What is the extent of our ability to ignore salient, irrelevant information in our environment? Some have argued that we have strong goal-driven abilities to resist distraction (e.g., Folk, Remington, & Johnston,
1992), although we may not always employ them (Bacon & Egeth,
1994). Others have argued that stimulus-driven factors primarily control our allocation of attention (e.g., Theeuwes,
1992). In the past few decades, the research literature has amassed a wealth of conflicting data that have been taken as evidence for either one viewpoint or the other, and attempts to reconcile the data have been fraught with further controversy (e.g., see Burnham,
2007; Theeuwes,
2010).
Recently, several researchers have adopted an explanatory framework based on the time-course of salience processing, a perspective that has attracted a great deal of intrigue. One such time-course based explanation is often referred to as the
rapid disengagement account (Theeuwes, Atchley, & Kramer,
2000; Theeuwes,
2010), by which salient stimuli initially capture attention but observers are able to implement goal-driven control to rapidly disengage from the irrelevant stimulus. By this account, individuals can mask any outwardly observable effects of distraction by delaying their responses until after any potential effects of capture have been overcome. A related account, the
short-lived salience account (Donk & van Zoest,
2008) similarly proposes that visual selection in the moments following the initial view of a stimulus is wholly determined by stimulus-driven salience, but this salience quickly wanes. By this account, individuals who delay their search until the salience wanes will evade distraction by salient, irrelevant stimuli. What the two accounts have in common is that time-course is critical for attentional control, and behavioral effects of distraction are generally only measurable when the responses—either manual or saccadic—are speeded (see also Hunt, von Mühlenen, & Kingstone,
2007; van Zoest, Donk, & Theeuwes,
2004; Theeuwes,
2010).
Some of the most impactful evidence in favor of these time-course based accounts has come from analysis of cumulative response time (RT) distributions (e.g., van Zoest et al.,
2004). In this analysis approach, trial data are typically
Vincentized (Ratcliff,
1979), by sorting RTs into multiple ordinal bins of data, starting from the fastest RTs all the way through the slowest RTs (e.g., Ansorge, Horstmann, & Carbone,
2005; Godijn & Theeuwes,
2002; Theeuwes & Burger,
1998). For instance, one could sort RTs into quintiles, so that each bin summarizes 20% of the cumulative distribution. When examining these data, the time-course dependent accounts make very specific predictions. By the short-lived salience account, observers should experience the strongest distraction effects when they commence their search, while the initial period of salience-dominated processing is active. Since quickly commenced searches should correspond with fast RTs, then the short-lived salience account predicts that fast RTs should be accompanied by the greatest degree of distraction. In contrast, when observers withhold their search until after the initial wave of salience has waned and the potency of the distractor is reduced, then slower RTs should be accompanied by weaker distraction. Indeed, van Zoest et al. (
2004) found just this pattern when measuring proportion correct for saccadic eye-movements toward the target. That is, in the fastest bin of saccadic RTs, a large proportion of saccades were directed to irrelevant distractors; proportion correct then increased gradually as a function of bin, with more than 90% of saccades correctly directed toward the target at the slowest of the five bins (see also van Zoest & Donk,
2005; van Zoest, Hunt, & Kingstone,
2010).
However, not all researchers have corroborated this finding. For instance, Ansorge et al. (
2005), using a two-alternative forced choice manual RT task in which a target discrimination was performed, found that distraction effects were actually
smallest in the fastest bin of RTs (see also Ansorge & Horstmann,
2007). Other researchers who have reported such an analysis have found either smaller distraction at the fastest RT bins or at least no modulation of distraction across the bins (e.g., Costello, Madden, Shepler, Mitroff, & Leber,
2010; Gibson & Bryant,
2008).
Taken together, it may appear unclear why the cumulative RT results are so conflicting, although some explanations have been offered. One leading explanation highlights differences in processing steps in manual versus saccadic tasks (see e.g., Hickey, van Zoest, & Theeuwes,
2010), focusing on the point that manual RTs employing a target discrimination are slowed on trials in which capture occurs; this is due to initially shifting covert attention to the distractor, then disengaging, and then shifting attention to the target. This slowing, which we can refer to as a
redirection cost, causes trials in which capture occurs to be sorted to slower segments of the RT distribution, leaving trials in which capture is successfully avoided—and thus not slowed following the initial deployment of attention—in the fastest segments. Thus, some have argued that rapid disengagement and short-lived salience accounts cannot be properly tested with cumulative RT analysis in a manual response task (e.g., Hickey et al.,
2010). Note that most saccadic RT studies do not suffer a redirection cost, since each trial in these studies is completed when the eyes land on any item (typically the target or distractor). This is because the dependent variable in these studies has always been the proportion of saccades to the target, rather than discrimination RT (e.g., van Zoest et al.
2004). It should also be noted that not all manual RT tasks need be susceptible to redirection costs. For instance, Hunt et al. (
2007) used joystick movements to the target, yielding a dependent measure that matches the measure used in the saccadic RT studies (i.e., proportion to target).
The existence of the redirection cost in typical manual discrimination tasks has offered a parsimonious explanation of the conflicting results of cumulative RT analyses. However, a closer look reveals that it cannot explain small or negligible capture effects at the fastest cumulative RT bins in the manual tasks. Consider that in the manual RT studies, capture at all bins is computed by examining the differences in distributions of trials expected to cause distraction versus those that are not. For instance, Ansorge et al. (
2005) compared quintiled bins of trials in which the distractor appeared at the target location to quintiled bins of trials in which the distractor appeared at a different location. If capture is present, it should cause a selective slowing on the different-location trials due to the need to redirect attention to the target location. Thus, if capture is greatest when search is most rapidly commenced, these different-location trials might not end up in the fastest RT bin. However, if these trials are moved to a slower bin, the remaining trials that populate the fastest bin are necessarily those in which the search was less rapidly commenced. As a result, when comparing the fastest bin for same and different location trials, a net positive RT difference must still emerge. Thus, it remains unclear how the time course explanations of capture can be reconciled with existing results from manual RT tasks.
In the present paper, we offer another explanation. As we will illustrate, the general enterprise of analyzing cumulative RT distributions in attention capture studies is susceptible to a potentially major pitfall. That is, because the sorting of trials by their RTs is inherently post hoc, the causes for why some RTs are fast versus slow can be greatly misinterpreted. We draw a critical distinction between two sources of variability in behavior that can influence cumulative RT analysis: (a) the observer's internal control state, or momentary readiness to perform the task (e.g., commence the search), and (b) incidental stimulus factors, or aspects of the environment that can be attributed to momentary “luckiness” (i.e., the observer will sometimes encounter trials whose stimulus aspects render them objectively easy compared to other trials). These two sources of variability are arguably confounded in cumulative RT analysis.
The key to the problem is that incidental factors of the environment can influence behavioral outcomes independently of internal control. Consider by analogy a roulette player who bets exactly the same way prior to every spin of the wheel. On some spins, the ball will come to rest on a number that yields the gambler a substantial win, while on others the ball will rest on a number that yields a substantial loss. Few researchers are superstitious enough to believe that the gambler's spin-by-spin performance is due to anything other than incidental aspects of the environment (e.g., the rotational speed of the roulette wheel as well as the starting point, speed and finesse placed on the ball); certainly, the gambler's internal control state could not possibly affect the results. However, this kind of superstition may creep into our experimental data interpretation, specifically when we employ cumulative RT analysis. Here, we demonstrate how incidental factors, which contribute largely to the variance in behavioral outcomes (i.e., getting lucky or unlucky on each trial due to some idiosyncratic visual aspect of the task), confound interpretations of data.
To build our argument, we focus on a typical behavioral task with a manual response, in which observers perform visual search in the face of distracting information. The left panel of
Figure 1 shows the task—an adaptation of the
additional singleton paradigm introduced by Theeuwes (
1991) —in which the participant is asked to find the single circle among squares as rapidly as possible and demonstrate his/her success by discriminating the orientation of the line segment inside of it. On half of the trials, the participant is confronted with a distracting, task-irrelevant stimulus, in the form of a color singleton, shown in the right panel of
Figure 1. While the task typically uses only the inside ring of shapes, our current adaptation adds an outside ring to boost the visual salience of the color singleton (see Theeuwes,
2004). Note that in our version of the task, neither targets nor distractors ever appear in the outside ring. It is easy to see the appeal of cumulative RT analysis in the present context. Yet, as we previewed, we are concerned about how the results of this important analysis method, and from related approaches, are interpreted. To articulate our concerns, we present Vincentized RT data collected from the additional singleton paradigm and then proceed to carefully scrutinize the results. Specifically, we aim to reveal whether incidental stimulus factors confound the analysis of Vincentized RT data, and if so, how. To preview, we will argue that susceptibility to distraction when observers are responding quickly is generally underestimated, due to the confounding influence of the incidental factors. This potentially resolves the inconsistent results of cumulative RT analysis and might be taken as support for time-course accounts such as short-lived salience and rapid disengagement. However, we ultimately will not endorse any particular theoretical framework, arguing that several critical issues remain open for well-deserved debate.