Competitor Rule Priming: Evidence for priming of task rules in task switching

ORIGINAL ARTICLE

Competitor Rule Priming: Evidence for priming of task rulesin task switching

Maayan Katzir • Bnaya Ori • Shulan Hsieh •

Nachshon Meiran

Received: 12 February 2014 / Accepted: 2 June 2014

� Springer-Verlag Berlin Heidelberg 2014

Abstract In task-switching experiments, participants

switch between task rules, and each task rule describes how

responses are mapped to stimulus information. Importantly,

task rules do not pertain to any specific response but to all

possible responses. This work examined the hypothesis that

task rules, as wholes, rather than (just) specific responses

are primed by their execution, such that, in the following

trial, response conflicts are exacerbated when the compet-

ing responses are generated by these recently primed rules,

and performance becomes relatively poor. This hypothesis

was supported in two task-switching experiments and re-

analyses of additional three published experiments, thus

indicating Competitor Rule Priming. Importantly, the

Competitor Rule-Priming effect was independent of

response repetition vs. switch, suggesting that it reflects the

priming of the entire task rule rather than the priming (or

suppression) of specific responses. Moreover, this effect

was obtained regardless of Backward Inhibition, suggest-

ing these effects are unrelated.

Introduction

Consider the following situation: you are on your way to

have lunch with your friends at the cafeteria, which is

located near the post office. When you arrive to the cafe-

teria and see the post office, you recall that you also have to

mail a letter. In such a situation, the action plan of mailing

the letter may compete with the action plan of having lunch

with your friends. However, action plans are derived from

abstract goals (e.g., the letter is your grant proposal, and

thus serves the goal of obtaining this grant; see Kruglanski

et al., 2002 for a similar idea). Thus, it is reasonable that

resolving the competition between action plans may

involve the goals that these actions serve. In this work, we

provide behavioral evidence indicating that the degree of

competition between action plans (i.e., responses) is

determined in part by the abstract goals that govern these

actions. To this end, we used a task-switching paradigm in

which actions (key-presses) are governed by goals (to

execute a specific task rule).

Recent evidence suggests that the choice between con-

flicting action plans takes place not only at the level of the

plans themselves, but also at the level of the abstract rules

that have generated these plans. Moreover, the argument is

that such conflict resolution is performed by determining

the relative strength (or activation) of competing action

plans as well as between the competing task rules that

generate them (Meiran, Hsieh, & Dimov, 2010; Astle,

Jackson, & Swainson, 2012; Tipper, Weaver, & Houghton,

1994). For example, Meiran, Hsieh and colleagues showed

recently that when an action plan generates a response

conflict, the entire task rule that has generated the conflict

is being suppressed. They called this rule ‘‘competitor

rule’’. In the present work, we addressed the question what

determines the strength of competition between action

plans? And in specific, we asked how this strength is

influenced by the priming of task rules?

The methodology we used is somewhat unique. Prior

research that employed two task setups (e.g., Allport,

Styles, & Hsieh, 1994; Allport & Wylie, 2000; Waszak,

Hommel, & Allport, 2003) could not distinguish between

the priming of what became the relevant rule in Trial N vs.

M. Katzir (&) � B. Ori � N. Meiran

Ben Gurion University of the Negev, Beer-Sheva, Israel

e-mail: [email protected]

S. Hsieh

National Cheng Kung University, Tainan, Taiwan

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Psychological Research

DOI 10.1007/s00426-014-0583-3

priming of what became the irrelevant rule in Trial N (see

more on that issue below). To distinguish between these

two options, studies must employ three or more rules.

Previous studies that used such setups mainly examined

what influences the priming of the relevant rule in Trial N

(e.g., Mayr & Keele, 2000; Meiran et al., 2010; Yeung &

Monsell, 2003a). In this study, we used a four-task setup

and focused on the priming of what became the irrelevant

rule in Trial N. In particular, we examined whether such

priming would influence the degree of interference posed

by the irrelevant rule when it becomes a competitor rule.

To this end, we capitalized on the Task-Rule Congruency

Effect (see Sudevan & Taylor, 1987, for the first demon-

stration and Meiran & Kessler, 2008, for review). In the

Task-Rule Congruency Effect, trials with competing

responses (i.e., incongruent trials) lead to performance cost

both in reaction time (RT) and proportion of errors (PE)

relative to trials with no competing responses (congruent

trials). Note that the interference in incongruent trials is

generated by responses. Nevertheless, considering that

responses are generated by task rules, we examined whe-

ther task rules have a unique contribution to the strength of

the interference in incongruent trials, above and beyond

that of responses. Our main assertion is that the strength of

the interference of the competitor rule (i.e., a rule that

generates a competing response) in Trial N is being

determined, in part, in Trial N–1 by either positive or

negative priming of this entire rule (for a similar idea see

Allport et al., 1994, and the ‘‘General discussion’’). In what

follows, we show that a rule which generates a competing

response (i.e., competitor rule) in Trial N poses enhanced

interference when this competitor rule was primed

beforehand, namely it was the relevant rule in Trial N–1.

We label this phenomenon Competitor Rule Priming.

Defining task rules

Prior to explaining the rationale for our assertion and

analysis, we explain in detail what we mean by ‘‘task rule’’,

and emphasize the difference between task rules and

response rules. Response rules associate keys (i.e.,

responses) with meanings. For example, a response rule

may associate key 1 with the meaning ‘red’ (e.g., IF red

PRESS key 1). In many task-switching paradigms, a few

meanings are attached to each key, and therefore key 1

could mean ‘green’ and also ‘circle’. In choice-RT tasks,

task rules involve several response rules. Each task rule

refers to one stimulus dimension (e.g., color, shape, etc.,).

Task rules therefore specify which stimulus dimension

determines the response, and also specify the correct

response to a given stimulus. As such, ‘task rule’ is a

broader concept than ‘response rule’.

The Task-Rule Congruency Effect

Task-switching experiments typically involve several task

rules that pertain to the same set of stimuli (e.g., colored

shapes, when switching between color classification and

shape classification). Such stimuli are labeled ‘‘multi-

valent’’. Moreover, the responses (typically, key-presses)

also overlap between the tasks (‘‘multivalent responses’’).

To illustrate, ‘‘IF red PRESS key 1; IF green PRESS key

2’’ is an example for a task rule that should be implemented

when the task is to classify the stimuli according to color.

On a given trial, a stimulus is presented and participants are

required to respond according to a specified task rule (e.g.,

color, henceforth ‘‘relevant rule’’). In such setups, the

correct response according to the relevant task rule (e.g.,

key 1) could be either congruent or incongruent with the

correct response generated by an irrelevant rule(s).

Whereas in congruent trials, the currently irrelevant task

rules activate the correct response (e.g., when classifying

the color of a red circle, both red and circle are mapped to

key 1), in incongruent trials, they activate a competing

response (e.g., when classifying the color of a green circle,

green is mapped to key 2 but circle is mapped to key 1).

The resultant interference in incongruent trials relative to

congruent trials is labeled ‘‘Task Rule Congruency Effect’’

(Sudevan & Taylor, 1987; Meiran & Kessler, 2008; see

Table 1). Additionally, the rule that generates a competing

response is labeled ‘‘competitor rule’’. Arguably, the Task-

Rule Congruency Effect seen in reaction time (RT-TRCE)

mostly reflects activated overlearned response category

codes in long-term memory (Kiesel, Wendt, & Peters,

2007; Meiran & Kessler, 2008; Wendt & Kiesel, 2008),

whereas the Task-Rule Congruency Effect observed in the

proportion of errors (PE-TRCE) mostly reflects the appli-

cation of the irrelevant task rule (Meiran & Daichman,

2005; cf. Steinhauser & Hubner, 2006).

The Task-Rule Congruency Effect is reflected in per-

formance costs in incongruent trials. This cost is enhanced

when the number of competitors increases (Meiran et al.,

2010). In this work, we additionally show that the inter-

ference from a single competitor rule is defined by the

priming of this rule in Trial N–1. Specifically, we show that

the execution of a rule primes this rule, and this priming

results in enhanced competition from that rule on the

subsequent trial.

