High bladder pressure reduces the ability to filter out interference from distractors in low perceptual load condition

Abstract

According to the load theory of attention, an active cognitive control mechanism is needed to ensure that behaviour is controlled by target-relevant information when distractors are also perceived. Although the active cognitive control mechanism consists of working memory, cognitive flexibility, and inhibition components, predictions regarding the load effects of this mechanism were derived mostly from studies on working memory. We aimed to test whether these predictions are also valid for an inhibition component. The inhibitory load was manipulated physiologically by creating different bladder pressure and its effects on distractor interference were examined under low and high perceptual load conditions. The results indicated that the availability of inhibitory control resources was important for decreasing the interference of distractors in the low perceptual load condition and that the high perceptual load reduced the effects of distractors independently from the availability of inhibitory resources. The results were consistent with the predictions of load theory, and to the best of our knowledge, the study provided the first piece of evidence in terms of the load effect of inhibition component on distractor interference.

Keywords

Selective attention inhibitory control load theory perception

Introduction

There are many stimuli around us. When walking down a crowded street, just think how different stimuli can be within our visual field. People, buildings, signs, cars, clouds, birds, cats, and more. Therefore, successfully performing a goal-directed behaviour in such an environment requires dealing only with the target-related stimuli while avoiding the effects of the irrelevant ones as much as possible (Driver, 2001). Selective attention is a key cognitive mechanism that enables individuals to achieve goal-directed behaviour with reduced interference of goal-irrelevant stimuli (Itthipuripat et al., 2017; Lavie, 2005). However, the fact that people sometimes are highly selective and at other times are not, raises the question of where the locus of attentional filtering is in the stream of information processing and several models have been proposed.

According to the early selection model of Broadbent (1958), for example, attentional filter excludes the unattended information at an early stage of processing due to the limited perceptual capacity. However, some other studies reporting that unattended information may have also influenced the behaviour led an alternative model to be developed. This late selection model (Deutsch & Deutsch, 1963) argued that perception has an unlimited processing capacity and can process all stimuli in the environment in a parallel fashion. Selection is carried out at a later stage to determine the relevant response after a complete perception (e.g., Allport, 1977). Various studies on this subject have supported both early (e.g., Johnston & Dark, 1982; Moray, 1959) and late selection (e.g., Miller, 1987; Posner, 1982) views and there has been no consensus on the locus of selection. Load theory of attention (Lavie, 1995) was proposed as a solution for the early and late selection debate.

Lavie (1995) has suggested that the perceptual capacity is limited but automatically allocated for all stimuli in the environment and that the differences observed in selectivity depend mainly on to what extent the perceptual capacity is loaded by target-relevant information (Lavie, 1995, 2005). According to this theory, if perceptual capacity is fully loaded by target-relevant information, there is no capacity left to process distractors. However, if perceptual capacity is not loaded enough by target-relevant information, the remaining capacity is involuntarily used for distractors (Lavie, 1995, 2001, 2010; Lavie & Tsal, 1994). Lavie (1995) tested this prediction by using a task that was based on Eriksen’s response competition paradigm (Eriksen & Eriksen, 1974). In this experiment, two target letters (“X” and “Z”) were presented in the middle of a computer screen, and participants were asked to determine whether the letter was an “X” or a “Z” as quickly as possible. Perceptual load of the task was manipulated by varying the number of target-related letters presented in the row where the target would appear. In the high perceptual load condition, in addition to the target letter, there were also five different letters arranged in a row. In the low perceptual load condition, however, the target was presented alone. A distractor letter that was asked to be ignored was also presented above or below the target letter. Depending on the condition, the distractor could be the same letter as the target letter (compatible condition), or it could be the other target letter (incompatible condition) or a neutral letter “P” (neutral condition). The magnitude of distractor interference was calculated by subtracting the mean reaction time (RT) of the neutral trials from the mean RT of the incompatible trials. Consistent with Lavie’s predictions, the results showed that the distractor interference, which was about 40 ms at the low perceptual load condition, decreased to almost non-existent under the high perceptual load condition. Lavie (1995) concluded that if the perceptual load of the task is high, the limited perceptual capacity is filled with the target-related stimulus, and the distractors are excluded perceptually (early selection). However, if the perceptual load of the task is low, the remaining perceptual capacity automatically processes the distractor and the selection of target among distractors is postponed. Many other studies have supported this conclusion (e.g., Lavie & Cox, 1997; Murphy & Greene, 2016; Raveh & Lavie, 2015; Rees et al., 1997; Schwartz et al., 2004; Wang et al., 2016).

The predictions of perceptual load theory raised another question that needs to be answered: if perceptual capacity is not filled by the target-related stimuli and distractors are not excluded perceptually, how are the targets selected among distractors? Lavie et al. (2004) proposed that there must be another mechanism that enables active cognitive control over attention to prevent distractors from taking control of the behaviour in low perceptual load conditions. The functions of this active cognitive/executive control mechanism are listed as frontal lobe functions: inhibition (self-control and interference control), working memory (WM), and cognitive flexibility (also called set-shifting or mental set-shifting; Diamond, 2013; Miyake et al., 2000) which are essential especially for selecting and maintaining priority information between targets and distractors, suppressing possible behaviours arising from distractors, and fulfilling target-compatible behaviours (Gaspelin & Luck, 2018; Lavie, 2000). Lavie et al. (2004) argued that depleting the capacities of these frontal lobe functions and so running short of their available resources is thought to have a distorting effect on performing goal-directed behaviour. Lavie et al. (2004) tested this prediction. They first presented a display containing one (low WM load condition) or six numbers (high WM load condition) to the participants. The participants were instructed that they had to keep these numbers in their minds and then they were asked a question about these numbers. While doing this, a selective attention task (SAT) based on the classical response competition paradigm was presented with a low perceptual load. In this task, the participants tried to determine the identity of the target letter as quickly as possible, as mentioned earlier. After the SAT, a question was asked about the numbers they had in mind. The analyses on the data with correct memory responses revealed that compared with one, keeping six digits in WM increased the interference of distractors on a concurrently performed SAT. Studies on the load effects of the active cognitive control mechanism have revealed similar results (Boot et al., 2005; Lavie & de Fockert, 2006). According to de Fockert (2013), WM and attention are highly correlated and WM resources are important for maintaining the priority of target information, especially in situations where both the target and the distractor are perceived.

