. In fact, in studies on so-called episodic feature binding and integration, it has been demonstrated that repetitions of independent features interact such that there are repetition benefits with

respect to the relevant feature primarily with complete feature repetitions, but much less so with only partial repetitions (Zmigrod & Hommel, 2010). Likewise, in studies on task switching, it has been found that task repetitions are beneficial primarily if the response repeats as well (e.g., Koch, Schuch, Vu, & Proctor, 2011; see also Kiesel et al., 2010, for a review). Therefore, in the present context, we might suspect that auditory task repetitions should be associated with better performance if the location of the relevant speaker repeats too, even though speaker location per se is relevant neither for stimulus selection nor for response selection. To explore this issue, we reran the above ANOVA but additionally included location change as an independent within-subjects variable. To avoid redundancy, we report only those effects that include this new variable. The ANOVA on RT indeed obtained an effect of location change, F(1, 29) = 37.39, MSE = 3,264, p < .01, η 2p = .563, indicating higher RTs in location changes than in location repetitions (1,088 vs. 1, 043 ms) and, thus, auditory location change costs of 45 ms. Notably, location change interacted significantly with auditory task switches (i.e., gender changes), F(1, 29) = 26.37, MSE = 4,001, p < .01, η 2p = .476, showing that the auditory switch costs decreased from 136 to 52 ms when the location of the relevant speaker also changed from one trial to the next. This finding suggests an effect of automatic feature binding with respect to gender and location, even though neither gender nor location is directly related to the response (which is actually based on categorizing the number word spoken by the relevant speaker, irrespective of its location). Hence, gender–location feature binding seems to modulate the size of auditory switch costs in the present paradigm. Importantly, though, RCI interacted with location change, F (1, 29) = 17.29, MSE = 2, 412, p < .01, η 2p = .374, indicating that the location change

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effect decreased from 71 to 19 ms with increasing RCI, whereas there was no evidence whatsoever for an influence of RCI on auditory task switch costs (F < 1; see also above), and there was also no hint of a RCI-based modulation of the feature-binding effect (i.e., the task switch×location switch interaction), F < 1. [The same ANOVA on the error rates confirmed the main effect of location change, F (1, 29) = 5.32, MSE = .001, p < .05, η 2p = .155, with 1.1 % more errors in location changes, but the other effects of location change were not significant, all Fs < 1.] In summary, Experiment 1B replicated the data of the auditory switch contrast and showed again that these are not reduced with long RCIs. Moreover, a supplemental analysis demonstrated the existence of costs of (task-irrelevant) location changes and that these modulate auditory task switch costs to produce an episodic-binding-like pattern (Zmigrod & Hommel, 2010). Importantly, while location change costs seem to dissipate over time, this is not the case with auditory task switch costs. This pattern suggests that perceptual auditory spatial location codes are automatically generated and, because they are not task relevant, decay over time, whereas the higher-level attention set that refers to the speaker gender, which is task relevant, does not seem to dissipate quickly (see the General Discussion section).

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expectancy that any cue change would go along with a task change, thus reducing the repetition benefit with task repetitions, but this suggestion is difficult to reconcile with the finding that error rates in Experiment 1A were not significantly smaller in cue repetitions than in task repetitions (see the individual analysis of Experiment 1A). Hence, we conclude that the pattern of error rates is not entirely clear with respect to the role of cue repetitions, but it is clear with respect to the absence of any RCI effects (particularly on switch costs). In summary, the inclusion of cue repetitions in Experiment 1A did not seem to have exerted a contaminating influence on the assessment of RT switch costs. Most notably, though, even with the increased power of combining Experiment 1A and Experiment 1B, we did not find an influence of RCI on auditory switch costs. In fact, a post hoc power analysis (repeated measures within–between interaction) indicated that with n = 50 and α = .05, the power to detect a medium-sized effect of f = 0.5 exceeds .93 (Faul, Erdfelder, Lang, & Buchner, 2007), suggesting that a temporal dissipation effect in auditory switch costs, if it exists, must be very small indeed.

