for all kinds of readable items (including color-congruent words, e.g., BLUEblue) and is, thus, different from the specific colorincongruency conflict occurring for color-incongruent Stroop words (e.g., BLUEgreen). This is because the individual's attention is drawn to an irrelevant task (i.e., word reading) instead of being fully focused on the relevant task (i.e., color naming), leading to the two task sets to compete (e.g., Monsell et al., 2001; Goldfarb and Henik, 2006, 2007; Kalanthroff et al., 2013a,b; Parris, 2014 for empirical demonstrations; see also, e.g., Aarts et al., 2009; Desmet et al., 2011; Elchlepp et al., 2013 for fMRI and EEG evidence).

Other accounts argue for the existence of stimulus (or semantic) conflict, which is thought to occur earlier in processing than the response conflict (but likely after task conflict – see Hershman and Henik, 2019). For instance, Seymour (1977) considers that this (early) conflict occurs at conceptual encoding of color-incongruent words (e.g., BLUEgreen) because the meaning of the word dimension (i.e., blue for BLUEgreen) and that of the color dimension (i.e., green here) both correspond to colors. Indeed, "(. . .) delays of processing occur whenever distinct semantic codes are simultaneously activated, and that these delays become acute when the conflicting codes are values on a single dimension or closely related dimensions" (p. 263; see also, e.g., Scheibe et al., 1967; Seymour, 1974; Seymour, 1977; Stirling, 1979; Luo, 1999; but see, e.g., Hock and Egeth, 1970 for the idea of perceptual rather than conceptual type of stimulus conflict). There is substantial evidence for the presence of conflict at this level of processing (Zhang and Kornblum, 1998; De Houwer, 2003; Manwell et al., 2004; Schmidt and Cheesman, 2005; Augustinova and Ferrand, 2014a; see also, e.g., van Veen and Carter, 2005; Szucs and Soltész, 2010; Chen et al., 2013; Killikelly and Szücs, 2013; Augustinova et al., 2015; for electrophysiological and fMRI evidence), although it has been proposed that stimulus conflict is an indirect measure of response conflict (Roelofs, 2003; see Parris et al., under review, for a review and evaluation of this evidence). It is, thus, not surprising that conceptualizations of multistage processing in the Stroop task assume that color-incongruent words (e.g., BLUEgreen) generate both stimulus and response conflicts (hereafter SC-RC accounts; see Augustinova et al., 2018b for this terminology and review of these accounts).

Given that considerable behavioral, electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) evidence points to the viability of both SC-RC and TC-RC multistage accounts of Stroop interference (see above), several lines of research highlighted the necessity to adopt an integrative perspective that would allow for bridging the two previously outlined multistage perspectives (Augustinova et al., 2018b; Parris et al., under review; for reviews). To implement this latter integrative proposal empirically, Augustinova et al. (2018b); see also Ferrand et al., in press) proposed that color-associated incongruent words (e.g., SKYgreen) and color-neutral letter strings (e.g., XXXgreen) supplement the standard color-incongruent words (e.g., BLUEgreen) and color-neutral words (e.g., DOGgreen) that are commonly used in the standard Stroop task (see above). Indeed, if the color-neutral letter strings (e.g., XXXgreen) and words (e.g., DOGgreen) only trigger task conflict, the color incongruency involved in both color-associated (e.g., SKYgreen) and standard (e.g., BLUEgreen) color-incongruent words triggers additional type(s) of conflict. More specifically, color-associated incongruent words (e.g., SKYgreen) trigger both task and semantic conflicts, and standard color-incongruent words (e.g., BLUEgreen) trigger all three types of conflict (i.e., task, semantic, and response; see the section "Present Study," for further developments).

Using this extended form of the Stroop paradigm – that builds on both SKY-PUT design suggested by Neely and Kahan's (2001) and Klein's (1964) semantic gradient – all three conflicts (i.e., task, semantic, and response conflicts) have been shown to contribute significantly to standard Stroop interference in both adults (Augustinova et al., 2018b) and reading-level children (Ferrand et al., in press) and have been shown to have specific developmental trajectories (Ferrand et al., in press). Taken together, these studies not only strongly reaffirm that the standard (i.e., overall) Stroop interference constitutes a composite and not a unitary (response-level) phenomenon but also clearly show the relevance of an integrative perspective bridging SC-RC and TC-RC multistage accounts. Yet, the extent to which these same components actually contribute to the overall Stroop interference collected with manual responses is a still-open issue. Therefore, the present study examined whether and the extent to which task, semantic, and response conflicts are affected by the type of response output (verbal vs. manual) that the Stroop task requires.

Additionally, the present study also examined how different forms of facilitation are modified by response modality. Indeed, to the best of our knowledge, only one published study has explored the potential variety in Stroop facilitation effects. Using a vocal response, Dalrymple-Alford (1972) reported a 42-ms semantic-associative facilitation effect (e.g., DOGblue – SKYblue) and a 63-ms standard facilitation effect (e.g., DOGblue – BLUEblue), suggesting a response facilitation effect of 21 ms. Interestingly, however, when compared to a letter string baseline (e.g., XXXblue), the congruent semantic associates actually produced interference, a finding implicating an influence of task conflict. These isolable forms of facilitation are interesting, require further study with more modern methods, and have the potential to shed light on impairments in selective attention and cognitive control. Of further interest of the present study is how these two forms of facilitation are modified by response modality.

## The Response Modality Effect Examined Within Multistage Accounts of Stroop Interference

In the aforementioned study of Augustinova et al. (2018b), the response modality effect was not an issue under consideration. Yet, the specific contributions of the task (e.g., DOGgreen – XXXgreen), semantic (e.g., SKYgreen – DOGgreen), and response conflict (e.g., BLUEgreen – SKYgreen) to the overall Stroop interference were examined with both manual (Experiment 1) and vocal responses (Experiment 2). While the contribution of

both response and semantic conflicts was significant in both experiments, with only the former being larger with the vocal response, the one of task conflict failed to reach significance when the Stroop task was administered with manual as opposed to vocal responses. Likewise, Kinoshita et al. (2017) observed task conflict with the vocal (Experiment 1) but not the manual (Experiment 2) Stroop task, but did not include a semantic Stroop condition to distinguish response and semantic conflict. However, more recently, Kinoshita et al. (2018) reported that both task and semantic conflicts were significant with both verbal (Experiments 1 and 3) and manual responses (Experiments 2 and 4), albeit with the magnitude of task conflict (but not of semantic conflict) being larger when a vocal (as opposed to manual) response output was required1 . They did not, however, include a measure of response conflict. Thus, only one of the above studies included all three conflict types in the same study (Augustinova et al., 2018b), but none investigated facilitation types, and in all the above studies, response modality was a between-subjects factor.

There is only one study as far as we are aware that has used a within-subject design to investigate all three conflict types in both manual and vocal responses. Sharma and McKenna (1998) reported that task conflict (which they referred to as the lexical component of the Stroop effect) and semantic conflict were present when a verbal but not manual response output was required but that response conflict was present with both response types (see also, e.g., Redding and Gerjets, 1977; McClain, 1983). Sharma and McKenna's original conclusion about the lack of semantic Stroop effects with the manual response Stroop task was based on comparisons of adjacent conditions (in terms of response times), but Brown and Besner (2001) reanalyzed Sharma and McKenna's data using non-adjacent conditions and revealed semantic Stroop effects with manual responses. However, given that the adjacent conditions did not reveal evidence of semantic conflict, its magnitude must have differed between response modes.

In summary, of the four studies reviewed here, three provide evidence for a lack of task conflict with a manual response, but one provided evidence for the presence of task conflict with a manual response. Of the three studies designed to assess semantic conflict, all three provide evidence for semantic conflict with a manual response, but one showed greater semantic conflict with the vocal response Stroop task. Of the two studies designed to assess the individual contribution of response conflict, both provide evidence for larger response conflict with a vocal response. However, in only two of these studies were all three conflict types manipulated in the same experiment, and in only one of these studies was response modality manipulated within subjects. Notably, none of the above studies considered varieties of Stroop facilitation.

## Present Study

The present study was designed to further explore the types of conflict and facilitation and, thus, the locus of Stroop effects, with manual and vocal responses. To this end, the aim of Experiment 1 was to generalize the findings of Augustinova et al. (2018b); without the response stimulus interval manipulation, with manual and vocal responses. The aim of Experiment 2 was to extend these findings by including measures of response and semantic facilitation and by employing a fully withinsubjects design.

To this end, the present study used the aforementioned extended form of the Stroop paradigm (Augustinova et al., 2018b). The irrelevant dimension of all stimuli included in this paradigm (i.e., color-neutral letter strings, color-neutral words, color-associated and standard color-incongruent words) is composed of letters and, thus, is assumed to generate task conflict. Importantly, they do so to the same extent, except for the non-readable color-neutral letter strings (e.g., XXXgreen). In line with the bimodal, interactive activation model with (amodal) semantics (McClelland and Rumelhart, 1981; McClelland, 1987; Grainger and Ferrand, 1996; Stolz and Besner, 1996; Ferrand and New, 2003; McNamara, 2005), the processing of the written dimension of these color-neutral letter strings (i.e., xxx) stops at the orthographic prelexical level. The processing of the written dimension for all other stimuli composed of words (e.g., dog, sky, and blue) stops, on the other hand, with access to meaning (i.e., after a full chain of visual, orthographic, lexical, and semantic processing has come to completion). Consequently, the significant difference in mean response latencies between Stroop color-neutral words and letter strings (e.g., DOGgreen - XXXgreen) is thought to solely reflect differences in activation of the irrelevant reading task set and, hence, of the differential amount of the task conflict that this entails. Indeed, because the meaning of colorneutral words (e.g., dog for DOGgreen) is not related to a color (unlike sky or blue), the aforementioned contribution of task conflict to overall Stroop interference is not intermixed with that of the semantic and response conflicts that are generated by color incongruency.

Turning now to the separation of semantic and response conflicts and facilitation, numerous studies have argued that color incongruency causes semantic conflict (see Seymour's reasoning outlined above). Also, and importantly, in line with Seymour (1977), semantic conflict is generated to the same extent by associated (e.g., SKYgreen) as compared to standard (e.g., BLUEgreen) Stroop words (e.g., see Augustinova et al., 2015 for N400-like evidence). Consequently, the significant difference in mean response latencies between color-associated and colorneutral trials (e.g., SKYgreen – DOGgreen) is likely to reflect the semantic conflict that color-associated (e.g., SKYgreen) unlike color-neutral (DOGgreen) Stroop words generate. Indeed, given that color-associated words do not activate (pre-)motor responses linked to the associated color (e.g., press a blue button on seeing SKY; see Schmidt and Cheesman, 2005 for a direct demonstration), the aforementioned contribution of semantic conflict to overall Stroop interference is not confounded with that of response conflict – generated by standard color-incongruent

1As suggested by one of the reviewers, it is worth mentioning that the semantic conflict observed in the manual response condition was quite small. Specifically, its magnitude was 14 ms in the condition of high proportion and 8 ms in the condition of low proportion of neutral distractors (i.e., # signs) that was also manipulated in this study, although it did not significantly affect the aforementioned amplitudes of semantic conflict.

words only (e.g., BLUEgreen, but see Hasshim and Parris, 2014, 2015 for a discussion of this study). Likewise, semantic facilitation with color-associated congruent stimuli (e.g., SKYblue) would not be confounded with response facilitation observed on standard congruent trials (e.g., BLUEblue).

Finally, the irrelevant word dimension of standard incongruent trials also primes the aforementioned (pre-)response tendency that – for these words (e.g., blue for BLUEgreen) – is part of the response set. It therefore interferes with the (pre- )response tendency primed by the meaning of the relevant color dimension (green here). Consequently, the significant difference in mean response latencies between standard and associated color-incongruent trials (e.g., BLUEgreen – SKYgreen) is thought to result from this (pre-)motor (i.e., response) conflict occurring at the level of response processing and/or output. Likewise, the difference between color-associated congruent trials and standard congruent trials (e.g., SKYblue – BLUEblue) would represent response facilitation. Indeed, both task and semantic conflicts are assumed to be equal in those two types of color-incongruent items (BLUEgreen and SKYgreen, see above) even though more complex interactions between these different conflicts cannot be excluded.

To sum up, in both Experiments 1 and 2, the positive difference in mean response latencies between standard color-incongruent words and color-neutral letter strings (e.g., BLUEgreen – XXXgreen) was used to measure the magnitude of overall Stroop interference. Furthermore, in both experiments (and as in Augustinova et al., 2018b's study), the positive difference in mean response latencies between color-neutral words and letter strings (e.g., DOGgreen – XXXgreen) was used as a proxy for assessing the specific contribution of task conflict to this overall Stroop interference. The positive difference in mean response latencies between color-associated incongruent and color-neutral trials (e.g., SKYgreen – DOGgreen) was used as a proxy for assessing the specific contribution of semantic conflict to overall Stroop interference. Finally, the positive difference in mean response latencies between standard color-incongruent and color-associated incongruent trials (e.g., BLUEgreen – SKYgreen) was used as a proxy for assessing the specific contribution of response conflict to overall Stroop interference.