Relevant sequential effects in task switching

Given the fact that the effect we demonstrate, namely,

Competitor Rule Priming, represents a sequential effect, in

the following section, we briefly review other sequential

effects in task switching, which are relevant to the current


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work. One such effect is ‘‘Backward Inhibition’’ (Mayr &

Keele, 2000; lag-2 repetition costs, Koch, Gade, Schuch, &

Philipp, 2010, for review). According to Backward Inhibi-

tion, in order to switch from task A to task B (in Trial N–1),

task A is being inhibited, presumably to facilitate the exe-

cution of task B. This inhibition incurs performance cost

when returning to task A (in Trial N). Thus, A–B–A

sequences are compared with C–B–A sequences, in which

rule C was inhibited in Trial N–1, and not rule A (see Table 1).

Competitor Rule Suppression is another relevant effect

that has been shown to be independent of Backward Inhi-

bition both in terms of its formal definition, and empirically

(Hsieh, Chang, & Meiran, 2012; Meiran et al., 2010;

Meiran, Hsieh, & Chang, 2011; see also Astle et al., 2012).

Competitor Rule Suppression presumably reflects the

operation of a mechanism that is responsible for resolving

response conflicts. Specifically, Meiran, Hsieh and col-

leagues showed that performance in Trial N was hampered

Table 1 Behavioral effects in task switching

Trial Effect Slow Moderately Slow

Moderately Quick

Quick

Task Rule Congruency Effect (competitor rules)

3 2 1 0

Trial N a1b2c2d2 a1b2c2d1 a1b2c1d1 a1b1c1d1

Backward Inhibition BI+ BI –

Trial N–2 a*b*c*d* a*b*c*d*


Trial N a*b*c*d* a*b*c*d*

Competitor RuleSuppression

CRS+ CRS –

Trial N–1 a1b2c*d* a1b1c*d*

Trial N a*b*c*d* a*b*c*d*

Competitor RulePriming

CRP+ CRP –


Trial N a2b1c1d1 a2b1c1d1

Competitor RuleRepetition

No Competitor

Rule Repetition

Competitor Rule

Repetition

Trial N–1 a1b*c1d2 a2b*c1d1

Trial N a2b1c*d1 a2b1c*d1

Lowercase letters (e.g., a, b) represent stimulus dimensions (e.g., color, shape) and the numbers on the right to each letter represent the dimension

values (e.g., red and green for the dimension color) that go with a specific response key (key 1, key 2) according to the task rules. The numbers

are replaced by an asterisk to denote that the key’s identity is unimportant. The relevant dimension is underscored and also emphasized by

boldface and italics. Thus, a1b1 represents a bivalent stimulus such as a circle that is also red in which both dimensions are associated with key 1

(namely, the correct response to circle and to red is key 1) and dimension A is the relevant dimension. a1b2 represents a bivalent stimulus such as

a circle that is also green in which the shape dimension is associated with key 1, but the color dimension is associated with key 2 and dimension

A is the relevant dimension. This logic is applied to four dimensions (a, b, c, d)

The critical comparison that defines a given phenomenon is always indicated by frame and a double-headed arrow /?. The condition (predicted

to be) associated with poorer performance (slow) is presented left to the arrow, whereas the quicker condition is presented on its right (with the

exception of the Task-Rule Congruency Effect, which does not have a double-headed arrow, because it is calculated as a linear trend)


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if the relevant task rule in this trial was a competitor rule in

Trial N–1. They termed this inhibitory effect Competitor

Rule Suppression, as it indicates that the competitor rule in

Trial N–1 was suppressed (see Table 1).

Backward Inhibition and Competitor Rule Suppression

are considered to reflect the carryover of inhibition/sup-

pression from from Trail N-1 to Trail N. Other sequential

effects are attributed to the carryover of activation, like the

switch cost asymmetry effect and the reversed Stroop

effect. Switch cost asymmetry is the increased task-

switching cost when switching from a difficult task (e.g.,

color naming) to an easy task (e.g., word reading) than

when switching in the opposite direction (cf. Allport &

Wylie, 2000; Barutchu, Becker, Carter, Hester, & Levy,

2013; Yeung & Monsell, 2003a, 2003b). Additionally,

when switching from (the difficult) color-naming task to

(the easy) word-reading task, an atypical interference effect

(known as ‘‘reversed Stroop effect’’, see Macleod, 1991,

for review) emerges. Specifically, a congruency effect in

the easy task (word reading) is observed, indicating inter-

ference from the (more difficult) color-naming task. Both

phenomena—the switch cost asymmetry and the reversed

Stroop effect—are attributed to the activation carryover of

the difficult task, which impairs performance when swit-

ched to the easy task (Allport & Wylie, 2000; Yeung &

Monsell, 2003a; but see Bryck & Mayr, 2008).

Whereas sequential effects in task switching usually

indicate influences on the relevant rule, in this work we

examine the sequential influence that priming has on a

competitor rule.

The current study

In the current study, we present direct behavioral evidence

to the influence that priming of a competitor rule has on the

degree of interference in incongruent trials. Our rationale

was that priming of the relevant rule in Trial N–1 is carried

over into Trial N, resulting in performance cost when the

previously relevant rule in Trial N–1 becomes the com-

petitor rule in Trial N. Thus, a competitor rule will incur a

high-performance cost when this competitor rule was

primed beforehand because it was the relevant rule in Trial

N–1. We compare this condition to trials in which the

competitor rule was not previously primed (as strongly),

because this rule was irrelevant in Trial N–1. The increased

competition from the previously primed competitor rule

should be seen in a performance cost in Trial N, thus

indicating Competitor Rule Priming.

We define ‘‘competitor rule’’ as an irrelevant rule that

generates response conflict. Note that we argue that it is the

entire rule, and not merely the response, that influences the

strength of the competition. If competition is determined at

the response level, then priming of the interfering response

will determine the amount of interference of a competing

response. However, if competition is determined at the rule

level, priming of the entire rule that generates this response

will determine the amount of interference of a competing

response, even if the specific response was not previously

primed. Our design enables us to examine the unique

contribution of task rules to the strength of the interference

in incongruent trials (as materializes in Competitor Rule

Priming). We will examine whether this phenomenon is

independent of the contribution of the response, by entering

the response variable (response repetition vs. response

switch) to the analysis.

Competitor Rule Priming will be demonstrated by

comparing trials in which the competitor rule in Trial N

was the relevant rule in Trial N–1 (CRP? trials) to trials in

which the competitor rule in Trial N was irrelevant in Trial

N–1 (CRP- trials). To illustrate, without loss of generality,

assume that rule B is the relevant rule in Trial N, and that

rule A is the only competitor rule in Trial N. In CRP?

trials, rule A was the relevant rule in Trial N–1. In CRP-

trials, rule C was the relevant rule in Trial N–1 (see

Table 1). The defining feature of Competitor Rule Priming

is that the competitor rule in Trial N was the relevant rule

in Trial N–1. The reason is that priming of the relevant rule

in Trial N–1 was carried into Trial N, resulting in a

stronger response competition in Trial N.

Note that, like with Competitor Rule Suppression and

Backward Inhibition, comparing CRP? trials to CRP-

trials cannot be performed in paradigms that incorporate

only two tasks. With regard to Competitor Rule Priming,

what distinguishes between CRP? and CRP- trials when

executing rule A (using the example from the previous

paragraph) is whether the relevant rule in Trial N–1 was

rule B or C, making it necessary to use at least three rules

(rules A, B, and C). For reasons that are clarified in the

Appendix, even three rules are insufficient to rule out

potential alternative accounts and a minimum of four rules

must be used.

To overcome this methodological problem, we used a

switching task that incorporated four tasks that were per-

formed on shapes (Experiment 1A) or digits (Experiment

1B). In both experiments, two tasks involved object clas-

sification (classifying the shape or the color of the object in

Experiment 1A; classifying the parity or magnitude of the

digit in Experiment 1B) and two tasks involved spatial

classification (classifying the shapes according to their

horizontal location or their vertical location). The motiva-

tion for running two experiments was not derived solely

from the need for replication and extension. In Experiment

1A, the decisions are perceptual and therefore stimulus

dimensions (i.e., color and shape) and the task rules are

indistinguishable. Competitor Rule Priming, if obtained,


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may indicate interference coming from a perceptual

dimension, not from a semantic task rule. In Experiment

1B, the object tasks involved semantic task rules, which

allowed us to examine whether a semantic task rule, that is

independent of a perceptual dimension, is primed in Trial

N–1 and then influences the strength of a competitor rule.

