What Is Shaping RT and Accuracy Distributions? Active and Selective Response Inhibition Causes the Negative Compatibility Effect

Abstract

Inhibitory control such as active selective response inhibition is currently a major topic in cognitive neuroscience. Here we analyze the shape of behavioral RT and accuracy distributions in a visual masked priming paradigm. We employ discrete time hazard functions of response occurrence and conditional accuracy functions to study what causes the negative compatibility effect (NCE)-faster responses and less errors in inconsistent than in consistent prime target conditions-during the time course of a trial. Experiment 1 compares different mask types to find out whether response-relevant mask features are necessary for the NCE. After ruling out this explanation, Experiment 2 manipulates prime mask and mask target intervals to find out whether the NCE is time-locked to the prime or to the mask. We find that (a) response conflicts in inconsistent prime target conditions are locked to target onset, (b) positive priming effects are locked to prime onset whereas the NCE is locked to mask onset, (c) active response inhibition is selective for the primed responses, and (d) the type of mask has only modulating effects. We conclude that the NCE is neither caused by automatic self-inhibition of the primed response due to backward masking nor by updating response-relevant features of the mask but by active mask-triggered selective inhibition of the primed response. We discuss our results in light of a recent computational model of the role of the Basal Ganglia in response gating and executive control.

Full text

Active, selective inhibition causes the NCE 1
Running head: Active, selective inhibition causes the NCE
What is shaping reaction-time and accuracy distributions?
Active and selective response inhibition causes the Negative Compatibility Effect
Sven Panis & Thomas Schmidt
Faculty of Social Sciences, Experimental Psychology Unit
University of Kaiserslautern
Germany
Abstract: 233 words
Main text: 12321 words
82 references
1 Table & 7 Figures
4 Tables and 11 Figures in Supplemental Material

Active, selective inhibition causes the NCE 2
Abstract
Inhibitory control such as active selective response inhibition is currently a major topic in cognitive
neuroscience. Here we analyze the shape of behavioral response time and accuracy distributions in
a visual masked priming paradigm. We employ discrete-time hazard functions of response
occurrence and conditional accuracy functions to study what causes the Negative Compatibility
Effect (NCE) – faster responses and less errors in inconsistent than in consistent prime-target
conditions – during the time-course of a trial. Experiment 1 compares different mask types to find
out whether response-relevant mask features are necessary for the NCE. After ruling out this
explanation, Experiment 2 manipulates prime-mask and mask-target intervals to find out whether
the NCE is time-locked to the prime or to the mask. We find that (a) response conflicts in
inconsistent prime-target conditions are locked to target onset, (b) positive priming effects are
locked to prime onset whereas the NCE is locked to mask onset, (c) active response inhibition is
selective for the primed response, and (d) the type of mask has only modulating effects. We
conclude that the NCE is neither caused by automatic self-inhibition of the primed response due to
backward masking, nor by updating response-relevant features of the mask, but by active mask-
triggered selective inhibition of the primed response. We discuss our results in light of a recent
computational model of the role of the basal ganglia in response gating and executive control.
Key-words
response time, conditional accuracy, hazard function, masked priming, negative compatibility
effect, basal ganglia, response inhibition, cognitive control, survival analysis
Address correspondence to: sven.panis@sowi.uni-kl.de

Active, selective inhibition causes the NCE 3
1. Introduction
The nature of inhibitory control processes including response inhibition is currently a major topic in
cognitive neuroscience (Banich & Depue, 2015; Houghton & Tipper, 1996; Middlebrooks & Schall,
2014; Stuphorn, 2015; Watanabe & Funahashi, 2015). Response inhibition can be triggered
automatically in a bottom-up, stimulus-driven fashion (as in lateral inhibition between two
stimulus-triggered candidate responses), or actively in a top-down, task-driven fashion (as in
cognitive control). Physiological evidence from choice reaction time (RT) tasks indicates that
response inhibition can be active (versus automatic) as well as selective (versus nonselective or
global; Burle, Vidal, Tandonnet, & Hasbroucq, 2004). However, it is often possible to come up with
an explanation without active selective response inhibition to explain mean performance patterns.
For example, in task conditions involving competing responses, slowing in mean RT may be
explained by lateral inhibition between response channels (which is considered automatic and
nonselective because it is reciprocal), without considering the possibility of the additional presence
of active and selective (as well as active and nonselective) response inhibition. For this reason
many mathematical two-choice RT process models such as Poisson accumulator models (Mattler
and Palmer, 2012; Schubert, Palazova, & Hutt, 2013; Vorberg, Mattler, Heinecke, Schmidt, &
Schwarzbach, 2003) may neglect the role of active inhibition in interference paradigms (Aron,
2011; Schroll & Hamker, 2013; Wiecki & Frank, 2013).
1.1 The masked (response) priming paradigm. The visual masked priming paradigm (e.g., Marcel,
1983; for reviews see Eriksen, 1960; Holender, 1986; Ansorge, Kunde, & Kiefer, 2014) is used to
study how an irrelevant and invisible prime stimulus interferes with responding to a subsequently
presented target. Typically, correct responses are faster and error rates are lower when the prime
contains features that are similar to the target than when prime and target features are dissimilar.
A special case of masked priming is the response priming paradigm (Klotz & Wolff, 1995;

Active, selective inhibition causes the NCE 4
Klotz & Neumann, 1999; Vorberg et al., 2003) where the invisible prime is either mapped to the
same response as the target (consistent trials) or to the opposite response (inconsistent trials).
Even in the absence of awareness of the critical prime features, the prime is often able to influence
motor responses, and even non-motor operations such as cognitive control (Kunde, Kiesel, &
Hoffmann, 2003; Mattler, 2003; Palmer & Mattler, 2013; van Gaal, Lamme, & Ridderinkhof, 2010).
Because of the fixed mapping of stimulus features to responses, a single prime feature is assumed
sufficient to elicit the associated response directly, without conscious mediation (Neumann, 1990).
It has been suggested that in response priming, prime and target elicit sequential
feedforward sweeps (Lamme & Roelfsema, 2000; VanRullen & Koch, 2003) that activate the
associated responses in strict sequence (Schmidt, et al., 2011). This leads to response conflict when
the prime is inconsistent with the target. Indeed, sequential response activation by primes and
targets can be observed in lateralized readiness potentials (Eimer & Schlaghecken, 1998; Leuthold
& Kopp, 1998; Vath & Schmidt, 2007) and in the time courses of pointing movements and response
forces (Schmidt, 2002; Schmidt & Schmidt, 2009; Schmidt, Weber, & Schmidt, 2014). The notion of
sequential triggering of prime- and target-related responses explains many interesting properties
of response priming. For instance, priming effects increase with prime-target stimulus-onset-
asynchrony (SOA), and error responses are typically fast and occur selectively in inconsistent trials
at long SOAs, which would be expected if longer SOAs leave the prime more time to direct the
response into the correct or incorrect direction. Moreover, it has repeatedly been shown that the
first motor responses are exclusively controlled by the prime and not influenced at all by the actual
target (e.g., Schmidt, Niehaus, & Nagel, 2006; Schmidt & Schmidt, 2009, 2010; Schmidt, et al.,
2014).
1.2 The negative compatibility effect. Positive response priming effects are typically observed for
prime-target SOAs up to about 100 ms (positive compatibility effect, PCE). For longer SOAs, the

Active, selective inhibition causes the NCE 5
masked priming effect has often been found to reverse, resulting in a negative index with better
mean performance in the inconsistent condition: the negative compatibility effect (NCE; Eimer &
Schlaghecken, 1998; Eimer, 1999; Lingnau & Vorberg, 2005). Eimer and Schlaghecken (1998)
employed double arrows (<< or >>) as primes and targets. A 17-ms prime was immediately
followed by a 100-ms mask constructed from the superposition of left- and right-pointing double
arrows. The mask was immediately followed by a target that was either consistent with the prime
(pointing in the same direction, thus requiring the same response) or inconsistent (requiring the
opposite response). To their surprise, the authors found that mean error rates were higher, and
mean correct RT longer, when primes and targets were consistent than when they were
inconsistent – a reversal of the expected PCE. They traced the effect in the lateralized readiness
potential, observing a sequence of three response activations: an initial activation of the prime-
related response, followed by a transient activation of the opposite (antiprime) response, and
finally an activation of the target-triggered correct response. It is generally accepted that it is the
emergence of antiprime activation that reverses the priming effect (Seiss, Klippel, Hope, Boy, &
Sumner, 2014).
At least three different hypotheses have been developed to explain the emergence of
antiprime activation in this particular paradigm (for reviews, see Jaśkowski and Verleger, 2007;
Sumner, 2007). First, according to the self-inhibition account, the initial motor activation elicited by
the prime is automatically and selectively inhibited due to self-inhibitory circuits at the motor level
when (a) the perceptual evidence for the prime is immediately removed by the mask, and (b) the
delay between the prime and target is long enough for this inhibition to become effective (Eimer &
Schlaghecken, 1998, 2002, 2003). Importantly, the inhibition itself is supposed to be triggered by
the prime, not the mask, but it only occurs when the mask sufficiently reduces the visibility of the
prime. Assuming lateral inhibition between response channels, the selective inhibition of the

Active, selective inhibition causes the NCE 6
primed response is then supposed to lead to the temporary activation (disinhibition) of the
opposite or antiprime response.
Second, according to the object-updating account, the NCE emerges when the mask
contains features that call for the response opposite to the prime – a so-called relevant mask. The
NCE thus simply reflects positive priming of the antiprime response by the corresponding features
in the mask, instead of selective response inhibition (Lleras & Enns, 2004, 2006). Critically, the NCE
is thus not expected to occur when an irrelevant mask is used that does not contain response-
relevant features. This explanation applies in the case of Eimer and Schlaghecken's (1998) original
stimuli, because their mask was a replica of the prime arrow with an additional antiprime arrow
superimposed. Under this account, a response-relevant mask is a necessary condition of the NCE.
Third, according to the mask-triggered inhibition account, the role of the mask is to stop
response accumulation by the prime by actively inhibiting the premature prime-triggered response
(Jaśkowski & Przekoracka-Krawczyk, 2005; Jaśkowski, 2007, 2008, 2009; Jaśkowski, Białuńska,
Tomanek, & Verleger, 2008). This active and selective response inhibition (sometimes called an
“emergency break”) requires a strong mask signal, but not necessarily strong masking of the prime.
Under the mask-triggered inhibition account, it is still expected that relevant masks are more
effective than irrelevant masks in triggering inhibition and activating the antiprime response
because of their response-relevant features. But in contrast to the self-inhibition account,
inhibition is predicted to be time-locked to the mask, not the prime.
1.3 Event history analysis. To evaluate these accounts, we take a longitudinal approach by apply-
ing event history analysis (EHA). EHA is the standard distributional method for analyzing time-to-
event data; it is also known as survival, hazard, duration, failure-time, or transition analysis
(Allison, 2010; Miller, 1981; Panis & Wagemans, 2009; Singer & Willett, 2003). We assume that for
each time point since target onset (in each trial from each participant) there is a risk for the event

Active, selective inhibition causes the NCE 7
(here: a response) to occur. The function relating this instantaneous likelihood or hazard of
response occurrence to time is known as the continuous-time hazard function (Luce, 1986).
Here we apply discrete-time (descriptive and inferential) methods (Allison, 1982, 2010;
Chechile, 2003; Panis & Hermens, 2014; Singer & Willett, 1993, 2003; Willett & Singer, 1993,
1995). We divide the first 600 ms after target onset into 15 bins of 40 ms indexed by t = 1 to 15,
and estimate the discrete-time hazard function of response occurrence: h(t) = P(T = t | T ≥ t),
where T ≥ t denotes the event that the response does not occur before the start of bin t. This
conditional probability function gives for each bin t the conditional probability of response
occurrence sometime during bin t, given that the response has not yet occurred in any previous bin
(t-1, t-2, ..., 1). The survivor function, S(t), gives the probability that the response has not occurred
yet by the time bin t is completed. It is the joint probability that the response has not occurred in
any of the bins prior to t: S(t) = P(T > t) = [1 - h(t)] · [1 - h(t-1)] · [1 - h(t-2)] · ... · [1 - h(1)]. Finally, P(t)
= P(T = t) = h(t) · S(t-1) gives the unconditional probability that the response occurs in bin t. Plotting
P(t) over t gives the (sub)probability mass function of response occurrence. Whenever there are
right-censored observations, the P(t) estimates will not sum to 1 (Chechile, 2006).
We cannot simply estimate the hazard functions for error and correct response occurrence
separately, because those two events cannot be assumed to be independent (Burle et al., 2004;
Eriksen, Coles, Morris, & O’Hara, 1985; Praamstra & Seiss, 2005). Therefore, we take the so-called
conditional-processes approach by extending the h(t) analysis of response occurrence by an
analysis of conditional accuracy (Allison, 2010, pp. 227-229). First, we estimate h(t) of response
occurrence regardless of response accuracy, to study whether and when responses occur. For each
bin t, the sample-based estimate of h(t) is obtained by dividing the total number of observed
responses in bin t by the risk set for bin t. The risk set equals the total number of trials that are
response-free in all bins earlier than t and are thus still eligible to experience the response at the

Active, selective inhibition causes the NCE 8
start of bin t (see Table 1). Note that right-censored observations – trials for which we only know
that RT > 600 ms – do contribute to the risk set in each bin.1
Second, once we know probabilistically whether and when responses occur, we estimate
the conditional accuracy function of observed responses: ca(t) = P(response correct | T = t), which
provides the conditional probability that an observed response is correct given that it occurs
sometime during bin t. Ca(t) is obtained by dividing the number of correct responses observed
during bin t by the total number of observed responses in bin t, as shown in Table 1. By using h(t)
functions in combination with conditional accuracy functions one can provide an unbiased, time-
varying, and probabilistic description of the latency and accuracy of responses.2
1.4 Current study. We investigate the role of response inhibition in the within-trial temporal
dynamics of positive and negative compatibility effects in the masked priming paradigm. Two goals
motivate our study. In Experiment 1 we compare different masks and use event history analysis to
look for sudden decreases in the hazard of response occurrence as indicators of inhibitory
processes. In Experiment 2 we manipulate the SOAs and investigate whether and when the PCE
and NCE emerge in the hazard and conditional accuracy functions. We focus on individual
performance patterns to evaluate similarities across participants, and fit statistical models to the
aggregated participant data (hazard models and conditional accuracy models). We find that the
hazard functions display sudden decreases that indicate response inhibition, that the temporal
dynamics of the NCE is very similar for relevant and irrelevant masks, that the onset of the NCE in
conditional accuracy is time-locked to mask but not prime onset, and that the inhibitory cause of
the NCE can be dissociated temporally from automatic lateral inhibition during response
competition.
2. Experiment 1: The effects of mask type on within-trial dynamics of compatibility effects
In Experiment 1 we examine the temporal dynamics of the PCE and NCE using event history and