The main prediction of the load theory regarding the load effect on the control mechanism, however, is mostly based on the findings of the research focused on the WM component (see Brand-D’Abrescia & Lavie, 2008 for the effect of multiple task coordination). Indeed, recent studies are now investigating how the load effects of different functions of WM, maintenance, and control may differ (see Konstantinou et al., 2014; Yao et al., 2020). However, a successful control can also be achieved by suppressing the distracting effects, that is, by the inhibition function of the active cognitive control mechanism (e.g., Gaspelin & Luck, 2018). The role of the inhibition function in selective attention has been usually revealed through studies on Negative Priming (NP). NP effect is defined as slowing RTs to previously ignored stimuli. Studies on NP suggest that the inhibition component has an important role in suppressing distracting effects (e.g., Tipper, 1985, 2001). However, Frings et al. (2015) argued that an NP paradigm is not an ideal task for studying only and exclusively inhibition function, because both inhibition and memory retrieval processes contribute to the NP effect (see Mayr & Buchner, 2007, for different accounts for NP effect). Therefore, the current study aimed to examine the role of the inhibition component on selective attention in a different way adopting the load approach. The load approach is important because it enables one to test whether the predictions of the load theory for both perceptual mechanism and the active cognitive control mechanism are valid for the inhibition component.

Considering the rationale of the experimental procedures of WM load studies (e.g., Lavie et al., 2004), we aimed to investigate the possible load effect of inhibition function on distractor interference by concurrently implementing a task in which inhibitory load can be manipulated and a task of selective attention. However, it seems impossible to carry out well-known inhibitory tasks such as Stroop (1935) and Stop-Signal (Lappin & Eriksen, 1966) simultaneously with a SAT. These tasks can only be done one after the other fashion. This kind of presentation would not guarantee that the inhibitory effects will be transferred to the subsequent SAT without any loss. Moreover, a task-switching effect caused by such an implementation would also increase the load of the active cognitive control mechanism which makes it hard to determine the actual source of any observed load effect. Indeed, the frequent use of a WM component in examining the load effects of the active cognitive control mechanisms on selective attention may be due to the lack of a paradigm that would allow to manipulate the load of the inhibition function simultaneously with a SAT.

Different inhibitory-related functions and neural pathways specific to them have been investigated to find alternative ways to manipulate inhibition load. Neurocognitive studies have shown that some brain regions such as the dorsolateral prefrontal cortex (DLPFC), right inferior frontal cortex (rIFC), and anterior cingulate cortex (ACC) play important roles in different inhibitory domains corresponding to the motor (Aron et al., 2003, 2004; Menon et al., 2001), physiological (Griffiths & Tadic, 2008), or cognitive inhibition (Anderson & Green, 2001; Jonides et al., 1998). Berkman et al. (2009) also showed that an inhibitory process in a brain region tends to diffuse across these different domains through a common inhibitory network. Based on the studies (Fenn et al., 2015; Tuk et al., 2011) showing that physiological inhibition can be activated via bladder pressure, and the idea that an attention task can be performed simultaneously in the case of bladder pressure, we focused on the physiological domain.

Increased bladder pressure triggers the social behaviour of urination. Although urination is a reflexive, automatic, and subconsciously executed action during infancy, this action becomes an active, voluntary decision following development (Mukhopadhyay & Stowers, 2020). Such voluntary urination requires control of the action. Researchers suggested that both the cortex and mid-brain structures are involved in the process of bladder control with their inhibitory effects. These researchers also showed that the inhibitory influences originating from the prefrontal cortex, where the voluntary decision of continence or void is made, and ACC, which enables motor control and monitoring of this decision, tend to increase with the increase in bladder pressure (Griffiths & Tadic, 2008). As a result, differentiation of bladder pressure was expected to result in a difference in the use of inhibitory resources.

Moreover, Tuk et al. (2011) showed that the performances of two groups of healthy participants with different bladder pressure levels differed significantly from each other in tasks such as monetary decision making or Stroop, each of which involves different inhibition components. These results have been interpreted as the individuals having a general inhibition system and inhibitory signals are not limited to focal tasks that require inhibition, and these effects can spread (see also Berkman et al., 2009). Based on the idea that inhibitory signals have a common network and bladder pressure manipulation can be applied simultaneously with the SAT, we planned to examine the interference effect of distractors on a SAT in two groups of participants differing in their bladder pressure levels (high and low).

We also aimed to investigate the perceptual load effect because according to the load theory, in the case of high perceptual load, the distractor is not processed, so the interference effect cannot be modulated by the load of the inhibitory function. Such a modulation effect must occur under the low perceptual load condition, where active cognitive control mechanism functions are needed. Considering that the participants with high bladder pressure would use more inhibitory resources for bladder control than the participants with low bladder pressure, we hypothesised that interference of distractors will be significantly greater for the participants with high bladder pressure than those with low bladder pressure in low perceptual load conditions. Yet, this difference will be eliminated in the high perceptual load condition as distractors will be eliminated perceptually.

In addition to these main hypotheses, secondary hypotheses have been formulated to test whether the SAT worked as it should and whether the bladder pressure modification was effective. These are (a) the RT and error rate of SAT will be higher in the high perceptual load condition than in the low perceptual load condition for both incompatible and compatible trials; (b) the compatibility effect will be higher in the low perceptual load condition than the effect in the high perceptual load condition for both RTs and error rates; (c) participants in the high bladder pressure group will have a higher post-experiment urination need than those in low bladder pressure condition; And (d) the post-experimental bladder pressure level for both groups will be higher than the pre-experimental bladder pressure level. In addition to these, we also examined how the mean RT and error rates vary as a function of different bladder pressure levels considering the possibility that a higher urge might affect the general attention performance of the participants.

Materials and methods

Participants

A priori power analysis was conducted via G-Power (Faul et al., 2007) with .05 of alpha level and .22 of effect size (Cohen’s F) and the results indicated that 20 participants in each group would be enough to reach .80 power for our main analyses of 2 × 2 (Group [low bladder pressure, high bladder pressure] × Perceptual Load [low, high]) mixed analyses of variance (ANOVAs) on distractor cost percentage. The effect size of .22 was based on the study of Tuk et al. (2011) in which similar bladder pressure manipulation was used. After sampling 40, reportedly healthy, college students, 3 of them whose data were not recorded properly were excluded. Therefore, 3 more participants were sampled again. The analyses were carried out with the 40 participants (33 women, M_age = 21.57 years, SD = 2.94) who had no psychological or neurological disorders. Neither of them had any vision problems and any situation threatening body-electrolyte balance like diarrhea.