To maximize the need for control processes, we fol-

lowed Meiran et al. (2010, 2011) and used a relatively short

cue–target interval (Druey & Hubner, 2007; but see Grange

& Houghton, 2009) and avoided task repetitions (Philipp &

Koch, 2006). We predicted a performance cost in trials in

which the competitor rule was the relevant rule in Trial N–

1 (CRP?) relative to trials in which the competitor rule

was not the relevant rule in Trial N–1 (CRP-).

Methods

Participants

In each experiment, 24 undergraduate students from Ben-

Gurion University of the Negev participated in return for

partial course credit or for 35 NIS (approximately 9 US$).

The participants reported having normal or corrected-to-

normal vision.

Stimuli and procedure

The software was programmed in E-Prime 1.0 (Experiment

1A) and E-Prime 2.0 (Experiment 1B) (Schneider, Esch-

man & Zuccolotto, 2002).

Experiment 1A

The stimuli consisted of an object presented inside a 2 9 2

grid subtending a visual angle of approximately 13.10

(width) 9 13.10 (height) degrees. The object was a colored

shape, the stimuli were two triangles (2.38� 9 2.86�) and

two circles (diameter 2.38�), two in red and two in green,

forming all the four combinations of shape and color. The

task cues were task-related icons that were presented in the

center of the grid (Fig. 1a), while the target-object was

presented in the grid’s quadrants (Fig. 1b). We asked

participants to classify the stimuli according to the task

cue. In the object tasks, they classified the stimuli either

according to their color or according to their shape. In the

spatial classification tasks, they classified the stimuli either

according to their horizontal (left–right) location or

according to their vertical (up–down) location.

The experiment began with verbal explanation and

illustration of the tasks and the stimuli. Afterwards, the

participants were required to execute one practice block

and 18 experimental blocks. Each block consisted of 64

trials, rendering a total of 1,052 trials for each participant,

excluding the practice block. We gave the participants a

2-min rest between the blocks in an effort to keep them

alert. The total session took approximately 1 h and 10 min.

The participants were asked to be as accurate and quick as

possible.

A trial started with a response–cue interval of 500 ms in

which a black screen was presented. This was followed by

the task–cue presentation in the center of the empty grid

array for 500 ms. Then the object was added to the display,

which was kept on the screen until the response was given

(Fig. 1b). A beep sound of 400 MHz was heard after the

response if an error was made. The task always switched

from trial to trial.

The response key arrangement was counterbalanced

between the participants. Half of the participants used the

upper left (7) and lower right (3) keys on the keypad to

indicate up/left or down/right, while the other half used

upper right (9) and lower left (1) keys to indicate up/right

or down/left (Fig. 2). The assignment of object values

(such as circle, green, etc.,) to the same response keys used

for the spatial tasks was counterbalanced as well, yielding a

total of eight counterbalancing combinations. The keypad

was aligned with the center of the screen, and the partici-

pants were instructed to respond with their two index

fingers.

Experiment 1B

The procedure was identical to that of Experiment 1A with

a few minor changes. Instead of colored shapes, we used

8 9 6 mm digits (1–9, excluding 5). The task cues were

the Hebrew words for parity, magnitude, and task-related

icons for the spatial tasks. Therefore, in the object tasks,

participants classified the stimuli either according to their

magnitude (smaller or larger than 5) or according to their

parity (Schuch & Koch, 2003).

Results

We followed Meiran et al. (2010, 2011) and excluded trials

that followed an error either immediately or after two trials.

We analyzed RT only for correct trials and excluded from

the analysis RTs \100 ms (anticipatory errors) or longer

than 3,000 ms (outliers). For each analysis, we computed

the cell means separately for each participant.

Sequential phenomena in task switching

First, we wanted to make sure that our paradigm produces

well-documented effects in task switching (i.e., Backward

Inhibition, Competitor Rule Suppression). Table 2 shows


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that Backward Inhibition and Competitor Rule Suppression

were reliable.

Task-Rule Congruency Effect analysis

Because Competitor Rule Priming is a phenomenon relat-

ing to the interference coming from competitor rules,

before demonstrating Competitor Rule Priming, we wished

to demonstrate that competitors in fact yielded interference

in this paradigm. To do that, we analyzed the Task-Rule

Congruency Effect. Because there were four tasks, there

could be 0, 1, 2, or 3 competitor rules on a given trial (see

Table 1). The Task-Rule Congruency Effect was expected

to manifest as a linear trend showing that interference is

increased when the number of competitors increases

(Meiran et al., 2010). We included the relevant task in Trial

N as a variable in the analysis to see if the effect may be

task specific. The design of this analysis, therefore,

employed two independent variables: Task (i.e., shape,

color, horizontal, or vertical), and Number of Competitor

Rules (0, 1, 2, and 3). To estimate the Task-Rule Con-

gruency Effect as a linear trend, we used a linear contrast

(Pinhas, Tzelgov, & Ganor-Stren, 2012).

Experiment 1A

Response time (RT) A repeated measures ANOVA of

Task (shape, color, horizontal, vertical) 9 Number of

Fig. 1 a Stimuli and task cues. b Example of a trial and of cue–target pairs and response key arrangements in Experiment 1A

Fig. 2 The two possible mapping conditions for the spatial tasks.

a Participants respond with key 1 to down and left, and with key 9 to

up and right. b Participants respond with key 7 to up and left, and with

key 3 to down and right. Note that the object task’s responses were

also mapped to the same keys (1 and 9 in scenario a, 3 and 7 in

scenario b), in a counterbalanced order, resulting in eight mapping

conditions (4 in scenario a and 4 in scenario b). Figure 2 presents

only one possible mapping of the object tasks


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Competitors (0, 1, 2, 3) on RT yielded a main effect of

Task, F(3, 69) = 54.69, MSE = 84,807, p \ .001,

gp2 = .70, representing a difference between mean RT of

935, 898, 508, and 571 ms for shape, color, horizontal, and

vertical, respectively. More importantly, there was a sig-

nificant linear trend for Number of Competitors (i.e.,

the Task-Rule Congruency Effect), F(1, 23) = 30.86, MSE

= 8,250, p \ .001, gp2 = .57, representing a difference

between mean RT of 690, 721, 741, and 760 ms for 0, 1, 2,

and 3 competing rules, respectively. The interaction

between Task and the linear trend of Number of Compet-

itors was non-significant.

Proportion of errors (PE) A repeated measures ANOVA

with the same design yielded a main effect of Task, F(3,

69) = 11.82, MSE = .008, p \ .001, gp2 = .34, representing

a difference between mean PE of .07, .08, .01, and .04 for

shape, color, horizontal, and vertical, respectively. More

importantly, there was a significant linear trend for Number

of Competitors (i.e., the Task-Rule Congruency Effect),

F(1, 23) = 26.51, MSE = .009, p \ .001, gp2 = .54, repre-

senting a difference between mean PE of .02, .03, .05, and

.09 for 0, 1, 2, and 3 competing rules, respectively. The

interaction between Task and the linear trend of Number of

Competitors was also significant, F(3, 69) = 8.78, MSE =

.003, p \ .001, gp2 = .28, but the linear trend of Number of

Competitors was significant in all the tasks, F’s(1,

23) = 19.41, 17.48, 5.73, 17.74, MSE’s = .008, .007,

.0004, .002, p’s \ .05, gp2’s = .46, .43, .20, .44, for shape,

color, horizontal, and vertical, respectively.

Experiment 1B

RT The ANOVA yielded a main effect of Task, F(3,

69) = 37.49, MSE = 46,346, p \ .001, gp2 = .62, repre-

senting a difference between mean RT of 733, 828, 533,

and 588 ms for parity, magnitude, horizontal, and vertical,

respectively. More importantly, there was a significant

linear trend for Number of Competitors (i.e., the Task-Rule

Congruency Effect), F(1, 23) = 23.35, MSE = 7,710,

p \ .001, gp2 = .50, representing a difference between

mean RT of 640, 665, 674, and 702 ms for 0, 1, 2, and 3

competing rules, respectively. The interaction between

Task and the linear trend of Number of Competitors was

also significant, F(9, 207) = 5.54, MSE = 3,761,

p = .002, gp2 = .19, but the linear trend of Number of

Competitors was significant or marginally significant in all

the tasks, F’s = 3.04, 3.62, 6.62, 36.92, MSE’s 4,688,

5,345, 4,023, 4,938, p’s = .094, .070, .017, \.001,

gp2 = .12, .14, .22, .62, in the parity, magnitude, horizontal,

and vertical, respectively.