In Experiment 2, the magnitude of overall Stroop facilitation was also measured. It corresponded to the positive difference in mean response latencies between color-neutral words and standard color-congruent words (e.g., DOGblue – BLUEblue). Furthermore, the positive difference in mean response latencies between color-neutral trials and color-associated congruent trials (e.g., DOGblue – SKYblue) was used to isolate the specific contribution of semantic facilitation to the aforementioned overall Stroop facilitation. Finally, the positive difference in mean response latencies between color-associated and standard color-congruent trials (e.g., SKYblue – BLUEblue) was used to capture the specific contribution of response facilitation to overall Stroop facilitation.

The implementations of the Stroop paradigm depicted above, thus, enabled us to further assess the nature of processes that are influenced by the variations in the response output commonly employed in the Stroop task. To this end, color identification items were collected with both vocal and manual responses in both experiments. The response modality varied between participants in Experiment 1 and within participants in Experiment 2. Given the important discrepancies between findings regarding whether and the extent to which task and semantic conflict occur, respectively, with manual and vocal responses, we only a priori predicted that in both studies, the magnitude of Stroop interference will be larger with vocal as compared to manual responses and that this difference should result at least in part from a difference in response conflict.

#### EXPERIMENT 1

## Method

#### Participants

Seventy-six psychology undergraduates (56 females and 10 males, all native French speakers reporting normal or corrected-tonormal vision, Mage = 19.5 years; Mmin = 18; Mmin = 24) at Université Clermont Auvergne, Clermont-Ferrand, France, took part in this experiment in exchange for a course credit. The data of four participants were excluded from the analyses2 , leaving a total of 72 participants (38 in the manual and 34 vocal response modality).

#### Design and Stimuli

Since the participants were randomly assigned to one of the two response modality conditions, the data were collected using a 2 (response modality: manual vs. vocal) × 4 (stimulus type: color-incongruent words vs. color-associated words vs. colorneutral words vs. color-neutral signs) design, with the first of these being used as a between-participants factor. There were 60 trials for each stimulus-type factor condition (resulting from five repetitions of the same set of stimuli), which varied randomly within a single block of 240 experimental trials.

The stimuli (presented in lowercase Courier font, size 18, on a black background) consisted of four color words: rouge [red], jaune [yellow], bleu [blue], and vert [green]; four colorassociated words: tomate [tomato], maïs [corn], ciel [sky], and salade [salad]; four color-neutral words: balcon [balcony], robe [dress], pont [bridge], and chien [dog]; and strings of Xs of the same length as the color-incongruent trials. The four colorassociated words were selected as strong associates (tomatored: 49.4%; corn-yellow: 30.2%; sky-blue: 44%; and salad-green: 31.5%) from French word association norms (Ferrand and Alario, 1998; De La Haye, 2003) and pretested as depicted in Augustinova and Ferrand (2007). In each condition, all the stimuli were similar in length (4.5, 5, 4.75, and 4.75 letters on average for the colorincongruent words, the color-associated words, the color-neutral words, and the strings of Xs, respectively) and frequency (74, 82, and 84 occurrences per million for the color-incongruent words, the color-associated words, and the color-neutral words,

2One participant made more than 33% of errors; the microphone did not detect responses for 2 participants, and EPrime failed to record responses for 1 participant.

respectively) according to Lexique (New et al., 2004). Colorincongruent and color-associated items always appeared in colors that were incongruent with the meaning of their word dimension.

#### Apparatus and Procedure

fpsyg-10-01786 August 19, 2019 Time: 16:56 # 6

EPrime 2.1 (Psychology Software Tools, Pittsburgh, PA, United States) running on a PC (Dell Precision) was used for stimulus presentation and data collection. The participants who were tested individually were seated approximately 50 cm from a 17-inch Dell color monitor. With both response modalities, their task was to identify the color of letter strings presented on the screen as quickly and accurately as possible while ignoring their meanings. To this end, they were instructed to concentrate on the white fixation cross (" + ") that appeared in the center of the (black) screen at the beginning of each trial. After 500 ms, the fixation point was replaced by the stimulus that continued to be displayed until the participant responded or until 2,000 ms had elapsed.

In the manual response modality, the participants were required to respond on a keyboard placed on a table between them and the monitor. The response keys were labeled with colored stickers such that a red, blue, yellow, and green roundshaped sticker covered, respectively, the "S," "D," "K," and "L" keys of an AZERTY-type keyboard. Consequently, the participants pressed the "red" key with the middle finger and "blue" key with the index finger of their left hand, and the "yellow" key with the index finger and "green" key with the middle finger of their right hand. In the vocal response modality, the participants were required to respond out loud. Their responses were recorded via a Koss 70-dB microphone headset and stored on a Sony IC Recorder-ICD PX333.

Before the beginning of the experimental block, the participants were familiarized with specificities of a given response modality. Following MacLeod (2005), 128 keymatching practice trials were used in the manual response modality so the participants can adequately learn the key–color correspondence. In the vocal response modality, the number of practice trials was reduced to 32 items. In both conditions, these practice trials consisted of strings of asterisks (presented in four aforementioned colors).

## Results and Discussion

Latencies greater than 3 SDs above or below each participant's mean latency for each condition (i.e., less than 2% of the total data in the task administered with manual responses and less than 3% of the total data in the task-administered with oral responses) were excluded from the analyses.

Mean reaction times for correctly identified items were subsequently analyzed in the 4 (stimulus type: standard colorincongruent words vs. associated color-incongruent words vs. color-neutral words vs. color-neutral signs) × 2 (response modality: manual vs. vocal) analysis of variance (ANOVA) (see **Table 1** for descriptive statistics). This analysis revealed the significant main effect of stimulus type [F(3,210) = 142.40; p < 0.001, ηp 2 = 0.670] and a marginally significant one of response modality [F(1,70) = 2.88; p = 0.094, ηp 2 = 0.039]. This latter effect was due to the fact that color identification times tended to be faster for vocal compared to manual responses. The latter main effects were also included in the significant stimulus type × response modality interaction [F(3,210) = 15.33; p < 0.001, ηp 2 = 0.180]3 .

Its decomposition further revealed that the simple main effect of stimulus type was significant in both manual [F(3,68) = 17.83; p < 0.001, ηp 2 = 0.440] and vocal [F(3,68) = 53.61; p < 0.001, ηp 2 = 0.703] response modalities. Additional contrast analyses of these simple main effects revealed that in both response modalities, latencies for standard color-incongruent words were significantly longer than those observed for color-neutral signs (both ps < 0.001). Thus, a substantial amount of Stroop interference (i.e., BLUEgreen – XXXgreen) occurred in both response modalities. Yet, latencies for color-neutral words were significantly longer than those observed for color-neutral signs (see **Tables 1**, **2**) only in the vocal (p < 0.001) but not in the manual (p = 0.159; Mdifference = 7 ms; 95%CI = −3 to 18) response modality. This latter result implies that, in the Stroop task administered with manual responses, the contribution of task conflict to the overall Stroop interference failed to reach significance, whereas the contribution of both semantic (i.e., SKYgreen – XXXgreen) and response conflicts (i.e., BLUEgreen – SKYgreen) was significantly independently of the response output that was required (all ps < 0.001).

To examine further the extent to which the variation in response modality specifically influences task vs. semantic vs. response conflict, magnitudes of these conflicts were analyzed in 3 (conflict type: task vs. semantic vs. response) × 2 (response modality: manual vs. vocal) ANOVA (see **Table 2**). This analysis revealed significant main effects of conflict type [F(2,140) = 20.46; p < 0.001, ηp 2 = 0.226] and of response modality [F(1,70) = 21.72; p < 0.001, ηp 2 = 0.237] that were also included in the significant conflict type × response modality interaction [F(2,140) = 5.10; p = 0.007, ηp 2 = 0.068]. Its decomposition further revealed that the simple main effect of response modality was significant on task [F(1,70) = 29.54; p < 0.001, ηp 2 = 0.297] and response conflicts [F(1,70) = 8.18; p = 0.006, ηp 2 = 0.105], such that their contribution to the overall interference was significantly larger when vocal (as opposed to manual) response output was required (see **Table 2**). This latter variation in the response output failed to influence the magnitude of semantic conflict [F(1,70) = 0.40; p = 0.532, ηp 2 = 0.006; Mdifference = 4 ms; 95%CI = −17 to 9]. The contribution of the latter conflict to the overall interference was significant but remained of the same magnitude with both types of the required response output (see **Table 2**).

3The results of the same analysis on percentages of errors somewhat mirrored those observed on RTs as it revealed the significant main effect of stimulus type [F(3,210) = 25.70; p < 0.001, ηp 2 = 0.269] that was also included in the significant stimulus × response modality interaction [F(3,210) = 15.36; p < 0.001, ηp 2 = 0.180] with the main effect of response modality remaining non-significant [F(1,70) = 1.08; p = 0.302, ηp 2 = 0.015]. As can be seen in **Table 1**, the decomposition of the overall interaction suggests that all types of items were equally error prone in manual response modality (all ps ≥ 0.418), whereas standard color-incongruent items were significantly more error prone than the other kinds of items (all ps < 0.001) in vocal response modality. In sum, these results are not only in line with past studies but also rule out the possibility of speed–accuracy trade-off.

TABLE 1 | Mean correct response times (in milliseconds), standard errors (in parentheses), and percentages of errors observed as a function of stimulus type and response modality.

M, manual; V, vocal; ni, not included.

TABLE 2 | Stroop-like effects (in milliseconds and percent ratios) observed as a function of response modality.

∗p < 0.001; ap = 0.008; bp < 0.01; M, manual; V, vocal; RT diff., reaction time differences; ni, not included. Bold values correspond to the Response Modality Effect.

Given the important differences in the speed of processing across the two types of response output, the previous analyses were supplemented by those of distinct conflict computed in the form of interference ratio (Augustinova et al., 2018a). Such that for each individual, the observed magnitude of response conflict for instance was divided by its appropriate baseline (BLUEgreen – SKYgreen/SKYgreen). Resulting ratios were subsequently analyzed in 3 (conflict-type percent: task vs. semantic vs. response) × 2 (response modality: manual vs. vocal) ANOVA (see **Table 2**). This analysis mirrored the aforementioned results, such that it revealed significant main effects of conflict type [F(2,140) = 16.49; p < 0.001, ηp 2 = 0.191] and of response modality [F(1,70) = 38.26; p < 0.001, ηp 2 = 0.353] that were also included in the significant conflict type × response modality interaction [F(2,140) = 7.07; p = 0.001, ηp 2 = 0.092]. Its decomposition further revealed that the simple main effect of response modality was significant on

the ratio of task [F(1,70) = 37.97; p < 0.001, ηp 2 = 0.352] and of response conflict [F(1,70) = 11.02; p = 0.001, ηp 2 = 0.136], such that their contribution to the overall interference was significantly larger when vocal (as opposed to manual) response output was required (see **Table 2**). Again, this latter variation in the response output failed to influence the ratio of semantic conflict [F(1,70) = 0.56; p = 0.458, ηp 2 = 0.008; Mdifference = −0.006; 95%CI = −0.023 to 0.010].

In line with past literature, the results reported show substantially larger magnitudes of Stroop interference with vocal as compared to manual responses (see **Table 2**). These differences were due to the fact that both response and task conflict contributed less when manual response output was required – to the point that the contribution of task conflict remained nonsignificant in this response modality replicating Augustinova et al. (2018b); Experiment 1. The contribution of semantic conflict to overall Stroop interference remained significant, but the size of its magnitude remained equivalent across the two types of response output (see **Table 2**).

These results have several potentially interesting implications. First, the possible absence of task conflict in the Stroop task administered with manual responses, at least when measured by comparing response to color neutral and repeated Xs baseline, suggests that qualitative (Sharma and McKenna, 1998; Kinoshita et al., 2017) rather than just quantitative (Brown and Besner, 2001; Augustinova et al., 2018b; Parris et al., in press) differences between response modes. Thus, the investigation of the response modality effect in the Stroop task might actually add to uncovering the different components of Stroop interference observed with manual as compared to vocal responses supporting the notion that the two tasks are not equivalent (e.g., naming vs. categorization task entailing the different processes; see, e.g., Kinoshita et al., 2017; Fennell and Ratcliff, 2019). Given the importance of this second implication of the results reported, the following experiment was designed to (a) replicate these results while the response modality was manipulated within participants, and (b) extend them to Stroop facilitation (i.e., difference in mean reaction times for color-neutral and standard color-congruent words; DOGgreen – BLUEblue).