Active, selective inhibition causes the NCE 9
conditional accuracy analyses. When looking at mean performance measures we expect a PCE
when the mask is absent, and an NCE when a mask is present. When looking at distributional
measures, response-relevant and -irrelevant masks should trigger the same, stimulus-independent
inhibition process and thus lead to the same NCE according to the self-inhibition account.
According to the object-updating account, an NCE should occur only for relevant but not for
irrelevant masks because only the response-relevant mask features can activate the antiprime
response. According to the mask-triggered inhibition account, any mask should be able to trigger
an NCE, but a relevant mask might trigger a stronger NCE than an irrelevant one because of its
additional response-relevant features.
2.1 Methods
Participants. Six volunteers (one male, all right-handed) participated. Mean age was 26.5 years
(range: 23 to 32). All had normal or corrected-to-normal vision. Our approach is to make precise
measurements on each single participant to demonstrate homogeneity of effects on an individual
basis, rather than to emphasize the total number of participants (Schmidt, Haberkamp, & Schmidt,
2011).
Materials. Targets were double arrowheads pointing left or right (Figure 1A). Primes could be
either blank (no prime, NP) or double arrows with direction consistent (CON) or inconsistent
(INCON) with that of the target. Masks could be either blank (no mask, NM), response-relevant
(REL, with arrow primes in both directions superimposed), response-irrelevant (IRREL, a
symmetrical figure consisting of horizontal and vertical line elements), or random lines (LIN, 32
randomly oriented lines, generated anew for each trial; Figure 1A). The arrow stimuli and relevant
mask subtended an area of approximately 1.2° x 1.2° in visual angle. The irrelevant and random-
lines masks subtended an area of approximately 1.5° x 1.5° and 1.7° x 1.7°, respectively. All stimuli
were presented in black on a white background in the center of a 40-cm 75-Hz CRT screen.

Active, selective inhibition causes the NCE 10
Design. The factors Prime Type (NP, CON, INCON) and Mask Type (NM, REL, IRREL, LIN) were
crossed orthogonally, resulting in 12 trial types (NP-NM, CON-NM, ..., INCON-LIN).
Procedure. Each trial started with a fixation cross for 536 ms (40 frames) followed by a blank for
402 ms (30 frames; Figure 1A). The prime stimulus (i.e., a double arrow or blank) was presented for
13 ms (1 frame), followed by a 1-frame blank. Then the mask stimulus (i.e., REL, IRREL, LIN, or a
blank) was presented for 94 ms (7 frames) followed by a blank for 67 ms (5 frames). Finally, the
target was presented for 94 ms and replaced with a blank. Prime-target SOA was fixed at 187 ms.
The participant´s task was to categorize the target as pointing left or right as quickly and as
accurately as possible by pressing one of two keyboard buttons (“F” for left, „J“ for right). The
response collection period started at target onset and lasted 800 ms. If no response was detected
before the end of this period, the message „Too slow“ was presented; if an error response was
detected, the message “Error” was presented (all in German).
There was one practice block and 70 experimental blocks with 22 trials per block. Every
seventh experimental block exclusively contained target-only trials (i.e., condition NP-NM; 10
blocks in total). We presented the target-only trials in separate blocks because we noticed during
pretesting that there was a tendency to withhold responding in target-only trials when all 12 trial
types were intermixed randomly (see General Discussion). The remaining 60 experimental blocks
each contained two trials for each of the remaining 11 conditions (one with target pointing right,
one with target pointing left). Order of presentation was randomized within each block.
Analysis of mean error rate and mean correct RT. Mean error rate was calculated for each
condition and participant based on the trials with observed responses between 250 and 600 ms
(4.2% of the data excluded). The mean correct RT was calculated after ignoring trials with an error,
trials without a response, and trials with a correct response that had a latency below 250 ms or
above 600 ms (10.1% of the data excluded). Repeated-measures ANOVAs are reported with

Active, selective inhibition causes the NCE 11
Greenhouse-Geisser corrections when necessary. All significant effects are reported.
Event history analysis. For each participant, we first calculated the sample-based descriptive
estimates of h(t), S(t), P(t), and ca(t) for each condition. Next, discrete-time hazard models and
conditional accuracy models were implemented as generalized linear mixed-effects regression
models in R (R Core Team, 2014; function glmer of package lme4). We used a logit link for the ca(t)
models and a complementary log-log (cloglog) link for the h(t) models (Allison, 2010).3 Next to
dummy-coding the levels of our experimental factors (prime type and mask type) we also included
TRIAL number as a predictor in the hazard models (centered on value 1000, and then divided by
1000) to model across-trial learning effects in the speed of responses. For both models, the
predictor variable TIME was the time bin index t centered on value 8, or 320 ms. We denote time
bins by the endpoint of the interval they span, so that bin 8 = bin 320 = (280,320]. The intercept
and the linear and quadratic effects of TIME were treated as random effects. For both analyses, we
started with a full model encompassing all possible fixed effects-of-interest and used an automatic
backward selection procedure to select a final model. Specifically, during each iteration, the effect
with the largest p-value that was not part of any higher-order effect was deleted, and the model
refitted. This continued until each of the remaining effects that was not part of any higher-order
effect had a p < .05 (see highlighted p-values in Tables S1 to S4). The NP-REL condition (no prime,
relevant mask) was chosen as a baseline condition to facilitate comparisons of relevant versus
irrelevant masks. Thus, with all effects set to zero, the h(t) model´s intercept refers to the
estimated cloglog[h(t)] for bin 320 in trial 1000 of the NP-REL condition.
To estimate the parameters of the h(t) model, we must create a dataset where each row
corresponds to a time bin of a trial of a participant. Specifically, each time bin that was at risk for
event occurrence in a trial was scored on the dependent variable EVENT (1 = response occurred; 0
= no response), the covariate TIME (centered on bin 320), and the dummy-coded predictor

Active, selective inhibition causes the NCE 12
variables including TRIAL. Because most responses occurred before 600 ms after target onset, all
trials with observed RTs > 600 ms and all trials without a response during the observation period
were treated as right-censored observations; they provide the information that the response did
not occur during the first 600 ms or 15 bins (i.e., each of these trials contributes 15 rows, and each
row has a value 0 for EVENT). In addition, we deleted the first two bins because all observed RTs
were larger than 80 ms. The resulting h(t) data set contained 75,180 rows.
For the ca(t) model, we used the usual dataset where each row corresponds to a trial of a
participant (1540 x 6 = 9240 trials). Each trial without a response, and each trial with a response
latency below 160 ms or above 480 ms was deleted (10.37 % of the data). This latency range was
chosen to avoid problems of linear separability during model fitting. Each remaining trial (a single
row in the dataset) was scored on the dependent variable ACCURACY (1 = correct; 0 = error), the
covariate TIME (centered on bin 320), and the dummy-coded independent variables. The ca(t) data
set contained 8,282 rows.
2.2 Results
2.2.1 Mean error rate and mean correct RT. As expected, priming effects are positive in the no-
mask condition (PCE, with faster responses and lower error rates in consistent than in inconsistent
trials) and negative in the three masked conditions (NCE). Accordingly, a two-way repeated-
measures ANOVA on mean correct RT shows a significant interaction between Prime Type and
Mask Type, F(1.414, 7.071) = 25.051, p = .001, ηp² = .834 (Figure 2A). There is also a significant
main effect of Mask Type, F(1.524, 7.622) = 4.927, p = .049, ηp² = .496. Planned dependent-samples
t-tests (two-tailed, uncorrected) show that the difference between consistent and inconsistent
conditions was significant for all mask types; NM (t(5) = 5.807, p = .002), REL (t(5) = -4.08, p = .010),
IRREL (t(5) = -4.158, p = .009), and LIN (t(5) = -2.598, p = .048). Response times in the no-prime
conditions were intermediate between those with consistent and inconsistent primes.

Active, selective inhibition causes the NCE 13
A one-way repeated-measures ANOVA on the priming effects in mean correct RT
(consistent minus inconsistent) shows a significant effect of Mask Type, F(3,15) = 27.841, p < .001,
ηp² = .848. Planned dependent-samples t-tests (two-tailed, uncorrected) showed that the priming
effect for the mask-absent condition was significantly different from that for REL (t(5) = 5.39, p =
.003), IRREL (t(5) = 5.861, p = .002), and LIN (t(5) = 5.316, p = .003), with no significant differences
between the three mask-present conditions.
Similarly, a two-way repeated-measures ANOVA on mean error rate shows a significant
main effect of Prime Type, F(2, 10) = 6.037, p = .015, ηp² = .547, and a significant interaction
between Prime Type and Mask Type, F(1.406, 7.028) = 5.865, p = .035, ηp² = .54 (Figure 2B).
Planned t-tests show that the difference between consistent and inconsistent conditions was
marginally significant for NM (t(5) = 2.458, p = .057), significant for REL (t(5) = -3.497, p = .017) and
IRREL (t(5) = -3.027, p = .029), and not significant for LIN (t(5) = -2.017, p = .1).
A one-way repeated-measures ANOVA on the priming effects in mean error rate (consistent
minus inconsistent) shows a significant effect of Mask Type, F(1.283,6.417) = 6.89, p = .032, ηp² =
.579. Planned dependent-samples t-tests (two-tailed, uncorrected) showed that the priming effect
for the mask-absent condition was different from that for REL (t(5) = 2.725, p = .042), IRREL (t(5) =
3.029, p = .029), and LIN (t(5) = 2.481, p = .056), with no significant differences between the three
mask-present conditions.
2.2.2 Event history analysis: Descriptive statistics. The 48 sample-based functions for a single
participant are shown in Figure 3. To grasp the correct interpretation of the functions, the key is to
shift along with the passage of time starting at target onset, and interpret the (vertical differences
between the) estimates for each successive bin. Let us first focus on the NP-NM (or target-only)
condition (black lines, first column in Figure 3; Table 1).
The first responses occur in bin 280 or (240,280] (n = 7), and there are 220 trials that are

Active, selective inhibition causes the NCE 14
response-free before 240 ms (the risk set for bin 280), so the sample estimate of h(280) = 7/220 =
.032. In other words, once the waiting time has reached 240 ms, then there is an estimated hazard
of .032 that the response is about to occur in the impending bin (240,280]. The probability that the
response has not yet occurred by the end of bin 280 equals S(280) = .968 (= 1 - .032), and the
unconditional probability of response occurrence equals P(280) = .032. If it does occur during bin
280, then there is an estimated conditional probability of ca(280) = 2/7 = .29 that it will be correct.
In bin 320 or (280,320], 13 responses occur and there are only 213 trials left that are
response-free before 280 ms, so h(320) = 13/213 = .061. So, once the waiting time has reached 280
ms, the estimated probability that the response will occur sometime in bin 320 or (280,320] is
.061 . The probability that the response has still not occurred by the end of bin 320 equals S(320) =
.909 (= [1-.032][1-.061]), and the unconditional probability of response occurrence in bin 320
equals P(320) = 13/220 = .059. If the response does occur during bin 320, then there is a
conditional probability of ca(320) = 10/13 = .77 that it will be correct. Continuing these calculations
for the other bins, we see that h(t) increases steadily and reaches its peak in bin 520, that S(t)
decreases monotonically over time towards zero, and that P(t) peaks in bin 440, with 48 responses
in (400,440]. The conditional accuracy equals 1 for bin 480 and onward, so every response that
occurs later than 440 ms after target onset was a correct response for target-only trials. Note that
the high hazard of .653 in bin (480,520] is experienced only by those 49 trials out of 220 that are
still response-free until at least 480 ms after target onset.
When an unmasked prime is added (green and red lines, column 1 of Figure 3), h(t) and P(t)
start to increase – and S(t) starts to decrease – earlier, i.e., around bin 200. Furthermore, if a
response occurs during bins 200-360, it is always correct when the prime is consistent (CON-NM;
green lines), but always incorrect when the prime is inconsistent (INCON-NM; red lines). Those
early responses in both unmasked prime conditions are thus triggered by the prime´s identity only.