PE The ANOVA yielded a main effect of Task, F(3,

69) = 12.15, MSE = .006, p \ .001, gp2 = .35, representing

a difference between mean PE of .08, .08, .02, and .05 for

parity, magnitude, horizontal, and vertical, respectively.

More importantly, the linear trend for Number of Com-

petitors was also significant, F(1, 23) = 8.52, MSE = .01,

p = .008, gp2 = .27, representing a difference between

mean PE of .04, .04, .06, and .09 for 0, 1, 2, and 3 com-

peting rules, respectively. The interaction between Task

and the linear trend of Number of Competitors was non-

significant.

Competitor Rule-Priming analysis

To demonstrate the basic Competitor Rule-Priming effect,

we compared trials in which the competitor rule was the

relevant rule in Trial N–1 (CRP?) with trials in which the

competitor rule was irrelevant in Trial N–1 (CRP-). As

explained above, the response variable was included in the

analysis, to assure that Competitor Rule Priming has a

unique contribution above and beyond that of the response.

Task (i.e., shape, color, horizontal, or vertical) was also

included as independent variable in the analysis (to be

explained in the ‘‘Selection criteria’’ section below). The

design of this analysis, therefore, employed three inde-

pendent variables: Task (e.g., shape, color, horizontal, or

vertical), Response (repetition, switch) and Competitor

Rule Priming (CRP? vs. CRP-).

Selection criteria

The straightforward Competitor Rule-Priming analysis

included many confounds. Elaborated description of these

confounds and how we controlled for them can be found in

the ‘‘Appendix’’. Briefly, we equated CRP? trials and

CRP- trials on the degree of interference in Trial N

(namely, the number of competitor rules), the degree of

interference in Trial N–1 (Botvinick, Braver, Barch, Carter,

& Cohen, 2001), Competitor Rule Suppression, and

Table 2 Analysis of sequential phenomena in task switching

Size-RT (ms) gp2 Size-PE gp

2

Experiment 1A

BI 31* .42 \.001 \.001

CRS 26* .29 .007* .07

Experiment 1B

BI 18* .26 -.006* .13

CRS 15* .13 .014* .46

Effect size in RT (in ms) and PE of well-documented effects in task

switching: Backward Inhibition and Competitor Rule Suppression

BI Backward Inhibition, CRS Competitor Rule Suppression, RT

response time, PE proportion of errors

* One-sided significance, p \ .05


123

competitor rule repetition (whether the competitor rule was

the same in Trials N–1 and N; Meiran et al., 2011).

Additionally, the other exclusion criteria led to the exclu-

sion of stimulus repetitions between Trials N–1 and N (see

‘‘Appendix’’). Eventually, we only analyzed trials with one

or two competing responses (because CRP- cannot be

materialized in trials with three competing rules, and

CRP? cannot be materialized in trials with zero competing

rules).

Controlling for all the possible confounds when con-

sidering Trial N–1 to Trial N transitions, reduces immen-

sely the amount of valid trials. Note that because tasks

were selected randomly, and so did the order of stimuli

selection, the number of trials in each condition varied for

each participant. In the switching task, which consisted of

1,052 trials, after excluding outlier trials and trials fol-

lowing errors, there were 203 trials (126–243), on average,

for each participant in the CRP- condition, and 206 trials

(129–256), on average in the CRP? condition, in Experi-

ment 1A. In Experiment 1B there were 196 trials

(133–228), on average, for each participant in the CRP-

condition, and 203 trials (132–248), on average in the

CRP? condition.

Given that performance on the spatial tasks was better

than performance on the object tasks, and given that tasks

were selected randomly, it could be that the random

selection would randomly yield more object trials in the

CRP? condition than in the CRP- condition. In such sit-

uations, the Competitor Rule-Priming effect would be a

result of noise coming from the task variable, and not the

real Competitor Rule-Priming effect. To control for this

possibility, we entered the task variable to the analysis.

When the task and the response variables were consid-

ered, there were 26 trials (8–44 in the CRP- condition and

8–41 in the CRP? condition), on average, in each cell in

the design in Experiment 1A. In Experiment 1B, there were

25 trials (10–36 in the CRP- condition and 12–41 in the

CRP? condition), on average, in each cell in the design.

Experiment 1A

RT A repeated measures ANOVA of Task (shape, color,

horizontal, vertical) 9 Response (repetition, switch) 9

Competitor Rule Priming (CRP?, CRP-) on RT yielded a

main effect of Task, F(3, 69) = 53.09, MSE = 83,896,

p \ .001, gp2 = .70, representing a difference between

mean RT of 924, 897, 511, and 569 ms for shape, color,

horizontal, and vertical, respectively. The analysis also

yielded a main effect of Response, F(1, 23) = 10.77, MSE

= 9,607, p = .003, gp2 = .32, indicating that responses were

slower when the response in Trial N–1 was repeated in trial

N (M = 742 ms) than when it switched (M = 709 ms). No

other effects were significant.

PE A similar ANOVA on PE yielded a main effect of

Task, F(3, 69) = 7.36, MSE = .01, p \ .001, gp2 = .24,

representing a difference between mean PE of .05, .07, .01,

and .04 for shape, color, horizontal, and vertical, respec-

tively. There was also a main effect for Response, F(1,

23) = 8.24, MSE = .004, p = .008, gp2 = .26, indicating

that participants performed more errors following response

repetition trials (M = .05), relative to response switch tri-

als (M = .03). The interaction Task 9 Response was also

significant, F(3, 69) = 4.13, MSE = .003, p = .009,

gp2 = .15, indicating that the response effect was present

only in the object tasks, F(1, 23) = 8.10, MSE = .008,

p = .009, gp2 = .26, but not in the spatial tasks, F \ 1.

More importantly, we found the predicted main effect of

Competitor Rule Priming, F(1, 23) = 4.76, MSE = .002,

p = .040, gp2 = .17, indicating that PE was higher in

CRP? trials (.05) than in CRP- trials (.04, demonstrating

a Competitor Rule-Priming effect of .01). Note that Com-

petitor Rule Priming did not interact with Task, Response,

or Task 9 Response interaction, Fs \ 1.

Experiment 1B

RT The ANOVA yielded a main effect of Task, F(3,

69) = 37.12, MSE = 47,308, p \ .001, gp2 = .62, repre-

senting a difference between mean RT of 736, 829, 538,

and 581 ms for parity, magnitude, horizontal, and vertical.

The main effects for Response, which indicated that

responses were slower when the response in Trial N–1

repeated in Trial N (M = 678 ms), than when it switched

(M = 664 ms), and the main effect for Competitor Rule

Priming, which indicated that as predicted, participants

were slower in CRP? trials (M = 677 ms) relative to

CRP- trials (M = 665 ms), failed to reach significance,

Fs(1, 23) = 2.67 and 2.69, MSE’s = 6,889 and 4,751,

ps = .116 and .114, gp2 = .10 and .10 for Response and

Competitor Rule Priming, respectively. Also, the interac-

tion, Task 9 Competitor Rule Priming was marginally

significant, F(3, 69) = 2.27, MSE = 4,838, p = .088,

gp2 = .09, indicating that the Competitor Rule-Priming

effect was significant in the spatial tasks (M’s = 573 and

546 ms, in the CRP? and CRP- trials, respectively), F(1,

23) = 7.94, MSE = 4,181, p = .010, gp2 = .26, but not in

the semantic tasks (Ms = 784 and 780 ms, in the CRP?

and CRP- trials, respectively), F \ 1. No other effects

were significant.