The rationale behind this extension corresponds to a further investigation of the fact that the magnitude of semantic conflict remained equivalent with both response modalities (see also Augustinova et al., 2018b but see Sharma and McKenna, 1998; Brown and Besner, 2001; Kinoshita et al., 2018). If semantic processing in the Stroop task is indeed invariant (and as such it cannot be prevented and/or reduced), results on Stroop facilitation should logically mirror those observed in the present experiment on Stroop interference. More specifically, Stroop facilitation observed with both manual and vocal responses should result from a substantial amount of semantic facilitation (i.e., differences in mean reaction times for color-neutral words and associated color-congruent words, e.g., DOGgreen – SKYblue) that should arise in both response modalities. However, the contribution of response facilitation (i.e., differences in mean reaction times for associated and standard color-congruent words; e.g., SKYgreen – BLUEblue) should be reduced in manual response modality. The following experiment was designed to test just these predictions.

## EXPERIMENT 2

#### Method

Forty-five psychology undergraduates (36 females and 9 males, all native French speakers reporting normal or corrected-to-normal vision, Mage = 21.04 years; Mmin = 19; Mmin = 26) at Université Clermont Auvergne, Clermont-Ferrand, France, took part in this experiment in exchange for a course credit. The data of nine participants were excluded from the analyses4 , leaving a total of 36 participants. Unlike in Experiment 1, response modality factor was manipulated within participants. Thus, the order of the two response modalities was counterbalanced in a random fashion, such as half of the participants responded with a manual, the other half with a vocal response modality first. Stimulustype factor used in Experiment 1 was supplemented with two new kinds of items: standard color-congruent (BLUEblue) and associated color-congruent (SKYblue) words. In other words, color words not only appeared in colors that were incongruent but also congruent with the meaning of their word dimension. There were 48 trials in each condition of stimulus-type factor (resulting from four repetitions of the same set of colorincongruent and color-neutral stimuli and 12 repetitions of the same set of color-congruent stimuli) that varied randomly within a single block of 288 experimental trials (that was executed in each of the two response modalities). Because of this balancing, the facilitation effect was subject to a contingency bias (Schmidt and Besner, 2008; Schmidt et al., 2015). However, our main interest lies in processes underlying the response modality effect; thus, the same randomization procedure was used (see also, e.g., Fennell and Ratcliff, 2019).

Finally, DMDX software (Forster and Forster, 2003) was used for stimulus presentation and data collection. Remaining aspects were identical to those depicted in the section "Methods" for Experiment 1, including the practice trials that were administered again before the beginning of each of the two experimental blocks.

#### Results and Discussion

Latencies greater than 3 SDs above or below each participant's mean latency for each condition (i.e., less than 1% of the total data in the task administered with manual responses and less than 2% of the total data in the task-administered with oral responses) were excluded from the analyses.

Mean reaction times for correctly identified items were first analyzed in the omnibus 6 (stimulus type: standard colorincongruent words vs. associated color-incongruent words vs. color-neutral words vs. color-neutral signs vs. associated colorcongruent words vs. standard color-congruent words) × 2

4This exclusion was due to the fact that in the vocal response modality, eight participants exhibited more than 33% of errors and/or no responses (because the microphone did not detect their responses), and 1 participant made irrelevant mouth/tongue movements that systematically triggered the voice key prematurely.

(response modality: manual vs. vocal) ANOVA (see **Table 1** for descriptive statistics). This analysis revealed the significant main effects of stimulus type [F(5,175) = 113.65; p < 0.001, ηp 2 = 0.765] and of response modality [F(1,35) = 6.40; p = 0.016, ηp 2 = 0.155]. This latter effect was due to faster color identification times for manual compared to vocal responses. The latter main effects that were also included in a stimulus × response modality [F(5,175) = 27.92; p < 0.001, ηp 2 = 0.444]5 .

The decomposition of stimulus × response modality interaction (see above) revealed that the simple main effect of stimulus type was significant in both manual [F(5,31) = 18.42; p < 0.001, ηp 2 = 0.748] and vocal [F(5,31) = 44.39; p < 0.001, ηp 2 = 0.877] response modalities. Additional contrast analyses of these simple main effects revealed that in both response modalities, latencies for standard color-incongruent words were significantly longer than those observed for color-neutral signs (both ps < 0.001), suggesting that a significant Stroop interference occurred with both types of response output.

As in Experiment 1, latencies for color-neutral words were significantly longer than those observed for color-neutral signs (see **Table 1**) in the vocal (p < 0.001) but not in the manual (p = 0.145; Mdifference = 5 ms; 95%CI = 2–12) response modality. This suggests again the absence of the significant contribution of the task conflict in this latter response modality, whereas both semantic (e.g., SKYgreen – DOGgreen) and response conflicts (e.g., BLUEgreen – SKYgreen) significantly contributed to Stroop interference in both response modalities (all ps < 0.001).

Additionally, these contrast analyses revealed that latencies for both color-neutral words were significantly longer than those observed for standard color-congruent items (both ps < 0.001), suggesting that a significant Stroop facilitation (e.g., DOGgreen – BLUEblue) occurred with both types of response output. The additional contrast analyses revealed that under both response modalities, the Stroop facilitation resulted from a significant contribution of semantic facilitation (e.g., DOGgreen – SKYblue; all ps < 0.01) modality, whereas the contribution of response facilitation (e.g., SKYbleu – BLUEblue) failed to reach when manual responses were used (p = 0.219; Mdifference = 8 ms; 95%CI = −20 to 5), while it was significant and of great magnitude (see **Table 2**) when vocal response output was required.

#### The Influence of Response Modality on Distinct Components of Stroop Interference

To examine further the extent to which the variation in response modality specifically influences task vs. semantic vs. response conflict, as in Experiment 1, magnitudes of these conflicts were analyzed in 3 (conflict type: task vs. semantic vs. response) × 2 (response modality: manual vs. vocal) ANOVA (see **Table 2**). This analysis revealed significant main effects of conflict type [F(2,70) = 35.00; p < 0.001, ηp 2 = 0.500] and of response modality [F(1,35) = 63.60; p < 0.001, ηp 2 = 0.645] that were also included in the significant conflict type × response modality interaction [F(2,70) = 8.65; p < 0.001, ηp 2 = 0.198]. Its decomposition further revealed that the simple main effect of response modality was significant on task [F(1,35) = 37.67; p < 0.001, ηp 2 = 0.518] and response conflicts [F(1,35) = 25.05; p < 0.001, ηp 2 = 0.417], such that their contribution to the overall interference was significantly larger when vocal (as opposed to manual) response output was required (see **Table 2**). This latter variation in the response output failed to influence the magnitude of semantic conflict [F(1,35) = 0.76; p = 0.388, ηp 2 = 0.006; Mdifference = -5 ms; 95%CI = −17 to 7]. Recall that the contribution of the latter conflict to the overall interference was significant but remained of the same magnitude with both types of the required response output (see **Table 2**).

Given the important differences in the speed of processing across the two types of response output even within participants, and as in Experiment 1, the previous analyses were supplemented by those of conflicts computed as interference ratios. Resulting ratios were subsequently analyzed in 3 (Conflict-type percent: task vs. semantic vs. response) × 2 (Response modality: manual vs. vocal) ANOVA (see **Table 2**). This analysis mirrored the aforementioned results. Such that it revealed significant main effects of Conflict-type [F(2,70) = 32.98; p < 0.001, ηp 2 = 0.485] and of Response-modality [F(1,35) = 72.65; p < 0.001, ηp 2 = 0.675] that were also included in the significant Conflict-type × Response-modality interaction [F(2,70) = 8.96; p < 0.001, ηp 2 = 0.204]. Its decomposition further revealed that the simple main effect of Responsemodality was significant on the ratio of task [F(1,35) = 41.95; p < 0.001, ηp 2 = 0.545] and of response conflict [F(1,35) = 24.05; p < 0.001, ηp 2 = 0.407] such that their contribution to the overall interference was significantly larger when vocal (as opposed to manual) response output was required (see **Table 2**). Again, this latter variation in the response output failed to influence the ratio of semantic conflict [F(1,35) = 0.33; p = 0.569, ηp 2 = 0.009; Mdifference = −0.005; 95%CI = −0.022 to 0.012].

#### The Influence of Response Modality on Distinct Components of Stroop Facilitation

To examine further the extent to which the variation in response modality specifically influenced the contribution of semantic vs. response facilitation to the overall Stroop facilitation, magnitudes of these facilitation effects were analyzed in 2 (facilitation type: semantic vs. response) × 2 (response modality: manual vs. vocal) ANOVA (see **Table 2**). This analysis revealed significant main effects of response modality [F(1,35) = 10.94; p = 0.002, ηp 2 = 0.238] and of facilitation type [F(1,35) = 18.19; p < 0.001, ηp 2 = 0.342]. The facilitation type × response modality interaction was also significant [F(1,35) = 55.341; p < 0.001, ηp 2 = 0.613]. It was also significant when these latter effects were analyzed as facilitation ratios. More specifically, with these latter

5The results of the same analysis on percentages of errors somewhat mirrored those observed on RTs as it revealed a significant main effect of Stimulustype [F(5,175) = 32.12; p < 0.001, ηp 2 = 0.479] that was also included in the significant Stimulus-type × Response-modality interaction [F(5,175) = 14.97; p < 0.001, ηp 2 = 0.300] with the main effect of Response-modality remaining non-significant [F(1,35) = 2.07; p = 0.158, ηp 2 = 0.056]. As can be seen in **Table 1**, the decomposition of the overall interaction suggests that standard colorincongruent items were significantly more error-prone than the other types of items (all ps < 0.001) in the vocal modality; it was also the case in the manual response modality (all ps < 0.03), except for the difference between standard color-incongruent items and associated color-incongruent items (p = 0.34).

indicators, the main effect of facilitation type was non-significant [F(1,35) = 2.42; p = 0.128, ηp 2 = 0.065], whereas the one of response modality [F(1,35) = 18.41; p < 0.001, ηp 2 = 0.345] was significant. As already mentioned, facilitation type × response modality interaction [F(1,35) = 12.24; p = 0.001, ηp 2 = 0.259] was significant. The further decomposition of this latter interaction revealed that the simple main effect of response modality was significant on the ratio of response facilitation [F(1,35) = 22.73; p < 0.001, ηp 2 = 0.394], such that its contribution to overall Stroop facilitation was significantly larger when vocal (as opposed to manual) response output was required (see **Table 2**). This latter variation in the response output failed to influence the ratio of semantic facilitation [F(1,35) = 0.41; p = 0.529, ηp 2 = 0.011; Mdifference = 0.006; 95%CI = −0.012 to 0.022], such that the semantic facilitation contributed significantly to overall Stroop facilitation phenomenon in both response modalities (see **Table 2** for the very same pattern of results observed with magnitudes of semantic vs. response facilitation).

It is important to note that the aforementioned pattern of results would have been diluted by the use of color-neutral signs (as opposed to words) as a baseline. Indeed, even though color-neutral signs and words are often used interchangeably as baselines, standard Stroop facilitation observed in vocal response modality (p = 0.558; Mdifference = 5 ms; 95%CI = 12– 24 ms) and semantic facilitation observed in manual response modality (p = 0.139; Mdifference = 9 ms; 95%CI = 3– 20 ms) would have no longer been significant if colorneutral signs were used to compute these contrasts (see, e.g., Redding and Gerjets, 1977; Brown, 2011 for other empirical demonstrations). Thus, these results are compatible with Brown's (2011) conclusion that if a baseline consists of color-neutral signs instead of words, not only is the magnitude of Stroop interference overestimated, but also, and importantly, the magnitude of Stroop facilitation is largely underestimated. In light of the present results, but also because some task conflict actually occurs even for color-congruent stimuli (Goldfarb and Henik, 2007), it still seems useful to nuance this latter conclusion by specifying that if a baseline consists, indeed, of color-neutral signs instead of words, the magnitude of the color incongruency effect is overestimated and, importantly, the magnitude of Stroop facilitation is largely underestimated.

#### GENERAL DISCUSSION

The results of Experiments 1 and 2 yielded an important response modality effect – the direction of which was consistent with past findings (e.g., White, 1969; Redding and Gerjets, 1977; Neill, 1977; McClain, 1983; Sharma and McKenna, 1998; Kinoshita et al., 2018; Fennell and Ratcliff, 2019; Zahedi et al., 2019; Parris et al., in press). Indeed, in both experiments reported above, the magnitude of Stroop interference was substantially larger when vocal responses as opposed to key presses were used. This means that both the Stroop interference effects and response modality effects are the same in both experiments and are, therefore, not affected by the inclusion of the additional congruent conditions in Experiment 2.