General discussion Comparison of auditory switch costs across Experiment 1A and Experiment 1B In a final analysis, we compared the data pattern across Experiments 1A and 1B to see whether the inclusion of immediate cue repetitions exerted any contextual influence on our measure of auditory switch costs. A mixed three-way ANOVA on RTs, using experiment as a between-subjects variable and transition (task switch vs. task repetition) as well as RCI as within-subjects variables, yielded significant effects of transition, F(1, 48) = 54.28, MSE = 253,839, p < .01, η 2p = .531, and RCI, F (1, 48) = 8.27, MSE = 17,078, p < .01, η 2p = .147. The interaction of transition and RCI was not significant, F (1, 48) = 1.021, MSE = 2,584, p > .3, η 2p = .021. The main effect of experiment was not significant, F < 1. Likewise, the interactions of experiment and RCI, F < 1, experiment and transition, F (1, 48) = 1.07, MSE = 4, 990, p > .3, η 2p = .022, and the three-way interaction, F < 1, were clearly not significant. The same ANOVA on error rates did not reveal any significant effects (all Fs < 1.21, p s > .27), except for the effect of transition, F (1, 48) = 38.52, MSE = .021, p < .01, η 2p = .445, and the interaction of experiment and transition, F (1, 48) = 5.09, MSE = .003, p < .05, η 2p = .096. Apparently, in Experiment 1A, the error rates are somewhat decreased for task switches and somewhat increased for task repetitions, resulting in reduced switch costs relative to Experiment 1B (1.3 % vs. 2.9 %). This might be taken as a slight hint at an

The aim of this study was to explore whether auditory switch costs passively dissipate over time. To this end, we employed a recently developed auditory attention-switching paradigm using task-switching methodology (Koch et al., 2011) and manipulated the RCI. Unlike previous studies on switching between visual tasks, which found substantial RCI effects on switch costs (see, e.g., Kiesel et al., 2010, for a review), the costs of switching auditory attention settings did not dissipate passively as a function of RCI across two experiments. Before we discuss the implications of this finding, we mention five other noteworthy observations. First, both experiments revealed auditory switch costs that were uncontaminated by any influence of direct cue repetitions, thus demonstrating the robustness of auditory switch costs. We assume that these costs are due to reconfiguration of internal attention settings that refer to the indicated selection cue. Specifically, for visual attention, a biasing process that directs processing toward the relevant stimulus features or dimension is often assumed (e.g., Logan & Gordon, 2001), and in our case, we assume a similar biasing of processing in the auditory modality (Shinn-Cunningham, 2008) that is presumably focused on both pitch and timbre of speaker voice, which are both associated with speaker gender. Our data suggest that any change in this auditory attentional control setting incurs some time costs, but once established, this control setting remains in place without much dissipation, at least over time ranges that have yielded substantial RCI

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effects in visual task switching (e.g., Horoufchin et al., 2011; Meiran et al., 2000). Second, it is noteworthy that auditory switch costs were about the same in Experiments 1A and 1B, showing that the costs were not significantly affected by the inclusion of immediate cue repetitions. Given the discussion about measurement of switch costs in 2:1 cue-to-task mappings (e.g., Altmann, 2006; Schneider & Logan, 2011), it is good news to know that the assessment of auditory switch costs (task switch vs. task repetition) seems to be uncontaminated by the inclusion of cue repetitions, which might have induced a bias that any change of cue is also associated with a changed task. This finding could be taken as suggesting that Monsell and Mizon’s (2006) proposal of avoiding immediate cue repetitions when one is not interested in cue-related priming processes is a feasible practice. Yet the present data also suggest that the presence of immediate cue repetitions does not seem to change processing in meaningful ways (see also Jost et al., 2013, for a review and discussion). Third, in Experiment 1A, we observed that relative to auditory switch costs, the contribution of cue-related repetition priming was small and even nonsignificant. Studies on visual task switching observed a wide range of differential task- and cue-related priming effects (e.g., Schneider & Logan, 2011), but typically a cue-related priming effect could be detected. In our previous study that used a constant overall interval (i.e., response–stimulus interval [RSI]) of 1,000 ms (Koch et al., 2011, Experiment 3) and manipulated the cuing interval (and RCI thus varied inversely), we found a substantial cue priming effect, which, however, diminished with increasing cuing interval. If we take the condition with a long RCI in the present study, which is the one that is most similar to that in our previous study, we found a cue priming effect of 29 ms that just failed to reach significance if tested separately (p = .072). We attribute this failure to replicate in Experiment 1A to increased variance based on the additional inclusion of a very short overall interval in the present study (i.e., an RSI of 200 vs. 1,100 ms). Fourth, even though we did not find that auditory switch costs dissipated over time, we found a general effect of RCI on RT in the sense that responses became generally somewhat slower when the interval was long. This effect might be attributable to a process of activation decay for task repetition trials, but the fact that the same effect occurred also on switch trials rather suggests that this drop in performance is due to a rather unspecific influence of increased temporal uncertainty with respect to cue onset (e.g., Los, 2010). We note, though, that this effect was not reflected in the error rates, which actually showed a tiny and nonsignificant improvement with a long RCI. Therefore, it seems prudent to refrain from specific interpretations of the main effect of RCI on RT. A final interesting finding refers to the influence of location change. Location change is entirely irrelevant for both