Bladder pressure manipulation

Bladder pressure manipulation was carried out in a way following Tuk et al. (2011). The participants were asked to drink five glasses of water labelled with numbers (1–5). Participants in the low bladder pressure group consumed a total of 50 mL of water, while participants in the high bladder pressure group consumed a total of 1,000 mL of water. A form requesting participants to make assessments about the waters they drank was then given. In this form, participants were asked to evaluate the waters in five different glasses according to their specific characteristics (e.g., sort by order of the amount of water). The form also included some other questions about the participants’ general water consumption (e.g., How many litres of water do you drink per day?). After the participants filled out the form, they were given the Spielberger State and Trait Anxiety Inventors (S-SAI and S-TAI; Spielberger, 1983) and Cognitive Failures Questionnaire (CFQ; Broadbent et al., 1982) as fillers. These tests were completed in approximately 40 min, which is also required for bladder pressure activation (see Tuk et al., 2011). The SAT was then applied which lasts about 30 min. The bladder pressure manipulation was checked and the change in urinary urgency levels was determined using a self-evaluation form presented just before and after the SAT. The form consisted of several questions about the participants’ general physiological status, including “How urgent is your need to urinate?” (1 – not at all urgent, 7 – very urgent) and it takes 30 s to be completed. The answers given to the question on the toilet need were included in the analysis as the participants’ urination need levels.

SAT

SAT was a task based on the response competition paradigm (Eriksen & Eriksen, 1974). The perceptual load manipulation was very similar to that used in Forster and Lavie’s (2007) study. Each trial started with a presentation of a central fixation point, which was randomly changed in duration between 400 and 600 ms. It was immediately followed by a 100-ms presentation of the task display consisting of a hypothetical circle (1.61° radius) of six letters centred at fixation, plus a peripheral distractor letter, presented to the left or right of the circle, 1.41° away from the nearest circle letter. Each of the circle letters subtended 0.61° by 0.41°, and the distractor letter subtended 0.81° by 0.51°. The search targets were Y, and N. Task display was followed by a 1,900 ms blank black screen. Thus, the participants had 2,000 ms (100 ms task display +1,900 ms blank screen) of response time in total. Participants were instructed to indicate which of the target letters was present in the circle by pressing either the “0” or “1” key on the keyboard as quickly as possible with the index fingers of the left and right hands, respectively, while not sacrificing accuracy (for each of the perceptual load conditions, half of the participants responded “1” for target N, “0” for target Y, while the other half responded “1” for Y and “0” for N). Participants were also instructed to ignore the distractor letter, which was equally likely to be Y or N. In the high perceptual load condition, the letters L, V, Z, H, and K were placed randomly in the nontarget circle positions in a different order on each trial. In the low-load condition, the nontarget letters were all small “O”s (0.151°). All the stimuli were presented in white on a black background. Positions of target and distractor and their identity were counterbalanced. After a training phase consisted of 16 trials (8 from each perceptual load condition), participants completed 5 blocks of 96 trials each in an ABBAABBA order (4 blocks of 384 trials for each load condition). At the end of each two blocks, the participant rested for 2 min (it was also possible to skip at any time) without moving the body (see Figure 1). All the stimulus presentations and response recordings of the SAT procedure were implemented by the E-Prime 2 stimulus presentation software (E–Prime 2.0 Professional, Pittsburgh, USA) on an Intel i7 processor computer with 16 GB of internal memory. The display was a 15.6-in. FHD monitor with a 1,920 × 1,080 pixel resolution.

Figure 1.

Experimental procedure.

Other measures and experimental procedure

The experimental procedure (see Figure 1) was started by providing a written informed consent form to the participants. The participants were told that the main purpose of the task was to determine the effect of some psychological and physiological factors on attention and the decision-making process. Those who agreed to participate were asked to use the restroom for similar initial content levels in the bladder. When the participant came back to the experimental environment, a demographic information form was given to get general information about the participant, such as gender and age. This was followed by the implementation of Stroop and Auditory Consonant Trigram (ACT) tests to ensure that both groups had similar cognitive functioning on these two tests, which otherwise may contaminate the findings. Stroop test (Karakaş et al., 1999; Stroop, 1935) was used to measure participants’ flexibility, attention, and automatic or parallel processing abilities. The ACT, on the other hand, was implemented to determine verbal WM, divided attention, and information processing capacity (Anil et al., 2003; Brown, 1958). When the participants completed the tests, bladder pressure manipulation was implemented. As in the Fenn et al. (2015) study, an attempt was made to keep the participants’ attention away from their bladders as much as possible while maintaining the attention task, and to ensure that each participant performs the same actions during the waiting period for bladder pressure to occur. Thus, it was said that the effect of physiological factors on the decision-making processes will be measured by a group of questions about the water they drink. Three filler tests (S-SAI, S-TAI, and CFQ) were given following the water form. Following these tests, the participants were told that they needed to perform an attention task so that we could evaluate the relationship between their performance on these psychological tests and their attention performance. The SAT was then administered and as mentioned earlier, self-evaluation forms were given just before and after the task. Therefore, the water drinking task was completed approximately 40 min before the beginning of the attention task and approximately 75 min before the end of the SAT (see also Figure 1). The experimental procedures were in accordance with the Declaration of Helsinki, including signed informed consent, as approved by the Hacettepe University Institutional Review Board (decision date: 14.03.2019, decision number: 28297300-900/00000505755).

Results

Groups with low and high bladder pressure were compared with independent-samples t-tests in terms of different demographic, cognitive, and clinical characteristics. It was shown that the groups were not statistically different from each other in terms of none of the characteristics (see Table 1). These results ensured that the related characteristics did not affect the results of the main analyses on SAT scores in favour of a group.

Table 1.

Comparisons of the demographic, clinical, and cognitive characteristics of the groups with low and high bladder pressure.

	Low bladder pressure	High bladder pressure	Comparisons
Age	M = 21.90, SD = 2.65	M = 21.25, SD = 3.24	t(38) = −.694, p = .492, d = 0.22
Stroop Int. (RT)	M = 6.30, SD = 2.70	M = 6.88, SD = 2.90	t(38) = .655 p = .516, d = 0.21
ACT (total)	M = 52.1, SD = 5.25	M = 52.5, SD = 5.21	t(38) = .212, p = .834, d = 0.08
S-SAI	M = 32.55, SD = 6.18	M = 32.00, SD = 6.87	t(38) = −.266, p = .792, d = 0.08
S-TAI	M = 40.09, SD = 7.39	M = 40.75, SD = 9.06	t(38) = .254, p = .801, d = 0.08
CFQ	M = 35.95, SD = 10.59	M = 43.30, SD = 16.07	t(38) = −1.83, p = .094, d = 0.54

d: Cohen’s d; Stroop Int.: Stroop interference; RT: reaction time; ACT: Auditory Consonant Trigram; S-SAI: Spielberger State Anxiety Inventory; S-TAI: Spielberger Trait Anxiety Inventory; CFQ: Cognitive Failures Questionnaire.