The present study further extended the past results in several important ways. Indeed, it has shed a more direct light on processes driving this effect. Specifically, results of both experiments showed that the response modality effect is due to a significantly lesser contribution of task and response conflicts (but not the one of semantic conflict) to overall Stroop interference when manual, as opposed to vocal response, output is required. Even more precisely, with key presses, the magnitude of task conflict is reduced to the point that it actually fails to contribute significantly to overall Stroop interference. The significantly reduced magnitude of response conflict contributed, on the other hand, to overall Stroop interference, exactly like the magnitude of semantic conflict that remained unchanged by the induced differences in the required response output. The aforementioned pattern of results occurred independently of whether the response modality was manipulated between (Experiment 1) or within (Experiment 2) participants.

These results therefore present several potentially important implications. First, they seem consistent with a rather puzzling idea – mentioned earlier (see, section "Introduction") – that the way participants identify the color of Stroop stimuli determines how (rather than the extent to which) different features of these compound stimuli are actually processed. Indeed, if all types of conflict (task, semantic, and response conflicts) seem to significantly contribute to the overall Stroop interference observed with vocal responses, only semantic and response conflicts clearly significantly contribute to Stroop interference observed with manual responses. Consequently, the second important implication of this pattern of results is that vocal and manual Stroop tasks might actually correspond to two different tasks. Specifically, in line with conclusions of several recent studies, the former might correspond to a naming task, whereas the latter to a categorization task, hence entailing qualitative rather than quantitative differences in processing (e.g., Kinoshita et al., 2017; Fennell and Ratcliff, 2019).

While this latter possibility remains plausible and therefore should be thoroughly addressed by additional studies, we are inclined to argue in favor of a, perhaps, more parsimonious possibility of quantitative, rather than qualitative, differences in processing between vocal and manual Stroop tasks (Roelofs, 2003). Indeed, in line with an integrative perspective that bridges both SC-RC and TC-RC multistage accounts of Stroop interference (Augustinova et al., 2018b; Parris et al., under review; for reviews), it still remains equally plausible that some amount of task conflict occurs with both types of response output. However, given a modest magnitude of this contribution with manual responses, response time might not be the most suitable indicator for capturing it (see, e.g., Augustinova et al., 2015; see also Kinoshita et al., 2018 for findings consistent with this latter conclusion). This latter reasoning is consistent with findings of Heil et al. (2004) in a letter search priming paradigm. They convincingly demonstrated that the absence of semantic

activation cannot be validly inferred from the lack of response time effects in this latter paradigm. Indeed, in their study, the absence of response time effect occurred while eventrelated potential (ERP) correlate of semantic activation (i.e., the N400 amplitude) was still significant and sensitive to experimental manipulations used. It therefore remains possible that the significant contribution of an early component of Stroop interference such as task conflict can still be found in electrophysiological measures such as ERPs (see Elchlepp et al., 2013 for ERP task set conflict correlates observed in a version of task-switching paradigm).

Another point to note is that the measure of task conflict used in this and the previous studies mentioned above (e.g., DOGgreen - XXXgreen) is not the only measure of task conflict. To investigate the potential role of conflict between task sets in the Stroop task, Goldfarb and Henik (2007); see also Kalanthroff et al. (2013a,b) reported a study in which they attempted to reduce task conflict control by increasing the proportion of non-word neutral trials (repeated letter strings) to 75%. Increasing the proportion of non-word neutral trials would create the expectation for a low task conflict context, and so, task conflict monitoring would effectively be offline. In addition to increasing the proportion of non-word neutral trials, on half of the trials, participants received cues that indicated whether the following stimulus would be a non-word or a color word, giving another indication as to whether the mechanisms that control task conflict should be activated. For non-cued trials, when presumably task conflict control was at its nadir, and therefore task conflict at its peak, RTs were slower for congruent trials than for non-word neutral trials, producing a negative facilitation effect. This measure of negative facilitation, indicating the presence of task conflict, was observed with a manual response. Thus, our argument is not that there is no task conflict with manual responses, but that our data provide evidence for larger task conflict with a vocal response, which would contribute to the difference in interference (and facilitation) effects often reported between the two response modes. The fact that observing negative facilitation requires an experimental manipulation that would modify facilitation and other forms of conflict (e.g., Kinoshita et al., 2018) means that it is not ideal when measuring the contribution of conflict and facilitation types to Stroop effects.

The present behavioral findings also suggest that the contribution of semantic conflict remains unaffected by variations in response modality. Several past ERP studies are in line with this result as they show that the amplitude of the aforementioned N400 – corresponding to an ERP correlate of semantic conflict (Augustinova et al., 2015) – also remains unaffected by the response modality (Liotti et al., 2000; Zahedi et al., 2019). Note, however, that the scalp distribution of N400 might eventually differ as a function of response output (Liotti et al., 2000). Taken together, the present and past results are consistent with the idea that semantic processing in the Stroop task occurs and to the same magnitude irrespective of the type of response output required (Augustinova and Ferrand, 2014a,b; Brown and Besner, 2001 for discussions, but see Sharma and McKenna, 1998; Hasshim and Parris, 2014, 2015 for different empirical findings). This latter idea is actually strengthened by the fact that semantic facilitation remained unaffected by the type of response output, whereas response facilitation was substantially reduced (to the point of its actual elimination) in manual response modality. This finding is inconsistent with the notion that Stroop effects observed with semantic Stroop stimuli are due to the indirect measurement of response conflict (Roelofs, 2003). That is, it has been argued that the connections that semantically related stimuli have to response colors (i.e., sky is related to blue, and it is the activation of the response blue that leads to the Stroop effect) is what leads to apparent semantic Stroop effects. In the present data, semantic Stroop effects are unaffected by response mode, whereas response conflict is affected. If semantic effects were due to connections at the response level, one would expect to see simultaneous modification of the semantic- and response-level effects. However, the semantic Stroop effects are much smaller than the response effects, and so, the preserved effects could be due to effect of magnitude as opposed to effect type (see Parris et al., under review, for a fuller discussion of this issue).

It is clear from the present data set that, as a percentage of overall Stroop interference and facilitation effects, response processing contributes less when using a manual response (compared to a vocal response), suggesting that the makeup of Stroop effects differs between response modes. This is, however, due to the substantially reduced amount of both response conflict and response facilitation. Indeed, the finding of eliminated response facilitation with the manual response is important and surprising and one that shows how facilitation with manual and vocal responses is quite different. Response facilitation then, like response conflict, is substantially reduced with manual responses, suggesting a commonality between Stroop interference and facilitation, indices that have recently been considered as potentially unrelated phenomena (Parris, 2014; see Brown, 2011 for a further discussion of this important issue).

Finally, the concluding implication of the present findings is related to the fact that the investigations of the response modality effect in the Stroop task seemingly contribute to uncovering the different components of Stroop interference. Even though these different components still remain to be further studied, namely, with more time- and, perhaps, locus-sensitive indicators, it seems rather obvious that the historically favored single-stage response competition accounts should be abandoned in favor of multistage accounts of Stroop interference (Risko et al., 2006; Augustinova et al., 2018b).

This paradigmatic shift is, indeed, important because singlestage response competition accounts still largely dominate both empirical research and clinical practice, such that many researchers and practitioners who are interested in Stroop interference itself and/or in its measurement still seem to be unaware that it goes far beyond a mere response competition and that it should, thus, be measured or at least interpreted as a composite phenomenon involving additional types of components. Consequently, the extended semantic Stroop paradigm used in the present study might turn out as an evaluation tool that is simple enough to be administered in both laboratory and field (i.e., clinical) settings. Indeed, the specific contribution of all three types of conflict (task, semantic,

and response conflicts), as well as the modulation (or the lack of thereof) of these distinct contributions, can be clearly seen within this paradigm – at least when administered with vocal responses. Also, and importantly, it is not restricted to manual responses as is the case with the so-called 2-to-1 paradigm (De Houwer, 2003; see also, e.g., van Veen and Carter, 2005; Hasshim and Parris, 2014, 2015). As already emphasized by Augustinova et al. (2018b), the extended form of the Stroop paradigm can therefore "be administered not only using an item-by-item (i.e., computerized) presentation but also, potentially, in a card version that is still in widespread use in clinical practice (see, e.g., Bugg et al., 2007 for an example and Augustinova and Ferrand, 2014b; Augustinova et al., 2016 for discussions of this issue)" (p. 61, see also Augustinova et al., 2018b). Thus, a more fine-grained measurement of Stroop interference would represent an added value, namely, for neuropsychological practice. Indeed, because different components of Stroop interference are likely to be associated with distinct neural substrates (Bench et al., 1993; Milham et al., 2001; van Veen and Carter, 2005; Chen et al., 2013), it remains highly plausible that the different conflicts involved in Stroop interference are selectively impacted by various clinical conditions (e.g., Alzheimer's disease, attention deficit hyperactivity disorder). The present study therefore motivates a comparison of the neural substrates of all three conflict types in the same neuroimaging study. Indeed, one of the rationales for the original proposal of a TC-RC account of Stroop interference by MacLeod and MacDonald (2000) lies in the observation that the Anterior Cingulate Cortext (ACC) appeared to be more activated by incongruent and congruent stimuli when compared to repeated letter neutral stimuli (e.g., XXX; see also Bench et al., 1993). That said, no study has yet directly investigated this possibility using the required contrast of color-neutral words to color-neutral letter strings, so the precise location of activation within the ACC associated with task conflict

## REFERENCES

is not known (see Milham et al., 2001; van Veen and Carter, 2005; Chen et al., 2013 for distinct locations of semantic vs. response conflicts). Therefore, and again, future studies need to address this remaining issue directly. Meanwhile, the present study largely reaffirms that Stroop interference and facilitation have several loci as opposed to just a single (i.e., response) locus, at least with vocal responses.

## DATA AVAILABILITY

The datasets generated for this study are available on request to the corresponding authors.

## ETHICS STATEMENT

Human subject research: The studies involving human participants were reviewed and approved by Comité de Protection des Personnes Sud- Est 6 (Clermont- Ferrand, France). The patients/ participants provided their written informed consent to participate in this study. Animal subjects: No animal studies are presented in this manuscript. Human images: No potentially identifiable human images or data is presented in this study.

## AUTHOR CONTRIBUTIONS

MA and LF designed the study, collected and analyzed the data, and wrote the first draft of the manuscript. All authors contributed to the interpretation of the data, critically revised the final version of the manuscript, and equally contributed to read, comment, and approve the submitted version.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Augustinova, Parris and Ferrand. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

## Preserved Proactive Control in Ageing: A Stroop Study With Emotional Faces vs. Words

Natalie Berger\*, Anne Richards and Eddy J. Davelaar

Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom

Previous studies regarding age-related changes in proactive control were inconclusive and the effects of emotion on proactive control in ageing are yet to be determined. Here, we assessed the role of task-relevant emotion on proactive control in younger and older adults. Proactive control was manipulated by varying the proportion of conflict trials in an emotional Stroop task. In Experiment 1, emotional target faces with congruent, incongruent or non-word distractor labels were used to assess proactive control in younger and older adults. To investigate whether the effects of emotion are consistent across different stimulus types, emotional target words with congruent, incongruent or obscured distractor faces were used in Experiment 2. Data from this study showed that older adults successfully deployed proactive control when needed and that task-relevant emotion affected cognitive control similarly in both age groups. It was also found that the effects of emotion on cognitive performance were qualitatively different for faces and words, with facilitating effects being observed for happy faces and for negative words. Overall, these results suggest that the effects of emotion and age on proactive control depend on the task at hand and the chosen stimulus set.

#### Edited by:

Benjamin Andrew Parris, Bournemouth University, United Kingdom

#### Reviewed by:

Robert West, DePauw University, United States Yoshifumi Ikeda, Joetsu University of Education, Japan

> \*Correspondence: Natalie Berger<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Cognition, a section of the journal Frontiers in Psychology

Received: 29 April 2019 Accepted: 02 August 2019 Published: 03 September 2019

#### Citation:

Berger N, Richards A and Davelaar EJ (2019) Preserved Proactive Control in Ageing: A Stroop Study With Emotional Faces vs. Words. Front. Psychol. 10:1906. doi: 10.3389/fpsyg.2019.01906 Keywords: proactive control, cognitive control, ageing, task-relevant emotion, Stroop task

## INTRODUCTION

Research suggests that the ability to exert cognitive control over incoming information is not a unitary process. According to the dual mechanisms of control (DMC) theory (Braver et al., 2007, 2009), there are at least two separable factors: proactive control refers to sustained control, which is recruited before the occurrence of conflict (Braver, 2012), whereas reactive control refers to transient control processes that are recruited once conflict has been detected (Botvinick et al., 2001). In recent years, research started to assess the effects of emotion on these two control modes (Kalanthroff et al., 2016; Grimshaw et al., 2017; Kar et al., 2017). However, none of these studies have investigated the effects of emotion on proactive control in ageing despite evidence of age-related changes in executive functions and in emotional functioning. The aim of the present research was therefore to investigate younger and older adults' ability to exert proactive control in two emotional Stroop tasks.