PE A similar ANOVA on PE yielded a significant main

effect of Task, F(3, 69) = 11.95, MSE = .005, p \ .001,

gp2 = .34, representing a difference between mean PE of

.08, .06, .02, and .05 for parity, magnitude, horizontal, and

vertical, respectively. The main effect for Response was

marginally significant, F(1, 23) = 2.97, MSE = .003,


123

p = .099, gp2 = .11, indicating that participants performed

more errors following response repetition trials (M = .06),

relative to response switch trials (M = .05).

More importantly, we found the predicted main effect of

Competitor Rule Priming, F(1, 23) = 4.87, MSE = .001,

p = .038, gp2 = .17, indicating that PE was higher in

CRP? trials (.06) than in CRP- trials (.05, demonstrating

a Competitor Rule-Priming effect of .01). Note that Com-

petitor Rule Priming interacted with task and response,

F(3, 69) = 4.16, MSE = .001, p = .009, gp2 = .15, indi-

cating that the Competitor Rule-Priming effect was found

in the parity and the horizontal tasks when the response

switched, but in the magnitude task it was found when the

response repeated. At the very least, we can conclude that

the Competitor Rule-Priming effect is not systematically

related to response switch or repetition.

Discussion

Experiments 1A and 1B revealed all the standard effects.

More importantly, in both experiments, we found the

predicted main effect for Competitor Rule Priming in PE

(although in Experiment 1B it was present in some, but not

all the conditions), indicating that if the competitor rule in

Trial N was also the relevant rule in Trial N–1, it poses

higher interference than if it was not the relevant rule in

Trial N–1. Note that the Competitor Rule-Priming effect

was independent of response repetition (or at least, not

consistently related to it), suggesting that rule priming

influences the degree of interference posed by a competitor

rule, above and beyond that of responses. Given the small

Competitor Rule-Priming effect, which manifested only in

PE, we wished to examine the robustness of this effect

before drawing conclusions.

Is Competitor Rule Priming a robust phenomenon?

Given the small Competitor Rule-Priming effect, and

since it was consistently found only in PE, we wished to

examine the robustness of this effect. To do that, we

analyzed previously published experiments. Overall, we

analyzed the results of three experiments that were

Table 3 ANOVA tables for the analysis of previously published experiments

MSE F p gp2

Competitor Rule-Priming analysis

RT

Experiment 101,043 18.61 \.001 .31

Response 1,141 34.01 \.001 .29

Response 9 Experiment 1,141 3.36 .04 .08

Competitor Rule Priming 1,275 17.32 \.001 .17

Competitor Rule Priming 9 Experiment 1,275 3.51 .03 .08

Simple Competitor Rule-Priming effects

Meiran et al. (2010) 1,275 13.57 \.001 .14

Hsieh et al. (2012)—Experiment 1 1,275 8.54 \.001 .09

Hsieh et al. (2012)—Experiment 2 1,275 0.15 .70 .00

Response 9 Competitor Rule Priming 649 0.79 .38 .01

Response 9 Competitor Rule Priming 9 Experiment 649 1.21 .30 .03

PE

Experiment .0022 2.84 .06 .06

Response .0004 17.35 \.001 .17

Response 9 Experiment .0004 0.38 .68 .01

Competitor Rule Priming .0002 26.70 \.001 .25

Competitor Rule Priming 9 Experiment .0002 4.71 .01 .10

Simple Competitor Rule Priming effects

Meiran et al. (2010) .0002 0.15 .70 .00

Hsieh et al. (2012)—Experiment 1 .0002 9.53 \.001 .10

Hsieh et al. (2012)—Experiment 2 .0002 30.34 \.001 .27

Response 9 Competitor Rule Priming .0001 0.84 .36 .01

Response 9 Competitor Rule Priming 9 Experiment .0001 1.05 .35 .02


123

published elsewhere (Hsieh et al., 2012; Meiran et al.,

2010). The experiments used a similar design in which

there were four tasks, two involving location-based clas-

sifications and two involving object-based classifications.

Meiran et al. (2010) used the same spatial tasks as

reported in Experiments 1A and 1B, but the stimuli they

used were faces. Hsieh et al. (2012) used four boxes

arranged in a column and participants indicated whether

the stimulus’ location was up versus down or inner versus

outer. The stimuli were colored shapes. We employed the

same Competitor Rule-Priming analysis we performed in

Experiments 1A and 1B, with a few minor changes. Since

the tasks varied across experiments, we did not enter the

Task variable to the analysis. However, to prevent noise

from the Task variable, we created the cells in the ana-

lysis by averaging across task (i.e., giving equal weight to

each task). We also included the Experiment variable in

the analysis.

RT

As Table 3 shows, a repeated measures ANOVA of

Response (repetition, switch) 9 Competitor Rule Priming

(CRP?, CRP-) with Experiment as a between-participants

variable showed a reliable main effect (gp2 = .17) for

competitor rule priming, indicating that as predicted,

responses were slower in CRP? trials (M = 769 ms) rel-

ative to CRP- trials (M = 754 ms, demonstrating a

Competitor Rule-Priming effect of 15 ms). The interaction

Competitor Rule Priming 9 Experiment was significant,

indicating that the Competitor Rule-Priming effect was

reliable in Meiran et al. (2010) and Hsieh et al. (2012)—

Experiment 1, but not in Hsieh et al. (2012)—Experiment

2, demonstrating a Competitor Rule-Priming effect of 27,

19 and 2 ms in Meiran et al. (2010), Hsieh et al. (2012)—

Experiment 1 and Hsieh et al. (2012)—Experiment 2,

respectively (see Fig. 3). Importantly, Competitor Rule

Priming did not interact with Response.

PE

As Table 3 shows, a similar ANOVA on PE also yielded a

reliable Competitor Rule-Priming main effect (gp2 = .25),

indicating that PE was higher in CRP? trials (.03) than in

CRP- trials (.02, demonstrating a Competitor Rule-Prim-

ing effect of .01). The interaction Competitor Rule Prim-

ing 9 Experiment was significant, indicating that the

Competitor Rule-Priming effect was reliable in Hsieh et al.

(2012)—Experiment 1 and Hsieh et al. (2012)—Experi-

ment 2, but not in Meiran et al. (2010), demonstrating a

Competitor Rule-Priming effect of .01, .01 and \.01 in

Hsieh et al. (2012)—Experiment 1, Hsieh et al. (2012)—

Experiment 2, and Meiran et al. (2010), respectively (see

Fig. 4). Importantly, Competitor Rule Priming did not

interact with response.

To conclude, Competitor Rule Priming is a robust

phenomenon, which was manifested in every experiment

either in RT or in PE,1 with no indication for speed accu-

racy tradeoff. Importantly, Competitor Rule Priming did

not interact with response, suggesting that the priming of

the entire rule contributes to the degree of the interference

posed by a competing response, above and beyond the

priming of the response.

General discussion

The present study asked the question what determines the

strength of competition between action plans. We spe-

cifically asked whether it is influenced by priming of

abstract task rules. Unlike previous studies, which tackled

similar questions by focusing on the strength of the rel-

evant rule in Trial N, we focused on the irrelevant rule in

Trial N and examined whether priming of the relevant

rule in Trial N–1 influences the degree of interference this

rule poses when it becomes the irrelevant competitor rule

in Trial N.

In two experiments using a task-switching paradigm that

incorporated four tasks and involved 100 % switch trials,

we found performance cost (mainly in PE) in trials in

which the competitor rule in Trial N was the relevant rule

in Trial N–1 (CRP? trials) relative to trials in which the

competitor rule in Trial N was not the relevant rule in Trial

N–1 (CRP– trials). Re-analyses of three additional pub-

lished experiments showed that Competitor Rule Priming,

despite being a small effect, is a robust effect that is usually

found in errors. This finding renders support to the idea that

rule execution (in Trial N–1) primes this task rule. This

priming then carries over to Trial N and incurs

1 It is important to note that the analyses reported in this paper

include a confound relating to the number of competitors in Trial N.

Specifically, due to the selection criteria of the Competitor Rule-

Priming effect, CRP? included more trials with two competitor rules

and fewer trials with one competitor rule than CRP– trials. This

confound works in favor of the Competitor Rule-Priming effect. To

assure that the Competitor Rule-Priming effect is not solely a result of

this confound, we ran another repeated measures ANOVA with

Congruency in Trial N, and Competitor Rule Priming as within

participants independent variables, and Experiment as between-

participants variable (cells were created by averaging across tasks).

The Competitor Rule-Priming effect remained significant both in RT,

F(1, 128) = 6.18, p = .014, gp2 = .05, and in PE, F(1, 128) = 19.51,

p \ .001, gp2 = .13. Importantly, the Competitor Rule-Priming effect

did not interact with Congruency in Trial N or with the interaction

Congruency in Trial N 9 Experiment, in RT and in PE, all Fs \ 1.

This analysis therefore rules out the aforementioned potential

confound.


123

performance cost, when this primed task rule becomes a

competitor rule in Trial N.2

This finding is in line with Allport et al.’s (1994) task set

inertia (TSI) hypothesis, according to which both positive

and negative priming (of the relevant and irrelevant task

rules in Trial N–1, respectively) linger from Trial N–1 to

Trial N and incur performance cost in Trial N. In the

present work, we distinguished between two types of

inertia: the inertia that influences the relevant rule in Trial

N and the inertia that potentiates the competitor rule in that

trial. These two types of inertia cannot be distinguished

when the experimental setup involves only two tasks, as

explained in the ‘‘Introduction’’. To disentangle the two

aspects of inertia, one must therefore use more complex

designs, involving more than two tasks. Previous works

have shown that events that took place in Trial N–1

influence the efficiency in which the relevant rule is exe-

cuted in Trial N (e.g., ‘‘Backward Inhibition’’, Mayr &

Keele, 2000; ‘‘Competitor Rule Suppression’’, Meiran

et al., 2010, see Koch et al., 2010, for review). Unlike these

works, the present work demonstrated a behavioral priming

effect which influenced the strength of the competitor rule

in Trial N.

Importantly, the Competitor Rule-Priming effect did not

interact with Response (with the single exception in

Experiment 1B in which Competitor Rule Priming incon-

sistently interacted with Response), suggesting that the

degree of interference posed by a competing response

results from the priming of the entire rule, above and

beyond the priming of the response. This result supports

the notion that competition between responses (action

plans) is influenced by the abstract task rules that govern

these responses (see also Hsieh & Liu, 2008, for additional

support to the notion that in task switching, cognitive

control operates at the task rule level).

Note, that since our paradigm involved 100 % switch

trials, priming of task rules was maladaptive in this par-

ticular situation (because it increases performance cost in

the following trial). This suggests that the magnitude of the

Competitor Rule-Priming effect may be underestimated in

our studies, and that it may increase in paradigms that

include repeat trials.

Interestingly, in most of the experiments, Competitor

Rule Priming was seen in errors. Moreover, the Competitor

Rule-Priming effect is seen among incongruent trials and

there is a tentative suggestion that errors in these trials

reflect the execution of the wrong task rule (Meiran &

Daichman, 2005). Thus, we can tentatively suggest that

activation carryover is mostly seen in perseverative ten-

dency to execute the previous task again, despite the fact

that it is no longer required.

Additionally, in most of the experiments, the CTI was

relatively long (500 ms), a condition which enabled

advance rule preparation in Trial N–1. Yet, the fact that we

showed a reliable Competitor Rule-Priming effect in Hsieh

et al. (2012, Experiment 2) experiment in which the CTI

was 100 ms shows that a long CTI is not required to obtain

this effect.

600

650

700

750

800

850

900

950

Meiran et al. (2010) Hsieh et al. (2012)Exp1

Hsieh et al. (2012)Exp2

Res

pons

e T

ime

CRP-

CRP+

Fig. 3 RT as a function of Competitor Rule Priming and Experiment.

Error bars represent mixed-model 95 % confidence interval (Jarmasz

& Hollands, 2009)

0.00

0.01

0.02

0.03

0.04

0.05

0.06

Meiran et al. (2010) Hsieh et al. (2012)Exp1

Hsieh et al. (2012)Exp2

Prop

ortio

n of

Err

ors

CRP-

CRP+

Fig. 4 Proportion of errors (PE) as a function of Competitor Rule

Priming and Experiment. Error bars represent mixed-model 95 %

confidence interval (Jarmasz & Hollands, 2009)

2 There is also the possibility that the Competitor Rule-Priming effect

does not represent cost in CRP? trials, but rather benefit in CRP-

trials. Either way, the difference in performance between the two

conditions can only result from priming (or the lack of priming) of the

previously relevant rule, which became a competitor rule in Trial N.

Specifically, it could be that in CRP? trials, the competitor rule is

primed and this leads to cost, and it could be that in CRP- trials, the

competitor rule is not primed, and this leads to benefit. Although we

find the former more plausible than the latter, the important issue is

that only priming can explain the results obtained in this paper. Note

that the question of cost vs. benefit is raised also with regard to

Competitor Rule Suppression. Therefore, the question whether

Competitor Rule Priming and Competitor Rule Suppression represent

cost in CRP?/CRS? trials or benefit in CRP–/CRS– trials awaits

future research.


123

We now turn to discuss a study which supports the

Competitor Rule-Priming effect. In Hubner et al. (2003)

study, participants switched between three tasks. Each task

was performed on different stimuli (i.e., digits, letters, and

shapes), and participants responded with different keys to

each task. Digits were categorized to odd and even, letters

to consonants and vowels, and shapes to shape with straight

lines only or shapes that also include curved lines. The

authors analyzed only switch trials. In these trials, the

target stimulus always appeared in the middle of the

screen, and it was either flanked by stimuli from the rele-

vant task in Trial N–1 (‘‘preceding’’ condition), or flanked

by stimuli form the third task (‘‘control’’ condition), or not

flanked at all (‘‘none’’ condition). In Experiment 1, half of

the switch trials were pre-cued with a cue that indicated the

relevant task in Trial N, and the other half they were not

cued. Experiment 2 was identical to Experiment 1, with the

exception that the cue only indicated a task switch, but did

not indicate which task is going to be the relevant task in

Trial N. The authors found performance costs on the

‘‘preceding’’ condition relative to the ‘‘control’’ condition

in three out of the four conditions, an effect reminiscent of

Competitor Rule Priming. However, when the pre-cue was

informative about the upcoming task (Experiment 1), they

found performance benefit on the ‘‘preceding’’ condition

(seemingly indicating a reversed Competitor Rule-Priming

effect). Hubner et al. (2003) interpreted this latter result as

expressing Backward Inhibition, suggesting that the for-

merly relevant task is being inhibited only when the to-be-

performed task is known in advance. Note that their latter

result is opposite to the results we obtained in the five

experiments reported here, in which tasks were always pre-

cued. Given the differences in the paradigms, we cannot

tell whether our results represent a failure to replicate

Hubner et al.’s results. However, note that unlike Hubner

et al.’s analyses, in our analyses we ruled out confounds

such as competitor rule repetition, Competitor Rule Sup-

pression, etc. (see ‘‘Appendix’’ for elaboration). In addi-

tion, unlike Hubner et al.’s paradigm, our paradigm

enabled the examination of response repetition effects, a

fact that enabled us to reach the conclusion that Competitor

Rule Priming represents priming of the entire task rule

rather than responses. Another advantage of our paradigm

is that it enables the examination of Backward Inhibition in

addition to Competitor Rule Priming. In this regard, we

find Competitor Rule Priming both in experiments that

show a reliable Backward Inhibition (3 out of the 5

experiments reported in this paper) and in experiments that

did not show a reliable Backward Inhibition (Experiments

1 and 2 in Hsieh et al., 2012), suggesting that Competitor

Rule Priming is unrelated to Backward Inhibition.

Finally, we presented a phenomenon that refers to the

priming of a competitor rule in Trial N if it was the relevant

rule in Trial N–1. Nevertheless, this work is limited in

deciding what is the cognitive process underlying this

phenomenon. The process underlying Competitor Rule

Priming may be the activation of the relevant rule when it

is executed (e.g., Allport & Wylie, 2000; Yeung & Mon-

sell, 2003a). However, the process underlying Competitor

Rule Priming may also be inhibition (e.g., Mayr & Keele,

2000; Koch et al., 2010), and more specifically the

decreased inhibition of that rule relative to other rules.

Namely, if we assume that executing a task is accompanied

by the inhibition of alternative tasks (e.g., Brown et al.,

2007; Mayr & Keele, 2000), task rules that were performed

more recently are inhibited to a lesser degree.3

To conclude, five experiments established the existence

of the Competitor Rule-Priming effect, in which priming of

the relevant rule in Trial N–1 is carried over to Trial N. The

Competitor Rule-Priming effect shows that interference

from competing action plans may be influenced by the

recent execution of the abstract rule that has generated

these plans.

Acknowledgments The research was supported by a Bi-National

Taiwan–Israel research grant to Shulan Hsieh and Nachshon Meiran,

by a research grant from the Israel Science Foundation to Nachshon

Meiran and by a research grant from the Israeli Foundation Trustees

to Maayan Katzir (Fund for Doctoral Students No. 30).

Appendix: Selection criteria

The straightforward Competitor Rule-Priming analysis

included many confounds, which involved the degree of

interference in Trial N (Meiran & Kessler, 2008), the

degree of interference in Trial N–1 (Botvinick et al., 2001),

Competitor Rule Suppression, and competitor repetition

(Meiran et al., 2011). In this section, we briefly explain

how we controlled for these confounds. CRP- can only

occur in trials with zero, one or two competing rules, but

not with three competing rules. This is because if there are

three competing rules, one of them must have been the

relevant rule in Trial N–1 (given that there are four tasks).

Furthermore, CRP? can only occur in trials with one, two

or three competing rules, but not with zero competing

rules, because to obtain Competitor Rule Priming there

must be a competitor rule to be primed. Given these con-

siderations, we excluded trials with zero or three competing

rules to remove this particular confound.

Moreover, we wanted to assure that CRP- and CRP?

trials are equally influenced by conflict in Trial N–1

(Botvinick et al., 2001) and conflict in Trial N (Meiran &

Kessler, 2008). Specifically, according to conflict adapta-

tion theory (Botvinick et al., 2001), control is recruited

3 We thank Andrea Kiesel for suggesting this option.


123

Table 4 Selection criteria Competitor Rule Priming

Competitor Rule Priming

Competitor

Rule

Suppression

Competitor

Repetition

CRP – CRP +

Number of competitors in Trial N=0

CRP+ cannot be materialized: There cannot be CRP+ if there is no

competitor rule in Trial N.


Number of competitors in Trial N–1=0

– – Trial N–1: a1b1c1d1 Trial N–1: a1b1c1d1

Trial N: a1b1c2d1 Trial N: a1b1c2d1

+ There cannot be CRS+ or CRR+ trials if there was no competitor rule

in Trial N–1, because in CRS+ a competitor (in N–1) becomes the

relevant rule (in N) and in CRR+ the competitor (in N–1) remains the

competitor (in N).

+


– – Trial N–1: a2b1c1d1 Trial N–1: a2b1c1d1


+ CRP+ cannot be materialized: If the only competitor in Trial N was the

competitor in Trial N–1 (creating CRR+), it could not have been the

relevant rule in Trial N–1 (to create CRP+).

+ – Trial N–1:a1b2c1d1 Trial N–1: a1b2c1d1


+ If there was only one competitor in Trial N–1, it cannot both be the

competitor in Trial N (creating CRR+) and, at the same time, be the

relevant rule (creating CRS+).


123

Table 4 continued


– – CRP– cannot be materialized: If the two competitors from Trial N–1

are neither the competitor in Trial N (creating CRR–) nor the relevant

in Trial N (creating CRS–), the relevant rule in Trial N–1 had to be the

competitor in Trial N (creating only CRP+ trials).

+ CRP+ cannot be materialized: if the only competitor in Trial N was the





+ CRP+ cannot be materialized: if the only competitor in Trial N was the




_ There cannot be CRS– trials because if there were 3 competitors in

Trial N–1, one of them will become the relevant rule.

+ – CRP– cannot be materialized: If the competitor in Trial N was not a

competitor in Trial N–1 (CRR–) then it had to be the relevant (creating

only CRP+).

+ CRP+ cannot be materialized: If the only competitor in Trial N was the


relevant rule in Trial N–1 (to create CRP+) .



– – Trial N–1:a1b1c1d1 Trial N–1: a1b1c1d1


+ There cannot be CRS+ or CRR+ trials if there was no competitor rule

in Trial N–1, because in CRS+ a competitor (in N–1) becomes the

relevant rule (in N) and in CRR+ the competitor (in N–1) remains the

competitor (in N).

+


123

Table 4 continued


– – CRP– cannot be materialized: If the only competitor in Trial N–1 does

not become one of the two competitors in Trial N (creating CRR–) and

does not also become the relevant in Trial N (creating CRS–), then it

has to be the compatible rule. Therefore, since the relevant rule in Trial

N–1 cannot be the relevant in Trial N and it cannot be the compatible

rule, then it has to become the competitor in Trial N (creating CRP+).

+ Trial N–1:a2b1c1d1 Trial N–1: a2b1c1d1




+ If there was only one competitor in Trial N–1, it cannot both be the

competitor in Trial N (creating CRR+) and, at the same time, be the

relevant rule (creating CRS+).


– – If there are two competitors in Trial N, and there were two competitors

in Trial N–1, one of the competitors from Trial N will be either the

competitor in Trial N (CRR+) or the relevant in Trial N (CRS+).

+ None of the competitors could

become the relevant (CRS–),

which means that the compatible

in Trial N–1 became the relevant,

and since the relevant was not a

competitor (CRP–), the

competitors repeated and created a

double CRR+.

The relevant in Trial N–1 became

one of the competitors in Trial N

(CRP+) and therefore, only one

competitor can repeat and create

CRR+.

+ – CRP– cannot be materialized: If the competitors in Trial N–1 do not

remain a competitor in Trial N (CRR–), the relevant in Trial N–1 has to

become one of the two competitors in Trial N.

+ Trial N–1:a2b2c1d1 Trial N–1: a2b2c1d1



123

after a conflict. This increased control mode is carried into

the following trial leading to more cautious responding,

which slows down the otherwise automatic response. We,

therefore, wanted to assure that CRP? and CRP- trials are

equally present following 0, 1, 2 and 3 competing rules.

Similarly, because trials with two competitor rules are

slower than trials with one competitor rule, we wanted to

equate CRP? and CRP- trials in this respect as well.

Additionally, since competitor rule repetition should be

seen in reduced interference because the competitor rule in

Trial N has already been suppressed in Trial N–1 (see

Meiran et al., 2011, p. 301, for support), we wanted to

assure that CRP- and CRP? trials were equated also on

competitor repetitions. Finally, we also controlled for

Competitor Rule Suppression (Meiran et al., 2010). To

control for the Competitor Rule Suppression confound, we

excluded trials if their Trial N-1 to Trial N transition

included CRS? (or CRS-) only in CRP? trials but not in

CRP- trials, and vice versa.

As Table 4 shows, controlling for all the possible con-

founds when considering Trial N–1 to Trial N transitions,

reduces immensely the amount of valid trials. The final

number of trials is detailed in the manuscript.

Finally, note that the exclusions removed all stimulus

repetitions. This resulted from a combination of several

factors. Consider that we had 100 % switch trials. Also,

only stimuli that had one or two competitors could repeat,

because only trials with one or two competitors in Trial N

were included in the analysis. Note that stimuli with one

competitor were stimuli with three competitors in Trial N–1

if the competitor in Trial N was the relevant in Trial N–1.

Nevertheless, trials with three competitors in Trial N–1

were removed (see Table 4). The remaining trials with one

competitor always had the same competitor in Trial N–1,

and therefore could only be CRP- trials with competitor

repetition. Because CRP? trials could not have competitor

repetition if they had only one competitor in Trial N (see

Table 4), these stimulus repetition trials were removed from

Table 4 continued


– There cannot be CRS– trials because if there were 3 competitors in

Trial N–1, one of them will become the relevant rule.

+ – There cannot be CRR– trials. If there are two competitors in Trial N, at

least one of them must have been one of the three competitors in Trial

N–1.

+ The relevant in Trial N–1 has to

become the compatible in Trial N

(CRP–). Hence, two of the

competitors has to repeat and

created a double CRR+.

The relevant in Trial N–1 became

one of the competitors in Trial N

(CRP+) and therefore, only one

competitor can repeat and create

CRR+.


CRP– cannot be materialized: If all the rules compete in Trial N, one of

the competitors had to be the relevant in Trial N–1 (creating CRP+).

All possible combinations of Trial N following Trial N–1. Lowercase letters (e.g., a, b) represent stimulus dimensions (e.g., color, shape), and the

numbers on the right to each letter represent the dimension values (e.g., red and green for the dimension color) that go with a specific response

key (key 1, key 2) according to the task rules. The relevant dimension is underscored

The left columns indicate the different levels in the confounding factors we wished to exclude. CRP? and CRP– trials were included in the

analysis only if they had the same number of competing rules in Trial N–1 and Trial N, and were equated on Competitor Rule Suppression and

competitor repetition. Brief explanations in each cell indicate why this cell was not included in the analysis. If a cell was included in the analysis,

one example for the Trial N–1 to Trial N transition is presented


123

the analysis. As for trials with two competitors in Trial N,

these trials can involve stimulus repetition only if there

were also two competitors in Trial N-1. Note that we

included trials with two competitors in Trial N and two

competitors in Trial N-1 only if they were CRS? trials with

competitor repetition (Table 4). In this scenario one of the

competitors necessarily remained the competitor rule and

the other necessarily became the relevant rule. Note that the

correct responses for these two rules had to be equal in Trial

N-1 (because they were both competitors). However, in

Trial N, these two rules were necessarily mapped to dif-

ferent keys (because one rule remained the competitor rule

and the other became the relevant rule), and therefore there

could not be stimulus repetition in this scenario.

References

Allport, A., Styles, E. A., & Hsieh, S. (1994). Shifting intentional set:

exploring the dynamic control of tasks. In C. Umilta & M.

Moscovitch (Eds.), Attention and performance XV: conscious

and nonconsciousinformation processing (pp. 421–452). Cam-

bridge: MIT Press.

Allport, D. A., & Wylie, G. (2000). ‘‘Task–switching’’, stimulus–

response bindings, and negative priming. In S. Monsell & J.

S. Driver (Eds.), Control of cognitive processes: attention and

performance XVIII (pp. 35–70). Cambridge: MIT Press.

Astle, D. E., Jackson, G. M., & Swainson, R. (2012). Two measures

of task-specific inhibition. The Quarterly Journal of Experimen-

tal Psychology, 65(2), 233–251.

Barutchu, A., Becker, S. I., Carter, O., Hester, R., & Levy, N. L.

(2013). The role of task-related learned representations in

explaining asymmetries in task switching. PLoS ONE, 8(4),

e61729. doi:10.1371/journal.pone.0061729.

Botvinick, M., Braver, T., Barch, D. M., Carter, C. S., & Cohen, J. D.

(2001). Conflict monitoring and cognitive control. Psychological

Review, 108(3), 624–652.

Brown, J. W., Reynolds, J. R., & Braver, T. S. (2007). A

computational model of fractionated conflict-control mecha-

nisms in task switching. Cognitive Psychology, 55(1), 37–85.

Bryck, R. L., & Mayr, U. (2008). Task selection cost asymmetry

without task switching. Psychonomic Bulletin and Review, 15(1),

128–134.

Druey, M., & Hubner, R. (2007). The role of temporal cue–target

overlap in backward inhibition under task switching. Psycho-

nomic Bulletin and Review, 14(4), 749–754.

Grange, J. A., & Houghton, G. (2009). Temporal cue–target overlap is

not essential for backward inhibition in task switching. Quarterly

Journal of Experimental Psychology, 62(10), 2068–2079.

Hsieh, S., Chang, C–. C., & Meiran, N. (2012). Episodic retrieval and

decaying inhibition in the competitor-rule suppression phenom-

enon. Acta Psychologica, 41(3), 316–321.

Hsieh, S., & Liu, H. (2008). Electrophysiological correlates of task

conflicts in task-switching. Brain Research, 1203, 116–125.

Hubner, M., Dreisbach, G., Haider, H., & Kluwe, R. H. (2003).

Backward inhibition as a means of sequential task-set control:

evidence for reduction of task competition. Journal of Exper-

imental Psychology. Learning, Memory, and Cognition, 29(2),

289–297.

Jarmasz, J., & Hollands, J. G. (2009). Confidence intervals in

repeated-measures designs: The number of observations

principle. Canadian Journal of Experimental Psychology, 63,

124–138.

Kiesel, A., Wendt, M., & Peters, A. (2007). Task switching: on the

origin of response congruency effects. Psychological Research,

71(2), 117–125.

Koch, I., Gade, M., Schuch, S., & Philipp, A. M. (2010). The role of

inhibition in task switching: a review. Psychonomic Bulletin and

Review, 17(1), 1–14.

Kruglanski, A. W., Shah, J. Y., Fishbach, A., Friedman, R., Chun, W.,

& Sleeth-Keppler, D. (2002). A theory of goal systems. In M.

P. Zanna (Ed.), Advances in experimental social psychology

(Vol. 34, pp. 331–378). San Diego: Academic Press.

MacLeod, C. M. (1991). Half a century of research on the Stroop effect:

an integrative review. Psychological Bulletin, 109(2), 163–203.

Mayr, U., & Keele, S. W. (2000). Changing internal constraints on

action: the role of backward inhibition. Journal of Experimental

Psychology: General, 129(1), 4–26.

Meiran, N., & Daichman, A. (2005). Advance task preparation

reduces task error rate in the cueing-task paradigm. Memory and

Cognition, 33, 1272–1288.

Meiran, N., Hsieh, S., & Chang, C. C. (2011). ‘‘Smart inhibition’’:

electrophysiological evidence for the suppression of conflict-

generating task rules during task switching. Cognitive, Affective

and Behavioral Neuroscience, 11(3), 292–308.

Meiran, N., Hsieh, S., & Dimov, E. (2010). Resolving task rule

incongruence during task switching by competitor rule suppres-

sion. Journal of Experimental Psychology: Learning, Memory,

and Cognition, 36(4), 992–1002.

Meiran, N., & Kessler, Y. (2008). The task rule congruency effect in

task switching reflects activated long term memory. Journal of

Experimental Psychology: Human Perception and Performance,

34(1), 137–157.

Philipp, A. M., & Koch, I. (2006). Task inhibition and task repetition

in task switching. European Journal of Cognitive Psychology,

18(4), 624–639.

Pinhas, M., Tzelgov, J., & Ganor-Stern, D. (2012). Estimating linear

effects in ANOVA designs: the easy way. Behavior Research

Methods, 44(3), 788–794.

Schneider, W., Eschman, A., & Zuccolotto, A. (2002). E-Prime

[Computer software]. Pittsburgh: Psychology Software Tools.

Schuch, S., & Koch, I. (2003). The role of response selection for

inhibition of task sets in task shifting. Journal of Experimental

Psychology: Human Perception and Performance, 29, 92.

Steinhauser, M., & Hubner, R. (2006). Response-based strengthening

in task shifting: evidence from shift effects produced by errors.

Journal of Experimental Psychology: Human Perception and

Performance, 32(3), 517–534.

Sudevan, P., & Taylor, D. A. (1987). The cuing and priming of

cognitive operations. Journal of Experimental Psychology:

Human Perception and Performance, 13, 89–103.

Tipper, S. P., Weaver, B., & Houghton, G. (1994). Behavioural goals

determine inhibitory mechanisms of selective attention. Quar-

terly Journal of Experimental Psychology, 47A, 809–840.

Waszak, F., Hommel, B., & Allport, A. (2003). Task-switching and

long-term priming: Role of episodic stimulus–task bindings in

task-shift costs. Cognitive psychology, 46, 361–413.

Wendt, M., & Kiesel, A. (2008). The impact of stimulus-specific

practice and task instructions on response congruency effects

between tasks. Psychological Research, 72(4), 425–432.

Yeung, N., & Monsell, S. (2003a). Switching between tasks of

unequal familiarity: the role of stimulus-attribute and response-

set selection. Journal of Experimental Psychology: Human

Perception and Performance, 29(2), 455–469.

Yeung, N., & Monsell, S. (2003b). The effects of recent practice on

task switching. Journal of Experimental Psychology: Human

Perception and Performance, 29(5), 919–936.


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Competitor Rule Priming: Evidence for priming of task rules in task switching

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