Psychological Research DOI 10.1007/s00426-013-0527-3

REVIEW

Auditory distractor processing in sequential selection tasks Christian Frings • Katja Kerstin Schneider Birte Moeller

•

Received: 2 July 2013 / Accepted: 5 November 2013  Springer-Verlag Berlin Heidelberg 2013

Abstract In this review, we analyze the cognitive processes contributing to selection in audition. In particular, we focus on the processing of auditory distractors in sequential selection paradigms in which target stimuli are accompanied by distractors. We review the evidence from two established tasks, namely the auditory negative priming and the auditory distractor–response binding task, and discuss the cognitive mechanisms contributing to the results typically observed in these tasks. In fact, several processes have been suggested as to explain how distractors are processed and handled in audition; that is, auditory distractors can be inhibited, encoded with a do-notrespond-tag, integrated into a stimulus–response episode containing the response to the target, or upheld in working memory and matched/mismatched with the following distractor. In addition, variables possibly modulating these cognitive processes are discussed. Finally, auditory distractor processing is compared with distractor processing in vision.

Introduction Selecting relevant information from the complex sensory inputs the human brain constantly receives is a key ability for controlled behavior. Selection can take place in a specific modality (e.g., in audition when one selectively listens to one of several speakers) or across modalities (e.g., reading a text message while hearing several speakers). In this review, we focus on selection in audition. More C. Frings (&)
K. K. Schneider
B. Moeller Cognitive Psychology, University of Trier, 54296 Trier, Germany e-mail: [email protected]

specifically, we focus on the cognitive mechanisms that contribute to what we call ‘auditory distractor processing’. Hence, instead of reviewing evidence for auditory selection in general, we concentrate on evidence for a specific type of auditory selection that typically takes place in sequential priming paradigms in which auditory targets have to be selected against auditory distractors. Before we detail and define this type of auditory distractor processing, we will shortly integrate it in the broader literature on auditory selection. If one scans the literature on auditory attention or auditory selection nearly all papers refer to Broadbent (1958) and Cherry (1953) and the dichotic-listening paradigm. The main interest of researchers using this paradigm is typically what we can recall from unattended auditory information. One can say that participants in a dichoticlistening paradigm have—by instruction—a ‘target-ear’ and a ‘distractor-ear’ and are to selectively attend to the target-ear. Many important findings have been observed with this auditory selection paradigm. For example, the popular Cocktail-party effect (Moray, 1959) shows that one’s own name has a higher chance for being noticed in the unattended auditory channel as compared to control names (see Wood & Cowan, 1995, for a somewhat newer perspective on this classic effect). Other paradigms analyze the impact of irrelevant auditory streams on the performance or the detection of irrelevant frequencies. In particular, the irrelevant speech paradigm (e.g., Baddeley & Salame´, 1986; Miles, Jones, & Madden, 1991) has been used to analyze how task irrelevant speech-like auditory streams influence memory performance. Nowadays, participants are typically instructed to remember serially presented visual items while ignoring the auditory channel. Thus, one can say that the irrelevant speech paradigm analyzes the influence of auditory distractors. In the same

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vein, effects of ‘expecting’ a specific target frequency or spatial location of an auditory target have been analyzed (Scharf, 1990). For example, tones that differ from the expected frequencies to a large margin are hardly perceivable at all (against white noise); the typical explanation is that the cognitive system focusses on a particular frequency (the target frequency) and filters out all other frequencies (the distracting ones). Many other paradigms exist that can be used to analyze some aspect of auditory attention and possibly auditory selection (e.g., the oddball paradigm). However, these popular auditory tasks cannot be used to investigate the kind of auditory selection we focus on in this review. The phenomenon we are interested in here, is concerned with a different kind of selection. In fact, we are going to analyze auditory distractor processing in tasks in which participants had to select a target against a distractor stimulus on a prime display and then again had to select a target against a distractor on a probe display; this kind of distractor processing is different from the just described selection research in audition for at least three reasons. First, the selection is purely auditory (in contrast, for example, to the irrelevant speech effect). Second, the irrelevant stimuli are always detected and participants are typically fully aware of them. This is due to the fact that only a small set of stimuli is used during the experiment (for example, only four), that all stimuli serve as relevant and irrelevant stimuli (this stands in sharp contrast to the typical selective auditory detection tasks or the oddball task), and that distractors share the same stimulus–response mapping with the targets. And third, as a consequence of the first two points, irrelevant stimuli are always mapped to a response that typically interferes with the to-be-executed response towards the target stimulus (this contrasts the classic dichotic-listening paradigm). In fact, as distractors and targets are drawn from the same set of stimuli, a distractor is not a stimulus that is completely irrelevant (because the stimulus may be the next target) but only interferes with correct responding on the current trial; selection serves to execute the correct response from a set of typically two competing responses. With ‘auditory distractor processing’ we label the cognitive mechanisms that allow to effectively handle these kinds of (auditory) selection situations. Generally speaking, research on the auditory variant of the flanker task (Eriksen & Eriksen, 1974) may be also relevant here. For example, Chan, Merrifield, & Spence, (2005) analyzed the interference of auditory distractors when participants had to identify an auditory target. Yet, we are interested the after-effects of ignoring an auditory distractor which cannot be measured in the standard flanker task. Thus, we turn to sequential paradigms in which auditory targets are selected against auditory distractors.