## Age-Related Changes in Cognitive and Emotional Functioning

Research indicates that reactive control is preserved in aging (Paxton et al., 2006; Braver, 2012). In contrast, research findings regarding age-related differences in proactive control have been mixed. Significantly impaired goal maintenance was found in older relative to younger adults

(e.g., Braver et al., 2005, 2009; Haarmann et al., 2005; Paxton et al., 2008, Exp. 1), which was interpreted as evidence for impaired proactive control in ageing. Other studies, however, reported intact (e.g., Paxton et al., 2008, Exp. 2; Staub et al., 2014) or even improved proactive control in older relative to younger adults (Staub et al., 2014). It should be noted that impaired proactive control in ageing was found in studies using the AX-Continuous Performance Task (AX-CPT; Rosvold et al., 1956), which requires participants to maintain goal-related information and to make target responses on cued trials and non-target responses on all other trials. This task not only tackles proactive control but also requires participants to remember a two-fold set of rules and to keep track of preceding items in order to make correct target and non-target responses to an X. Previous research has shown that these abilities are impaired in ageing: Older adults were found to show difficulties in maintaining two different tasks in working memory (Verhaeghen and Cerella, 2002; Reimers and Maylor, 2005; Wasylyshyn et al., 2011) and in working memory updating (Van der Linden et al., 1994; Hartman et al., 2001; Salthouse et al., 2003; De Beni and Palladino, 2004; Chen and Li, 2007; Schmiedek et al., 2009). Thus, age-related differences in AX-CPT performance might be found due to impairments in processes other than proactive control.

So far, the effects of emotion on proactive control in ageing have received little attention, despite evidence that emotioncognition interactions change with age (for comprehensive reviews, see Mather, 2004; Mather and Carstensen, 2005; Murphy and Isaacowitz, 2008; Kensinger, 2009). Research from the domain of WM has shown that older adults can benefit from the inclusion of emotional and particularly positive material (Mikels et al., 2005; Mammarella et al., 2013a,b). For instance, Mikels et al. (2005) found age-related impairments in a delayedresponse task when participants had to compare the brightness of two neutral pictures but not when they had to compare the emotional intensity of two emotional pictures. Moreover, older adults outperformed younger adults when they had to compare the emotional intensity of positive pictures, whereas younger adults showed better performance than older adults on trials with negative pictures. Age-related impairments were also found when neutral but not when emotional words were used in a modified version of the operation WM span test, in which participants had to maintain words while solving mathematical operations (Mammarella et al., 2013a,b).

Age-related changes in emotion-cognition interactions are usually interpreted within the socioemotional selectivity theory (SST; Carstensen, 1993), according to which older adults use cognitive resources to direct their attention to emotional and particularly positive information to enhance their well-being (for reviews, see Scheibe and Carstensen, 2010; Reed and Carstensen, 2012). It was found that cognitive load can eliminate this emotional bias in ageing (Mather and Knight, 2005), suggesting that older adults' preference for positive material requires controlled, resource-demanding processes. Based on this assumption, which centers around the availability of cognitive resources, specific hypotheses can be suggested regarding the effects of emotion on cognitive control in ageing. As goal representations are maintained continuously under proactive control, this control mode is thought to be resource-consuming (Braver, 2012) and thus, fewer cognitive resources should be available. If older adults indeed use cognitive resources in order to direct their attention to positive information, it can be expected that a positivity effect in ageing should be less pronounced under conditions requiring high proactive control relative to conditions requiring low proactive control.

#### Proactive Control in the Stroop Task

The Stroop task (Stroop, 1935) has been widely used to assess cognitive control. In the classic color version task, color words are printed in a congruent or an incongruent ink color (e.g., "red" printed in red vs. green ink) and participants have to name the color of the ink while ignoring the color word. It is assumed that there is a strong tendency to read the word due to lifelong experience with reading (Verhaeghen and De Meersman, 1998) and thus, cognitive control is required to selectively attend to and respond to the weak but task-relevant (i.e., the color of the ink) attribute in the presence of a strong but task-irrelevant (i.e., written color word) attribute (Miller and Cohen, 2001). Typically, incongruent trials are associated with slower responses than non-word trials, a pattern that is known as the Stroop effect (Lindsay and Jacoby, 1994).

However, research suggests that in contrast to non-word trials, not only incongruent but also congruent trials elicit task conflict between word reading and color naming due to the presence of both color and word information (Goldfarb and Henik, 2007, 2013; Kalanthroff et al., 2015; for a review, see Kalanthroff et al., 2013, 2018). Previous studies have used expectancy of task conflict to manipulate the recruitment of proactive control (De Pisapia and Braver, 2006; Funes et al., 2010; Krug and Carter, 2012; Kalanthroff et al., 2015). Goldfarb and Henik (2007), for instance, increased the number of non-word trials (see also Tzelgov et al., 1992) and added cues that informed participants on half of the trials whether the next trial would be a Stroop trial or a non-word trial. On the other half of the trials, the cues were uninformative. This was aimed at reducing or relaxing proactive control in participants on un-cued relative to cued trials, as most of the trials only had task-relevant color information. It was found that on non-cued trials, reaction times (RTs) were longer for congruent compared to non-word trials, which was labeled reversed facilitation. Additionally, RTs were longer for non-cued congruent stimuli compared to cued stimuli and incongruent trials were slower than non-word and congruent trials throughout. These results suggest that participants were less efficient in resolving task conflict on both incongruent and congruent trials when proactive control was low.

Neuroimaging studies also found that conditions with a high expectancy (HE) of conflict (i.e., congruent and incongruent) trials in a Stroop task were associated with sustained activity in the dorsolateral prefrontal cortex (DLPFC) that is linked to the deployment of cognitive control (De Pisapia and Braver, 2006; Krug and Carter, 2012). In contrast, conflict trials under conditions with a low expectancy (LE) of conflict trials were associated with event-related activation of a medial and lateral prefrontal cognitive control network, including the anterior cingulate cortex (ACC), which has been linked to conflict monitoring (De Pisapia and Braver, 2006; Krug and Carter, 2012). Behaviorally, two indices for the recruitment of proactive control in a Stroop paradigm can be used: interference, which is the difference between RTs for incongruent and non-word trials, and facilitation, which is the difference between RTs for congruent and non-word trials. High levels of proactive control under conditions of HE of task conflict are thought to be associated with reduced interference and facilitation. In contrast, low levels of proactive control under conditions of LE of task conflict are thought to be associated with increased interference and no or even reversed facilitation (Tzelgov et al., 1992; Goldfarb and Henik, 2007; Kalanthroff et al., 2013, 2015).

## The Present Research

fpsyg-10-01906 August 30, 2019 Time: 17:34 # 3

The aim of this research was to assess the effects of age and emotion on proactive control in two emotional Stroop tasks. Expectancy of task conflict was used to manipulate proactive control and emotional faces and words were used to test whether the role of emotion is consistent across different stimulus sets. Experiment 1 assessed older and younger adults' ability to exert proactive control in an emotional Stroop task with faces. Although the Stroop task has been used to investigate the effects of emotion on proactive control, emotional items were often included as task-irrelevant distractors (e.g., Kalanthroff et al., 2016; Grimshaw et al., 2017). The effects of taskrelevant emotional targets, on the other hand, were often not considered, despite evidence that emotion can improve cognitive performance through enhanced target processing (Pessoa, 2009). In a study by Krug and Carter (2012), for instance, participants responded to the emotion of neutral and fearful faces, while these were shown with congruent and incongruent emotion labels ("neutral" or "fearful"). The authors reported higher interference by an irrelevant emotional (i.e., "fearful") relative to an irrelevant neutral label distractor. An alternative interpretation, which was not explored by the authors, is that interference was actually reduced for emotional targets (fearful face with irrelevant neutral label) rather than increased for emotional distractors (neutral face with irrelevant emotional label). In another study, Kar et al. (2017) used happy vs. sad (Exp. 1) or happy vs. angry target faces (Exp. 2) with congruent and incongruent distractor labels in a Stroop task and found that conflict adaptation, a measure of proactive control, varied as a function of previously presented emotion. However, neutral faces were not included and this absence of a neutral baseline makes it difficult to interpret differential effects of sad vs. happy or angry vs. happy faces.

## EXPERIMENT 1

To address the limitations of previous research, three emotions were included in the present facial Stroop task: happy, neutral, and angry target faces. Based on research showing that happy faces are more efficiently detected than other expressions (Kirita and Endo, 1995; Becker et al., 2011; Becker and Srinivasan, 2014), it was predicted that happy targets would be associated with higher accuracy and faster RTs relative to neutral or angry targets. As research (Carstensen, 1993) suggests that older adults focus on positive material more than younger adults and that this focus requires cognitive resources, it was hypothesized that older adults would show particularly improved performance for happy faces relative to younger adults. However, this was expected under LE conditions requiring low levels of proactive control, as more resources would be available to focus on happy faces relative to HE conditions requiring high levels of resource-demanding proactive control.

## Methods

#### Participants

Thirty younger (19–40 years old) and 30 older adults (62–85 years old) participated in the experiment (see **Table 1** for participant characteristics). One younger and one older participant were excluded from the analysis due to RTs that were 2.5 SD slower than the respective age group's mean RTs. Younger adults were undergraduate and postgraduate students at Birkbeck, University of London, and received either course credits or £7.50 per hour for their participation. Older adults were recruited from the University of the Third Age in London and were paid at the same rate as younger adults for their participation. Participants were community-dwelling and were pre-screened for psychiatric disorders and a history of neurological disorders. They reported to be in good health and had normal or corrected-to-normal vision. Older participants had a score of 27 or above on the Mini-Mental State Examination (MMSE; Folstein et al., 1975). Older adults had better verbal knowledge as assessed with the NART (Nelson and Willison, 1991) and showed slower processing speed as measured by the Digit Symbol Substitution Test (Wechsler, 1955). No further differences were observed. The ethics board of Birkbeck, University of London, approved the procedure prior to the start of the study and written informed consent was obtained from each participant.

#### Materials

The stimuli were 36 faces from the FACES database (Ebner et al., 2010), a validated set of photographs of naturalistic faces of different ages in front view. Faces showed angry, neutral or happy expressions (12 items per emotion). The age group (younger,

#### TABLE 1 | Participant characteristics, Experiment 1.

NART = The National Adult Reading Test, BDI II = Beck Depression Inventory II, STAI = State-Trait Anxiety Inventory, MMSE = Mini-Mental State Examination.

TABLE 2 | Combinations of facial expressions and labels that formed congruent, non-word and incongruent stimuli in Experiment 1.

Congruent stimuli are color-coded in green, non-word stimuli in yellow and incongruent stimuli in red.

middle-aged, older) and sex (male, female) of the faces were balanced in each emotion category. The faces were taken from a pool of stimuli that had been previously rated by younger and older adults and were selected based on high agreement ratings between both age groups (for evaluation details, see Berger et al., 2017). Congruent items were created by printing matching emotion labels across the emotional faces (e.g., neutral face with "neutral" label).  Non-word items were created by printing a string of "xxxxx" across the faces. Combinations of faces and labels are summarized in **Table 2**. Face images were turned to gray-scale, whilst labels were printed in red, 38-point Courier New font, and placed between eyes and mouths of the faces. Example stimuli are presented in **Figure 1**.

#### Procedure

After giving informed consent, participants completed a demographic questionnaire and were seated in front of a computer screen. A visual acuity test (Bach, 1996) was conducted at a distance of 65 cm to ensure that vision was in the normal range. Participants were then asked to remain at this distance to the screen and performed the computerized Stroop task, which was prepared and presented using E-Prime Version <IP_ADDRESS>3 (Schneider et al., 2002) on a 24-inch computer screen with a resolution of 1920 × 1200 pixels. The task consisted of two blocks, counterbalanced across participants. In the HE block, 75% of the trials were either congruent or incongruent (37.5%, respectively), while 25% of the trials were non-words. In the LE block, 25% of the trials were either congruent or incongruent (12.5%, respectively) and 75% of the trials were non-words. There were equal numbers of angry, neutral, and happy faces across congruent, non-word and incongruent trials as well as across the two blocks. Each block consisted of 288 trials and presentation of trials was random. In each trial, a fixation cross appeared for 500 ms. It was then replaced by the distractor label "angry," "neutral," "happy" or "xxxxx," which was presented for 100 ms. This was done to facilitate label reading, following prior procedures by Krug and Carter (2012). The presentation of the label was followed by the simultaneous presentation of the label and the target face. Participants were instructed to indicate the emotion of the face (angry, neutral or happy) as accurately and quickly as possible by pressing one of three labeled keys. On the computer keyboard, the buttons "1," "2," and "3" on the numeric keypad were used. Button presses initiated the presentation of a blank screen for 2000 ms, after which the next trial started. The assignment of emotion labels to buttons was counterbalanced across participants. Participants were instructed to leave the fingers on the buttons for the duration of the task. With the option to take short breaks after every 48 trials, there were five short breaks in each block and one in-between blocks. Participants were tested individually and each session lasted approximately 60–75 minutes in total.