Bladder pressure manipulation

To investigate whether our bladder pressure manipulation worked well and could have differentiated groups properly in terms of bladder pressure, we analysed the need for urination scores of the participants by 2 x 2 (Group [low bladder pressure, high bladder pressure] × Time [pre-SAT, post-SAT]) mixed ANOVA with Time as a within factor and Group as a between factor. The results showed statistically significant main effect of the Group F(1, 38) = 25.765, p < .001, $η_{p}^{2}$ = .404, indicating that participants in the high bladder pressure condition reported significantly higher need for urination than those in the low bladder pressure condition (M₁ − M₂ = 2.15, SE = .42). The main effect of Time was also significant F(1, 38) = 77.07, p < .001, $η_{p}^{2}$ = .670, indicating that need for urination just before the SAT (compared with just after the SAT) was significantly lower (M₁ − M₂ = −1.85, SE = .21). Importantly, the interaction effect of Group and Time was also significant F(1, 38) = 11.03, p = .002, $η_{p}^{2}$ = .225. Accordingly, need for urination scores for the high bladder pressure group was significantly higher than the low bladder pressure group’s scores in both just before (M₁ − M₂ = 1.45, SE = .47, p < .05, r = .448) and just after (M₁ − M₂ = 2.85, SE = .47, p < .001, r = .701) the SAT. At the same time, need for urination scores after SAT were significantly higher than before the SAT in both the groups with high and low bladder pressure (M₁ − M₂ = 2.55, SE = .30, p < .001, r = .809; M₁ − M₂ = 1.15, SE = .30, p < .01, r = .528, respectively; see Figure 2). These findings verified that our manipulation worked well and successfully differentiated the groups in terms of bladder pressure.

Figure 2.

Mean “need for urination” scores obtained before and after the SAT procedure.

Main analyses

SAT’s RTs less than 100 ms or over 1,500 ms were considered as invalid trials and were not included in the analyses. In addition, response latencies that were three standard deviations away from their mean correct RTs in each condition (minimum “0,” maximum “10” trials) were excluded from analyses. Error percentages of each participant were less than 50% for any condition.

To facilitate the following of the “Results” section, we reported the results of the main analyses under three sections. A 2 × 2 × 2 (Group [low bladder pressure, high bladder pressure] × Perceptual Load [low, high] × Distractor Compatibility [compatible, incompatible]) mixed ANOVA with “Group” as a between and both “Perceptual Load” and “Distractor Compatibility” as within-subject factors was carried out on both correct RTs and error rates (see Table 2). While the results on correct RTs were reported under the section of “Results for the RTs,” the results on error rates were reported under the section “Results for the error rates.”

Table 2.

Mean SATs correct RTs and error rates (E%) across participants as a function of distractor compatibility and bladder pressure.

Trial type	Distractor compatibility				Difference
	Incompatible (I)		Compatible (C)		I − C
	RT	E%	RT	E%	RT	RT%	E%
Low perceptual load
Low bladder pressure	512	6	493	4	19	4	1
High bladder pressure	530	6	496	4	34	7	2
High perceptual load
Low bladder pressure	739	24	735	25	4	1	−0
High bladder pressure	769	24	767	23	2	0	1

SAT: selective attention task; RT: reaction time.

Finally, considering the possible differences in the baseline RTs between the two groups, we carried out a 2 × 2 (Group [low bladder pressure, high bladder pressure] × Perceptual Load [low, high]) mixed ANOVA on a new cost value calculated as percentage to cut down noise from variability in baseline RTs. The results of this two-way analysis were reported under the section of “Results for the ratio of distractor cost.” All multiple comparisons were reported as Bonferroni corrected.

Results for the RTs

For the correct RTs, the results yielded significant main effect of perceptual load, F(1, 38) = 545.677 p < .001, $η_{p}^{2}$ = .935 and distractor compatibility, F(1, 38) = 26.48, p < .001, $η_{p}^{2}$ = .411. The findings indicated that RTs were higher in high perceptual load condition than in low perceptual load condition (M₁ − M₂ = 245.27, SE = 10.50). Similarly, compared with the compatible trials, RTs were significantly higher in incompatible trials (M₁ − M₂ = 14.68, SE = 2.85). The main effect of Group, however, was non-significant F(1, 38) = .45, p = .508, $η_{p}^{2}$ = .012.

More importantly, interaction effects of Perceptual Load × Distractor Compatibility were also significant F(1, 38) = 35.26, p < .001, $η_{p}^{2}$ = .481. Pairwise comparisons indicated that distractor compatibility effect was significant in low perceptual load condition (M_incompatible − M_compatible = 26.42, SE = 2.64, p < .001, r = .851) but non-significant in high perceptual load condition (M_incompatible − M_compatible = 2.94, SE = 4.14, p = 482, r = .114). Moreover, the RTs in high perceptual load condition were significantly higher than in low perceptual load condition for both compatible (M₁ − M₂ = 257.01, SE = 10.84, p < .001, r = .968) and incompatible (M₁ − M₂ = 233.53, SE = 10.52, p < .001, r = .963) trials. However, the interaction effects of Group × Perceptual Load F(1, 38) = .93, p = .343, $η_{p}^{2}$ = .024 and Group × Distractor Compatibility F(1, 38) = 1.22, p = .276, $η_{p}^{2}$ = .031 were non-significant (see Figure 3). Finally, the three-way interaction was marginally significant F(1, 38) = 4.03, p = .052, $η_{p}^{2}$ = .096 (see section of “Results for the ratio of distractor cost” for a detailed investigation).

Figure 3.

Reaction times (left) and error rates (right) of SAT as a function of perceptual load and distractor compatibility.

Results for the error rates

For the error rates, the result yielded significant main effects of perceptual load F(1, 38) = 185.02, p < .001, $η_{p}^{2}$ = .830, and distractor compatibility F(1, 38) = 7.43, p < .01, $η_{p}^{2}$ = .164. The findings indicated that high perceptual load compared with low perceptual load (M₁ − M₂ = 19.15, SE = 1.41) and incompatible trials compared with compatible trials (M₁ − M₂ = 1.15, SE = 0.42) significantly increased error rates. The main effect of Group, however, was non-significant F(1, 38) = 0.17, p = .682, $η_{p}^{2}$ = .004.

The interaction effect of Perceptual Load × Distractor Compatibility was marginally significant, F(1, 38) = 3.02, p = .090, $η_{p}^{2}$ = .074, indicating that the compatibility effect was significant in the low perceptual load condition (M_incompatible − M_compatible = 1.78, SE = 0.46, p < .001, r = .531) but not in the high perceptual load condition (M_incompatible − M_compatible = 0.52, SE = 0.64, p = 420, r = .131). It was also revealed from this interaction effect that the error rates in high perceptual load condition was significantly higher than low perceptual load condition for both compatible (M₁ − M₂ = 19.78, SE = 1.36, p < .001, r = .921) and incompatible (M₁ − M₂ = 18.52, SE = 1.54, p < .001, r = .890) trials (see Figure 3). The interaction effects of Group × Perceptual Load F(1, 38) = 0.07, p = .793, $η_{p}^{2}$ = .002, Group × Distractor Compatibility F(1, 38) = 2.72, p = .108, $η_{p}^{2}$ = .067, and also Group × Perceptual Load × Distractor Compatibility were found to be non-significant F(1, 38) = 0.35, p = .558, $η_{p}^{2}$ = .009 for the error rates.

The effects not including Group variable for both RTs and error rate replicated previous findings that perceptual load significantly reduces interference of distractors and so ensured that the SAT worked as it should.

Results for the ratio of distractor cost

Although the three-way interaction on RTs was marginally significant F(1, 38) = 4.03, p = .052, $η_{p}^{2}$ = .096, considering the possibility that the intergroup variability in baseline RT may have confounded the results, we calculated a new cost value to cut down noise from variability in baseline RT. Costs were calculated as the percentage value of the amount of increase in the mean RTs from compatible to incompatible conditions. Therefore, increasing percentage values corresponded to the increase in distractor interference. Then we tested our main predictions with a 2 × 2 (Group [low bladder pressure, high bladder pressure] x Perceptual Load [low, high]) mixed ANOVA.

The main effect of Group was non-significant (F(1, 38) = 1.60, p = .213, $η_{p}^{2}$ = .040). This non-significant main effect indicated that mean distractor cost for both groups were quite similar (M₁ − M₂ = 1.17, SE = 0.09). The main effect of Perceptual Load, was significant (F(1, 38) = 74.34, p < .001, $η_{p}^{2}$ = .662), indicating that the distractor cost was greater in the low perceptual load condition compared with the high perceptual load condition (M₁ − M₂ = 5.07, SE = 0.59, p < .001, r = .812). More importantly, the interaction effect of Group × Perceptual Load was also significant (F(1, 38) = 5.30, p = .027, $η_{p}^{2}$ = .122). According to pairwise comparisons, the mean distractor cost in the low perceptual load condition were greater than the cost in the high perceptual load condition for both groups under low bladder pressure (M₁ − M₂ = 6.42 SE = .83, p < .001, r = .782) and high bladder pressure (M₁ − M₂ = 3.71, SE = .83, p < .001, r = .587). The results also revealed that mean distractor cost for the high bladder pressure group was greater than the cost for the low bladder pressure group in the low perceptual load condition (M₁ − M₂ = 2.52, SE = 1.06, p = .023, r = .360) as expected. In the high perceptual load condition, however, there was no significant difference between the two groups (M₁ − M₂ = 0.18, SE = 1.13, p = .871, r = .023; see Figure 4).

Figure 4.

Distractor cost level as a function of perceptual load and group.

Discussion

The load theory of attention suggests that perceptual processing resources are limited, and all of these resources are allocated to the stimuli in the environment. If these resources are completely used by target and target relevant stimuli, no resource is left to process distractors but if there are residual processing resources, they are automatically allocated to other irrelevant stimuli like distractors. In the former, the selection between target and distractors occurs perceptually at an early stage of the processing (early selection), while in the latter case, the selection is made at a later time with active control (late selection). In other words, if the perceptual demand of the target task is high enough, the selection takes place perceptually and the distractors for which adequate resources are not allocated are easily rejected. However, if the perceptual demand of the task is low and therefore both the target and the distractor are processed, higher-order cognitive functions are needed to respond to the target selectively and reduce the distractor’s effects. Therefore, Lavie (2005) suggested that an active cognitive control mechanism is needed when both target-relevant and target-irrelevant stimuli are in the attention radar to ensure that the behaviour of an individual is controlled by the target rather than distractors. Although WM, cognitive flexibility, and inhibition have all been described as the main components of cognitive/executive control (Diamond, 2013), research on the load effect of this mechanism has generally focused on the WM component (de Fockert & Bremner, 2011; Gil-Gómez de Liaño et al., 2016; Lavie et al., 2004; Simon et al., 2016). In these studies, the amount of WM resources requested by another task carried out simultaneously with the SAT was manipulated and it was observed that as the WM load increased, the distracting effects on the task also increased. Therefore, the load theory predicts that as the load of the active cognitive mechanism increases, the interference of distractors on the task will increase, in contrast to the perceptual mechanism.

In the presence of a distractor and a target, target selection can be accomplished by suppressing the distractor effects as well as highlighting the target with WM. Therefore, it is important to examine the role of the inhibition function on the distracting effect. NP studies on that issue generally concluded that NP happens because the inhibition function suppresses the distractors. However, there are also criticisms against this explanation. Therefore, we aimed to reveal the load effects of the inhibition component by adopting an approach used in studies on the load effect of WM (e.g., Lavie et al., 2004). For this purpose, we considered using a task known to demand inhibitory resources concurrently with a SAT. As it is difficult to perform well-known inhibitory tasks like Stroop or Simon simultaneously with an attention task, we decided to physiologically recruit the inhibitory resources. We examined the physiological inhibitory load induced by bladder pressure as a between-group variable by varying the amount of pressure. As increased bladder pressure will demand more resources of inhibition, we predicted that the participants with high bladder pressure (compared with low) would be subject to distractor interference more in the low perceptual load condition. We also hypothesised that high perceptual load would eliminate this difference between groups.

Although the three-way interaction effect on RTs was found to be marginally significant, the medium-high effect size showed that the result was worth further investigation. Moreover, considering the possibility that the baseline response time might have differed between groups, the analyses regarding our main hypothesis were repeated with a recalculated dependent variable. In this analysis, the distractor interference was calculated as a percentage value, and the results were statistically significant this time. Analyses revealed that only under the low perceptual load condition, the participants with high bladder pressure (high inhibitory load condition) were more susceptible to distractor interference than those with low bladder pressure (low inhibitory load condition) as hypothesised. Moreover, the interference effect of distractors almost disappeared in the high perceptual load condition for both groups of participants (see Figure 4) expectedly. As distractors were perceptually excluded, in the high perceptual load condition, the interference effect did not differ as a function of the inhibitory load. The results also confirmed that the inhibition function effectively had control over attention in the low perceptual load conditions in which distractors compete with the targets to gain control of the behaviour (Lavie, 2010; Lavie et al., 2005). Therefore, the results not only revealed that the inhibition component of the active control mechanism has a similar load effect like the WM component but also provided supportive evidence for perceptual selection. Considering the crucial role of inhibition function in suppressing the distractor effect (Gaspelin & Luck, 2018), it can be said that the increasing demand for inhibitory resources with bladder pressure increased the interference effect of distractors on a concurrent attention task by reducing the inhibitory resources required for suppressing this undesired effect.

As we know that any differences regarding WM functioning between the two group of participants may affect the distractor interferences (de Fockert et al., 2001), we evaluated participants’ WM task performance using ACT. Moreover, the Stroop test was used to assess initial attentional inhibition performances of both groups. The results indicated no significant differences between the two groups in terms of Stroop and ACT scores. Thus, it was ensured that there was no contaminative effect of WM and attentional inhibition performances on the results.

Although we suggested that the load effect pattern found in the current study was similar to the patterns reported in the studies on WM load, it was reported in these studies that the necessity of dual-task coordination also demanded cognitive control resources and therefore the distracting interference effect reached greater values (2–3 times higher) than it should be even under low WM load (Lavie et al., 2004). This situation also pointed out the importance of using an approach away from multitasking, if possible, in revealing the load effect of a cognitive control component without other confounding effects. Moreover, the possibility of the additional load caused by multitasking was also evaluated for the current study. The distractor effects reported in previous experiments using a similar flanker paradigm under a single-task condition were around 10–40 ms (e.g., Forster & Lavie, 2007; Lavie, 1995). We investigated whether performing a SAT under different bladder pressure had a similar kind of effect. Distractor costs varying between this range mentioned above for both groups indicated no additional load effect on the active control mechanism (see Table 2), therefore that our manipulation of the inhibitory load does not seem to act as an additional task, which makes sense as it implicitly creates its effect.

However, one may argue that high bladder pressure caused discomfort and adversely affected the attention performance of the participants. Yet, analyses revealed that there were no significant differences between groups in terms of RTs and error rates. The analysis on cost value revealed group-related differences only for the low perceptual load condition but not for the high perceptual load condition. These results demonstrated that higher pressure did not lead to a general decrease in attentional performance.

Another issue regarding our manipulation method is related to the inhibitory spillover effect (ISE) phenomenon asserting that an inhibitory process in a brain region tends to diffuse through a common inhibitory network (Berkman et al., 2009). The idea of a common inhibitory network strengthened the assumption of the current study that changing the load of an inhibitory domain (physiological) will also change the load of another domain (resistance to distractor interference). However, studies on ISE using the same bladder pressure manipulation to create a concurrent inhibitory activation revealed that the diffusion of the inhibitory effects usually manifests itself by facilitation or an improvement in simultaneously occurring inhibitory-related task performances regardless of the type of inhibition (e.g., Fenn et al., 2015; Tuk et al., 2011). Considering this, the distractor interference may have been expected to be less for participants with high bladder pressure in the current study. Our results were not as predicted by the ISE phenomenon.

An explanation may be that the spillover effect provided a greater benefit in suppressing compatible distractors than incompatible distractors. However, even in such a case, high bladder pressure may have decreased the RT and error rates for both incompatible and compatible stimuli, which was not the case for our study (see Table 2). These inconsistent results raised the question of whether spillover of inhibitory signals has the same facilitating effect for different inhibitory domains. Even if the facilitative effect of concurrent bladder pressure on a colour-naming Stroop task requiring attentional inhibition was shown before (Tuk et al., 2011), we thought that different suppression mechanisms may have been used for performing the task in the current study than those used in the Stroop task in Tuk et al.’s study. Although both tasks are based on response competition, the distracting stimuli and target positions are overlapped in the Stroop task and the response (reading the word) triggered by the distractors was a common, dominant response in nature. In this case, it can be thought that the possibility of using the reactive suppression mechanism is high in the Stroop task to disengage the attention captured by the distractor. In the SAT of the current study, however, the distractor-related response cannot be considered as a common response and the location of the distractors did not overlap with the location of the target (but see Möller et al., 2019). Thus, the participant may have used the mechanism of proactive suppression by suppressing the expected features of the distractor such as its possible location, and so the need to disengage attention captured by the distractors may have been much less than the need in the Stroop task. Therefore, differences in the level of involvement of proactive and reactive suppression mechanisms in performing target behaviour may have differed ISE.

Individual factors such as strategic choices and impulsivity have been reported to affect which strategies people use during a task (see Geng, 2014), so it is important to investigate in detail the circumstances that cause us to choose one of these strategies over the other and the neural mechanisms underlying these strategies. Moreover, further studies aiming directly to show whether ISE effects vary according to the suppression strategies used for performing target behaviour will provide a clearer and more precise understanding.

In the current study, the distractor was an expected stimulus for the participants and therefore the suppression function was possibly expectation-based. Yet, distractors are not always expected in everyday life. For this reason, it would be interesting to demonstrate how the availability of inhibitory resources affects suppressing expected versus unexpected distractors in future research.

The results of the current study may also have clinical implications. In the light of our findings indicating the load created in an inhibitory domain (physiological domain in the current study) had an effect on another inhibitory domain regarding resistance to distractor interference, it can be thought that rather than trying to strengthen the weak domain, making therapeutic interventions on other interference-control-related inhibitory domains that are known to be intact in people with obsessive/compulsive disorder (OCD) or post-traumatic stress disorder (PTSD), may positively affect their selective attention performances which enable them to achieve goal-directed behaviour with the minimum effect of goal-irrelevant information. In addition, as the group difference found in the low perceptual load condition was eliminated in the high perceptual load condition, using methods that increase the perceptual load of the target behaviour (e.g., increasing the number of task-related stimuli) may be an effective method to reduce the likelihood of distractions of individuals with PTSD (Catarino et al., 2015), attention deficit and hyperactivity disorder (Wodka et al., 2007), OCD (Bannon et al., 2002; Chamberlain et al., 2006), or older people (Morrone et al., 2010) who have been shown to have inhibition-related problems.

To conclude, the findings of the current study supported the load theory of attention by showing the importance of the availability of inhibitory control resources for achieving a goal-directed behaviour. The findings also demonstrated that the perceptual load of the target task may reduce the effects of distractors independently from the availability of inhibitory resources. Therefore, we provided additional support for that perceptual selection is the primer determinant of selective attention processes and the active cognitive control functions are needed when the perceptual capacity is not loaded enough by target-relevant information.

However, it is also useful to keep in mind that, there have been several criticisms and some alternative accounts for the predictions of the load theory. For example, the dilution approach suggested that the effect of high perceptual load on the distractor may be related not to a decrease in allocation of perceptual resources to the distractors but to an increase in the number of target-related stimuli that dilutes the distractor (Benoni & Tsal, 2010; see also Lavie & Torralbo, 2010 for their reply). Attentional zoom is also considered as an important variable for distractor interference. When the attention is zoomed in to a part of the visual field, the dilution ability of target-related stimuli which are not inside of that part of the visual area is smaller (see Chen & Cave, 2013). This finding was important for our study because the block design of the current study may have changed the size of the area that attention zoomed. In the high perceptual load, for example, participants may have zoomed their attention to the target-related area for a more effective target search and this may have resulted in a decrease in interference of the distractor which was presented out of the target-related area (e.g., Chen & Cave, 2015). The demand of any higher cognitive functioning for maintaining a narrow or wider attentional zoom during different blocks may have differed and so the amount of additional load that could have been added on the active cognitive control mechanism may have differed for each perceptual load condition. This might have led to misleading results such as the inhibition load effects appearing weaker or stronger than they should have been. Especially considering the criticisms of the theory, future studies in which trials on perceptual load conditions will be presented as intermixed rather than in blocks, distractors will be presented in the middle of the task-related area rather than at the periphery, and the type of stimulus that has been changed in number will be considered as an independent variable, may provide detailed information on whether the key findings of the current study hold up strongly under different conditions.

Footnotes

Acknowledgements

We thank Dr. Kyle Cave and two anonymous reviewers for their contributions.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Hasan Gunduz

References

Allport

D. A.

(1977). On knowing the meaning of words we are unable to report: The effects of visual masking. In Dornic

(Ed.), Attention and performance VI (pp. 505–533). Erlbaum.

Anderson

M. C.

Green

(2001). Suppressing unwanted memories by executive control. Nature, 410(6826), 366–369.

Anil

A. E.

Kivircik

B. B.

Batur

Kabakçi

Kitis

Güven

. . .Arkar

(2003). The Turkish version of the Auditory Consonant Trigram Test as a measure of working memory: A normative study. The Clinical Neuropsychologist, 17(2), 159–169.

Aron

A. R.

Fletcher

P. C.

Bullmore

E. T.

Sahakian

B. J.

Robbins

T. W.

(2003). Stop-signal inhibition disrupted by damage to right inferior frontal gyrus in humans. Nature Neuroscience, 6(2), 115–116.

Aron

A. R.

Robbins

T. W.

Poldrack

R. A.

(2004). Inhibition and the right inferior frontal cortex. Trends in Cognitive Sciences, 8(4), 170–177.

Bannon

Gonsalvez

C. J.

Croft

R. J.

Boyce

P. M.

(2002). Response inhibition deficits in obsessive-compulsive disorder. Psychiatry Research, 110(2), 165–174.

Benoni

Tsal

(2010). Where have we gone wrong? Perceptual load does not affect selective attention. Vision Research, 50(13), 1292–1298.

Berkman

E. T.

Burklund

Lieberman

M. D.

(2009). Inhibitory spillover: Intentional motor inhibition produces incidental limbic inhibition via right inferior frontal cortex. NeuroImage, 47(2), 705–712.

Boot

W. R.

Brockmole

J. R.

Simons

D. J.

(2005). Attention capture is modulated in dual-task situations. Psychonomic Bulletin & Review, 12(4), 662–668.

10.

Brand-D’Abrescia

Lavie

(2008). Task coordination between and within sensory modalities: Effects on distraction. Perception & Psychophysics, 70(3), 508–515.

11.

Broadbent

D. E.

(1958). Perception and communication. Pergamon Press.

12.

Broadbent

D. E.

Cooper

P. F.

FitzGerald

Parkes

K. R.

(1982). The cognitive failures questionnaire (CFQ) and its correlates. British Journal of Clinical Psychology, 21(1), 1–16.

13.

Brown

(1958). Some tests of the decay theory of immediate memory. Quarterly Journal of Experimental Psychology, 10(1), 12–21.

14.

Catarino

Küpper

C. S.

Werner-Seidler

Dalgleish

Anderson

M. C.

(2015). Failing to forget: Inhibitory-control deficits compromise memory suppression in posttraumatic stress disorder. Psychological Science, 26(5), 604–616.

15.

Chamberlain

S. R.

Fineberg

N. A.

Blackwell

A. D.

Robbins

T. W.

Sahakian

B. J.

(2006). Motor inhibition and cognitive flexibility in obsessive-compulsive disorder and trichotillomania. American Journal of Psychiatry, 163(7), 1282–1284.

16.

Chen

Cave

K. R.

(2013). Perceptual load vs. dilution: The roles of attentional focus, stimulus category, and target predictability. Frontiers in Psychology, 4, Article 327.

17.

Chen

Cave

K. R.

(2015). Singleton search is guided by knowledge of the target, but maybe it shouldn’t be. Vision Research, 115, 92–103.

18.

de Fockert

J. W.

(2013). Beyond perceptual load and dilution: A review of the role of working memory in selective attention. Frontiers in Psychology, 4, Article 287.

19.

de Fockert

J. W.

Bremner

A. J.

(2011). Release of inattentional blindness by high working memory load: Elucidating the relationship between working memory and selective attention. Cognition, 121(3), 400–408.

20.

de Fockert

J. W.

Rees

Frith

C. D.

Lavie

(2001). The role of working memory in visual selective attention. Science, 291(5509), 1803–1806.

21.

Deutsch

J. A.

Deutsch

(1963). Attention: Some theoretical considerations. Psychological Review, 70(1), 80–90.

22.

Diamond

(2013). Executive functions. Annual Review of Psychology, 64, 135–168.

23.

Driver

(2001). A selective review of selective attention research from the past century. British Journal of Psychology, 92(1), 53–78.

24.

Eriksen

B. A.

Eriksen

C. W.

(1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & Psychophysics, 16(1), 143–149.

25.

Faul

Erdfelder

Lang

A. G.

Buchner

(2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral and biomedical sciences. Behavior Research Methods, 39(2), 175–191.

26.

Fenn

Blandón-Gitlin

Coons

Pineda

Echon

(2015). The inhibitory spillover effect: Controlling the bladder makes better liars. Consciousness and Cognition, 37, 112–122.

27.

Forster

Lavie

(2007). High perceptual load makes everybody equal. Psychological Science, 18(5), 377–381.

28.

Frings

Schneider

K. K.

Fox

(2015). The negative priming paradigm: An update and implications for selective attention. Psychonomic Bulletin & Review, 22(6), 1577–1597.

29.

Gaspelin

Luck

S. J.

(2018). The role of inhibition in avoiding distraction by salient stimuli. Trends in Cognitive Sciences, 22(1), 79–92.

30.

Geng

J. J.

(2014). Attentional mechanisms of distractor suppression. Current Directions in Psychological Science, 23(2), 147–153.

31.

Gil-Gómez de Liaño

Stablum

Umiltà

(2016). Can concurrent memory load reduce distraction? A replication study and beyond. Journal of Experimental Psychology: General, 145(1), e1–e12.

32.

Griffiths

Tadic

S. D.

(2008). Bladder control, urgency, and urge incontinence: Evidence from functional brain imaging. Neurourology and Urodynamics, 27(6), 466–474.

33.

Itthipuripat

Cha

Byers

Serences

J. T.

(2017). Two different mechanisms support selective attention at different phases of training. PLoS Biology, 15(6), Article e2001724.

34.

Johnston

W. A.

Dark

V. J.

(1982). In defense of intraperceptual theories of attention. Journal of Experimental Psychology: Human Perception and Performance, 8(3), 407–421.

35.

Jonides

Smith

E. E.

Marshuetz

Koeppe

R. A.

Reuter-Lorenz

P. A.

(1998). Inhibition in verbal working memory revealed by brain activation. Proceedings of the National Academy of Sciences of the United States of America, 95(14), 8410–8413.

36.

Karakaş

Erdoğan

Sak

Soysal

A. Ş.

Ulusoy

İ. Y.

Alkan

(1999). Stroop Test TBAG Form: Standardisation for Turkish Culture, Reliability and Validity. Turkish Journal of Clinical Psyhiatry. 2(2), 75–88

37.

Konstantinou

Beal

King

J. R.

Lavie

(2014). Working memory load and distraction: Dissociable effects of visual maintenance and cognitive control. Attention, Perception, & Psychophysics, 76(7), 1985–1997.

38.

Lappin

J. S.

Eriksen

C. W.

(1966). Use of a delayed signal to stop a visual reaction-time response. Journal of Experimental Psychology, 72(6), 805–811.

39.

Lavie

(1995). Perceptual load as a necessary condition for selective attention. Journal of Experimental Psychology: Human Perception and Performance, 21(3), 451–468.

40.

Lavie

(2000). Selective attention and cognitive control: Dissociating attentional functions through different types of load. In Monsell

Driver

(Eds.), Attention and performance XVIII (pp. 175–194). MIT Press.

41.

Lavie

(2001). Capacity limits in selective attention: Behavioral evidence and implications for neural activity. In Braun

Koch

Davis

J. L.

(Eds.), Visual attention and cortical circuits (pp. 49–68). MIT Press.

42.

Lavie

(2005). Distracted and confused? Selective attention under load. Trends in Cognitive Sciences, 9(2), 75–82.

43.

Lavie

(2010). Attention, distraction, and cognitive control under load. Current Directions in Psychological Science, 19(3), 143–148.

44.

Lavie

Cox

(1997). On the efficiency of visual selective attention: Efficient visual search leads to inefficient distractor rejection. Psychological Science, 8(5), 395–396.

45.

Lavie

de Fockert

(2006). Frontal control of attentional capture in visual search. Visual Cognition, 14(4–8), 863–876.

46.

Lavie

Hirst

De Fockert

J. W.

Viding

(2004). Load theory of selective attention and cognitive control. Journal of Experimental Psychology: General, 133(3), 339–354.

47.

Lavie

Torralbo

(2010). Dilution: A theoretical burden or just load? A reply to Tsal and Benoni (2010). Journal of Experimental Psychology: Human Perception and Performance, 36(6), 1657–1664.

48.

Lavie

Tsal

(1994). Perceptual load as a major determinant of the locus of selection in visual attention. Perception & Psychophysics, 56(2), 183–197.

49.

Mayr

Buchner

(2007). Negative priming as a memory phenomenon. Zeitschrift für Psychologie/Journal of Psychology, 215(1), 35–51.

50.

Menon

Adleman

N. E.

White

C. D.

Glover

G. H.

Reiss

A. L.

(2001). Error-related brain activation during a Go/NoGo response inhibition task. Human Brain Mapping, 12(3), 131–143.

51.

Miller

(1987). Evidence of preliminary response preparation from a divided attention task. Journal of Experimental Psychology: Human Perception and Performance, 13(3), 425–434.

52.

Miyake

Friedman

N. P.

Emerson

M. J.

Witzki

A. H.

Howerter

Wager

T. D.

(2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49–100.

53.

Möller

Mayr

Buchner

(2019). Inhibition of irrelevant response codes is affected by matching target-distractor modalities. Journal of Experimental Psychology: Human Perception and Performance, 45(2), 189–208.

54.

Moray

(1959). Attention in dichotic listening: Affective cues and the influence of instructions. Quarterly Journal of Experimental Psychology, 11(1), 56–60.

55.

Morrone

Declercq

Novella

J.-L.

Besche

(2010). Aging and inhibition processes: The case of metaphor treatment. Psychology and Aging, 25(3), 697–701.

56.

Mukhopadhyay

Stowers

(2020). Choosing to urinate. Circuits and mechanisms underlying voluntary urination. Current Opinion in Neurobiology, 60, 129–135.

57.

Murphy

Greene

C. M.

(2016). Perceptual load induces inattentional blindness in drivers. Applied Cognitive Psychology, 30(3), 479–483.

58.

Posner

M. I.

(1982). Cumulative development of attentional theory. American Psychologist, 37(2), 168–179.

59.

Raveh

Lavie

(2015). Load-induced inattentional deafness. Attention, Perception, & Psychophysics, 77(2), 483–492.

60.

Rees

Frith

C. D.

Lavie

(1997). Modulating irrelevant motion perception by varying attentional load in an unrelated task. Science, 278(5343), 1616–1619.

61.

Schwartz

Vuilleumier

Hutton

Maravita

Dolan

R. J.

Driver

(2004). Attentional load and sensory competition in human vision: Modulation of fMRI responses by load at fixation during task-irrelevant stimulation in the peripheral visual field. Cerebral Cortex, 15(6), 770–786.

62.

Simon

S. S.

Tusch

E. S.

Holcomb

P. J.

Daffner

K. R.

(2016). Increasing working memory load reduces processing of cross-modal task-irrelevant stimuli even after controlling for task difficulty and executive capacity. Frontiers in Human Neuroscience, 10, Article 380.

63.

Spielberger

C. D.

(1983). State-trait anxiety inventory for adults. Mind Garden.

64.

Stroop

J. R.

(1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18(6), 643–662.

65.

Tipper

S. P.

(1985). The negative priming effect: Inhibitory priming by ignored objects. The Quarterly Journal of Experimental Psychology, 37(4), 571–590.

66.

Tipper

S. P.

(2001). Does negative priming reflect inhibitory mechanisms? A review and integration of conflicting views. The Quarterly Journal of Experimental Psychology Section A, 54(2), 321–343.

67.

Tuk

M. A.

Trampe

Warlop

(2011). Inhibitory spillover: Increased urination urgency facilitates impulse control in unrelated domains. Psychological Science, 22(5), 627–633.

68.

Wang

Feng

Mai

Jia

Zhu

Luo

Y. J.

(2016). The impact of perceptual load on the non-conscious processing of fearful faces. PLOS ONE, 11(5), Article e0154914.

69.

Wodka

E. L.

Mark Mahone

Blankner

J. G.

Gidley Larson

J. C.

Fotedar

Denckla

M. B.

Mostofsky

S. H.

(2007). Evidence that response inhibition is a primary deficit in ADHD. Journal of Clinical and Experimental Neuropsychology, 29(4), 345–356.

70.

Yao

Guo

Liu

Shen

Gao

(2020). Visual working-memory capacity load does not modulate distractor processing. Attention, Perception, & Psychophysics, 82(7), 3291–3313.