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In research on vision, such processes have been analyzed in two variants of sequential priming tasks, namely the negative priming and the distractor–response binding tasks. We review the evidence on the auditory variants of these tasks and compare the assumed underlying mechanisms with the ones from research on visual distractor processing.

Auditory negative priming The negative priming effect refers to a slowed or more error-prone response to a stimulus that was previously ignored (Tipper, 1985). This effect has been extensively studied in the visual domain (for reviews, see Fox, 1995 or May, Kane & Hasher, 1995). A typical negative priming trial consists of two consecutive displays each requiring a response. The first of these is called the ,,prime‘‘, while the second is called the ,,probe’’. In both prime and probe display, a target stimulus is presented together with a distractor stimulus. If the prime distractor stimulus is used as the following probe target (distractor-to-target repetition), slowed and more error-prone responses are typically observable (this condition is termed ignored repetition) as compared to a control condition with no stimulus repetitions between prime and probe displays. For example, a prime-probe sequence in which at the prime the word TABLE has to be pronounced while the word BOOK has to be ignored is followed by a probe display at which the word BOOK has now to be pronounced while the word TREE has to be ignored (i.e., an ignored repetition trial). The reaction time of the probe display is compared to the reaction of a probe display of a prime-probe sequence in which at the prime the word TABLE has to be pronounced while the word BANK has to be ignored, followed by a probe display at which the word BOOK has to be pronounced while the word TREE has to be ignored (i.e., a control trial). In other words, the elegance of this paradigm is that two identical probe displays are compared that differ only in whether the probe target played the role of the previous prime distractor or not. Visual variants of the negative priming task include identity tasks (participants have to report the identity of the target; e.g., Tipper, 1985), spatial tasks (participants have to report the location of the target; e.g., Tipper, Brehaut & Driver, 1990) and semantic tasks (participants also have to report the identity of the target but the prime distractor and the probe target of the ignored repetition condition are not identical but only semantically related—for example, ignoring CAT at the prime and responding to DOG on the probe; e.g., Tipper & Driver, 1988). A coarse-grained taxonomy of negative priming theories differentiates between inhibition- (Houghton & Tipper,

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1994; Tipper, 1985) and retrieval-based accounts (Neill, Valdes, Terry, & Gorfein, 1992). The inhibition theory assumes that the abstract representation of the distractor stimulus is actively suppressed by mechanisms of selective attention during the processing of the prime episode and that this inhibition persists until the presentation of the next display. Thus, when the ignored distractor from the prime trial becomes the probe target, the recently inhibited representation has to be activated in order for the participant to respond, and hence negative priming occurs. By contrast, retrieval theories argue that negative priming is caused by the fact that perceiving a target activates memory traces associated with that particular stimulus. In the ignored repetition condition, the last memory trace of the current target stimulus may contain information like ‘‘distractor’’ or ‘‘do-not-respond’’, and it is this information that interferes with a person’s ability to respond quickly and accurately to the target. Both accounts are well supported by the empirical literature, and nowadays most researchers agree that both inhibitory mechanisms and retrieval processes likely contribute to negative priming (see e.g., Kane, May, Hasher, Rahhal, & Stoltzfus, 1997; Neill, 2007; Tipper, 2001). Several other approaches have been suggested, for example, the temporal discrimination theory (Milliken, Joordnes, Merikle, & Seiffert, 1998), transfer-(in)appropriate processing (Neill & Mathis, 1998), the stimulus response retrieval theory (Rothermund, Wentura, & De Houwer, 2005), or the prime response retrieval (Mayr & Buchner, 2006). Yet, all of these approaches can at least partially be interpreted as variants of the two classic approaches. Most studies on negative priming have been conducted for the visual domain. A full-on adaption of negative priming for the auditory domain was first presented by Banks, Roberts, and Ciranni (1995), who used a variant of auditory shadowing. The participants repeated words that were spoken by a female voice, but ignored those spoken by a male voice. Alternatively, participants attended to a specific location (e.g., either the left or the right ear). Other auditory adaptations of the negative priming task used classifications of different tones. Buchner and Steffens (2001) used groups of musical instruments. In their experiments, participants first heard a 20 ms metronome click that specified the to-be-attended ear. After a 250 ms cue-target interval, two tones were simultaneously presented (one tone to each ear). The participants had to identify the tone presented to the indicated ear, which could either belong to a group of string instruments (piano, balalaika, violin) or to a group of wind instruments (flute, trumpet, saxophone). The relevant comparison in this task was whether a tone just ignored on the previous prime trial would lead to performance cost if it was repeated at the following trial as the target.