#### Design and Statistical Analysis

Responses and RTs were recorded for each trial and accuracy and median rather than mean RTs for correct trials were calculated for each participant for each condition to account for the skewed distribution of RT data. Statistical analyses of

FIGURE 1 | Examples of Stroop stimuli in Experiment 1.  Pictures are taken from the FACES database (Ebner et al., 2010) and can be accessed at: https://faces.mpdl.mpg.de/imeji/. Publication and display of the shown pictures for the purpose of illustrating research methodology are permitted under the FACES Platform Release Agreement.

the data were conducted with SPSS 22 (IBM Corp., Armonk, NY). Accuracy and RTs were analyzed by 2 × 3 × 3 × 2 mixed factors ANOVA including the within-subjects factors expectancy (LE vs. HE), congruency (congruent vs. non-word vs. incongruent) and emotion (angry vs. neutral vs. happy) as well as the between-subjects factor of age (younger vs. older). Post hoc t-tests with a Bonferroni adjustment to the 5% alpha level were performed to follow up significant main effects and interactions. Due to significant differences in the two age groups' verbal knowledge and processing speed, all analyses were repeated with NART verbal IQ and Digit Symbol as centered covariates. The results with age as a factor reported here were qualitatively the same and significant in the analysis including covariates. RTs varied considerably between younger and older adults. To guard against spurious interactions between age and experimental conditions due to general slowing in older adults (Faust et al., 1999), log-transformed RTs were used for the analysis (e.g., Kray and Lindenberger, 2000; Tun and Lachman, 2008). To aid interpretation, pre-transformed RTs are reported in the descriptives and figures.

## Results

#### Accuracy

Accuracy scores for younger and older adults are presented in **Figure 2**. The analysis yielded a significant main effect of congruency, F(2, 112) = 46.23, MSE = 0.007, p < 0.001, partial η 2 = 0.45, with higher accuracy for congruent (M = 96.9%, SD = 2.7%) compared to non-word (M = 95.6%, SD = 3.6%), t(57) = 3.98, p < 0.001, or incongruent trials (M = 92.0%, SD = 6.4%), t(57) = 7.32, p < 0.001. Accuracy was also higher for non-word than for incongruent trials, t(57) = 6.47, p < 0.001. There was also a main effect of emotion, F(2, 112) = 29.45, MSE = 0.026, p < 0.001, partial η 2 = 0.34, with higher accuracy for happy faces (M = 97.7% SD = 3.1%) compared with neutral (M = 96.3%, SD = 4.2%), t(57) = 2.88, p = 0.005, or angry faces (M = 90.5%, SD = 8.5%), t(57) = 6.53, p < 0.001. Accuracy was also higher for neutral than for angry faces, t(57) = 4.81, p < 0.001. These main effects were qualified by a significant congruency × emotion interaction, F(4, 224) = 4.26, MSE = 0.003, p = 0.007, partial η 2 = 0.07. Follow-up t tests revealed that for angry faces, accuracy was higher for congruent (M = 93.8%, SD = 7.2%) relative to non-word trials (M = 91.1%, SD = 8.5%), t(57) = 3.80, p < 0.001. In contrast, the difference in accuracy between congruent and non-word trials was not significant for neutral (p = 0.079) or for happy faces (p = 0.102). Accuracy was higher for non-word than for incongruent trials for all three valences (all t values ≥ 4.26). There was also a significant expectancy × congruency × emotion × age interaction, F(4, 224) = 3.45, MSE = 0.004, p = 0.026, partial η 2 = 0.06. Accuracies under HE and LE conditions were analyzed separately to follow up this interaction. The congruency × emotion × age interaction was non-significant under LE conditions (p = 0.560), but was significant under HE conditions, F(4, 224) = 5.94, MSE = 0.002, p = 0.001, η 2 = 0.10. Separate analyses for angry, neutral, and happy faces were conducted and while the congruency × age interaction was significant for angry faces, F(2, 112) = 3.45, MSE = 0.005, p = 0.048, partial η 2 = 0.06, and for neutral faces, F(2, 112) = 5.26, MSE = 0.002, p = 0.012, partial η 2 = 0.07, it was non-significant for happy faces (p = 0.237). Follow-up t-tests showed different response patterns to angry faces in younger and older adults: Under HE conditions, younger adults showed higher accuracy for congruent (M = 93.8%, SD = 9.0%) relative to non-word angry faces (M = 88.5%, SD = 10.5%), t(28) = 3.81, p = 0.001, and no difference between incongruent (M = 87.4%, SD = 11.1%) and non-word angry faces (p = 0.459). In contrast, older adults showed no difference (p = 0.515) in accuracy for congruent (M = 93.1%, SD = 8.2%) relative to nonword angry faces (M = 92.4%, SD = 7.5%). Instead, older adults' accuracy was significantly lower for incongruent (M = 85.5%, SD = 13.5%) relative to non-word angry faces, t(28) = 3.17, p = 0.004. Response patterns also differed for neutral faces. In younger adults, accuracy was lower for incongruent (M = 91.9%, SD = 7.9%) relative to non-word neutral faces (M = 97.0%, SD = 5.0%), t(28) = 3.76, p = 0.001, whereas the difference was non-significant in older adults (p = 0.239). Lastly, there was also a main effect of age, F(1, 56) = 5.77, MSE = 0.024, p = 0.020, partial η 2 = 0.09, driven by higher accuracy in older (M = 96.0%, SD = 2.9%) than in younger adults (M = 93.7%, SD = 4.3%). No further significant main effects or interactions were observed for accuracy.

#### Reaction Times

Reaction times for younger and older adults are presented in **Figure 3**. The analysis yielded a main effect of congruency, F(2, 112) = 124.06, MSE = 0.019, p < 0.001, partial η 2 = 0.69, with overall faster RTs for congruent (M = 724 ms, SD = 138 ms) than for non-word trials (M = 750 ms, SD = 137 ms), t(57) = 7.89, p < 0.001, or incongruent trials (M = 833 ms, SD = 203 ms), t(58) = 12.40, p < 0.001. RTs were also faster for non-word than for incongruent trials, t(57) = 9.86, p < 0.001. This main effect was qualified by an expectancy × congruency interaction, F(2, 112) = 12.25, MSE = 0.006, p < 0.001, partial η 2 = 0.18. To follow up on this interaction, the analysis was repeated with the factor congruency only comprising the factor levels congruent and non-word trials and there was no significant expectancy × congruency interaction (p = 0.878). In contrast, in the analysis with the factor congruency comprising the factor levels non-word and incongruent trials, there was a significant expectancy × congruency interaction, F(1, 57) = 15.87, MSE = 0.006, p < 0.001, partial η 2 = 0.22. Followup t-tests revealed that under HE conditions, RTs were slower for incongruent (M = 815 ms, SD = 200 ms) than for non-word trials (M = 754 ms, SD = 146 ms), t(57) = 8.22, p < 0.001. Under LE conditions, the difference in RTs between incongruent (M = 850 ms, SD = 226 ms) and non-word trials (M = 745 ms, SD = 142 ms) was more pronounced, t(57) = 9.95, p < 0.001. Moreover, there was a significant main effect of emotion, F(2, 112) = 50.61, MSE = 0.022, p < 0.001, partial η 2 = 0.48, and follow-up analyses revealed that RTs for happy faces (M = 716 ms, SD = 124 ms) were faster than for neutral faces (M = 788 ms, SD = 177 ms), t(57) = 7.36, p < 0.001, or angry faces (M = 802 ms, SD = 181 ms), t(57) = 9.20, p < 0.001. The difference between RTs for neutral and angry faces was not significant (p = 0.139). Lastly,

there was also a main effect of age, F(1, 56) = 27.32, MSE = 0.421, p < 0.001, partial η 2 = 0.33, as older adults were overall slower (M = 853 ms, SD = 162 ms) than younger adults (M = 684 ms, SD = 92 ms). No further significant main effects or interactions were observed for RTs.

#### Discussion

Experiment 1 assessed the effects of emotion on proactive control in older and younger adults. Both younger and older adults showed reduced interference in RTs from incongruent relative to non-word trials when expectancy of conflict was high (HE conditions). This suggests that both age groups deployed proactive control under HE conditions, which helped to prime task-relevant processing pathways before the onset of conflict trials. It was also observed that emotional faces affected performance in both age groups. Happy faces improved overall performance as evidenced by higher accuracy and faster RTs for happy compared to neutral or angry faces across conditions with no age-related differences. In contrast, accuracy was lowest and RTs were slowest for angry faces. Younger adults were more accurate when responding to congruent relative to non-word angry faces, whereas older adults showed reduced accuracy for incongruent relative to non-word negative information under HE conditions. Although this could suggest greater impairments in the presence of angry faces in older than in younger adults, this effect was in fact driven by lower accuracy for angry non-word trials in younger than older adults as can be seen in **Figure 2**. No age-related differences in accuracy were observed for congruent and incongruent angry faces. When presented with neutral faces under HE conditions, younger but not older adults showed lower accuracy for incongruent relative to non-word trials. Thus, there was not only no evidence for age-related impairments in proactive control, but older adults even outperformed younger adults when presented with neutral material under conditions requiring proactive control.

Higher accuracy and faster RTs in the presence of happy relative to neutral or angry faces were observed in both age groups and this is in line with previous research showing improved WM performance for happy faces relative to other expressions (Levens and Gotlib, 2010, 2012; Cromheeke and Mueller, 2015).

Enhanced performance for happy faces was found across conditions and did not interact with control in the present research. This indicates that more general processes, for instance emotion recognition, were facilitated by happy faces rather than specific control processes. This is in line with studies showing more accurate and faster recognition of happy relative to other emotional expressions (Juth et al., 2005; Becker et al., 2011; Becker and Srinivasan, 2014). Besides this perceptual advantage it is also likely that happy faces contributed to improved performance due to the rewarding value they carry (O'Doherty et al., 2003; Tsukiura and Cabeza, 2008), which might have facilitated particularly efficient processing of happy faces. In contrast to neutral and angry faces, all happy faces used in this experiment showed teeth, a perceptual cue that could have facilitated recognition of happy faces. Previous research indicates that despite a recognition advantage of open-relative to closed-mouth versions of happy faces, happy expressions are still identified more accurately than other emotional expressions with open or with closed mouth (e.g., Tottenham et al., 2009; Becker et al., 2011).

The facilitating effect of happy faces was not more pronounced in older relative to younger adults, neither in general nor in any of the two conditions, which is not fully in line with the SST (Carstensen, 1993). According to this theory, older adults focus on positive information in order to improve wellbeing, which is reflected in a positivity effect in their cognitive performance. In the present experiment, older adults were very accurate in both conditions, which suggests that the task was not too demanding and that additional cognitive resources were still available. Despite this availability of cognitive resources, the

data suggest that older adults did not use them to sustain an emotional bias. However, the results could be reconcilable with the SST when considering that specific task instructions may supplant chronically active emotion regulation goals in older adults in contrast to more open instructions (e.g., those allowing participants to view items as if watching TV; for a review, see Reed and Carstensen, 2012). In the present study, participants were instructed to respond to the emotional expression of each face, which might have hindered the processing of emotional stimuli in a motivation-based way. Previous studies that have also used specific and therefore restrictive task instructions in the domain of working memory and that have observed age-related differences in emotion-cognition interactions, have interpreted these within the SST theory (e.g., Mikels et al., 2005; Borg et al., 2011; Truong and Yang, 2014). Thus, it is important that the role of specific task instructions for age-related emotional biases is clarified in future research so that the theory's validity can also be tested in the domain of working memory, where specific task instructions are the norm.

It should be noted that accuracy was not improved for congruent relative to non-word trials when neutral or happy faces were shown. As incongruent distractors did interfere with responses for neutral faces in younger adults and happy faces in both age groups, it appears unlikely that participants were able to ignore distractors when presented with neutral or happy faces. In contrast, it is possible that the failure to observe facilitation for neutral and happy faces was due to ceiling effects, as accuracy was very high for these faces. When responding to neutral faces, younger adults showed lower accuracy for incongruent relative to non-word trials under HE conditions, whereas older adults did not show differences in accuracy between incongruent and non-word trials. On the one hand, this seems to suggest that older adults did not rely on external cues when responding to neutral targets under conditions requiring proactive control. On the other hand, it is also possible that the task conflict created by target words and distractor faces was not high enough under conditions requiring proactive control to affect accuracy in older adults. It is not possible to disentangle these two explanations in the present paradigm. However, the result suggests that older adults were able to overcome information conflict elicited by incongruent trials under conditions requiring proactive control and highlights preserved or even improved proactive control in older relative to younger adults.