Active, selective inhibition causes the NCE 15
Interestingly, after the h(t) functions in conditions CON-NM and INCON-NM increase
together until bin 280, response hazard keeps increasing for a while in consistent trials, but
declines and even reaches zero in bin 400 in inconsistent trials. This temporary decline in h(t) for
an unmasked inconsistent prime may represent response competition due to the target, which is
becoming overtly available in bin 280 (see NP-NM) and activates the opposite (correct) response.
After bin 400, h(t) starts to increase again in the inconsistent condition, and if a response occurs at
least 400 ms after target onset, it is always correct. Thus, the response conflict has now been
resolved in favor of the target, and these late responses are triggered entirely by the target´s
identity. Note that h(t) in the consistent condition is always higher than in the inconsistent
condition from bin 280 onward, which would not be apparent by looking only at P(t). Conversely,
most responses for NP-NM, CON-NM, and INCON-NM occur in bin 440, 320, and 280, respectively,
which is not apparent by looking only at h(t). Finally, the estimated median RT is defined as
percentile S(t).50 and is lowest for CON-NM, intermediate for NP-NM, and highest for INCON-NM.
When a relevant mask is followed by the target in the absence of primes (NP-REL; black
lines in column 2 of Figure 3), the first responses appear in bin 320 (compared to bin 280 when no
mask is presented). Compared to the target-only condition, h(t) starts to increase a bit later and
S(t) starts to decrease later. Thus, the mask seems to interfere with target processing and to delay
the occurrence of the first responses (e.g., by forward-masking the visual target signal).
More importantly, when a prime is presented before the relevant mask, earlier responses
occur compared to NP-REL, similar to the no-mask conditions. Specifically, h(t) and P(t) are a bit
higher in bin 320 for CON-REL and INCON-REL compared to NP-REL, so their S(t) functions start to
decrease a bit earlier. Furthermore, if a response occurs in bin 320, it is always correct in INCON-
REL and always incorrect in CON-REL. This striking pattern is opposite to the PCE in the no-mask
condition and indicates an NCE in ca(320). This NCE also appears in the hazard functions, but later

Active, selective inhibition causes the NCE 16
than in the conditional accuracies. While h(t) keeps increasing after bin 360 for an inconsistent
prime (INCON-REL), there is a temporary dip in the h(t) function (or a delay in its rise) for a
consistent prime (CON-REL). Following this dip, h(t) sharply increases, at a similar rate as in INCON-
NM, and all responses emitted after 480 ms are correct, indicating that a response conflict has
been solved in favor of the target. The NCE in h(t) emerges first in bin 360 and thus must represent
the consequence and not the cause of the NCE in ca(320). Finally, in this participant, the overall
time-course with relevant masks is similar to that with irrelevant or random-lines masks.
In Figures S1 to S5 we present the sample distributional data for the other five participants.
Their time-varying behavior is qualitatively similar to that in Figure 3 except for the following. First,
when a mask is present, some participants also show a few very early responses (before bin 240).
These seem to be triggered by the prime´s identity as they occur around the same time when the
first responses appear in the primed no-mask conditions. Second, for many participants the NCE in
h(t) and ca(t) is smaller for random-line masks compared to relevant or irrelevant masks.
Figure 4A shows how priming effects (differences between consistent and inconsistent
conditions) in conditional accuracy functions develop over time within and across individual
participants. In these state transition plots, we see that all participants tend to show perfect
performance for responses emitted after about 400 ms. Furthermore, all participants show a PCE
sometime between 80 and 400 ms after target onset in the mask-absent condition. In the
conditions with relevant and irrelevant masks, all participants show an NCE sometime between
200 and 400 ms after target onset. With a random-line mask, participants tend to show an NCE of
a shorter duration sometime between 240 and 400 ms.
2.2.3 EHA: Inferential statistics. To test whether the main and interaction effects including TIME,
prime type, and mask type are significant across participants, we fitted discrete-time hazard
models and conditional accuracy models to the aggregated data (generalized linear mixed-effects

Active, selective inhibition causes the NCE 17
models). The predicted cloglog[h(t)] and logit[ca(t)] functions from both selected models are
shown in Figure 5, as well as the predicted h(t) and ca(t) functions, which are obtained by applying
the inverses of the cloglog and logit link functions, respectively. Specifically, an increase in cloglogs
by an additive constant a corresponds to a multiplicative increase in harzards (or hazard ratio, HR)
by a factor of exp(a). Parameter estimates and test statistics are shown in Tables S1 and S2. During
model selection, TIME was centered on bin 320 (t = 8). For the hazard models, TRIAL was centered
on value 1000, so that any numerical values of main and interaction effects not explicitly involving
TIME or TRIAL refer to bin 320 of trial number 1000. To explicitly model the temporally localized
dip in the sample h(t) functions for condition INCON-NM, we added a predictor INH (for
“inhibition”) to the hazard model which took on value 1 for bins 320 and 360, and 0 otherwise,
and included the three interaction effects involving INH, INCON, and NM (see Table S1). Thus, the
interaction INCON:NM:INH is included to model a specific drop in response hazard around bins 320
and 360 in INCON-NM. After initial model selection, we refitted both selected models three times
with TIME centered on bins 240 (t = 6), 400 (t = 10), and 480 (t = 12), to see explicitly what values
the parameter estimates take on in these bins, and whether they represent a significant effect or
not.
2.2.3.1 Discrete-time hazard model. In this and later sections we start with a fairly complete
interpretation of the model parameters in the reference condition (no prime and relevant mask,
NP-REL) and then we will switch to a more panoramic description of the significant changes that
occur when adding primes or changing mask type.
No prime and relevant mask. The first five parameter estimates in Table S1 model the shape of the
cloglog[h(t)] function for the reference condition, trial 1000 of NP-REL (Figure 5, row 1, column 2,
black line). Because TIME and TRIAL are centered, the intercept of our regression model refers to
the model estimate at bin 320 of trial 1000. Because INH is coded as 1 in bins 320 and 360 and zero

Active, selective inhibition causes the NCE 18
otherwise, the predicted cloglog[h(320)] value is the sum of the estimates of intercept and INH, -
2.943 - .101 = -3.044, when the effects CON, INCON, NM, IRREL, LIN, TIME, and TRIAL are set to
zero. Converting back from cloglogs to hazards, h(320) = .047 (= 1 - exp[-exp(-3.044)]; Figure 5, row
2, column 2, bin 320). Parameters 2-4 show a significant linear and quadratic effect of TIME on this
intercept estimate, such that the predicted response hazard increases over time: h(240) = 0.0015,
h(320) = .047, h(400) = 0.38, and h(480) = 0.68, respectively. This reflects the intuition that the
more waiting time passes in a trial, the greater the likelihood that a response is emitted in the next
time instant given that it has not occurred yet.
Primes and relevant mask. Now we add a consistent prime to the reference condition NP-REL.
Parameters 6-9 show a significant main effect of a consistent prime (CON) in bin 320 (parameter 6,
PE = .268, p = .022), and CON interacts with TIME, changing from positive to negative over time.
Compared to the cloglog[h(t)] estimates in the reference condition (Figure 5, row 1, column 2,
black), adding a consistent prime increases the estimated cloglog[h(t)] by 1.774 units in bin 240,
which corresponds to an increase in response hazard by a factor of 5.9 (HR(240) = exp[1.774] = 5.9)
compared to the corresponding condition without a prime. Similarly, HR(320) = 1.3, HR(400) = 0.7,
and HR(480) = 0.74, indicating that the consistent prime increases the hazard of an early response
and lowers the hazard of a later response (Figure 5, row 2, column 2, green).
What happens if we add an inconsistent instead of a consistent prime? Parameters 10-13
show a significant main effect of an inconsistent prime (INCON) in bin 320 (parameter 10, PE= .563,
p < .001), and interactions with TIME. Additionally, there is an interaction between INCON and INH
(parameter 14), revealing a very slight increase in response hazards during bins 320 and 360 for
inconsistent primes. In sum, adding an inconsistent prime to the reference condition NP-REL
increases the hazard of an early response by a factor of HR(240) = 4.7, just like the addition of a
consistent prime does. However, while the effect of a consistent prime reverses over time, the

Active, selective inhibition causes the NCE 19
inconsistent prime's early positive effect simply vanishes over time: HR(320) = 2.12, HR(400) =
1.16, and HR(480) = 1.04 (Figure 5, row 2, column 2). This demonstrates an NCE emerging in h(t)
around 280-320 ms after target onset.
No mask. In the following, we will switch to a more qualitative description of the effects. Note that
from here on, all additional regression parameters have to be interpreted relative to the reference
condition: for instance, there will be an additional main effect of mask absence (a dummy variable
coded 0 when any mask is present and 1 when no mask is presented). This variable may form
interactions will all the variables described so far for the relevant mask condition (CON, INCON,
INH, TIME, TIME2, and TIME3).
Parameters 15-18 in Table S1 show a significant main effect of mask absence (NM)
(parameter 15, PE = 1.174, p < .001), and significant interactions with TIME. Compared to the
reference condition (NP-REL), removing the mask increases the hazard of response occurrence in
bins 240-400 (Figure 5, row 2, column 1 versus 2, black lines).
Now we add the interaction effects between NM and consistent (CON) versus inconsistent
primes (INCON) and compare their additional effects with the effects of CON and INCON in the
relevant-mask condition to examine what removal of the mask does to the negative compatibility
effect. In line with the effects in mean RT, removal of the relevant mask turns the priming effect
from slightly negative to strongly positive (Figure 5, column 1, row 2). This switch in the sign of the
priming effect is marked by a significant interaction of the independent variables CON and NM that
varies over TIME (parameters 20-21), and by a significant interaction between INCON and NM that
varies over TIME (parameters 22-24). The former reflects an upward shift of the hazard function in
consistent trials when the mask is removed. The latter reflects a decrease in the response hazards
in inconsistent trials from bin 320 onwards, next to an early increase in bin 240. This leads to a
reversal in the ordering of the hazard functions: In opposition to the relevant-mask condition, the

Active, selective inhibition causes the NCE 20
hazard function for consistent trials is now above that for inconsistent trials from bin 280 onwards
(Figure 5, row 2, column 1). As expected, the interaction INCON:NM:INH is significantly negative
(parameter 25), reflecting the marked dip in h(t) during bins 320 and 360 of the unmasked
inconsistent prime condition.
Irrelevant mask. Compared to the large differences between the relevant-mask and no-mask
conditions, the differences between relevant and irrelevant masks are minimal. Parameters 26-29
mainly show a significant positive main effect of IRREL that tends to change over time, reflecting
slightly elevated hazards of responding compared to NP-REL: HR(240) = 1.09, HR(320) = 1.72,
HR(400) = 1.43, and HR(480) = 1.05 (Figure 5, rows 1 and 2, columns 3 versus 2; black lines).
Parameters 30-37 show the interaction effects of IRREL with the variables for consistent primes
(CON), inconsistent primes (INCON), and TIME. None of them is statistically significant, in line with
the overall impression that compatibility effects are similar to the relevant-mask condition. (We
only left them in the model to make the comparison between the masks explicit).
Random-lines mask. Parameters 38-41 show that LIN interacts with TIME. Compared to NP-REL, LIN
decreases the hazard of fast response occurrence and somewhat elevates the hazard of later
responses: HR(240) = 0.26, HR(320) = 1.16, HR(400) = 1.23, and HR(480) = 0.9 (Figure 5, rows 1 and
2, columns 4 versus 2, black lines). Parameters 42-49 show that LIN significantly neutralizes REL's
negative effect of CON in bin 480 and REL's positive effect of INCON in bin 320. The NCE is thus
smaller and reduced in duration relative to REL.
Trial Number. The significant main effect of trial number in bin 320 interacts with TIME (parameters
50-53). Each additional trial increases the estimated response hazards by a factor of 1.000058 in
bin 240, 1.00029 in bin 320, 1.00025 in bin 400, and 1.00014 in bin 480.
2.2.3.2 Conditional accuracy model. No prime and relevant mask. The first four parameters in
Table S2 model the shape of the logit[ca(t)] function for the reference condition: NP-REL. The

Active, selective inhibition causes the NCE 21
intercept of 0.843 is the predicted logit[ca(320)] value in the reference condition, so that ca(320) =
exp(.843)/[1+exp(.843)] = .70; see Figure 5, row 3, column 2, bin 320). Parameters 2-4 show a
significant linear, quadratic, and cubic effect of TIME on the intercept, ca(240) = 0.58, ca(320) =
0.70, ca(400) = 0.98, and ca(480) = 0.99, respectively (Figure 5, row 3, column 2, black line). We
only plotted a ca(t) estimate in Figure 5 if the corresponding h(t) estimate is larger than a threshold
value of 0.02, thus omitting accuracy estimates when responses are too unlikely.
Primes and relevant mask. Parameters 5-6 in Table S2 show a significant main effect of adding a
consistent prime, CON, in bin 320 (Figure 5, row 4, column 2). Compared to the reference
condition, adding a consistent prime decreases the estimated logit[ca(t)] in bins 240-480 – an NCE
in conditional accuracy. As a measure of effect size, one can exponentiate the parameter estimate
to obtain odds ratios (OR). Thus, the odds of a correct response in bin 240 of CON-REL are
estimated to be 0.12 (= exp[-2.148]) times those in bin 240 of NM-REL, or OR(240) = 0.12. Similarly,
OR(320) = 0.2, OR(400) = 0.32, and OR(480) = 0.62 (Figure 5, row 3, column 2, green versus black).
Parameters 7-10 show a significant main effect of an inconsistent prime (INCON) in bin 320,
and interactions with TIME. Compared to the reference condition, adding an inconsistent prime
massively increases response accuracies: OR(240) = 0.99, OR(320) = 6.2, OR(400) = 5.75, and
OR(480) = 13.6 (Figure 5, row 3, column 2, red versus black). Again, this indicates an NCE in
conditional accuracies.
No mask. Conditional accuracy functions in the relevant-mask condition show an NCE that vanishes
with time. This should turn into a PCE when the mask is removed. In the following, all additional
regression effect parameters have to be interpreted relative to NP-REL.
Parameters 11-13 show a significant main effect of mask absence (NM) in bin 320 (Figure
5, row 4, columns 1 versus 2, black lines). Compared to the reference condition, removing the
mask increases the odds of a correct response by a factor of 1.5 in bin 240. Similarly, OR(320) = 2.4,

Active, selective inhibition causes the NCE 22
OR(400) = 3.2, OR(480) = 6.8. Thus, compared to NP-REL, the removal of the mask leads to higher
conditional accuracies, especially for mid-range latencies.
In line with the effects in mean error rate, removal of the mask turns the priming effect in
ca(t) from mildly negative to strongly positive (Figure 5, row 3, column 1). This switch in the sign of
the priming effect is marked by a significant interaction between CON and NM that varies linearly
over TIME (parameters 14-15), and by a significant interaction between INCON and NM that varies
linearly and quadratically over TIME (parameters 16-18).
Irrelevant mask. Again, differences between the relevant and irrelevant mask conditions are
relatively small. Parameters 19-21 show that presenting an irrelevant instead of a relevant mask
significantly increases the odds of a late correct response, OR(400) = 3.3, OR(480) = 15.5. Overall,
the model predicts similar time-courses for irrelevant and relevant masks (Figure 5, row 3, column
3 versus 2).
Random-lines mask. Parameters 25-26 show a nonsignificant main effect of LIN in bin 320 but a
linear interaction with time: OR(240) = .13, OR(320) = .9, OR(400) = 5.3, and OR(480) = 31.0 (Figure
5, row 4, columns 4 versus 2, black lines). Parameters 27-30 show that both interaction effects
CON:LIN and INCON:LIN change linearly with TIME, from positive to negative. The model thus
predicts a smaller and shorter NCE in conditional accuracies for the random-line mask as compared
to the relevant mask (Figure 5, row 3, columns 4 versus 2).
2.3 Discussion
The classical indices of priming effects (mean correct RT and mean error rate) show large positive
priming effects when the mask is absent. Interestingly, any type of mask in our study reverses the
positive priming effect (PCE) into a negative one (NCE), with faster and more accurate responses in
inconsistent that in consistent trials. This finding is in line with the self-inhibition and the mask-
triggered inhibition accounts, which both maintain that response inhibition can be elicited by