Groups of musical tones were juxtaposed with animal sounds by Mayr, Niedeggen, Buchner, & Pietrowsky (2003). Mondor, Leboe, and Leboe (2005) applied a similar task in which four abstract sounds (for example, a ‘‘croak’’ or a ‘‘buzz’’) were utilized. In another study by the same group (Leboe, Mondor, & Leboe, 2006), the cues signaling the ‘target-ear’ were auditory (a metronome click) or visual (an arrow pointing to the right or left side). Regardless of the differences between the auditory stimuli in these studies, distractor-to-target repetitions always led to worse performance, i.e., auditory negative priming was observed. In addition, auditory negative priming was examined in regard to correlates of event-related brain potentials (Mayr et al., 2003). The negative priming condition was associated with a diminished parietal positive complex, starting at about 300 ms. This complex strongly resembles the socalled ‘‘old/new’’ ERP effect (Rugg & Doyle, 1994) that signifies the onset of an ‘‘old’’ stimulus compared to a ‘‘new’’ stimulus; that is, a previously ignored probe target was encoded as a ‘‘new’’ stimulus (see also, Mayr, Niedeggen, Buchner, & Orgs, 2006). In sum, auditory negative priming with identity or categorization tasks seems to be a reliable phenomenon. The results can be and have been explained in terms of inhibition and retrieval processes (or their variants). Most findings could be explained by both theories (although every article favors one over the other). For example, Mayr and Buchner (2006) found auditory negative priming and the respective ERP correlates of auditory negative priming mainly in the higher reaction times of participants’ individual reaction time distributions; in turn, they argued that the retrieval of conflicting stimuli is time consuming and thus not always completed in the case of the fast responses, many of which could be correct guesses and would then not reflect negative priming processes. However, in a similar vein, inhibition could not always be fully built up in cases of fast reactions, so that a smaller negative priming effect would be predicted from this account as well for the fast responses. Of course, the ERPs were located at parietal and not at frontal recording sides of the head (note, usually frontal regions are linked to inhibitory functions; Frings & Groh-Bordin, 2007; Yeung, Botvinick, & Cohen, 2004). Nevertheless, location analyses with the electroencephalogram are tentative at best. So, most findings on auditory negative priming can be interpreted in terms of inhibition and/or episodic retrieval. Hence, if identity negative priming is observed in vision as well as in audition, and both effects are mainly explained with the same theoretical assumptions, an important question is whether these effects reflect modality-independent processes. On the one hand, in a crossmodal study by Buchner, Zabal and Mayr (2003) using all combinations of visual and auditory primes and

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probes, the magnitude of the negative priming effect was comparable for the visual and auditory modality. On the other hand, unusually large auditory negative priming effects have been reported (see Experiment 2, Mondor et al., 2005; Buchner & Steffens, 2001; Banks, Roberts, & Ciranni, 1995). This could be due to the specific experimental designs, which in the case of Mondor et al. (2005) was atypical insofar as no response was required for the prime stimuli (however, in vision, this design does not lead to untypical large negative priming effects). Yet, these findings may also be an indication for differences in the mechanisms of visual and auditory negative priming. Banks et al. (1995) have hypothesized that auditory selection is more strongly influenced by central mechanisms than visual selection, as there are less peripheral means of attenuating input from unattended locations in this modality. Shifts of visual fixation from one position to another are accompanied by shifts in visual acuity; the presently fixated position is viewed most clearly, while others are attenuated. Since shifts of fixation happen very quickly, the input of central selective processing could be somewhat limited in comparison to auditory selection processes, which are not aided by equivalents of eye and gaze movements. This might account for larger auditory negative priming effects. In addition, the average reaction times should be used to adjust the negative priming effects when compared between modalities as they are typically higher in auditory as compared to visual variants of negative priming. For the tactile modality, Frings, Amendt and Spence (2011) have reported larger than usual negative priming effects, even after controlling for differences in processing difficulty. Yet, despite a comparison regarding the size of the negative priming effect there is evidence for a difference in strategic control of negative priming between vision and audition. For instance, Frings and Wentura (2008) have shown that the size of the visual negative priming effect increases with the proportion of attended repetition trials (that is repeating the prime target as the probe target). Attended repetition trials are thought to activate an episodic retrieval strategy when they are encountered in large numbers because in this case stimulus repetitions typically mean response repetitions as well. In the auditory modality, however, the size of the negative priming effect was not influenced by the proportion of attended repetition trials (Mayr & Buchner, 2010). This possibly indicates that the processes resulting in auditory negative priming have a stronger automatic component than the processes underlying visual negative priming. In conclusion, whether (auditory) negative priming is based on mostly amodal processes and hence modality independent cannot be pinpointed at this point in time.

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It should be noted, that all auditory experiments discussed so far used identity tasks, that is, the ignored prime distractor identity served as the probe target identity in the ignored repetition condition. In recent experiments, evidence for spatial auditory negative priming was presented (Mayr, Hauke, & Buchner, 2009; Mayr, Buchner, Mo¨ller, & Hauke, 2011; see also Mayr, Mo¨ller, & Buchner, in this special issue). The authors used a spatial, forced choice localization task using four spatially differently arranged loudspeakers, that is, participants had to indicate the location of the target sound by a spatially corresponding key press. A short visual cue indicated what sound would be the target sound (e.g., either a musical sound or an animal sound). For example, if a participant had to identify the location of an animal sound and it appeared at the loudspeaker on the participants’ front at the right side, the participant would press the right-frontal key on the response pad. In the prime and probe display, a distractor and target sound were simultaneously presented at two of the four possible locations. The sound category could stay the same or change between the prime and probe. Spatial negative priming was found (i.e., worse performance if the prime distractor position was repeated as the probe target position); however, it completely depended on whether physical incongruence occurred at a repeated location, that is, only if the same location was repeated as the prime distractor and probe target location while the stimulus identity changed, performance was hampered. This finding is hard to explain in terms of inhibition or episodic retrieval theories. However, it fits nicely with a theory, which has been somewhat neglected in vision in recent years, namely the feature mismatching theory suggested by Park and Kanwisher (1994). This theory proposes that a locationidentity discrepancy between the prime and probe display causes negative priming. Altering the match between location and identity of stimuli is thought to be the driving factor of negative priming, not the classification as target or non-target. To demonstrate this influence, Park and Kanwisher (1994) used a modified spatial paradigm (Tipper, Brehaut, & Driver, 1990) in which the participants had to locate the ‘‘O’’ stimulus in the prime and simultaneously ignore the ‘‘X’’ stimulus. In the probe, they were asked to respond to the ‘‘X’’ stimulus and to ignore the ‘‘O’’. Negative priming occurred when the probe target was located at the position of the former prime target (when the probe ‘‘X’’ appeared at the position of the prime ‘‘O’’). In the same experiment, participants showed facilitated reactions when the probe target was located at the position of the former prime distractor (when the probe ‘‘X’’ appeared at the position of the prime ‘‘X’’). In short, slowed or facilitated responses were observed not in dependence of a stimulus having previously been ignored or attended, but of a location-identity mismatch having occurred or not