It should be noted that facilitation in RTs was found for both age groups in both conditions. This finding suggests that the priming of task-relevant processing pathways improved performance for congruent relative to non-word trials irrespective of expectancy of conflict. Although research suggests that low levels of proactive control are associated with no or even reversed facilitation (Tzelgov et al., 1992; Goldfarb and Henik, 2007; Kalanthroff et al., 2013, 2015), a review by Roelofs (2003) has shown that facilitation occurs when distractors precede target stimuli as they did in Experiment 1: participants were presented with the distractor label 100 ms before the target face appeared. According to Roelofs (2003), such a preview can prime a particular response, resulting in facilitation in congruent trials, and this effect is considered to be "automatic" with preview times under 250 ms. Thus, it appears that the implementation of a distractor-first design in Experiment 1 resulted in facilitation across both experimental conditions.

## EXPERIMENT 2

Experiment 1 showed that emotional material affected cognitive performance in an emotional Stroop paradigm. More specifically, participants responded more accurately and faster when Stroop targets were happy faces, whereas accuracy was lowest and RTs were slowest for angry faces. However, it is not clear whether these effects of emotion can be expected for other stimulus sets such as words. On the one hand, research has shown more efficient processing of emotional relative to neutral material using a wide range of stimulus sets, including faces (e.g., Juth et al., 2005; Brosch et al., 2008; Calvo and Nummenmaa, 2008), images (e.g., Fox et al., 2007; Langeslag and Van Strien, 2008; Olofsson et al., 2008) and words (e.g., Hamann and Mao, 2002; Gotoh, 2008; Kopf et al., 2013). This suggests that effects of emotion can be expected to be consistent across different stimulus sets. On the other hand, there is also evidence that orienting to affective material was more pronounced for faces than for words (Kensinger and Corkin, 2003; Vuilleumier, 2005; Kensinger and Schacter, 2006) and that enhanced processing of emotional content was automatic for faces but not for words (Rellecke et al., 2011). Such differences in the effects of emotional faces and words were usually explained by differences in extracting emotional significance from words and faces. For instance, it was suggested that words must be processed to a higher level than faces before their meaning could be assessed (Kensinger and Corkin, 2003) and that their emotional significance needs to be extracted based on semantic knowledge (Schacht and Sommer, 2009; Rellecke et al., 2011). In contrast, perceptual features are used to extract emotional significance in faces (Vuilleumier, 2005; Vuilleumier and Huang, 2009). Given these differences in the processing of emotional words and faces, it is likely that verbal stimuli affect cognitive control differently than facial stimuli.

By using verbal stimuli in the same task as in Experiment 1, the aim was to assess whether cognitive control of emotional words would be associated with comparable effects as were observed for emotional faces. Should emotional words produce similar effects as in Experiment 1, this would suggest that the valence (i.e., pleasantness) is sufficient to affect performance independently of their biological preparedness. In contrast, if differential effects of emotion were to be observed, this would suggest that stimulus features that are not shared by faces and words contribute to the effects of emotional items on cognitive control.

## Methods

#### Participants

Thirty younger (20–38 years old) and 30 older adults (63–78 years old) participated in the experiment (see **Table 3** for participant characteristics). One younger and one older adult were excluded from the analysis due to RTs that were 2.5 SD slower than the respective group's mean RTs. Additionally, one younger adult was excluded due to high BDI-II scores, indicating moderate levels of depression. The recruitment criteria were the same as in Experiment 1 and none of the participants had taken part in the

#### TABLE 3 | Participant characteristics, Experiment 2.

fpsyg-10-01906 August 30, 2019 Time: 17:34 # 9

NART = The National Adult Reading Test, BDI II = Beck Depression Inventory II, STAI = State-Trait Anxiety Inventory, MMSE = Mini-Mental State Examination.

previous experiment. As can be seen in **Table 3**, older adults had better verbal knowledge than younger adults as assessed with the NART (Nelson and Willison, 1991) and scored lower on the Digit Symbol Substitution Test (Wechsler, 1955), suggesting slower processing speed in older than in younger adults. Whereas these results are commonly observed in ageing research as highlighted above, it was also found that older adults reported fewer years of education than younger adults. Additionally, younger adults reported higher levels of trait anxiety than older adults as assessed by the A-Trait version of the STAI (Spielberger et al., 1983). No further differences were observed between the two age groups. Older participants had a score of 27 or above on the MMSE (Folstein et al., 1975). The ethics board of Birkbeck, University of London, approved the procedure prior to the start of the study and written informed consent was obtained from each participant.

#### Materials

Stimuli consisted of a selection of 36 words from the ANEW database (Bradley and Lang, 1999), which provides normative emotional ratings for a large number of words in the English language. Words were either negative (e.g., abuse, wounds, crime), emotionally neutral (e.g., bench, board, moment) or positive (e.g., thrill, hug, love) and there were 12 words per category. The words had been rated in a preliminary evaluation study and were selected based on high agreement ratings between younger and older raters (see **Supplementary Materials** for evaluation details). Congruent items were created by printing the word on emotionally matching faces that were used in Experiment 1 (e.g., word "thrill" with happy face). Incongruent items were created by printing a word on non-matching emotional faces (e.g., word "bench" with angry face). "Nonface" items (equivalent to non-word items used in the previous experiments) were created by printing the word on a face picture, in which the area of the face was obscured. Combinations of words and faces are summarized in **Table 4**. Target words were printed in navy blue, 38-point Courier New font, and placed between the face's eyes and mouth. The face images were colored photographs that appeared 100 ms before the word, in TABLE 4 | Combinations of words and facial expressions that formed congruent, non-face and incongruent stimuli in Experiment 2.

Congruent stimuli are color-coded in green, non-face stimuli in yellow and incongruent stimuli in red.

accordance with the procedures used in Experiment 1. Example stimuli are presented in **Figure 4**.

#### Procedure

The procedure for Experiment 2 was identical to that of Experiment 1 as were the proportions of congruent, incongruent and non-face trials in the HE and LE blocks. There were equal numbers of negative, neutral and positive words across trials of different congruencies and across the two blocks. Each trial began with the presentation of the distractor face that was happy, neutral, angry, or obscured for 100 ms, followed by the simultaneous presentation of the distractor face and the target word. Participants were instructed to indicate the emotional valence of the word (negative, neutral or positive) as accurately and quickly as possible by pressing one of three labeled buttons.

#### Design and Statistical Analysis

The recording and exclusion of data were identical as in Experiment 1. Accuracy and RTs were analyzed by 2 × 3 × 3 × 2 mixed factors ANOVA including the within-subjects factors expectancy (LE vs. HE), congruency (congruent vs. non-face vs. incongruent) and emotion (negative vs. neutral vs. positive) as well as the between-subjects factor of age (younger vs. older). Procedures to conduct post hoc tests and to determine significance were as described above. Due to significant differences in the two age groups' reported years of education, verbal knowledge, processing speed and anxiety scores, all analyses were repeated with years of schooling, NART verbal IQ, Digit Symbol and STAI Trait Anxiety as centered covariates. The results with age as a factor reported here were qualitatively the same and significant in the analysis including covariates. As latencies varied considerably between younger and older adults, log-transformed RT data were used for the analysis. To aid interpretation, pre-transformed RTs are reported in the descriptives and figures.

#### Results

#### Accuracy

Accuracy scores for younger and older adults are shown in **Figure 5**. The analysis yielded a main effect of emotion, F(2, 110) = 12.64, MSE = 0.081, p < 0.001, partial η 2 = 0.19, as accuracy was generally higher for negative words (M = 97.7%, SD = 3.9%) than for neutral (M = 89.2%, SD = 13.2%), t(56) = 4.72, p < 0.001, or positive words (M = 91.7, SD = 7.8),

FIGURE 4 | Examples of Stroop stimuli in Experiment 2. Panel (A) shows a negative word with a congruent face, panel (B) shows a neutral word with an incongruent face, and panel (C) shows a positive word with an obscured face (non-face condition). Pictures are taken from the FACES database (Ebner et al., 2010) and can be accessed at: https://faces.mpdl.mpg.de/imeji/. Publication and display of the shown pictures for the purpose of illustrating research methodology are permitted under the FACES Platform Release Agreement.

t(56) = 6.33, p < 0.001. Accuracy scores for neutral and positive words were not significantly different (p = 0.267). There was a significant main effect of congruency, F(2, 110) = 7.23, MSE = 0.004, p = 0.002, partial η 2 = 0.12, as accuracy was lower for incongruent trials (M = 91.9%, SD = 6.0%) than for congruent (M = 93.4%, SD = 5.2%), t(56) = 2.98, p = 0.004, or non-face trials (M = 93.2%, SD = 5.0%), t(56) = 2.96, p = 0.005. There was no difference in accuracy between

Berger et al. Emotional Stroop in Ageing

non-face and congruent trials (p = 0.602). This main effect was qualified by a marginally significant congruency × age interaction, F(2, 110) = 3.13, MSE = 0.004, p = 0.053, partial η 2 = 0.05, as in younger adults, accuracy was significantly higher for congruent (M = 93.4%, SD = 5.7%) relative to incongruent trials (M = 90.8%, SD = 7.1%), t(27) = 3.35, p = 0.002. In older adults, accuracy scores for congruent and incongruent trials were not significantly different (p = 0.451). There was also an expectancy × congruency × emotion × age interaction, F(4, 220) = 3.96, MSE = 0.003, p = 0.007, partial η 2 = 0.07. Accuracy scores under HE and LE conditions were analyzed separately to follow up this interaction. The congruency × emotion × age interaction was significance under HE conditions, F(4, 220) = 3.27, MSE = 0.002, p = 0.019, η 2 = 0.06, but not under LE conditions (p = 0.142). As a next step, younger and older adults' data were analyzed separately and a congruency × emotion interaction was significant in younger adults, F(4, 108) = 2.91, MSE = 0.002, p = 0.039, η 2 = 0.10, but not in older adults (p = 0.471). Further analyses of younger adults' data showed that there was a main effect of congruency for neutral words, F(2, 54) = 6.38, MSE = 0.004, p = 0.003, η 2 = 0.19, but not for negative (p = 0.402) or positive words (p = 0.372). Follow-up t-test indicated that under HE conditions, younger adults showed higher accuracy for congruent neutral words (M = 92.6%, SD = 14.8%) than for incongruent neutral words (M = 87.4%, SD = 15.2%), t(27) = 4.45, p < 0.001, or non-face neutral words (M = 91.8%, SD = 12.1%), t(27) = 2.61, p = 0.014. No further significant main effects or interactions were observed for accuracy.

#### Reaction Times

RTs for younger and older adults are shown in **Figure 6**. The analysis yielded a main effect of congruency, F(2, 110) = 42.70, MSE = 0.006, p < 0.001, partial η 2 = 0.44, with overall slower RTs for incongruent (M = 802 ms, SD = 160 ms) than for non-face trials (M = 762 ms, SD = 142 ms), t(56) = 7.05, p < 0.001, or congruent trials (M = 762 ms, SD = 151 ms), t(56) = 8.42, p < 0.001. There was no significant difference in RTs for non-face compared to congruent trials (p = 0.582). This main effect was qualified by a significant expectancy × congruency interaction, F(2, 110) = 6.71, MSE = 0.004, p = 0.005, partial η 2 = 0.11. To follow up this interaction, the analysis was repeated with the factor congruency only comprising the factor levels congruent and non-face trials, which resulted in a significant expectancy × congruency interaction, F(1, 56) = 7.32, MSE = 0.004, p = 0.009, partial η 2 = 0.12. The analysis with the factor congruency comprising the factor levels non-face and incongruent trials also resulted in a significant expectancy × congruency interaction, which was more pronounced, F(1, 56) = 11.98, MSE = 0.006, p = 0.001, partial η 2 = 0.18. Follow-up t-tests revealed that under HE conditions, RTs on congruent trials (M = 755 ms, SD = 163 ms) were slightly faster than on non-face trials (M = 765 ms, SD = 154 ms), t(56) = 2.24, p = 0.029 (marginally significant after Bonferroni correction), whereas under LE conditions, the comparison between congruent and non-face trials was not significant (p = 0.155). Moreover, under HE conditions, RTs were slower for incongruent (M = 785 ms, SD = 153 ms) than for non-face trials (M = 765 ms, SD = 154 ms), t(56) = 3.20, p = 0.002. Under LE conditions, the difference in RTs between incongruent (M = 818 ms, SD = 188 ms) and non-face trials (M = 759 ms, SD = 159 ms) was even more pronounced, t(56) = 7.16, p < 0.001. Additionally, there was a significant main effect of emotion, F(2, 110) = 13.90, MSE = 0.031, p < 0.001, partial η 2 = 0.20, and follow-up analyses revealed that RTs for negative words (M = 746 ms, SD = 128 ms) were faster than for neutral words (M = 811 ms, SD = 192 ms), t(56) = 4.88, p < 0.001. RTs for positive words (M = 769 ms, SD = 155 ms) were also faster than for neutral words, t(56) = 3.32, p = 0.002, with no significant difference between RTs for positive and negative words (p = 0.064). Lastly, there was a main effect of age, F(1, 55) = 21.99, MSE = 0.437, p < 0.001, partial η 2 = 0.29, as older adults were overall slower (M = 849 ms, SD = 142 ms) than younger adults (M = 699 ms, SD = 115 ms). No further significant main effects or interactions were found for RTs.