Active, selective inhibition causes the NCE 23
different types of masks, not only those containing response-relevant features. However, our data
are inconsistent with the object-updating account, which predicts that only a response-relevant
mask should lead to an NCE. We thus conclude that the NCE cannot be explained on basis of
response-relevant stimulus features only, and that a selective response inhibition process is
required. The major question for Experiment 2 thus becomes whether this inhibition process is
triggered by the prime or the mask.
Event history analysis allows us to trace the within-trial time-course of positive and
negative compatibility effects. It shows that the summary indices of mean RT and mean error rate
only give a very cursory account of the dynamics of underlying processes. For example, the positive
priming effect in mean RT has a magnitude of about 90 ms (the only time estimate available when
looking at mean performance). In contrast, an EHA can provide time estimates for both h(t) and
ca(t). Hazard and conditional accuracy functions reflect different aspects of the ongoing,
continuous decision process (Mulder & van Maanen, 2013), as indicated by the fact that the time
courses of an effect they reveal typically differ in duration and location: the PCE in h(t) actually
lasts at least 320 ms (8 bins) and the (earlier emerging) PCE in ca(t) lasts about 240 ms (6 bins;
Figure 4A). Similarly, the NCE in h(t) and ca(t) lasts at least 120 ms, compared to the 20 to 40 ms
differences measured in mean RT. Differences in mean RT clearly underestimate the actual
duration of the effect in h(t) and ca(t). Similarly, priming effects in mean error rates conceal that
there are stretches of time where the response is controlled completely by the prime or target,
leading to conditional accuracy functions that can move from 0 % to 100 % correct as the target
takes over the response process from the inconsistent prime. Event history analysis thus reveals
that analyzing only mean performance can give a misleading picture of the underlying processing
dynamics.
In the no-mask condition the response dynamics suggest that the response is at first

Active, selective inhibition causes the NCE 24
controlled exclusively by the prime (Schmidt et al., 2006, 2011), that a response conflict develops
via automatic lateral inhibition as target information becomes available, and that the response
conflict is finally resolved in favor of the target, which ultimately controls the response on its own
(provided that a prime-triggered response has not occurred in the trial). When a mask is present,
some early prime-triggered responses before bin 240 can occur, but in general response
occurrence is delayed. The early responses for consistent and inconsistent trials in bin 280 only
differ in their conditional accuracy, with more errors in the consistent condition than in the
inconsistent one. This earlier emergence of the NCE in ca(t) than in h(t) implies that by this time in
the trial, selective inhibition has already taken place that was directed specifically against the
primed response and so reversed the priming effect. In the hazard functions, the NCE becomes
visible a bit later, around bin 320, reflecting the consequence of (a) the resulting target-triggered
response conflict in the consistent condition (which temporarily increases over time) or (b) the
head-start in correct response activation in the inconsistent condition (which decreases over time).
Two other observations are worth mentioning. First, the NCE in h(t) and ca(t) for the
random-line mask was smaller and more short-lived than for the relevant and irrelevant masks. It is
possible that the changing line mask interfered with the feedforward signal from the prime more
than both unchanging mask patterns did, curtailing its effective duration relatively more than the
other masks. Also, the line mask decreased the hazard and conditional accuracy of early responses
compared to the relevant mask. It is unknown at present whether the magnitude of the NCE
depends on the strength of the initial prime signal; but if so, differences across mask types might
be explained by different degrees of mask-prime interference in the prime's feedforward signal
(not to be confused with the degree of masking in visual awareness).
Second, notice how hazard increases at a similar rate in all conditions except for those with
unmasked primes. The difference with the other ten conditions is that participants are perceptual-

Active, selective inhibition causes the NCE 25
ly aware of two and not one task-relevant double arrowhead stimulus. In the general discussion we
will revisit the distributional data of Experiment 1 and argue that active response inhibition is also
present in both visible-prime conditions.
3. Experiment 2: Is response inhibition time-locked to the prime or to the mask?
After having rejected the object-updating account, we vary the prime-mask (PM) and mask-target
(MT) SOAs to see whether the NCE is time-locked to prime or mask onset, thus allowing us to
decide between the remaining accounts. According to the self-inhibition account, the prime
triggers its own inhibition, which should accordingly be time-locked to prime onset, and inhibition
should be triggered only when the prime is effectively masked. According to the mask-triggered
inhibition account, inhibition is time-locked to mask onset, and because the mask only serves to
signal the prematureness of the prime-induced response, efficient masking of the prime is not
necessary. We employ the same materials as in Experiment 1, except that only the relevant mask is
used, and that instead of the no-prime condition we employ a neutral prime (NEU, a black ring).
3.1 Methods
Participants. Six volunteers participated (2 males, 1 left-handed). Mean age was 25.7 years (range:
23 to 29). All had normal or corrected-to-normal vision.
Materials. The same materials were employed as in Experiment 1, except that only the relevant
mask was used, and that instead of the no-prime condition we employed a neutral prime (a black
ring subtending 1.2° x 1.2°).
Design. The factors Prime Type (NEU, CON, INCON) and SOA Combination (PM-SOA long and MT-
SOA short: L-S, similarly S-L, S-S, and L-L) were crossed orthogonally, resulting in 12 trial types (NP-
S-S, CON-S-S, …, INCON-L-L).
Procedure. Each trial started with a fixation cross for 536 ms (40 frames) followed by a blank for
402 ms (30 frames). This was followed by a sequence of a 27-ms prime, a 67-ms mask, and a 67-ms

Active, selective inhibition causes the NCE 26
target with their respective SOAs (Figure 1B). PM-SOA was either 40 or 120 ms, MT-SOA was either
80 ms or 160 ms. Note that L-L and S-L as well as L-S and S-S share the same mask-target SOA (160
and 80 ms, respectively), and that S-L and L-S share the same prime-target SOA (200 ms).
The participant´s task was the same as in Experiment 1, and there was the same feedback.
The response collection period started at target onset and lasted 700 ms. There was one practice
block and 60 experimental blocks with 24 trials per block. Each block contained two trials from
each condition (one with target pointing to the right, one with target pointing to the left). Order of
presentation was randomized.
Analysis of mean error rate and mean correct RT. Mean error rate was calculated based on
observed responses between 250 and 600 ms (3.0% of the data excluded). The mean correct RT
was calculated after ignoring trials with an error, trials without a response, and trials with a correct
response but a latency below 250 ms or above 600 ms (6.1% of the data excluded).
Event history analysis. The reference condition was chosen to be the NEU-S-S condition. To fit the
h(t) model, trials were right-censored at 600 ms after target onset, and the first two bins of each
trial were deleted. The h(t) data set contained 71,084 rows. For the ca(t) model, each trial without
a response and each trial with a response latency below 160 ms or above 480 ms was deleted
(7.53% of the data). The ca(t) data set contained 7,989 rows.
3.2 Results
3.2.1 Mean error rate and mean correct RT. As expected, priming effects are negative for all SOA
combinations in mean correct RT as well as mean error rate. A two-way repeated-measures ANOVA
on mean correct RT shows a significant main effect of Prime Type, F(2,10) = 60.983, p < .001, ηp² =
.924, and a significant main effect of SOA Combination, F(3,15) = 46.137, p < .001, ηp² = .902
(Figure 2C). Planned dependent-samples t-tests (two-tailed, uncorrected) show that differences
between consistent and inconsistent prime conditions were significant for each SOA combination,

Active, selective inhibition causes the NCE 27
all t(5) ≤ -4.885, all p ≤ .005. A one-way repeated-measures ANOVA on the priming effects in mean
correct RT (consistent minus inconsistent) shows no significant effect of SOA Combination.
A two-way repeated-measures ANOVA on mean error rate shows only a significant main
effect of Prime Type, F(2,10) = 14.916, p < .001, ηp² = .749 (Figure 2D). Differences between
consistent and inconsistent prime conditions were significant for S-S, S-L, and L-L, all t(5) ≤ -3.806,
all p < .013, and marginally significant for L-S, t(5) = -2.514, p = .054. A one-way repeated-measures
ANOVA on the priming effect in mean error rate (consistent minus inconsistent) shows no
significant effect of SOA Combination.
3.2.2 EHA: Descriptive statistics. The sample-based estimates for each participant are shown in
Figures S6 to S11 and the state transition plots are shown in Figure 4B. The latter clearly show how
positive and negative compatibility effects are time-locked to primes and masks, respectively. In all
SOA conditions, we see an NCE in ca(t). It last for two or three time bins (80-120 ms) and is time-
locked to mask onset: It occurs about 80 ms earlier for long rather than short mask-target SOAs, a
delay corresponding exactly to the difference in mask onsets. Thus, in all conditions, the NCE
appears a fixed 360 ms after mask onset.
When the prime is immediately followed by a mask (S-S and S-L), we only see this NCE. But
when there is a 120-ms gap between prime and mask (L-L and L-S), a PCE unexpectedly precedes
the NCE. This PCE last for about 120 ms and is time-locked to prime onset just as the NCE is time-
locked to mask onset, shifted by the same 80 ms that separate prime onsets in the L-L and L-S
conditions. It thus occurs a fixed 320 ms after prime onset, comparable to Experiment 1.
3.2.3 EHA: Inferential statistics. The predicted cloglog[h(t)] and logit[ca(t)] functions from both
selected models are shown in Figure 6, together with the predicted h(t) and ca(t) functions. The
parameter estimates are shown in Tables S3 and S4. During model selection, TIME was centered on
bin 280, and TRIAL was centered on value 1000. To explicitly model the systematic temporary drop

Active, selective inhibition causes the NCE 28
in response hazards for consistent primes in the S-L and L-L conditions (see Figures S6 to S11), we
added a predictor „INH8“ to the hazard model which took on value 1 for bin 8 or (280,320] and 0
otherwise, as well as the relevant interactions (see Table S3).
3.2.3.1 Discrete-time hazard model. Neutral prime, short-short condition. The first five parameter
estimates in Table S3 model the shape of the cloglog[h(t)] function for the reference condition:
trial 1000 of NEU-S-S. The estimated intercept of -5.488 is the predicted cloglog[h(280)],
corresponding to h(280) = .004 (Figure 6, row 2, column 1, black). Parameters 2-4 show that
response hazards increase over time: h(280) = 0.004, h(320) = .024, h(360) = 0.16, and h(440) = 0.7
(Figure 6, row 2, column 1, black line).
Response-relevant primes, short-short condition. Now we change the neutral prime into a
consistent one (CON-S-S). Parameters 6-9 show no main effect of CON in bin 280 but significant
interactions with TIME in a linear and quadratic fashion. The interaction between CON and INH8 is
not significant (parameter 10). Compared to the reference condition, adding a consistent prime
decreases the likelihood of response occurrence as expected under the NCE, HR(280) = 0.64,
HR(320) = 0.46, HR(360) = 0.29, and HR(440) = 0.37 (Figure 6, row 2, column 1, green versus black).
Conversely, adding an inconsistent instead of a consistent prime (parameters 11-14) increases the
likelihood of response occurrence, especially for faster responses, HR(280) = 2.16, HR(320) = 1.71,
HR(360) = 1.41, and HR(440) = 1.08 (Figure 6, row 2, column 1, red versus black). These patterns
show that the nature of the NCE in h(t) changes over time: It starts out as a higher hazard for
inconsistent compared to neutral primes in bin 280, and it turns into a lower hazard for consistent
compared to neutral primes in later bins.
Short-long condition. Again, our analysis describes this data pattern relative to NEU-S-S by
introducing a new dummy variable (S-L, coding for the difference between S-S and S-L) and its
interactions with previous effects. Parameters 15-18 show a significant main effect of S-L in bin 280

Active, selective inhibition causes the NCE 29
and significant interactions with TIME. The interaction between S-L and INH8 is also significant
(parameter 19). As a result and compared to the reference condition, changing the SOA
combination from S-S to S-L increases the response hazards, especially for earlier bins: HR(280) =
6.0, HR(320) = 4.0, HR(360) = 1.9, and HR(440) = 1.1 (Figure 6, row 2, column 2 versus 1, black
lines). Thus, compared to NEU-S-S, increasing the MT-SOA leads to earlier response occurrence.
Parameters 20-28 describe how the effects of CON and INCON change when changing from
S-S to S-L. Parameters 20-24 show that the disadvantage in h(t) in CON versus NEU as observed for
S-S (see parameter 6 in Table S3) becomes even larger in bin 320 (-0.445 = 0.666-1.111; see
parameters 20 and 24), but smaller in bins 9 and 11 (+0.727 and +0.432; Figure 6, row 2, column
2). Parameters 25-28 show that the h(t) advantage for INCON versus NEU as observed for S-S
(parameter 11) is similar for S-L compared to S-S, and thus decreases over time (just as in the
mask-present conditions of Experiment 1).
Long-short condition. Again, we introduce a new parameter, L-S. Parameters 29-32 show that
changing the SOA combination from S-S to L-S leads to earlier response occurrence (but less strong
than with S-L; compare parameter lines 29 and 15). Parameters 33-40 show that the compatibility
effects for L-S are in general similar to those for S-S, except that the h(t) advantage for inconsistent
compared to neutral primes (parameter line 11) becomes even larger in bin 280 (+0.577), but
smaller in bin 360 (-0.248; Figure 6, row 2, columns 3 vs. 1).
Long-long condition. Parameters 41-44 show that, compared to NEU-S-S, the L-L condition shows
earlier response occurrence, and more so than L-S and S-L (compare parameter lines 41, 29, 15).
The compatibility effects for L-L are very similar to those for S-L (parameters 45-48; Figure 6, row 2,
column 4 versus 2). Note that our model does not capture the few early responses around bin 120
(see Figures S6 to S11).
Trial number. Each additional trial increases the estimated response hazards by a factor of 1.00024

Active, selective inhibition causes the NCE 30
in bin 280, 1.00036 in bin 320, 1.00037 in bin 360, and 1.0002 in bin 440 (parameters 49-52).
3.2.3.2 Conditional accuracy model. Short-short condition. The first three parameters in Table S4
model the shape of the logit[ca(t)] function for the reference condition, NEU-S-S (Figure 6, row 4,
column 1, black line). The intercept corresponds to a predicted ca(280) = .78. Parameters 2-3 show
that there is a significant linear effect of TIME on the intercept such that conditional accuracy
increases over time; ca(280) = 0.78, ca(320) = 0.91, ca(360) = 0.97, and ca(440) = 0.99, respectively
(Figure 6, row 3, column 1, black line).
Parameters 4-6 show that, compared the reference condition, changing the neutral prime
to a consistent one greatly decreases the accuracy of fast responses in particular, OR(280) = 0.14,
OR(320) = 0.04, and OR(360) = 0.03 (row 3, column 1, green versus black). Note that we only
plotted a ca(t) estimate in Figure 6 if the corresponding response hazard was at least .002.
Parameters 7-9 show that, compared to the reference condition, changing the neutral prime to an
inconsistent one greatly increases response accuracy, resulting in OR(280) = 6.9, OR(320) = 4.3,
OR(360) = 2.7. The model thus predicts an NCE in conditional accuracy for bins 280-400 in the
short-short condition (Figure 6, row 3, column 1).
Short-long condition. While there is no significant effect of S-L in any bin (parameter line 10), the
interaction between CON and S-L is significant in bin 360 (PE = 1.307, p = .03) and bin 440 (PE =
3.394, p = .002). The significant interaction between INCON and S-L does not change over time
(parameter 15, PE = 2.333, p = .04). The model thus predicts an NCE in conditional accuracy for
bins 200-360 in the short-long condition (Figure 6, row 3, column 2).
Long-short condition. The main effect of L-S is significant in bin 360 (PE = -0.959, p = .02) and bin
440 (PE = -1.619, p = .02), and the interaction effect between CON and L-S does not change over
time (parameter 19, PE = 1.582, p = .001). The interaction effect between INCON and L-S is
significant in bin 280 (PE = -2.66, p = .002) and bin 360 (PE = 1.805, p = .02). The model thus

Active, selective inhibition causes the NCE 31
predicts a PCE in conditional accuracies for bins 200 and 240, and an NCE for bins 320 and 360
(Figure 6, row 3, column 3; see also Figure 4B).
Long-long condition. The main effect of L-L is not significant in any bin (parameters 23-24), and the
interaction between CON and L-L is significant in bin 320 (PE = 1.392, p = .01), bin 360 (PE = 2.979,
p < .001), and bin 440 (PE = 6.154, p < .001). The model thus predicts an NCE in conditional
accuracies for bins 200 to 320 (Figure 6, row 3, column 4). Again, our model does not capture the
unexpectedly early responses around bin 120.
3.3 Discussion
The classical indices of priming effects (mean RT and error rate) consistently show negative
compatibility effects under all SOA combinations. NCE effects are invariably time-locked to the
mask and start about 360 ms after mask onset, as predicted by mask-triggered inhibition. In
contrast, the self-inhibition account would have predicted time-locking to the prime. Moreover, it
predicts that the NCE should only occur when the prime is effectively masked (i.e., at short prime-
mask intervals), which is not observed here. Our findings thus reject the self-inhibition account.
In both conditions with long prime-mask SOAs (L-S and L-L), event history analysis reveals a
sequence of three motor states: first a PCE state where the primed response is activated, then an
NCE state where the antiprime response is activated, and finally a target-controlled state that
invariably leads to correct responses. The PCE state lasts about 120 ms and emerges around 320
ms after prime onset. This state is not observed for short prime-mask SOAs (S-S and S-L),
suggesting that an early mask abolishes the initial overt positive priming effect. The PCE state is
followed by an NCE state, which is time-locked to the mask just like the PCE state is time-locked to
the prime. The NCE state starts about 360 ms after mask onset, and its duration depends on the
mask-target interval: It lasts about 80 ms for short intervals (S-S and L-S), and about 120 ms for
long intervals (S-L and L-L) and thus outlasts the prime-mask interval, indicating that some impact

Active, selective inhibition causes the NCE 32
of the prime persists after mask onset. The NCE thus seems to end when target-controlled (correct)
responses start to emerge, which occurs sooner when the MT-SOA is longer. Note that a sequence
of PCE and NCE states has also been observed in lateralized readiness potentials (Eimer &
Schlaghecken, 1998). Moreover, Jaśkowski et al. (2008) have shown that lateralized readiness
potentials in antiprime direction are time-locked to the mask. In sum, our findings thus suggest
that the NCE is a case of active, selective inhibition of the prime-induced response (Jaśkowski &
Przekoracka-Krawczyk, 2005).
4. General Discussion
4.1 Evidence for an active selective response inhibition process. Our results are compatible with
the main tenet of mask-triggered inhibition, namely that a stimulus signaling that the current
response activation is premature can trigger selective response inhibition directed specifically
against the activated response (Verleger, 2011). The same conclusion can be drawn from a study of
primed pointing movements by Schmidt, Hauch, and Schmidt (2015). In that study, participants
performed movements in different directions by pointing to the horizontal one of two target bars
(basically, a 2AFC task; but see the paper for details). Targets were preceded by primes (identical to
the targets in consistent trials, spatially switched in inconsistent trials), so that consistent primes
induced a movement in the same vectorial direction as the targets, whereas inconsistent primes
induced a movement in the opposite direction. An NCE occurred only when a mask was presented
early after the prime, and it depended on the time of response initiation (Ocampo & Finkbeiner,
2013). While fast responses (quartiles 1 and 2) started out in the direction of the prime, slower
responses (quartiles 3 and 4) started out in the precise opposite vectorial direction, even in
consistent trials. Thus, when the prime and target pairs both afforded a response to, say, the lower
left, responses first went to the upper right, in the opposite direction to both stimuli. This
surprising movement in antiprime direction for slow responses, named thrust reversal, was much

Active, selective inhibition causes the NCE 33
larger than suggested by the modest NCEs in response times (i.e., arrival times at the correct target
location). Thrust reversal started about 350 ms after mask onset, very similar to the 360 ms
observed here. Importantly, even though different types of mask modulated the time-course of the
pointing movements, thrust reversal was indistinguishable between response-relevant and
response-irrelevant masks. In addition, Schmidt et al. (2015) point out the role of global response
inhibition for the NCE. Because prime-target SOAs are long, participants are required to actively
withhold their response until the target has appeared, in order to avoid responding to the prime
and incurring a high error rate. Indeed, during pretesting of Experiment 1 participants actively
withheld their response to the target temporarily in target-only trials when these were intermixed
with the other trial types, presumably because in many trials that start with a visible arrow, a
second visible arrow can be expected (the target in both unmasked prime conditions).
Boy, Husain, and Sumner (2010) likewise conclude that response inhibition in the NCE is
active (not merely due to automatic lateral inhibition) and selective (directed specifically against
the primed response). They combine a masked-priming paradigm with an Eriksen flanker task,
concluding that the inhibitory mechanisms involved in masked priming and flanker paradigms are
overlapping whereas the previous-trial or Gratton effect does not interact with response inhibition.
They suggest that the NCE is related to a post-stimulus reactive control process that selectively
inhibits a specific motor response, and not to a proactive control process that modulates
perceptual processing through attention.4
Interestingly, Schlaghecken, Münchau, Bloem, Rothwell, and Eimer (2003) showed that
slow-frequency repetitive transcranial magnetic stimulation of motor and premotor cortex slows
mean RT but does not affect masked priming effects, concluding that masked priming effects are
generated at earlier stages of visuomotor processing, such as the basal ganglia. Similarly, D'Ostilio,
Collette, Philips, and Garraux (2012) used fast event-related fMRI and a weighted parametric

Active, selective inhibition causes the NCE 34
analysis to show that, over and above mere response conflict, the NCE is related to activity changes
in a cortico-subcortical network, involving the cortical supplementary motor area (SMA) and
subcortical striatum, the input nucleus of the basal ganglia.
4.2 The role of the basal ganglia in response inhibition. Understanding the functional anatomy of
inhibitory motor and cognitive control processes during RT tasks is the current goal of many studies
in cognitive neuroscience (Alexander & Crutcher, 1990; Cai, Oldenkamp, & Aron, 2011; Mink, 1996;
Redgrave, Prescott, & Gurney, 1999; Schmidt, Leventhal, Mallet, Chen, & Berke, 2013; Seiss &
Praamstra, 2004). Here, we focus on a computational model of the basal ganglia (BG) by Frank
(2006; Wiecki & Frank, 2013). The model supports (a) stimulus-triggered action selection, (b)
selective and nonselective inhibitory control, (c) response-conflict management, and (d) volitional
action generation. There are three main pathways linking frontal cortex with the BG (Figure 7): the
direct “Go” pathway, the indirect “NoGo” pathway, and the hyperdirect pathway. The direct "Go"
pathway (cortex → striatum “Go” → GPi → thalamus → cortex) and indirect "NoGo" pathway
(cortex → striatum “NoGo” → GPe → GPi → thalamus → cortex) together implement a selective
gating mechanism by facilitating or suppressing each of the candidate motor actions relevant in a
given task.
After stimulus onset, sensory cortical representations project to the cortical response units
in the SMA, whose activity is modulated by the thalamus. By itself, this SMA activation is not
sufficient to initiate response generation immediately because the thalamus is under tonic
inhibition from the basal ganglia's output nucleus, the GPi (Figure 7). The tonic inhibition is
removed by activation of striatal Go units in the direct pathway, which inhibit the GPi and
therefore disinhibit the thalamus. Acting in opposition to the direct GO pathway, striatal NoGo
units in the indirect pathway further excite the GPi indirectly by removing tonic inhibition from GPe
to GPi. Direct pathway activity thus results in gating of a manual response (Go) while indirect

Active, selective inhibition causes the NCE 35
pathway activity prevents its gating (NoGo). One such gating mechanism is assumed to exist for
each candidate response. Lateral inhibition between individual responses in SMA (or feedforward
inhibition from SMA to M1) in case of response conflict is detected by the anterior cingulate cortex
(Botvinick, Cohen, & Carter, 2004; van Veen & Carter, 2002), and activates the hyperdirect pathway
(anterior cingulate cortex → STN → GPi). This prevents premature responses by raising thresholds
for all candidate responses. This model is able to gate stimulus-driven responses and to
automatically slow down when those responses get into conflict.
However, in most response-conflict tasks, an initial (primed) response gets activated but
then needs to be suppressed in favor of a more controlled response. To allow executive control to
inhibit and override response activation, Wiecki and Frank (2013) add an executive control layer
(assumed to reside in DLPFC). Once DLPFC determines the correct response based on integrating
stimuli and task instructions, it (1) projects to the correct SMA response units supporting the
controlled response, (2) activates striatal NoGo units to prevent gating of the initial response, and
(3) activates striatal Go units to gate the controlled (correct) response. If due to random noise
executive control is slower on some trials, it might be too late to activate the correct rule
representation before the primed response is gated (Gratton, Coles, & Donchin, 1992). Note that
this leaves the model with two inhibition mechanisms: global threshold adjustment (hyperdirect
pathway), and selective response inhibition (indirect pathway; Wiecki & Frank, 2013).
This model is able to explain the PCE in the no-mask conditions of Experiment 1. The prime
activates its associated response representation in SMA as well as a response-specific Go signal in
the direct pathway. If this gating signal crosses the global response threshold, the thalamus is
disinhibited, and an overt response is emitted that is exclusively controlled by the prime. This
response is always correct when the prime is consistent and always incorrect when it is
inconsistent, thus explaining the PCE in the conditional accuracies, i.e., fast errors on inconsistent

Active, selective inhibition causes the NCE 36
trials. On trials where the global response threshold is initially not crossed, time passes on and the
target eventually activates the SMA representation of its associated response. This leads to
response conflict when the target is inconsistent with the visible prime, and to the temporary
decline in h(t) in the no-mask inconsistent trials due to the temporary global raising of response
thresholds by the hyperdirect pathway. The DLPFC eventually recognizes the target as the
imperative stimulus and activates SMA and striatal Go units for the controlled response and NoGo
units for the primed response. However, this process cannot yet explain the response-specific,
mask-locked NCE because the identity of the target response is not yet known by DLPFC right after
mask onset detection.
4.3 Active, selective response inhibition as the cause of the NCE. In the stop-signal task (Aron,
2011; Aron & Poldrack, 2006) a signal is presented at a variable delay after an imperative Go
stimulus, instructing the participant to withhold responding. Wiecki and Frank (2013) simulated
this task by including the right VLPFC (also known as the right inferior frontal cortex) with direct
projections to the subthalamic nucleus (STN), the key structure of the hyperdirect pathway (Figure
7). Specifically, the stop signal excites the right VLPFC, which excites the STN, which raises all
response thresholds. Furthermore, in addition to this fast and nonselective mechanism, the right
VLPFC selectively inhibits the active response via activating the corresponding population of
striatal NoGo units (perhaps via DLPFC as in Figure 7). Critically, to account for empirical
observations, this selective mechanism is slower but remains active after the STN returned to
baseline in the model of Wiecki and Frank (2013). We propose that this active dual reaction of
VLPFC to a stop-signal can explain the emergence of the NCE in masked priming, as follows.
We assume that the prime triggers a fast automatic response, that the mask acts as a stop-
signal detected by VLPFC, and that the target triggers the controlled response through DLPFC. The
first mask-triggered mechanism, a fast, nonselective, and transient raising of response thresholds

Active, selective inhibition causes the NCE 37
(hyperdirect pathway), can explain why response hazards tend to drop after the earliest prime-
triggered responses in conditions L-S and L-L in Experiment 2 (see Figures S6 to S11); in conditions
S-S, S-L, and the three masked prime conditions in Experiment 1, this mask-triggered transient
global inhibition prevents observing an overt PCE. The second mechanism, a slower, but lasting
and selective activation of striatal NoGo units can explain why the NCE emerges: It selectively
inhibits the primed response, leading to disinhibition of the antiprime response due to lateral
inhibition in SMA – in other words, thrust reversal (Schmidt et al., 2015). When the target signal
then activates the gating of the controlled response, this antiprime activation will have created a
head-start in correct response activation in inconsistent trials but leads to temporary response
conflict in consistent trials in SMA. The special feature of the masked priming paradigm is thus that
the cues for activating the primed and controlled response are separated in time, compared to
other response inhibition tasks such as the antisaccade task, the Simon task, and the Stroop task.
A neural stop-signal response might also be invoked by target onset in both unmasked prime
conditions of Experiment 1, which would explain why hazard eventually increases at a slower rate
and reaches a lower peak when the prime is visible (see Figure 5).
In sum, our results are in line with the mask-triggered inhibition account (Jaśkowski &
Przekoracka-Krawczyk, 2005; Jaśkowski, 2007, 2008; Jaśkowski, Białuńska, Tomanek, & Verleger,
2008), which holds that the mask stimulus can trigger selective response inhibition directed
specifically against the initial response. Active selective inhibition via the indirect pathway of the
BG is different from mere lateral inhibition in SMA (or feedforward inhibition from SMA to M1),
because it occurs even if prime and target afford the same response and never generate a
response conflict (Schmidt et al., 2015). We conclude that the NCE is due to active, selective,
stimulus-triggered inhibition of a premature response, likely involving the indirect pathway of the
basal ganglia.

Active, selective inhibition causes the NCE 38
References
Alexander, G. E., & Crutcher, M. D. (1990). Functional architecture of basal ganglia circuits: Neural
substrates of parallel processing. Trends in Neurosciences, 13 (7), 266-271.
Allison, P. D. (1982). Discrete-time methods for the analysis of event histories. Sociological
Methodology, 13, 61-98.
Allison, P. D. (2010). Survival analysis using SAS: A practical guide, Second Edition. SAS Institute Inc.,
Cary, NC, USA.
Ansorge, U., Kunde, W., & Kiefer, M. (2014). Unconscious vision and executive control: How
unconscious processing and conscious action control interact. Consciousness and Cognition,
27, 268-287.
Aron, A. R. (2011). From reactive to proactive and selective control: Developing a richer model for
stopping inappropriate responses. Biological Psychiatry, 69 (12), e55-e68.
Aron, A. R., & Poldrack, R. A. (2006). Cortical and subcortical contributions to stop signal response
inhibition: Role of the subthalamic nucleus. The Journal of Neuroscience, 26 (9), 2424-2433.
Banich, M. T., & Depue, B. E. (2015). Recent advances in understanding neural systems that support
inhibitory control. Current Opinion in Behavioral Sciences, 1, 17-22.
Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring and anterior cingulate
cortex: An update. Trends in Cognitive Sciences, 8 (12), 539-546.
Boy, F., Husain, M., & Sumner, P. (2010). Unconscious inhibition separates two forms of cognitive
control. Proceedings of the National Academy of Science, 107 (24), 11134-11139.
Burle, B., Vidal, F., Tandonnet, C., & Hasbroucq, T. (2004). Physiological evidence for response
inhibition in choice reaction time tasks. Brain and Cognition, 56, 153-164.
Cai, W., Oldenkamp, C. L., & Aron, A. R. (2011). A proactive mechanism for selective suppression of
response tendencies. The Journal of Neuroscience, 31 (16), 5965-5969.

Active, selective inhibition causes the NCE 39
Chechile, R. A. (2003). Mathematical tools for hazard function analysis. Journal of Mathematical
Psychology, 47, 478-494.
Chechile, R. A. (2006). Memory hazard functions: A vehicle for theory development and test.
Psychological Review, 113 (1), 31-56.
D´Ostilio, K., Collette, F., Philips, C., & Garraux, G. (2012). Evidence for a role of a cortico-subcortical
network for automatic and unconscious motor inhibition of manual responses. Plos One, 7
(10), e48007.
Eimer, M. (1999). Facilitatory and inhibitory effects of masked prime stimuli on motor activation
and behavioural performance. Acta Psychologica, 101, 293-313.
Eimer, M., & Schlaghecken, F. (1998). Effects of masked stimuli on motor activation: Behavioral and
electrophysiological evidence. Journal of Experimental Psychology: Human Perception and
Performance, 24 (6), 1737-1747.
Eimer, M., & Schlaghecken, F. (2002). Links between conscious awareness and response inhibition:
Evidence from masked priming. Psychonomic Bulletin & Review, 9 (3), 514-520.
Eimer, M., & Schlaghecken, F. (2003). Response facilitation and inhibition in subliminal priming.
Biological Psychology, 64, 7-26.
Eriksen, C. W. (1960). Discrimination and learning without awareness: a methodological survey and
evaluation. Psychological Review, 67, 279-300.
Eriksen, C. W., Coles, M. G. H., Morris, L. R., & O’Hara, W. P. (1985). An electromyographic
examination of response competition. Bulletin of the Psychonomic Society, 23, 165-168.
Frank, M. J. (2006). Hold your horses: A dynamic computational role for the subthalamic nucleus in
decision making. Neural Networks, 19, 1120-1136.
Gratton, G., Coles, M. G. H., & Donchin, E., (1992). Optimizing the use of information: Strategic
control of activation of responses. Journal of Experimental Psychology: General, 121, 480-

Active, selective inhibition causes the NCE 40
506.
Holender, D. (1986). Semantic activation without conscious identification in dichotic listening,
parafoveal vision, and visual masking: A survey and appraisal. Behavioral and Brain Sciences,
9, 1-23.
Houghton, G., & Tipper, S. P. (1996). Inhibitory mechanisms of neural and cognitive control:
Applications to selective attention and sequential action. Brain and Cognition, 30, 20-43.
Jaśkowski, P. (2007). The effect of nonmasking distractors on the priming of motor responses.
Journal of Experimental Psychology: Human Perception and Performance, 33 (2), 456-468.
Jaśkowski, P. (2008). The negative compatibility effect with nonmasking flankers: A case for mask-
triggered inhibition hypothesis. Consciousness and Cognition, 17, 765-777.
Jaśkowski, P. (2009). Negative compatibility effect: the object-updating hypothesis revisited.
Experimental Brain Research, 193, 157-160.
Jaśkowski, P., Białuńska, A., Tomanek, M., & Verleger, R. (2008). Mask- and distractor-triggered
inhibitory processes in the timing of motor responses: An EEG study. Psychophysiology, 45,
70-85.
Jaśkowski, P., & Przekoracka-Krawczyk, A. (2005). On the role of mask structure in subliminal
priming. Acta Neurobiologiae Experimentalis, 65, 409-417.
Jaśkowski, P., & Verleger, R. (2007). What determines the direction of subliminal priming. Advances
in Cognitive Psychology, 3, 181-192.
Klotz, W., & Neumann, O. (1999). Motor activation without conscious discrimination in
metacontrast masking. Journal of Experimental Psychology: Human Perception and
Performance, 25 (4), 976-992.
Klotz, W., & Wolff, P. (1995). The effect of a masked stimulus on the response to the masking
stimulus. Psychological Research, 58, 92-101.

Active, selective inhibition causes the NCE 41
Kunde, W., Kiesel, A., & Hoffmann, J. (2003). Conscious control over the content of unconscious
cognition. Cognition, 88, 223-242.
Lamme, V. A. F., & Roelfsema, P. R. (2000). The distinct modes of vision offered by feedforward and
recurrent processing. Trends in Neuroscience, 23, 571-579.
Leuthold, H., & Kopp, B. (1998). Mechanisms of priming by masked stimuli: Inferences from event-
related brain potentials. Psychological Science, 9 (4), 263-269.
Lingnau, A., & Vorberg, D. (2005). The time course of response inhibition in masked priming.
Perception & Psychophysics, 67 (3), 545-557.
Lleras, A., & Enns, J. T. (2004). Negative compatibility or object updating? A cautionary tale of
mask-dependent priming. Journal of Experimental Psychology: General, 133 (4), 475-493.
Lleras, A., & Enns, J. T. (2006). How much like a target can a mask be? Geometric, spatial, and
temporal similarity in priming: A reply to Schlaghecken and Eimer (2006). Journal of
Experimental Psychology: General, 135 (3), 495-500.
Loftus, G. R., & Masson, M. E. J. (1994). Using confidence intervals in within-subject designs.
Psychonomic Bulletin & Review, 1 (4), 476-490.
Luce, R. D. (1986). Response times. Their role in inferring elementary mental organization. New
York: Oxford University Press Inc.
Marcel, A. J. (1983). Conscious and unconscious perception: Experiments on visual masking and
word recognition. Cognitive Psychology, 15, 197-237.
Mattler, U. (2003). Priming of mental operations by masked stimuli. Perception & Psychophysics, 65
(2), 167-187.
Mattler, U., & Palmer, S. (2012). Time course of free-choice priming effects explained by a simple
accumulator model. Cognition, 123, 347-360.
Middlebrooks, P. G., & Schall, J. D. (2014). Response inhibition during perceptual decision making

Active, selective inhibition causes the NCE 42
in humans and macaques. Attention, Perception, & Psychophysics, 76, 353-366.
Miller, R. G. (1981). Survival analysis. Wiley, New York.
Mink, J. W. (1996). The basal ganglia: Focused selection and inhibition of competing motor
programs. Progress in Neurobiology, 50, 381-425.
Mulder, M. J., & van Maanen, L. (2013). Are accuracy and reaction time affected via different
processes? PLos One, 8 (11): e80222.
Neumann, O. (1990). Direct parameter specification and the concept of perception. Psychological
Research, 52 (2-3), 207-215.
Ocampo, B., & Finkbeiner, M. (2013). The negative compatibility effect with relevant masks: a case
for automatic motor inhibition. Frontiers in Psychology, 4, Article 822.
Palmer, S., & Mattler, U. (2013). On the source and scope of priming effects of masked stimuli on
endogenous shifts of spatial attention. Consciousness and Cognition, 22, 528-544.
Panis, S., & Hermens, F. (2014). Time course of spatial contextual interference: Event history
analyses of simultaneous masking by nonoverlapping patterns. Journal of Experimental
Psychology: Human Perception & Performance, 40 (1), 129-144. Doi:10.1037/a0032949
Panis, S., & Wagemans, J. (2009). Time-course contingencies in perceptual organization and object
identification of fragmented object outlines. Journal of Experimental Psychology: Human
Perception and Performance, 35, 661-687.
Praamstra, P., & Seiss, E. (2005). The neurophysiology of response competition: Motor cortex
activation and inhibition following subliminal response priming. Journal of Cognitive
Neuroscience, 17(3), 483-493.
R Core Team (2014). R: A language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
Redgrave, P., Prescott, T. J., & Gurney, K. (1999). The basal ganglia: A vertebrate solution to the

Active, selective inhibition causes the NCE 43
selection problem? Neuroscience, 89 (4), 1009-1023.
Schlaghecken, F., Münchau, A., Bloem, B. R., Rothwell, J., & Eimer, M. (2003). Slow frequency
repetitive transcranial magnetic stimulation affects reaction times, but not priming effects, in
a masked prime task. Clinical Neurophysiology, 114, 1272-1277.
Schmidt, F., Haberkamp, A., & Schmidt, T. (2011). Dos and don'ts in response priming research.
Advances in Cognitive Psychology, 7, 120-131.
Schmidt, F., & Schmidt, T. (2010). Feature-based attention to unconscious shapes and colors.
Attention, Perception, & Psychophysics, 72 (6), 1480-1494.
Schmidt, F., Weber, A., & Schmidt, T. (2014). Activation of response force by self-splitting objects:
Where are the limits of feedforward Gestalt processing? Journal of Vision, 14(9):20, 1-16.
Schmidt, R., Leventhal, D. K., Mallet, N., Chen, F., & Berke, J. D. (2013). Canceling actions involves a
race between basal ganglia pathways. Nature Neuroscience, 16 (8), 1118-1124.
Schmidt, T. (2002). The finger in flight: Real-time motor control by visually masked color stimuli.
Psychological Science, 13, 112-118.
Schmidt, T., Haberkamp, A., Veltkamp, G. M., Weber, A., Seydell-Greenwald, A., & Schmidt, F.
(2011). Visual processing in rapid-chase systems: Image processing, attention, and
awareness. Frontiers in Psychology, 2:169, 1-16.
Schmidt, T., Hauch, V., & Schmidt, F. (2015). Mask-triggered thrust reversal in the negative
compatibility effect. Attention, Perception, & Psychophysics, online publication. doi:
10.3758/s13414-015-0923-4
Schmidt, T., Niehaus, S., & Nagel, A. (2006). Primes and targets in rapid chases: Tracing sequential
waves of motor activation. Behavioral Neuroscience, 120, 1005-1016.
Schmidt, T., & Schmidt, F. (2009). Processing of natural images is feedforward: A simple behavioral
test. Attention, Perception, & Psychophysics, 71 (3), 594-606.

Active, selective inhibition causes the NCE 44
Schroll, H., & Hamker, F. H. (2013). Computational models of basal-ganglia pathway functions:
focus on functional neuroanatomy. Frontiers in Systems Neuroscience, 7, Article 122.
Schubert, T., Palazova, M., & Hutt, A. (2013). The time course of temporal attention effects on
nonconscious prime processing. Attention, Perception, & Psychophysics, 75, 1667-1686.
Seiss, E., Klippel, M., Hope, C., Boy, F., & Sumner, P. (2014). The relationship between reversed
masked priming and the tri-phasic pattern of the lateralized readiness potential. Plos One, 9
(4), e93876.
Seiss, E., & Praamstra, P. (2004). The basal ganglia and inhibitory mechanisms in response
selection: Evidence from subliminal priming of motor responses in Parkinson's disease. Brain,
127, 330-339.
Singer, J. D., & Willett, J. B. (1991). Modelling the days of our lives: Using survival analysis when
designing and analyzing longitudinal studies of duration and the timing of events.
Psychological Bulletin, 110 (2), 268-290.
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modelling change and event
occurrence. New York: Oxford University Press Inc.
Stuphorn, V. (2015). Neural mechanisms of response inhibition. Current Opinion in Behavioral
Sciences, 1, 64-71.
Sumner, P. (2007). Negative and positive masked-priming - implications for motor inhibition.
Advances in Cognitive Psychology, 3 (1-2), 317-326.
van Gaal, S., Lamme, V. A. F., & Ridderinkhof, K. R. (2010). Unconsciously triggered conflict
adaptation. Plos One, 5 (7), e11508.
VanRullen, R., & Koch, C. (2003). Visual selective behavior can be triggered by a feed-forward
process. Journal of Cognitive Neuroscience, 15, 209-217.
van Veen, V., & Carter, C. S. (2002). The anterior cingulate as a conflict monitor: fMRI and ERP

Active, selective inhibition causes the NCE 45
studies. Physiology & Behavior, 77, 477-482.
Vath, N., & Schmidt, T. (2007). Tracing sequential waves of rapid visuomotor activation in
lateralized readiness potentials. Neuroscience, 145, 197-208.
Verleger, R. (2011). Slowing of mask-triggered inhibition in the elderly. Frontiers in Psychology, 2,
Article 138.
Vorberg, D., Mattler, U., Heinecke, A., Schmidt, T., & Schwarzbach, J. (2003). Different time courses
for visual perception and action priming. Proceedings of the National Academy of Sciences,
100, 6275-6280.
Watanabe, K., & Funahashi, S. (2015). Primate models of interference control. Current Opinion in
Behavioral Sciences, 1, 9-16.
Wiecki, T. V., & Frank, M. J. (2013). A computational model of inhibitory control in frontal cortex
and basal ganglia. Psychological Review, 120 (2), 329-355.
Willett, J. B., & Singer, J. D. (1993). Investigating onset, cessation, relapse, and recovery: Why you
should, and how you can, use discrete-time survival analysis to examine event occurrence.
Journal of Consulting and Clinical Psychology, 61 (6), 952-965.
Willett, J. B., & Singer, J. D. (1995). It’s déjà vu all over again: Using multiple-spell discrete-time
survival analysis. Journal of Educational and Behavioral Statistics, 20, 41-67.

Active, selective inhibition causes the NCE 46
Author Note
We would like to thank Christina Arnold for help with data collection. Correspondence may be sent
to the first author (sven.panis@sowi.uni-kl.de).
Footnotes
1) While we used a fixed response collection period of 800 ms in each trial of Experiment 1,
most responses occurred before 600 ms after target onset. For analysis purposes, we therefore
censored all trials at 600 ms after target onset. This means that trials without a response in the
first 600 ms after target onset were treated as right-censored observations which are not ignored
but contribute to each bin´s risk set – see Table 1.
2) Standard errors for h(t), P(t), and ca(t) can be estimated using the formula for a
proportion p – the square root of {p(1-p) / N} – where N equals respectively the risk set for bin t,
the total number of trials, and the number of observed responses in bin t. The standard errors for
S(t) were estimated using the formula on page 350 of Singer and Willett (2003).
3) The complementary log-log link is preferred over the logit link for a discrete-time hazard
model when the events can in principle occur at any time during each time bin, which is the case
for RT: cloglog[h(t)] = ln{-ln[1-h(t)]}; logit[ca(t)] = ln[ca(t) / 1-ca(t)]. Inverses of the links: h(t) = 1 -
exp{-exp{cloglog[h(t)]}}; ca(t) = exp{logit[ca(t)]} / (1+exp{logit[ca(t)]}).
4) Proactive control can involve more than top-down attentional facilitation and inhibition
of task-relevant sensory channels. For example, in the model of Wiecki and Frank (2013), speed-
accuracy adjustments are implemented by increasing functional connectivity between frontal
motor regions and striatum to decrease the decision threshold under speed emphasis. Also,
response caution can be increased by increasing the baseline VLPFC activity to slow responding via
the VLPFC-STN hyperdirect pathway (see Figure 7).

Active, selective inhibition causes the NCE 47
Tables
Table 1. Life-table for the 220 trials of one participant in one condition (NP-NM; see below) of Experiment 1.
ID = identity; # Events = number of observed responses in bin t; hazard function h(t) = P(T = t | T ≥ t);
survivor function S(t) = P(T > t); probability function P(t) = P(T = t); conditional accuracy function ca(t) =
P(correct | T = t); NA = undefined. Four trials were right-censored at 600 ms (i.e., 600 < RT ≤ 800 ms or no
response occurred during the entire 800-ms response collection period).
time bin time bin
ID index t # Censored # Events Risk Set h(t) 1-h(t) S(t) P(t) # Correct # Error ca(t)
(0,40] 1 0 0 220 0 1 1 0 0 0 NA
(40,80] 2 0 0 220 0 1 1 0 0 0 NA
(80,120] 3 0 0 220 0 1 1 0 0 0 NA
(120,160] 4 0 0 220 0 1 1 0 0 0 NA
(160,200] 5 0 0 220 0 1 1 0 0 0 NA
(200,240] 6 0 0 220 0 1 1 0 0 0 NA
(240,280] 7 0 7 220 .032 .968 .968 .032 2 5 0.29
(280,320] 8 0 13 213 .061 .939 .909 .059 10 3 0.77
(320,360] 9 0 26 200 .130 .870 .791 .118 24 2 0.92
(360,400] 10 040 174 .230 .770 .609 .182 40 0 1.00
(400,440] 11 048 134 .358 .642 .391 .218 47 1 0.98
(440,480] 12 037 86 .430 .570 .223 .168 37 0 1.00
(480,520] 13 032 49 .653 .347 .077 .145 32 0 1.00
(520,560] 14 0 9 17 .529 .471 .036 .041 9 0 1.00
(560,600] 15 4 4 8 .500 .500 .018 .018 4 0 1.00

Active, selective inhibition causes the NCE 48
Figure captions
Figure 1. Trial design. (A) Experiment 1. Numbers above each panel represent the
approximate duration in ms. Cartoons of the three types of mask stimuli are depicted. (B)
Experiment 2. Note that prime-mask and mask-target SOAs are always multiples of 40 ms.
Figure 2. Mean behavioral results. (A,B) Mean error rate (B) and mean correct RT (A) in
Experiment 1 as a function of mask type (NM, REL, IRREL, LIN) and prime type (CON, NP, INCON).
(C,D) Mean error rate (D) and mean correct RT (C) in Experiment 2 as a function of SOA
combination (S-S, L-S, S-L, L-L) and prime type (CON, NEU, INCON). Error bars represent +/- 1 SEM
after removing intersubject variance (Loftus & Masson, 1994).
Figure 3. Sample-based estimates of h(t), S(t), P(t), and ca(t) for participant 6 in Experiment
1, for the first 15 bins (or 600 ms) after target onset. Bin width equals 40 ms. Green lines represent
consistent primes, red lines inconsistent primes, and black lines the prime-absent condition.
Figure 4. Sample-based ca(t)-state transition plots. For each participant, bin, and mask type
(A, Experiment 1) or SOA combination (B, Experiment 2), we first coded the type of difference in
observed performance in ca(t) between consistent (CON) and inconsistent (INCON) prime
conditions, and then applied a color code (green = evidence for PCE; pink = evidence for NCE; cyan
= no evidence for either). For bins where responses are observed for both CON and INCON: „P“:
ca(t) = 1 for CON and ca(t) = 0 for INCON; „p“: CON minus INCON ≥ .2; „N“: ca(t) = 0 for CON and
ca(t) = 1 for INCON; „n“: CON minus INCON ≤ -.2; “all”: ca(t) > .8 for both CON and INCON. For bins
where responses exclusively occur in either CON or INCON: „cc“: ca(t) = 1 for CON and no
responses for INCON; „ii“: no responses for CON and ca(t) = 0 for INCON; „ic“: ca(t) = 0 for CON and
no responses for INCON; „ci“: no responses for CON and ca(t) = 1 for INCON. Remaining bins: „x“:
no responses observed in CON and INCON; “?”: other cases.
Figure 5. Model predictions in Experiment 1. The predicted cloglog[h(t)] functions and

Active, selective inhibition causes the NCE 49
corresponding h(t) functions for trial 1000 of the selected discrete-time hazard model in Table S1
are shown in rows 1 and 2, respectively. The predicted logit[ca(t)] functions and corresponding
ca(t) functions of the selected conditional accuracy model in Table S2 are shown in rows 4 and 3,
respectively.
Figure 6. Model predictions in Experiment 2. The predicted cloglog[h(t)] functions and
corresponding h(t) functions for trial 1000 of the selected discrete-time hazard model in Table S3
are shown in rows 1 and 2, respectively. The predicted logit[ca(t)] functions and corresponding
ca(t) functions of the selected conditional accuracy model in Table S4 are shown in rows 4 and 3,
respectively.
Figure 7. Network of human cortico-BG-thalamo-cortical loops for manual motor
responses. Four frontal areas (DLPFC, VLPFC, SMA, ACC) and the three BG pathways (D, direct; ID,
indirect; HD, hyperdirect) are shown, together with the motor loop (SMA → BG → thalamus →
SMA; srriped black arrows) and the cortico-BG-thalamo-cortical loop involving DLPFC (DLPFC → BG
→ thalamus → DLPFC; grey arrows). BG, basal ganglia; GPe, globus pallidus external segment; GPi,
globus pallidus internal segment; STN, subthalamic nucleus; L, left; R, right.

Active, selective inhibition causes the NCE 50
Figures
Figure 1

Active, selective inhibition causes the NCE 51
Figure 2

Active, selective inhibition causes the NCE 52
Figure 3

Active, selective inhibition causes the NCE 53
Figure 4

Active, selective inhibition causes the NCE 54
Figure 5

Active, selective inhibition causes the NCE 55
Figure 6

Active, selective inhibition causes the NCE 56
Figure 7

Active, selective inhibition causes the NCE 57
Supplemental material
Table S1. Selected cloglog[h(t)] model in Experiment 1. The parameter estimates (PE), their standard errors
(SE), and test statistics of the (53) fixed effects of the selected model are displayed for t=8 or bin (280,320].
The estimated variances of the three random effects are displayed for each refit of the selected model with
TIME centered on t=6 or (200,240], t=10 or (360,400], and t=12 or (440,480]. Outlined p-values indicate
effects that had to be significant to stay in the model because they are not part of any higher-order effect.
TIME = (bin rank t minus 8); Loglik = log-likelihood.
(200,240] (280,320] (360,400] (440,480]
effect PE pPE SE z p PE pPE p
1 Intercept -6.509 < 2e-16 *** -2.943 0 .1 78 -16.506 < 2e-16 *** -0.730 .000262 *** 0.139 .416725
2 TIME 1.444 0.116 12.433 < 2e-16 ***
3 -0.169 0.028 -5 .9 65 .45e-09 ***
4 0.000 0.002 0.060 .952472
5 INH -0.101 0.053 -1 .9 07 .056578 .
6 CON 1.774 .45e-11 *** 0.268 0.117 2.287 .022184 * -0.351 .26e-09 *** -0.300 .00015 7 ***
7 TIME:CON -0.513 0.061 -8 .2 90 < 2e-16 ***
8 0.110 0.020 5.377 .58e-08 ***
9 -0.004 0.002 -1 .5 63 .118136
10 INCON 1.548 .21e-06 *** 0.563 0.126 4.447 .72e-06 *** 0.146 .031811 * 0.037 .674834
11 TIME:INCON -0.328 0.076 -4 .2 79 .88e-05 ***
12 0.070 0.024 2.927 .0034 25 **
13 -0.005 0.002 -1 .8 20 .068745 .
14 INCON:INH 0.189 0.084 2.246 .024717 *
15 NM 1.371 .62e-10 *** 1.174 0.116 10.074 < 2e-16 *** 0 .431 .73e-13 *** -0.210 .0073 80 **
16 TIME:NM -0.288 0.053 -5 .3 89 .09e-08 ***
17 -0.068 0.012 -5 .3 26 .00e-07 ***
18 0.013 0.001 7.141 .27e-13 ***
19 NM:INH 0.000 0.080 0.006 .9950 24
20 CON:NM 0.825 .000138 *** 0.502 0.13 1 3.825 .000131 *** 0.179 .028154 * -0.143 .244448
21 TIME:CON:NM -0.161 0.048 -3 .3 08 .000940 ***
22 INCON:NM 0.775 .009314 ** -0.331 0.148 -2.229 .02579 5 * -0.920 < 2e-16 *** -0.991 < 2e-16 ***
23 TIME:INCON:NM -0.423 0.074 -5 .6 77 .37e-08 ***
24 0.064 0.013 4.885 .04e-06 ***
25 INCON:NM:INH -1.412 0.167 -8 .4 30 < 2e-16 ***
26 IRREL 0.084 .859392 0.544 0.124 4.390 .13e-05 *** 0.357 .26e-08 *** 0.048 .619675
27 TIME:IRREL 0.024 0.109 0.222 .8246 40
28 -0.080 0.045 -1 .7 92 .073125 .
29 0.010 0.005 1.942 .0521 43 .
30 CON:IRREL 0.101 .839686 -0.161 0.161 -0.998 .318098 0.039 .659399 0.166 .1872 12
31 TIME:CON:IRREL 0.029 0.116 0.251 .802 117
32 0.057 0.046 1.246 .2127 11
33 -0.011 0.006 -1 .8 41 .065643 .
34 INCON:IRREL 0.116 .828131 -0.245 0.155 -1.579 .11444 0 -0.02 8 .7 50392 0.180 .202462
35 TIME:INCON:IRREL 0.012 0.126 0.100 .9206 39
36 0.072 0.050 1.446 .1481 81
37 -0.012 0.006 -1 .8 67 .061964 .
38 LIN -1.350 .026836 * 0 .148 0.142 1.046 .2956 53 0.205 .001632 ** -0.107 .2502 95
39 TIME:LI N 0.299 0.139 2.145 .031983 *
40 -0.180 0.053 -3 .3 94 .000690 ***
41 0.022 0.006 3.702 .0002 14 ***
42 CON:LIN 0.530 .429401 -0.08 8 0.180 -0 .492 .62 3013 0.155 .085019 . 0.375 .002690 **
43 TIME:CON:LIN -0.019 0.154 -0 .1 27 .898744
44 0.107 0.059 1.822 .0684 43 .
45 -0.018 0.006 -2 .6 58 .007869 **
46 INCON:LIN 0 .1 70 .815960 -0.388 0.178 -2.178 .029411 * -0.150 .1021 75 0.081 .549282
47 TIME:INCON:LIN -0.013 0 .1 68 -0.078 .937938
48 0.099 0.065 1.525 .1272 47
49 -0.016 0.007 -2 .1 83 .029005 *
50 TRIAL 0.058 .468179 0 .293 0.043 6.793 .10e-11 *** 0.249 < 2e-16 *** 0.135 .000867 ***
51 TIME:TRI AL 0.030 0.022 1.353 .17 6207
52 -0.034 0.008 -4 .2 53 .11e-05 ***
53 0.004 0.001 3.228 .0012 45 **
0.46 0.13 0.23 0.15
0.17 0.06 0.008 0.02
0.002 0.002 0.002 0.002
AIC 34354 BIC 34898 LogLi k -17118 Deviance 342 36
TIME2
TIME3
TIME2:CON
TIME3:CON
TIME2:INCON
TIME3:INCON
TIME2:NM
TIME3:NM
TIME2:INCON:NM
TIME2:IRREL
TIME3:IRREL
TIME2:CON:IRREL
TIME3:CON:IRREL
TIME2:INCON:IRREL
TIME3:INCON:IRREL
TIME2:LI N
TIME3:LI N
TIME2:CON:LIN
TIME3:CON:LIN
TIME2:INCON:LIN
TIME3:INCON:LIN
TIME2:TRI AL
TIME3:TRI AL
σ2 intercept
σ2 TIME
σ2 TIME2

Active, selective inhibition causes the NCE 58
Table S2. Selected logit[ca(t)] model in Experiment 1. Same conventions as in Table S1.
(200,240] (280,320] (360,400] (440,480]
effect PE pPE SE z p PE pPE p
1 (Intercept) 0.325 0.557199 0.843 0.430 1.960 0.049998 * 3.676 2.23e-08 *** 4.429 < 2e-16 ***
2 TIME 1.210 0.287 4.213 2.52e-05 ***
3 0.215 0.077 2.792 0.005237 **
4 -0.056 0.015 -3.738 0.000186 ***
5 CON -2.148 4.48e-06 *** -1.619 0.228 -7.105 1.20e-12 *** -1.138 2.08e-06 *** -0.484 0.325253
6 TIME:CON 0.240 0.149 1.611 0.107247
7 INCON -0.007 0.991793 1.824 0.378 4.820 1.44e-06 *** 1.750 0.000642 *** 2.613 0.092017 .
8 TIME:INCON 0.206 0.280 0.737 0.461077
9 -0.244 0.093 -2.618 0.008841 **
10 0.061 0.028 2.131 0.033074 *
11 NM 0.406 0.565743 0.871 0.264 3.296 0.000981 *** 1.160 0.000574 *** 1.916 0.100728
12 TIME:NM 0.052 0.216 0.244 0.807115
13 0.045 0.087 0.522 0.601408
14 CON:NM 9.265 1.78e-07 *** 5.956 0.931 6.394 1.61e-10 *** 2.652 0.006695 ** -0.581 0.759545
15 TIME:CON:NM -1.651 0.582 -2.838 0.004540 **
16 INCON:NM -11.847 7.84e-05 *** -5.527 0.715 -7.728 1.10e-14 *** -2.951 4.24e-05 *** -4.030 0.061043 .
17 TIME:INCON:NM 2.369 0.848 2.793 0.005221 **
18 -0.540 0.249 -2.167 0.030271 *
19 IRREL 0.201 0.747627 0.361 0.232 1.554 0.120148 1.191 2.15e-05 *** 2.744 0.011105 *
20 TIME:IRREL 0.204 0.185 1.104 0.269678
21 0.105 0.085 1.233 0.217409
22 INCON:IRREL -1.461 0.132442 0.185 0.514 0.360 0.718942 -0.895 0.188216 -4.556 0.030138 *
23 TIME:INCON:IRREL 0.116 0.318 0.368 0.713189
24 -0.328 0.145 -2.251 0.024363 *
25 LIN -2.062 0.020605 * -0.115 0.369 -0.312 0.754669 1.666 0.001184 ** 3.432 0.001925 **
26 TIME:LIN 0.891 0.317 2.803 0.005069 **
27 CON:LIN 3.818 0.000236 *** 1.315 0.444 2.963 0.003051 ** -1.114 0.046907 * -3.578 0.003090 **
28 TIME:CON:LIN -1.214 0.356 -3.408 0.000654 ***
29 INCON:LIN 2.932 0.027105 * 0.108 0.595 0.181 0.855999 -2.646 0.000446 *** -5.362 0.000614 ***
30 TIME:INCON:LIN -1.377 0.454 -3.029 0.002450 **
0.00 0.82 2.29 0.000
0.34 0.32 0.01 1.20
0.003 0.015 0.015 0.046
AIC 2318 BIC 2571 LogLik -1123 Devi ance 2246
TIME2
TIME3
TIME2:INCON
TIME3:INCON
TIME2:NM
TIME2:INCON:NM
TIME2:IRREL
TIME2:INCON:IRREL
σ2 intercept
σ2 TIME
σ2 TIME2

Active, selective inhibition causes the NCE 59
Table S3. Selected cloglog[h(t)] model in Experiment 2. TIME=t-7. Otherwise same conventions as in Table
S1.
(240,280] (280,320] (320,360] (400,440]
effect PE SE z p PE pPE pPE p
1 (Intercept) -5.488 0.332 -16.485 < 2e-16 *** -3.419 < 2e-16 *** -1.767 < 2e-16 *** 0.178 0.107943
2 TIME 2.268 0.148 15.300 < 2e-16 ***
3 -0.194 0.033 -5.830 5.54e-09 ***
4 -0.004 0.003 -1.510 0.131069
5 INH8 -0.285 0.100 -2.841 0.004492 ** -0.285 0.004491 **
6 CON -0.450 0.338 -1.331 0.183040 -0.986 5.67e-07 *** -1.232 < 2e-16 *** -1.007 < 2e-16 ***
7 TIME:CON -0.694 0.195 -3.555 0.000379 ***
8 0.164 0.042 3.903 9.51e-05 ***
9 -0.006 0.003 -1.822 0.068449 .
10 CON:INH8 0.205 0.233 0.880 0.378800 0.205 0.378 788
11 INCON 0.769 0.107 7.128 1.02e-12 *** 0.537 2.48e-13 *** 0 .347 3.72e-11 *** 0.079 0.149311
12 TIME:INCON -0.252 0.077 -3.244 0.001180 **
13 0.021 0.028 0.767 0.442796
14 -0.000 0.003 -0.131 0.895808
15 SL 1.786 0.217 8.197 2.47e-16 *** 1.076 < 2e-16 *** 0.616 5.13e-16 *** 0.134 0.062682 .
16 TIME:SL -0.861 0.143 -5.985 2.17e-09 ***
17 0.164 0.041 3.982 6.83e-05 ***
18 -0.013 0.004 -2.845 0.004442 **
19 SL:INH8 0.302 0.139 2.172 0.029878 * 0.302 0.029 871 *
20 CON:SL 0.316 0.382 0.826 0.408776 0.666 0.004408 ** 0.727 2.57e-07 *** 0.432 1.18e-05 ***
21 TIME:CON:SL 0.533 0.223 2.384 0.0171 09 *
22 -0.201 0.056 -3.545 0.000393 ***
23 0.018 0.005 3.280 0.001038 **
24 CON:SL:INH8 -1.111 0.350 -3.174 0.001503 ** -1.111 0.001503 **
25 INCON:SL 0.012 0.176 0.072 0.942344 0.093 0.439932 0.038 0.655 318 -0.118 0.24262 2
26 TIME:INCON:SL 0.178 0.126 1.409 0.158956
27 -0.112 0.048 -2.342 0.019194 *
28 0.014 0.006 2.481 0.013113 *
29 LS 0.889 0.292 3.045 0.002324 ** 0.779 7.46e-09 *** 0.494 2.50e-10 *** -0.080 0.2457 92
30 TIME:LS 0.020 0.252 0.081 0.935581
31 -0.152 0.073 -2.085 0.037110 *
32 0.021 0.006 3.220 0.001280 **
33 CON:LS 0.543 0.422 1.287 0.198102 0.166 0.481345 0.115 0.418649 0.315 0.000937 ***
34 TIME:CON:LS -0.596 0.295 -2.021 0.043330 *
35 0.247 0.079 3.109 0.001877 **
36 -0.028 0.007 -3.873 0.000107 ***
37 INCON:LS 0.577 0.278 2.075 0.038030 * -0.051 0.718292 -0.248 0.007507 ** -0.016 0.864333
38 TIME:INCON:LS -0.900 0.241 -3.733 0 .000190 ***
39 0.299 0.074 4.014 5.97e-05 ***
40 -0.027 0.007 -3.735 0.000188 ***
41 LL 2.250 0.188 11.955 < 2e-16 *** 1.372 < 2e-16 *** 0.692 < 2e-16 *** -0.074 0.158438
42 TIME:LL -0.977 0.110 -8.837 < 2e-16 ***
43 0.099 0.015 6.196 5.79e-10 ***
44 LL:I NH8 0.350 0.132 2.636 0.008379 ** 0.350 0.0083 74 **
45 CON:LL 0.389 0.371 1.049 0.294248 0.711 0.00119 0 ** 0.883 2.36e-12 *** 0.779 < 2e-16 ***
46 TIME:CON:LL 0.396 0.193 2.049 0.040501 *
47 -0.074 0.025 -2.941 0.003267 **
48 CON:LL:INH8 -0.720 0.306 -2.354 0.018593 * -0.720 0.01858 7 *
49 TRIAL 0.244 0.072 3.383 0.000717 *** 0.357 9.61e-12 *** 0.370 < 2e-16 *** 0.202 1.03e-08 ***
50 TIME:TRIAL 0.172 0.039 4.342 1.41e-05 ***
51 -0.063 0.013 -4.592 4.40e-06 ***
52 0.004 0.001 2.671 0.007562 **
0.46 0.31 0.20 0.06
0.04 0.03 0.02 0.02
0.0004 0.0004 0.0004 0.004
AIC 30161 BIC 30693 LogLi k -15023 Devia nce 30045
TIME2
TIME3
TIME2:CON
TIME3:CON
TIME2:INCON
TIME3:INCON
TIME2:SL
TIME3:SL
TIME2:CON:SL
TIME3:CON:SL
TIME2:INCON:SL
TIME3:INCON:SL
TIME2:LS
TIME3:LS
TIME2:CON:LS
TIME3:CON:LS
TIME2:INCON:LS
TIME3:INCON:LS
TIME2:LL
TIME2:CON:LL
TIME2:TRIAL
TIME3:TRIAL
σ2 intercept
σ2 TIME
σ2 TIME2

Active, selective inhibition causes the NCE 60
Table S4. Selected logit[ca(t)] model in Experiment 2. TIME=t-7. Otherwise same conventions as in Table S1.
(240,280] (280,320] (320,360] (400,440]
effect PE SE z p PE pPE pPE p
1 I ntercept 1.255 0.57 2.19 0.028059 * 2.281 4.01e-08 *** 3.493 < 2e-16 *** 6.477 < 2e-16 ***
2 TIME 0.932 0.34 2.72 0.006498 **
3 0.093 0.07 1.21 0.226022
4 CON -1.990 0.69 -2.86 0.004226 ** -3.223 6.31e-11 *** -3.447 5.85e-16 *** -0.869 0.197514
5 TIME:CON -1.737 0.43 -3.98 6.65e-05 ***
6 0.504 0.09 5.18 2.19e-07 ***
7 I NCON 1.932 0.36 5.27 1.32e-07 *** 1.457 3.18e-05 *** 0.985 0.008150 ** 0.049 0.958054
8 TIME:INCON -0.476 0.34 -1.39 0.164285
9 0.001 0.11 0.01 0.989414
10 SL -0.331 0.67 -0.48 0.625765 0.473 0.363470 0.652 0.187200 -0.867 0.311986
11 TIME:SL 1.118 0.47 2.35 0.018373 *
12 -0.313 0.11 -2.80 0.005010 **
13 CON:SL -0.779 0.88 -0.88 0.378295 0.264 0.679042 1.307 0.029204 * 3.394 0.002296 **
14 TIME:CON:SL 1.043 0.40 2.58 0.009709 **
15 INCON:SL 2.333 1.11 2.09 0.036266 * 2.333 0.036263 * 2.333 0.036264 * 2.333 0.036263 *
16 LS -0.542 0.67 -0.80 0.419846 -0.720 0.114628 -0.959 0.016719 * -1.619 0.022229 *
17 TIME:LS -0.148 0.47 -0.31 0.754173
18 -0.030 0.10 -0.27 0.779917
19 CON:LS 1.582 0.49 3.19 0.001408 ** 1.582 0.001408 ** 1.582 0.001408 ** 1.582 0.001408 **
20 INCON:LS -2.660 0.87 -3.03 0.002444 ** 0.189 0.766042 1.805 0.016719 * 1.337 0.267233
21 TIME:INCON:LS 3.466 0.90 3.83 0.000127 ***
22 -0.616 0.20 -3.05 0.002251 **
23 LL -0.620 0.61 -1.01 0.309421 -0.403 0.338181 -0.186 0.626571 0.247 0.745183
24 TIME:LL 0.217 0.28 0.75 0.449478
25 CON:LL -0.195 0.80 -0.24 0.808351 1.392 0.012248 * 2.979 2.99e-07 *** 6.154 5.04e-07 ***
26 TIME:CON:LL 1.587 0.42 3.70 0.000214 ***
0.02 0.05 0.12 0.58
0.01 0.02 0.03 0.06
0.0004 0.0004 0.0004 0.0004
AIC 1493 BIC 1716 LogLik -714.5 Devia nce 1429
TIME2
TIME2:CON
TIME2:INCON
TIME2:SL
TIME2:LS
TIME2:INCON:LS
σ2 intercept
σ2 TIME
σ2 TIME2

Active, selective inhibition causes the NCE 61
Figure S1

Active, selective inhibition causes the NCE 62
Figure S2

Active, selective inhibition causes the NCE 63
Figure S3

Active, selective inhibition causes the NCE 64
Figure S4

Active, selective inhibition causes the NCE 65
Figure S5

Active, selective inhibition causes the NCE 66
Figure S6

Active, selective inhibition causes the NCE 67
Figure S7

Active, selective inhibition causes the NCE 68
Figure S8

Active, selective inhibition causes the NCE 69
Figure S9

Active, selective inhibition causes the NCE 70
Figure S10

Active, selective inhibition causes the NCE 71
Figure S11