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occurred. However, in vision, several papers reported negative priming in spatial tasks without feature mismatch (e.g., Milliken, Tipper, & Weaver, 1994; Tipper, Weaver, & Milliken, 1995). As a result, in vision this theory was abandoned since the mid 90s. Yet, the evidence on spatial auditory negative priming so far does suggest feature mismatch as the sole explanation for auditory spatial negative priming and thereby stands in sharp contrast to the evidence on spatial negative priming in vision. Interestingly, feature mismatch can be understood in terms of binding stimulus features to specific locations and thus can be integrated into somewhat more general theories that do not particularly focus on negative priming alone (see second part of this review). Taken together, three main conclusions for auditory negative priming can be drawn. For identity tasks, auditory negative priming is a reliable phenomenon that seems to be comparable to vision concerning the underlying mechanisms. Yet, the size of this effect is still a matter for future research to pinpoint. Still, regarding the currently available evidence, modality-specific differences in identity negative priming between audition and vision are possible. Finally, spatial auditory negative priming can be explained in terms of feature-mismatch, a theory that has more or less been discarded for negative priming in vision.

Auditory distractor–response binding One mechanism by which distractors can have an influence on behavior includes the retrieval of earlier stimulus– response episodes. For example, based on the instance theory of automatization (Logan, 1988, 1990), Neill and Valdes (1992; see also Neill et al., 1992) suggested the episodic retrieval theory in negative priming. As discussed above, retrieval of a previous episode is also assumed by the stimulus response retrieval theory (Rothermund et al., 2005; see also Frings, Rothermund, & Wentura, 2007). However, this account proposes distractor influence on action control due to retrieval of a specific response instead of a ‘do-notrespond’ tag. Based on the theory of event coding (Hommel, 1998, 2004; Hommel, Mu¨sseler, Aschersleben, & Prinz, 2001), it is assumed that a repeated distractor stimulus reactivates the last episode in which the stimulus was encountered, including the response executed to the target in this episode. In a typical negative priming design, each stimulus is mapped to a specific response. Consequently, if the prime distractor is presented as the probe target, the prime response that is retrieved by the repeated stimulus conflicts with the required probe response, hampering the response (labeled prime response retrieval by Mayr & Buchner, 2006; Mayr et al., 2011; see also Mondor & Leboe, 2008, Mondor, Hurlburt, & Thorne, 2003 for auditory retrieval effects).

Regarding the investigation of stimulus–response retrieval, one difficulty with the negative priming design is that disadvantages in the response times in distractor-to-target trials can be accounted for both by effects of distractor inhibition (see e.g., Tipper, 1992) and also by retrieval effects. Yet, evidence for retrieval of the prime response has been provided by analyzing the kinds of errors participants make if required to respond to a former distractor stimulus (e.g., Mayr & Buchner, 2006). If the prime distractor was repeated as the probe target, a significantly larger proportion of errors could be accounted for by (inaccurate) response repetition than by random errors. This was not the case if no stimulus was repeated from prime to probe (e.g., Buchner, Zabal, & Mayr, 2003; Mayr & Buchner, 2006; Mayr et al., 2011). Thus, at least some of the disadvantage in responding on distractor-to-target trials in a negative priming design seems to be due to retrieval of the prime response by the former distractor. Importantly, retrieval is not necessarily triggered by a target but can also be initiated by a repeated distractor stimulus. This mechanism is evidenced by a phenomenon termed distractor–response binding (Frings et al., 2007); in a typical distractor–response binding task, participants have to identify target stimuli versus distractor stimuli that are drawn from the same set of stimuli. For example, participants have always to identify via a keypress the central letter flanked by two distractors (e.g., DFD). As in the negative priming task, prime-probe sequences are analyzed. Repetitions of responses and repetitions of distractors are orthogonally varied. A distractor stimulus is assumed to be integrated with the response to the target stimulus at the first encounter and to retrieve this response at the next presentation (in the example above it would be assumed that the letter ‘D’ is integrated with pressing the F-key). If a repeated distractor retrieves the prime response, distractor repetition leads to an advantage in response times and accuracy if the prime response is also required on the probe (that is, if the participant has to press the F-key again), but to a disadvantage if different responses are required on the prime and the probe. Thus, distractor–response binding is evidenced by an interaction of response relation and distractor relation (see, Frings & Rothermund, 2011; Frings et al., 2007). This result pattern cannot be accounted for by an inhibition account of distractor influence, which would expect the same advantage due to distractor repetition for response repetition and response change trials (see Tipper, 1992). In sum, a distractor stimulus that competes with the target for the response can be bound to the response executed in reaction to the target stimulus, and influence later responding. Moreover, the effect of distractor–response binding evidences that retrieval of an earlier response is also possible if the retrieving stimulus is presented as a distractor.

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target:

low pitched sound:

distractor:

* until Space Bar

300 ms

+

+

+

+

200 ms

Prime 300 ms

until Response

500 ms

300 ms

+

+

+

200 ms

Probe 300 ms

until Response

time

Fig. 1 Sequence of events in the experiment. A cue presented before each prime or probe display indicated the target pitch for the next sound pair. Participants reacted to the identity of the target by pressing the corresponding key

Response retrieval due to distractor repetition (i.e., distractor–response binding) is a very reliable effect and has been shown in a number of studies (e.g., Frings, 2011; Frings & Moeller, 2010; Frings, Moeller, & Rothermund, 2013; Frings & Rothermund, 2011; Giesen & Rothermund, 2011; Moeller & Frings, 2011; Moeller, Rothermund, & Frings, 2012). However, the mechanism is not entirely automatic, but arises only under certain conditions regarding spatial and temporal, as well as attentional distributions. For example, grouping of target and distractor stimuli has been shown to influence binding of distractor and response (Frings & Rothermund, 2011). If the target and the distractor were grouped the effect was large, while it was significantly smaller or altogether absent if target and distractor were not grouped (see also Giesen & Rothermund, 2011, for effects of grouping by valence). Importantly, auditory distractors can also be bound into distractor–response episodes and thereby influence responding. In a typical auditory distractor–response binding experiment, we used four digitized artificial sounds (bell, buzzer, beep, and siren) as target and distractor stimuli which could be presented in high or low pitch. Participants (here N = 30) worked through 192 primeprobe sequences. On each prime and each probe they heard two different stimuli simultaneously on both ears for 300 ms—one in high and one in low pitch—and identified one of the sounds via key press while ignoring the other. Before the onset of each sound pair, participants were informed about the target pitch via a white arrow that pointed upwards or downwards. For example, if the arrow pointed upwards, the participant was supposed to identify the sound presented in high pitch and to ignore the sound presented in low pitch. The events in a single trial are depicted in Fig. 1. Importantly, both the target and the distractor sound could independently repeat or change from prime to probe, resulting in four different conditions: Response repetition/distractor repetition (RRDR), response repetition/distractor change (RRDC), response change/ distractor repetition (RCDR), and response change/

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Table 1 Mean reaction times (in ms) and mean error rates (in %) as a Function of response relation and distractor relation Response relation

Distractor relation Distractor change (DC)

Response repetition (RR)

Response change (RC)

1,570 (37.0)

1,744 (36.8)

Distractor repetition (DR)

1,301 (34.3)

1,618 (35.0)

Priming effecta

?269 [57]

?126 [49]

a

Priming effect is computed as the difference between distractor change minus distractor repetition, standard error of the mean in squared brackets

distractor change (RCDC). If distractors retrieve responses they have been integrated with, repeating the distractor stimulus would lead to response facilitation if the response has to be repeated, as well. In contrast, distractor repetition would lead to less advantage or even hamper responding if the response has to be changed from prime to probe. Mean response times and error rates are shown in Table 1. In a 2 (response relation: RR vs. RC) 9 2 (distractor relation: DR vs. DC) MANOVA of the response times, both main effects of response relation [F(1, 29) = 29.06, p \ 0.001, g2p = 0.50] and distractor relation [F(1, 29) = 23.64, p \ 0.001, g2p = 0.45] were significant. Reactions to repeated targets (1,435 ms) were faster than reactions to changed targets (1,681 ms), and reactions to sound pairs with repeated distractors (1,459 ms) were faster than reactions to sound pairs with changed distractors (1,657 ms). The effect of distractor repetition was significant for both response repetition [tRR(29) = 4.76, p \ 0.001] and response change trials [tRC(29) = 2.55, p \ 0.05]. Importantly, the interaction of response relation and distractor relation was significant [F(1, 29) = 4.43, p \ 0.05, g2p = 0.13], indicating that repeated distractors triggered retrieval of the prime response. These results also illustrate that evidence for distractor inhibition is oftentimes found in addition to response

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retrieval with this paradigm. An inhibition account would predict a general positive priming effect of distractor-todistractor repetitions (e.g., Frings & Wu¨hr, 2007; Houghton & Tipper, 1994): It should be easier to process the target and to select the appropriate response if the probe distractor still suffers from the inhibition that it received during the prime. This is indicated in the results above by the main effect of distractor repetition. However, an inhibition theory would also predict equal distractor repetition benefits in all conditions (i.e., response repetition and response change), since the persisting inhibition of a prime distractor is assumed to be independent of the relation between prime and probe response. Thus, the difference in the distractor repetition effects for response repetition and response change trials (i.e., the interaction of response relation and distractor relation) cannot be explained by distractor inhibition, but must be due to retrieval effects. In fact it has been shown that the effect of distractor–response binding and distractor inhibition effects influence behavior independently; that is, using a prime-probe task Giesen, Frings, and Rothermund (2012) found that distractor inhibition was dependent of the strength of interference in the prime display (the stronger the interference the stronger the inhibition) while in the same experiment distractor-based retrieval effects where independent of the strength of interference in the prime display (i.e., even distractors that did not at all interfere with target responding were integrated in the prime episode and lead to retrieval when repeated at the probe). Another possibility to investigate effects of response retrieval due to distractor repetition in our data is to analyze the frequencies of specific types of errors within a multinomial processing tree model that was introduced by Mayr and Buchner (2006). Using the HMMTree program (Stahl & Klauer, 2007), the parameters of this model were estimated on the basis of the frequencies of different types of error responses that were observed in the experiments (cf. Fig. 2). The model compares probe reactions for distractor repetition and distractor change trials only in conditions in which the response changed from prime to probe (RCDR vs. RCDC). In case of response repetition trials, a retrieval of the prime response leads to a correct response in the probe, and thus cannot be distinguished from the process that identifies the probe target correctly. The probability of correct responses to the probe target is estimated by pcorr. With a probability of 1 - pcorr, participants would then show an erroneous response in the probe. In case of an error, participants can show the response that corresponds to the probe distractor. The probability for this type of an erroneous response is estimated as pdistr. If the incorrect response does not correspond to the probe distractor, it can correspond to the prime target, indicating a repetition or retrieval of the prime response. The

Correct Probe Target Response

pcorrC

Distractor Change

Incorrect Probe Distractor Response

pdistrC 1-pcorrC ppriC

Incorrect Prime Target Response

1-ppriC

Other Incorrect Response

1-pdistrC

Correct Probe Target Response

pcorrR Distractor Repetition

Incorrect Probe Distractor Response

pdistrR 1-pcorrR ppriR

Incorrect Prime Target Response

1-ppriR

Other Incorrect Response

1-pdistrR

Fig. 2 Multinomial processing tree model used to estimate response frequencies for response change conditions (see also Mayr & Buchner, 2006, Fig. 2). R distractor repetition from prime to probe, C distractor change from prime to probe, corr correct response, distr response corresponding to the probe distractor, pri prime response Table 2 Accumulated absolute frequencies of correct probe responses and of the different types of probe errors for the response change conditions Distractor relation Distractor repetition (RCDR)

Distractor change (RCDC)

Correct probe target responses (pcorr)

641

638

Incorrect probe distractor responses (pdistr)

165

203

52

27

43

53

Incorrect prime target responses (ppri) a

Other incorrect responses (1 - ppri) a

RCDR trials, incorrect responses using the key that was assigned to the non-presented stimulus; RCDC trials, incorrect prime distractor response

probability of this process is estimated by the parameter ppri. Finally, an error can consist in the remaining fourth response that corresponds neither to the probe target, nor to the probe distractor, nor to the prime target (1 - ppri). For the observed frequencies of all described response possibilities see Table 2. The parameter estimates for the critical error types (ppri-DR and ppri-DC) are illustrated in Fig. 3. As can be seen from the figure, the probability of committing an error that corresponds to the prime response is higher for the distractor repetition condition, indicating an increased retrieval of the prime response in this condition. Equating the two probability estimates in the model leads to a significant decrease of model fit, G2(1) = 7.81,

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Probability estimates

0.6 0.5 0.4 0.3 0.2 0.1 0

p(pri)DR

p(pri)DC

Fig. 3 Probability estimates for the model parameters representing the probability of prime response retrieval as a function of trial type (RCDR and RCDC). The error bars depict the 0.95 confidence intervals. p(pri) DR, prime response retrieval in distractor repetition trials; p(pri) DC, prime response retrieval in distractor change trials

p \ 0.01, allowing the conclusion that repeating the distractor in the probe leads to a significant increase in the probability of prime response retrieval. Note that in this variant of the distractor–response binding task the analysis of error types has one advantage over the reaction time analysis mentioned above. In fact, as response and target repetitions were confounded, it remains possible that the effect in the reaction times is due to target (and not response) binding. Although Frings et al. (2007) have shown that distractor–response binding emerges with pure response repetitions (see Frings, Rothermund, & Moeller, 2013, for an auditory-visual variant), in this experiment the confound remains. Of course, as the errors were analyzed only in response change trials, for the error rates the confound is pointless. In sum, both the response times and also the error distribution in the reported experiment indicate that distractor sounds become integrated with a co-occurring response and retrieve this response if they are presented again as distractors. Another potentially interesting issue here is that the task-relevant pitch could change between the prime and the probe (in fact, it was orthogonally varied to all other factors). This made our task structurally analog to a variant of the dichotic-listening task recently introduced by Koch and colleagues (Koch, Lawo, Fels & Vorla¨nder, 2011; see also Lawo & Koch, 2013). They used a task in which a visual cue indicated the task-relevant gender in the upcoming trial. In each trial, a female and a male voice spoke a number, with the participants’ task being to categorize the relevant number as smaller or as larger than five. The data indicated that an instructed switch in attending to a different speaker gender incurred substantial switch costs. In our experiment, the same pattern was observed. The main effect of target pitch change was indeed significant, F(1, 29) = 65.89, p \ 0.001, g2p = 0.69, that is, responses were

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faster if target pitch was repeated (M = 1,387 ms, SD = 333 ms) than if target pitch changed (M = 1,740 ms, SD = 403 ms). This data pattern was mainly due to response repetition trials; in fact, if the taskrelevant pitch changed from prime to probe while the response repeated, reaction times were much higher as compared to prime-probe sequences in which the taskrelevant pitch and the response repeated. For response change trials, change or repetition of the task-relevant pitch did not matter. Koch and colleagues interpreted the attentional switch costs as some kind of attentional inertia possibly caused by persisting inhibition from the prime to the probe or by interference due to different filter settings in the prime and probe. While interesting in itself, it is worthwhile to analyze whether this mechanism modulates the binding effect. In our experiment, the interaction of response relation, distractor relation, and target pitch relation was significant, F(1, 29) = 6.36, p = 0.017, g2p = 0.18. Separate analyses revealed a significant effect of distractor–response binding only if the target pitch repeated, F(1, 29) = 6.30, p = 0.018, g2p = 0.18, but not if the target pitch changed, F(1, 29) = 1.15, p = 0.293, g2p = 0.04. One could speculate that changing the target pitch might diminish the occurrence of distractor-based retrieval here because the distractors were still inhibited from prime selection. However, an analog analysis was published by Frings and Moeller (2010), using a visual-spatial distractor–response binding task, in which the change of the cue signaling the target (location) did not modulate the binding effect. Whether changing the feature used for target selection modulates distractor-binding remains for future research to analyze. To better understand the mechanisms behind retrieval influences by auditory distractors, it is worthwhile to look at similarities to the mechanisms in other modalities. So far, evidence indicates that the auditory effect of distractor–response binding is restricted by similar mechanisms as the effect in the visual modality. For example, Moeller et al. (2012) found similar modulating effects of stimulus grouping on the distractor–response binding effect using auditory material. Participants in a condition with grouped target and distractor stimuli heard one high and one lowpitched sound on both ears (resulting in the perception of the same location for the two sounds), while participants in a condition with non-grouped stimuli heard a high-pitched sound on one and a low-pitched sound on the other ear. Their task was always to identify one of the sounds and ignore the other. In line with visual findings by Frings and Rothermund (2011), distractor–response binding was significantly stronger if target and distractor were grouped than if they were not grouped.

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In addition, the repetition of a distractor identity retrieved the response that was integrated with a distractor even if the distractor modality (auditory or visual) changed between prime and probe (Frings et al., 2013). For example, if participants ignored the sound (picture) of a chicken on the prime, ignoring the picture (sound) of a chicken on the probe led to facilitation if the prime response had to be repeated but not if different responses were required on prime and probe. Thus, one might assume that binding of distractor stimuli and responses takes place at a conceptual rather than a perceptual level (see also Spape´ & Hommel, 2008). This is also in line with assumptions that integration effects within and across perception and action are comparable across different modalities (see Zmigrod & Hommel, 2013), and that the retrieval of prime responses contributes to the negative priming effect irrespective of stimulus or response modality (Mayr, Mo¨ller, & Buchner, 2011). In fact, effect sizes for distractor–response binding effects were found to be similar in studies using visual, auditory, and tactile stimuli (see Moeller & Frings, 2011). Together, these results may indicate that binding processes regarding response-irrelevant stimuli occur at a relatively high level of processing and are independent of stimulus modality. Yet, more evidence is required to add more certainty to this assumption. For example, so far, no evidence exists regarding influences of timing or focused attention on the effect of auditory distractor–response binding. Typically auditory material inherently has a more sequential quality than visual material (see Albert, 1972). For example, we are used to listen to different people one at a time, even in a conversation in which we see all of them simultaneously. It is therefore possible that timing has a different effect on auditory than on visual effects of distractor–response binding. Further, given that sounds are not as easily filtered out of perception as visual stimuli are (e.g., by closing one’s eyes), auditory stimuli may be assumed to be generally more salient and response relevance of the distractors may play a different role for auditory than for visual distractors. For example, one might speculate that less attention on an auditory distractor as compared to a visual one is necessary to ensure distractor–response binding effects. Regarding binding and retrieval effects for non-responses in audition and vision, at first sight the mechanisms seem to differ between modalities. Giesen and Rothermund (2013) found binding and retrieval of a general ‘‘do not respond’’ tag if participants stopped response execution to a visual stimulus display on the prime. In contrast, if participants did not respond to auditory stimuli on the prime, or had to stop their response 150 ms after prime presentation, Mayr, Buchner, & Dentale (2009) report no evidence for response retrieval (but see Frings, Bermeitinger,

& Gibbons, 2011, for different findings). Yet, two important differences in the design of the two studies were the time at which participants were informed about the required response restraint and the sort of responses that were analyzed. While participants in the study by Giesen and Rothermund received a stop signal after prime stimulus presentation and response initiation according to their stopping performance (i.e., the time of the stop signal was adjusted during the experiment with respect to the participant’s stopping success), no-go signals in the study by Mayr and colleagues were presented before prime stimulus onset or at a fixed time interval of 150 ms after prime onset, resulting in differences in the effort necessary to restrain the response. In addition, Mayr and colleagues analyzed whether the response that was not executed in the prime was nevertheless retrieved by a repeated stimulus on the probe, they did not report results regarding the binding of non-responses. A third difference between the studies is of course the role of the stimulus that was supposed to trigger response retrieval. In the negative priming paradigm used by Mayr and colleagues it was the probe target while it was the probe distractor in the distractor–response binding paradigm used by Giesen and Rothermund. Thus, with the results that are available so far, it cannot be decided whether non-responses are integrated with and retrieved by auditory distractor stimuli in a similar way as in the visual modality. As distractor–response binding is a special case of binding between stimulus and response features as suggested by the theory of event coding (Hommel et al., 2001), it seems worthwhile to shortly discuss some findings on feature binding in general. Binding perceptual features into objects has been explained in terms of object files (Kahneman, Treisman, & Gibbs, 1992). Generally, object binding processes in the auditory modality have been proposed to be similar to those in the visual modality (Hall, Pastore, Acker, & Huang, 2000). Moreover, binding of target and response features seems to be similar for auditory and visual stimuli. Hommel and colleagues (e.g., Hommel, 2005; Zmigrod & Hommel, 2009, 2010, 2011; Zmigrod, Spape´, & Hommel, 2009) thoroughly investigated featureresponse binding of visual, auditory and multisensory stimuli. In a typical trial, participants were first instructed (for example via an arrow) which response had to be executed as soon as the prime stimulus appeared on the screen. For example, if participants saw a right pointing arrow and then heard a low-pitched sound on the prime, they responded with a right button press regardless of the prime stimulus features. In contrast, the response to the probe stimulus was defined by a feature of the stimulus. For example, participants responded with a right button press to a high-pitched sound and with a left button press to a lowpitched sound. Thus, the authors were also able to vary

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response and feature repetition orthogonally. Importantly, they found evidence for bindings between responses and both response relevant (e.g., pitch) and also responseirrelevant (e.g., loudness) auditory stimulus features (Zmigrod & Hommel, 2009) even if stimulus features were of different modalities (Hommel, 2005). In the mentioned studies, response-irrelevant stimulus features were typically part of the target stimulus and were never mapped to any of the responses in the response set. In contrast, distractor stimuli as discussed in this review are typically defined as additional stimuli that are different from the target stimulus and mapped to responses in the current response set. Thus, a distractor competes with the target stimulus for the response both on the prime (i.e., at integration) and on the probe (i.e., at retrieval). However, very similar binding mechanisms are assumed by Hommel and colleagues and for response retrieval by repeated distractor stimuli. Therefore, findings that shed light on the similarities of feature-response binding mechanisms in different modalities might also give an idea about similarities for those mechanisms in distractor–response binding. In fact, Hommel and colleagues come to the conclusion that feature-response binding mechanisms are very likely the same in different modalities. For example, intentional weighting of target features seems to modulate feature-response integration similarly for visual and auditory material: task relevant features receive more weight and are more likely to be integrated with and later on retrieve responses (Zmigrod & Hommel, 2011, 2013). In addition, timing restrictions regarding the integration and duration of event files seem to be similar in audition and vision (Zmigrod & Hommel, 2009, 2010, 2013). In summary, there is a large amount of evidence suggesting response retrieval effects that are due to the repetition of an auditory stimulus that was presented as a distractor at an earlier encounter. Moreover, such retrieval cannot only be triggered by a target, but also by a distractor stimulus, evidencing what we call auditory distractor– response binding effects. Whether or not the auditory distractor–response binding effect is controlled by the same mechanisms as in the visual modality cannot be decided at this point in time. However, so far evidence suggests quite similar mechanisms in the different modalities, possibly due to bindings taking place at a relatively high level of stimulus processing.

Conclusion and future directions In this review, we summarized the evidence on auditory distractor processing in sequential priming tasks (see Table 3 for the key findings). By and large, the effects of negative priming and distractor response binding seem to

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Table 3 Key findings on auditory distractor processing in sequential selection Key findings on auditory distractor processing 1

Repeating an auditory distractor as the consecutive auditory target in a prime-probe design with targets being accompanied by distractors leads to slower response times and more errors (i.e., auditory negative priming); this effect is quite robust and has been reported by several different research groups

2

Auditory negative priming with identity or categorization tasks is typically explained via the same mechanisms as negative priming in vision, namely by inhibition and/or retrieval theories

3

The size of the auditory negative priming effect was sometimes larger than the one in vision, but sometimes not; strategic control of negative priming was different between vision and audition; so far modality-specific influences cannot be ruled out

4

Auditory spatial negative priming is entirely dependent on feature-mismatch; this finding contrasts research from vision on spatial negative priming

5

Auditory distractors retrieve responses to previous targets if they are repeated as distractors (i.e., distractor–response binding); this effect can be observed for auditory distractors in the reaction times and the specific errors (prime response retrieval)

6

Auditory distractor response binding does not differ in size from distractor–response binding in vision and touch when compared between experiments; it has been explained exactly the same way as in the other modalities by binding between stimulus and response features in sensu of the theory of event coding (Hommel et al., 2001)

7

Auditory distractors can retrieve responses encoded with their visual pendant possibly pointing to stimulus–response binding at a conceptual level

8

So far evidence suggests that non-responses are possibly different integrated with auditory versus visual distractors

be comparable between audition and vision in that (a) almost the same empirical pattern aroused, and (b) the same underlying mechanisms were suggested as to explain these patterns, namely retrieval-based and inhibitory theories. Noteworthy, exceptions are the spatial variant of negative priming which yielded different data patterns in vision and audition and the possibly different patterns for the integration of non-responses between auditory and visual distractor–response binding. Yet, the role of modality-specific influences is not entirely clear at this point in time. It has been argued that the task defines at which level selection takes place (e.g., Neill, 2007), and in turn, one could expect less modality-specific influences at later processing stages (central processing, response selection) as compared to earlier stages (perceptual processing). Future research should therefore compare auditory and visual distractor processing in tasks, in which the processing stage at which selection takes place can be manipulated independently of stimulus modality. In addition, the binding between distractor and response features seems to be comparable for auditory and visual distractor processing although distractor-binding does possibly differ

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as location is concerned. This would be in line with results showing different effects of spatial separation between targets and distractors in audition and vision in the flanker task (e.g., Chan et al., 2005). Another path for future research is therefore to pinpoint modality-specific effects on binding between object features and location.

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