#### Discussion

Experiment 2 investigated the effects of emotional target words on cognitive control in a Stroop paradigm. It was found that both younger and older adults showed reduced interference and facilitation in RTs under HE compared to LE conditions, suggesting that they engaged in proactive control when the proportion of conflict-generating trials was high. It was also found that emotion facilitated task performance in both younger and older adults, with more accurate responses to negative relative to neutral or positive words. Responses were faster for both negative and positive words relative to neutral words in younger and older adults. Age-related differences emerged for accuracy under HE conditions: When neutral words were the targets, younger adults showed lower accuracy for incongruent relative to non-face or congruent trials, whereas older adults did not show differences in accuracy between congruent, non-face and incongruent trials.

The enhancing effect of emotion on cognitive control in an emotional Stroop task was observed for both positive and negative words, as participants responded faster when presented with emotional rather than neutral words. This could be due to enhanced sensory processing of emotional material including words (Phelps and LeDoux, 2005; Vuilleumier, 2005; Phelps et al., 2006; Vuilleumier and Huang, 2009). It should be noted, however, that the enhancing effect on performance was particularly pronounced for negative words, as accuracy was higher for negative relative to neutral or positive words. This contrasts with findings from Experiment 1 showing improved performance for happy relative to neutral or angry faces. Differences in the effects of words and faces on Stroop performance will be discussed in the general discussion below.

When responding to neutral words, younger adults showed lower accuracy for incongruent relative to congruent or nonface trials under HE conditions in the present experiment. In contrast, older adults did not show differences in accuracy between congruent, non-face or incongruent trials. This mirrors the results in Experiment 1 that showed no interference effect for neutral targets in older adults and suggests that older adults were

relatively more successful than younger adults in responding to neutral words without being affected by distractors under HE conditions. Thus, the data do not support the notion of reduced proactive control in aging but suggest that older adults can even outperform younger adults under conditions requiring proactive control.

## GENERAL DISCUSSION

Two emotional Stroop experiments were conducted to assess proactive control in younger and older adults. The deployment of proactive control was manipulated by varying the expectancy of congruent and incongruent trials relative to trials without conflict (i.e., non-words in Experiment 1 and non-face items in Experiment 2). Besides addressing age-related differences in cognitive control, these experiments also investigated the effects of emotion on cognitive control using facial and verbal stimuli. These experiments revealed the following critical findings: First, older adults successfully deployed proactive control when the proportion of conflict-inducing items in a Stroop task was high. Second, emotion affected cognitive performance in a Stroop task similarly in both age groups. Third, the effects of emotion on performance were not uniform across facial and verbal stimuli. In the following, the implications of these findings will be discussed.

## No Evidence for Age-Related Impairments in Proactive Control in Emotional Stroop Tasks

The present findings extend the empirical evidence obtained in studies using the AX-CPT task (Rosvold et al., 1956) and suggest that older adults can deploy proactive control when needed. Across two experiments using an emotional Stroop paradigm, older adults showed reduced interference from incongruent relative to non-word/non-face trials when expectancy of conflict was high. Moreover, both age groups showed facilitation across both HE and LE conditions in Experiment 1 and under HE conditions in Experiment 2, with no age-related differences. These results are in accordance with prior research showing no

age-related impairments in proactive control (Paxton et al., 2008, Exp. 2; Staub et al., 2014).

In contrast, these results deviate from findings of previous research with the AX-CPT task (Rosvold et al., 1956). These indicated that older adults have greater difficulties than younger adults to efficiently use the context for a target response in AX-CPT tasks and were viewed as evidence for age-related decline in proactive control (e.g., Braver et al., 2005, 2009; Haarmann et al., 2005; Paxton et al., 2008, Exp. 1). The contrasting pattern of results suggests that ageing is not associated with a general impairment in proactive control but that older adults' ability to deploy it successfully might depend on the demand characteristics of the task at hand. The AX-CPT task can be used to assess proactive control "locally" at the level of trials, whereas the present study used a global approach to manipulate proactive control across an entire block of trials in a Stroop paradigm. Older adults were outperformed by younger adults in the former but not in the latter task. This suggests that, although they might find it difficult to adapt their performance flexibly on a trial-by-trial basis or under conditions of uncertainty (see also Mayr, 2001; Mutter et al., 2005), older adults can adapt to task conflict and deploy proactive control over a period of time (see also West and Baylis, 1998; cf. Staub et al., 2014). Moreover, participants have to maintain a two-fold set of rules and to update information in working memory in the AX-CPT task, which is not required in a Stroop task. Previous research has shown agerelated impairments in maintaining multiple tasks in working memory (Verhaeghen and Cerella, 2002; Reimers and Maylor, 2005; Wasylyshyn et al., 2011) and in working memory updating (Van der Linden et al., 1994; Hartman et al., 2001; Salthouse et al., 2003; De Beni and Palladino, 2004; Chen and Li, 2007; Schmiedek et al., 2009). Thus, it is possible that older adults showed no impairments in proactive control in the Stroop but in the AX-CPT task as the latter additionally involves processes that are known to undergo age-related changes.

## Stimulus-Specific Effects of Emotion on Cognitive Control

The experiments assessed the effects of emotion on cognitive control in younger and older adults. In Experiment 1, emotional faces were used as targets and happy faces were found to improve both accuracy and RTs in younger and older adults. This finding is in line with previous literature showing improved performance for happy faces relative to other expressions in WM tasks with facial stimuli (Levens and Gotlib, 2010, 2012; Cromheeke and Mueller, 2015). The effects of emotion were largely consistent across conditions, suggesting that emotion affected more general processes rather than cognitive control per se. More specifically, it is likely that improved performance for happy faces was driven by a recognition advantage of happy faces (Juth et al., 2005; Becker et al., 2011; Becker and Srinivasan, 2014) and their overall rewarding effect (O'Doherty et al., 2003; Tsukiura and Cabeza, 2008) as discussed above. Importantly, the facilitating effects of emotion did not differ in the two age groups and there was no evidence for an increased positivity effect in ageing. This finding is not fully compatible with SST (Carstensen, 1993), according to which older adults focus on positive information in order to improve their wellbeing. However, it has been argued that the positivity effect emerges under instructions encouraging participants to process material freely (for a review, see Reed and Carstensen, 2012). In the present experiments, participants received specific instructions how to respond to stimuli, giving less room for older adults to process material the way they wanted. Thus, it is possible that their chronically active bias to focus on positive material was overridden by specific task requirements in Experiment 1.

In contrast to the facilitating effects of happy faces and impairing effects of angry faces, a somewhat reversed pattern of results was found for emotional words in Experiment 2. Both younger and older adults responded more accurately to negative relative to neutral and positive words, whereas RTs were faster for both negative and positive words relative to neutral words. Together, the results from Experiment 1 and 2 add to growing evidence that the effects of emotion on cognitive performance are not consistent across different stimulus sets. Previous studies focused on the processing of emotional stimuli (Kensinger and Schacter, 2006; Rellecke et al., 2011) as well as attention (Vuilleumier, 2005; Vuilleumier and Huang, 2009) and reported that orienting to emotional material was more pronounced for faces than for words (Vuilleumier, 2005; Kensinger and Schacter, 2006; Rellecke et al., 2011). Such differences were usually explained with reference to the biological preparedness of emotional faces in contrast to words. More specifically, it was suggested that differences arise as emotional significance of faces can be extracted from perceptual features (Vuilleumier, 2005; Vuilleumier and Huang, 2009), whereas emotional significance needs to be extracted based on semantic knowledge from words (Kensinger and Corkin, 2003; Schacht and Sommer, 2009; Rellecke et al., 2011).

Consistent with previous research that found a stronger effect for faces than for words on cognitive performance (Kensinger and Corkin, 2003) the present research showed that the effects of emotion were greater for emotional faces in Experiment 1 (Accuracy: η 2 = 0.34; Reaction times: η 2 = 0.48) than for emotional words in Experiment 2 (Accuracy: η 2 = 0.19; Reaction times: η 2 = 0.20). However, the effects differed not only in size but also in their overall qualitative pattern. It is possible that extracting emotional significance by using semantic knowledge modified the effects of emotion on cognition not in a quantitative but a qualitative way. To gain a better understanding of why differences in effects between facial and verbal stimuli were observed, emotional pictures could be used in future studies. These allow the extraction of emotional significance through perceptual features but can convey the same meaning as words (e.g., picture of bomb rather than word "bomb"). Similar findings between pictures and words would indicate that faces are special in their effect on cognitive performance, which could be due to their evolutionary importance. In contrast, similar effects between pictures and faces would suggest that the extraction of emotional significance through perceptual features or semantic knowledge is relevant for the effect of emotion on cognitive performance.

Facilitation in RTs (i.e., faster RTs for congruent relative to non-word trials) was found for both age groups across both conditions in Experiment 1, which is consistent with

Roelofs (2003) suggestion that preceding distractors can prime a response. However, despite the same distractor-first paradigm in Experiment 2, facilitation was eliminated under LE relative to HE conditions. A reason for varying findings for facilitation in the two experiments could lie in differences in distractor priming between the different stimulus sets. In Experiment 1, irrelevant labels were presented 100 ms before the target face, whereas in Experiment 2, irrelevant faces were presented 100 ms before the target word. It is possible that priming was more effective for label than for face distractors for several reasons. For instance, the verbal modality of label distractors was congruent with the modality of responses in Experiment 1, as participants were required to respond to faces by using labels ("happy," "neutral" or "angry"). In contrast, there was no modality congruency between face distractors and target responses using labels ("positive," "neutral" or "negative") in Experiment 2. It is also possible that priming of words was particularly efficient in Experiment 1, as participants' attention was already directed to the word by the previously presented fixation cross. In contrast, the area of the face that the participants' attention was directed to by the fixation cross in Experiment 2 was unlikely the most diagnostic one as it was in the face's center rather than in the eye or mouth region. Thus, participants would have needed to saccade to the eye or mouth region to assess the expression in a short period of time. Taken together, it appears that despite using the same distractorfirst design in both experiments, distractor-first priming was more efficient in Experiment 1 with target faces and distractor labels than in Experiment 2 with target words and distractor faces.

## CONCLUSION

The present study contributes to research on proactive control in ageing and its effectiveness in the presence of emotional material. No age-related differences in proactive control were found in an emotional Stroop paradigm, which contrasts with results from AX-CPT studies that found age-related impairments in proactive control. Moreover, it was found that task-relevant emotion affected performance similarly in younger and older adults and that the effects of emotion on performance were qualitatively different for emotional faces and emotional words. Overall, these results highlight that the effects of emotion and

#### REFERENCES

age on proactive control depend on the task at hand and the chosen stimulus set.

## DATA AVAILABILITY

The datasets generated for this study are available on request to the corresponding author.

## ETHICS STATEMENT

All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the ethics board of Birkbeck, University of London.

## AUTHOR CONTRIBUTIONS

NB contributed to the conception and design of the work, data collection, data analysis, and interpretation, drafting of the manuscript. AR and ED contributed to the conception and design of the work, the interpretation of the data, and critical revision of the manuscript.

## FUNDING

This work was supported by the Wellcome Trust Institutional Strategic Support Fund at Birkbeck, University of London, awarded to NB.

## ACKNOWLEDGMENTS

We thank all the participants for taking part in this research.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg. 2019.01906/full#supplementary-material

Variation in Working Memory, eds A. R. A. Conway, C. Jarrold, and M. J. Kane, (New York, NY: Oxford University Press), 76–106. doi: 10.1093/acprof: oso/9780195168648.003.0004

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Berger, Richards and Davelaar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

## Attentional Reorientation and Inhibition Adjustment in a Verbal Stroop Task: A Lifespan Approach to Interference and Sequential Congruency Effect

Eric Ménétré\* and Marina Laganaro

Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland

#### Edited by:

Ludovic Ferrand, Centre National de la Recherche Scientifique (CNRS), France

#### Reviewed by:

Andrew Aschenbrenner, Washington University in St. Louis, United States Miriam Gade, Medical School Berlin, Germany

> \*Correspondence: Eric Ménétré<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Cognition, a section of the journal Frontiers in Psychology

Received: 14 June 2019 Accepted: 20 August 2019 Published: 06 September 2019

#### Citation: