Experiment 1 extended the Risko and Stolz (2010b) investigation, in which participants were required to perform a standard cueing task in which peripheral rectangle cues were used to direct attention. In addition to manipulating cue validity, we explicitly instructed half of the participants to pay attention to the relation between the cues and spatial locations, to enhance the process of their becoming aware of cue–target contingencies. An implicit-processing account predicts that the PVE should be independent of awareness as indexed by subjective reports, and should be unaffected by the instruction manipulation. A purely volitional account predicts that the PVE should only occur if participants are instructed to pay attention to the cue–target contingencies or (previous limitations of the subjective report method notwithstanding) if participants become aware of the cue–target contingencies, as indexed by subjective reports.
To further provide the opportunity for endogenous orienting to impact the PVE, we extended the cue–target stimulus onset asynchronies (SOA) that had been used by Risko and Stolz (2010b) from 150 to 350 ms (the SOA that was used for their central cue). Risko and Stolz (2010b) had used the shorter SOA for the peripheral cue to try to engage rapid exogenous orienting while preventing the involvement of any endogenous orienting. By lengthening the SOA in the present study, and thereby providing the opportunity for endogenous processes to impact the PVE, the present study provides a strong test of the volitional account. Furthermore, by keeping the SOA fixed at 350 ms in both experiments, we avoided confounding cue type with SOA, and thus ensured that any performance change between experiments could be attributed to attentional processes related to cue type (see López-Ramón, Chica, Bartolomeo, & Lupiáñez, 2011, for data from a similar experiment)
A total of 80 participants with normal or corrected-to-normal vision were either given credit or paid $10 for their time.
The study used a 2 (Instruction: instructed and not instructed) × 2 (Cue Validity: valid and invalid) × 3 (Percentage Valid: 25%, 50%, and 75%) mixed design in which instruction was manipulated between participants and cue validity and percentage valid were manipulated within participants.
E-Prime 2.0 controlled the timing and presentation of the stimuli and logged response accuracy and RTs. Stimuli were presented on standard 15-in. CRT monitors with a resolution of 1,024 × 728. Participants viewed the monitor unrestrained at a distance of approximately 60 cm.
The stimulus display consisted of a central fixation cross that measured 0.6 cm2. The cues were solid rectangles (1.0 cm vertical, 1.4 cm horizontal) displayed 4.2 cm above or below fixation. The targets were either “%” or “#” signs measuring 0.9 cm vertically and 0.6 cm horizontally. The targets appeared 3.0 cm above or below fixation equally often. All stimuli were white and were presented on a black background.
Following Risko and Stolz (2010b), all participants were asked to indicate by buttonpress (either “c” or “m”) whether a target was “%” or “#.” We stressed that participants should try to keep their eyes at fixation at all times, and that both speed and accuracy were equally important in responding. The only procedural difference between participants was the instructions. The participants in the instructed condition were instructed to try to determine the different relationships that existed between the rectangle cues and targets in each block (e.g., in some blocks the cue would indicate the correct location of the upcoming targets more than in others). The participants in the not-instructed condition did not receive any such instruction prior to beginning the task. Half of the participants were assigned to the instructed condition and the other half to the not-instructed condition.
Each trial began with the fixation cross for 500 ms, followed by the onset of a cue either above or below the cross for 50 ms. After cue offset, there was a 300-ms delay before the target was presented. A response extinguished the display for 500 ms before the next trial began. See Fig. 1 for an example of the trial sequences for valid and invalid cues. Participants performed 16 practice trials to get acquainted with the task. The experiment consisted of three blocks of 416 trials,Footnote 2 with the percentage of valid cues in a given block being 25%, 50%, or 75%. In all conditions and both experiments, the term valid refers to a spatial correspondence between a cue and target (e.g., cue left–target left), regardless of the cue predictability. The order of the blocks was counterbalanced across participants. After each block, participants were asked to fill out a questionnaire that asked them (1) to indicate whether the target appeared more frequently in the same location as the “white rectangle” (i.e., the cue) or in a different location from the “white rectangle,” and (2) to write down the percentage of trials on which the “white rectangle” (i.e., the cue) indicated the correct location of the upcoming target. The experiment took approximately 60 min in total.
Prior to the RT and error analysis, outliers were removed using a modified recursive trimming procedure (see Van Selst & Jolicœur, 1994, for a justification). Using this procedure, data are trimmed over several cycles by using a unique cutoff criterion (i.e., a certain number of standard deviations away from the mean) that is set independently during each cycle, for each participant, in each condition. The value of the cutoff is dynamically altered on the basis of the sample size in each cycle of the procedure. This data-trimming procedure resulted in 4.3% of the RT data being discarded. Mean correct RTs and errors were analyzed using a three-way mixed analysis of variance (ANOVA) with Instruction (two levels: instructed and not instructed) as a between-participants factor and Cue Validity (two levels: valid and invalid) and Percentage Valid (three levels: 25% valid, 50% valid, and 75% valid) as within-participants factors. For all analyses in all experiments, if Mauchly’s test of sphericity was significant (p < .25), the relevant degrees of freedom were adjusted using the Greenhouse–Geisser (if ε ≤ .70) or Huynh–Feldt (if ε > .70) method. Furthermore, it should be noted that although all of the analyses reported below collapse across blocks, we also conducted all of the analyses with Block as a factor; for example, if endogenous processes impact the PVE, one might expect participants in the instructed condition to discover the cue–target relationship before the not-instructed participants. Block never interacted with instruction, cue validity, or percentage valid (all ps > .2).Footnote 3 Mean RTs, percentage errors, and cueing effects are presented in Table 1.
We observed a main effect of cue validity [F(1, 78) = 63.05, MSE = 701.66, p < .001], such that responses were quicker to validly cued targets (524 ms) than to invalidly cued targets (543 ms). There was also a significant interaction between percentage valid and cue validity [F(2, 156) = 27.79, MSE = 391.47, p < .001], such that the effect of cue validity increased as the percentage of valid trials increased: This is the PVE. No other main effects or interactions were significant (all Fs < 2.2).
To further assess the interaction between cue validity and percentage valid, the magnitude of the cueing effect was determined by subtracting the RTs in the valid condition from the RTs in the invalid condition. Three two-tailed repeated measures t tests (which, after applying a Bonferroni correction for multiple comparisons, had a family-wise error rate of .05) showed that cueing effects were larger in the 75%-valid condition (37 ms) than in both the 50%-valid condition (16 ms) [t(79) = 4.62, p < .001] and the 25%-valid condition (15 ms) [t(79) = 6.87, p < .001], and also were larger in the 50%-valid condition than in the 25%-valid condition [t(79) = 2.75, p < .008].
No main effects or interactions were significant (all Fs < 2.8).
Participants’ estimates of the percentages of valid trials in each block were analyzed using a two-way ANOVA with Instruction (two levels: instructed and not instructed) as a between-participants factor and Percentage Valid (three levels: 25%, 50%, 75%) as a within-participants factor. The mean participant estimates are shown in Fig. 2.
A main effect of percentage valid emerged [F(1.69, 133.37) = 39.30, MSE = 379.47, p < .001], such that participants’ estimates of the percentage of valid trials increased as the percentage of valid trials increased. No other main effects or interactions were significant (all Fs < 2.5).
To further assess the main effect of percentage valid, three two-tailed repeated measures t tests revealed that the estimates were significantly higher in the 75%-valid condition (65%) than in both the 50%-valid condition (54%) [t(79) = 4.34, p < .001] and the 25%-valid condition (40%) [t(79) = 7.24, p < .001]. Estimates were also higher in the 50%-valid condition than in the 25%-valid condition [t(79) = 5.77, p < .001].
Additionally, one-sample t tests revealed that the estimates for the 25%-valid block were significantly higher than 25% [t(79) = 6.25, p < .001], the estimates for the 50%-valid block were significantly higher than 50% [t(79) = 2.52, p < .05], and the estimates for the 75%-valid block were significantly lower than 75% [t(79) = 4.91, p < .001].
Relation between participant-specific estimates and performance
A Pearson correlation analysis revealed that the participant estimates and the magnitudes of their cueing effects were not significantly correlated in either RTs or errors in the 25% [RT, r(80) = .17, p = .13; errors, r(80)= –.07, p= 052], 50% [RT, r(80) = –.14, p = .21; errors, r(80) = –.02, p = .84], or 75% [RT, r(80) = .13, p = .24; errors, r(80) = –.18, p = .11] valid conditions. Furthermore, the same analysis was conducted on each instruction condition separately and revealed no significant correlations.
Relationship between the general change in participant estimates and cueing effects as a function of percentage of valid trials
We calculated the slopes relating the participants’ estimates of the numbers of valid trials and the magnitudes of their cueing effects with respect to the percentage of valid trials (i.e., 25%, 50%, or 75%) for each participant, in both the instructed and uninstructed conditions separately. Next, we correlated these measures to examine the relationship between the change in general awareness of cue utility (i.e., the change in the participant estimates as a function of percentage valid) and the PVE (i.e., the change in the magnitude of cueing effects with change in the percentage of valid trials). We found no correlation between the change in participants’ estimates and the change in the magnitudes of their cueing effects as a function of the percentage of valid trials, regardless of whether participants were instructed, r(40) = .14, p = .40, or not, r(40) = .11, p = .52. The correlation is plotted in Fig. 3.
In Experiment 1, the magnitude of the cueing effect increased as the percentage of valid trials increased (the PVE). Specifically, cueing effects were largest when 75% of the trials were valid, and smallest when 25% of the trials were valid. In addition, participants underestimated the actual percentage valid in the 75%-valid condition, overestimated the actual percentage valid in the 50%-valid condition, and overestimated the actual percentage valid in the 25%-valid condition. Critically, in Experiment 1, participants’ awareness of the cue–target relations had no influence on the PVE or on their estimates of the percentage of valid trials.
Finding that participants’ specific estimates of the percentage of valid peripheral cues do not predict the magnitude of the RT cueing effect indicates that a precise level of awareness of the cue–target contingency is not crucial to the PVE. This replicates Risko and Stolz (2010b). Furthermore, the failure to find a relationship between the relative change in participants’ estimates and the change in the magnitude of cueing effects demonstrates that a general awareness of changes in the cue–target contingency is also not critical to the PVE. This replicates Bartolomeo et al. (2007).
Collectively, the results of Experiment 1 confirm that the PVE for peripheral cues occurs regardless of participants’ specific or general awareness of cue–target contingencies. These data suggest that the PVE reflects implicit processes rather than explicit awareness of the cue–target relationship. This conclusion is reinforced by our finding that the PVE is insensitive to whether or not participants are explicitly instructed to pay attention to the cue–target relationship.
Finally, note that our conclusion is not that top-down processes are unable to modulate the effect of a nonpredictive peripheral cue, but rather that the PVE—produced by variation in cue–target validities—is driven by implicit processes. The former point, that top-down processes can impact a nonpredictive peripheral cue, has been made frequently in the past, both with regard to the early facilitation effect of a peripheral cue (e.g., López-Ramón et al., 2011) and the subsequent emergence of a reverse cueing effect, called inhibition of return (e.g., Tipper & Kingstone, 2005). For instance, López-Ramón et al. (2011) suggested that uninstructed participants who attempt to determine the relationship between a nonpredictive (50% valid) peripheral cue and target (those participants who they call “good estimators”) may modulate (in this case, reduce) the facilitatory effect of a peripheral cue.