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stimulus selection and response selection. Yet, in Experiment 1B, we conducted a supplemental analysis of the data and found that auditory switch costs were modulated by location change. Specifically, switch costs (or repetition benefits, for that matter) were larger when the location of the task-relevant speaker repeated than when it changed. This interaction of the task-relevant stimulus feature (relevant speaker) with an irrelevant feature (speaker location) resembles somewhat the pattern of episodic integration in studies on feature binding (e.g., Zmigrod & Hommel, 2010). In the present context, we assume that both features become integrated into an episodic event file, so that cuing the relevant speaker gender associatively reactivates the corresponding spatial location codes, which then primes attention to that side. This automatic auditory spatial priming is beneficial if the relevant speaker is actually presented at that location, but with location changes, this priming is detrimental and, therefore, partially offsets the auditory task repetition benefit (which is the flipside of switch costs). Unlike auditory switch costs, this spatial sequential priming effect did seem to dissipate over time, which is consistent with the idea that perceptual codes decay passively over time, whereas task-relevant codes are more durable (see also Horoufchin et al., 2011, for a discussion). The absence of a modulation of the effect of episodic binding of location and gender (i.e., the two-way interaction of task switch and location change) might suggest that this particular interaction is due to associative reactivation of location priming, which might be independent of the effect of lingering but decaying spatial stimulus priming, but this is an issue that cannot be closed on the basis of the present data. Taken together, the present set of findings suggests that auditory attentional control settings remain rather stable once established. The finding that auditory switch costs do not passively dissipate is consistent with the idea that “decay” of attentional sets in the auditory domain is probably not the major driving factor in attentional change but, rather, interfering events, in the form of either active, internal reconfiguration processes or stimulus-based attentional capture (Shinn-Cunningham, 2008). But auditory attention does not seem to change simply as a function of the passage of time alone. Note that despite the methodological analogy to cued visual task switching, there are potentially critical differences to our auditory switching paradigm. In the absence of a clear definition of what constitutes a “task” (e.g., Monsell, 1996), we define a task operationally on the basis of a feature that systematically changes on a switch trial. In many taskswitching studies, several features can change at the same time, such as stimulus categorization rules (e.g., parity vs. magnitude judgments for visually presented digits), whereas in the present study, the auditory stimulus categorization always remained the same and only the gender of the taskrelevant speaker switched across trials. Moreover, most visual

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switching studies have used overlapping category–response mappings, so that responses can change their “meaning” in a task switch (e.g., Schuch & Koch, 2004), whereas response meanings remain unchanged in our dichotic-listening paradigm. What is similar, though, across visual switching and our auditory switching task is that the selection criterion for the relevant stimulus attribute varies from trial to trial, requiring flexible attentional control. In conclusion, the present study demonstrated auditory switch costs and showed that these, unlike their pendant in the visual domain, do not seem to dissipate passively over time. This persistence of the auditory switch costs also suggests that the underlying attentional control settings are fairly robust and are primarily responsive to interfering internal events (active reconfiguration) or external events (exogenous attentional capture), but not to the simple passage of time. Given existing similarities to switches in visual attention (e.g., with respect to preparation effects) and potential differences, such as the apparent absence of temporal dissipation in the auditory domain, further research is required to examine whether these partially diverging patterns of findings are due to the various methodological differences (such as presence vs. absence of changing stimulus–response mappings) across paradigms or, instead, represent true modality-specific differences in attentional processes. Acknowledgements We would like to thank Frank Wefers from the Institute of Technical Acoustics for help in producing the stimulus material and Caterina Schiffner for help in preparing the experiments. We are also grateful for helpful comments by Simon Grondin, Sander Los, and two anonymous reviewers. This research was supported by the Deutsche Forschungsgemeinschaft (DFG; KO 2045/11-1).

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