Reward anticipation enhances brain activation during

Reward anticipation enhances brain activation during response inhibition

Patricia Rosell-Negre, Juan Carlos Bustamante, Paola Fuentes-Claramonte, Víctor Costumero, Sergio Benabarre & Alfonso Barros-Loscertales Cognitive, Affective, & Behavioral Neuroscience ISSN 1530-7026 Cogn Affect Behav Neurosci DOI 10.3758/s13415-014-0292-9

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Author's personal copy Cogn Affect Behav Neurosci DOI 10.3758/s13415-014-0292-9

Reward anticipation enhances brain activation during response inhibition Patricia Rosell-Negre & Juan Carlos Bustamante & Paola Fuentes-Claramonte & Víctor Costumero & Sergio Benabarre & Alfonso Barros-Loscertales

# Psychonomic Society, Inc. 2014

Abstract The chance to achieve a reward starts up the required neurobehavioral mechanisms to adapt our thoughts and actions in order to accomplish our objective. However, reward does not equally reinforce everybody but depends on interindividual motivational dispositions. Thus, immediate reward contingencies can modulate the cognitive process required for goal achievement, while individual differences in personality can affect this modulation. We aimed to test the interaction between inhibitionrelated brain response and motivational processing in a stop signal task by reward anticipation and whether individual differences in sensitivity to reward (SR) modulate such interaction. We analyzed the cognitive–motivational interaction between the brain pattern activation of the regions involved in correct and incorrect response inhibition and the association between such brain activations and SR scores. We also analyzed the behavioral effects of reward on both reaction times for the “go” trials before and after correct and incorrect inhibition in order to test error prediction performance and postinhibition adjustment. Our results show enhanced activation during response inhibition under reward contingencies in frontal, parietal, and subcortical areas. Moreover, activation of the right insula and the left putamen positively correlates with the SR scores. Finally, the possibility of reward outcome affects not only response inhibition Electronic supplementary material The online version of this article (doi:10.3758/s13415-014-0292-9) contains supplementary material, which is available to authorized users. P. Rosell-Negre : J. C. Bustamante : P. Fuentes-Claramonte : V. Costumero : A. Barros-Loscertales (*) Departamento de Psicología Básica, Clínica y Psicobiología, Universitat Jaume I, Castellón, Av. de Vicent Sos Baynat, s/n, 12071 Castelló de la Plana, Spain e-mail: [email protected] S. Benabarre Unidad Docente Medicina Familiar y Comunitaria, Instituto Aragonés de Ciencias de la Salud (IACS), Avenida San Juan Bosco, nº13, 50009 Zaragoza, Spain

performance (e.g., reducing stop signal reaction time), but also error prediction performance and postinhibition adjustment. Therefore, reward contingencies improve behavioral performance and enhance brain activation during response inhibition, and SR is related to brain activation. Our results suggest the conditions and factors that subserve cognitive control strategies in cognitive motivational interactions during response inhibition. Keywords Reward . Response inhibition . Sensitivity to reward

Introduction Cognitive control capacity is the ability to regulate, coordinate and sequence thoughts and actions in accordance with internally maintained behavioral goals (Braver, 2012). Executive processes, such as working memory, switching, planning, or inhibition, constitute a set of processes that are particularly important for behavioral control toward achieving a goal (Pessoa & Engelmann, 2010). The dual mechanism of control (DMC) framework hypothesizes that cognitive control operates via two distinct operating modes: proactive control and reactive control (Braver, 2012). Under the former, triggering of goal representations occurs before their implementation; that is, goal-relevant information continues to be actively maintained in a sustained manner before cognitively demanding events take place to optimally bias attention, perception, and action systems in order to attain such a goal (top-down bias). In contrast, under reactive control, activation of goal representations (or retrieved) occurs only when they are needed; thus recruitment of attention occurs as a late correction mechanism (bottom-up bias). Among some known factors that favor one type of control strategy instead of another (e.g., working memory load, fluid intelligence), interindividual differences, such as affectiverelated traits like sensitivity to reward (SR), apparently play a main role (Braver, 2012; Jimura, Locke, & Braver, 2010).

Author's personal copy Cogn Affect Behav Neurosci

Previous studies have investigated how motivation can potentially affect cognition and behavior by using reward contingencies to correct performance in different types of paradigms related to cognitive control functions, such as attention (Krebs, Schott, Schütze, & Düzel, 2009; Padmala & Pessoa, 2011; Stoppel et al., 2011), task switching (Braem, Verguts, Roggeman, & Notebaert, 2012), working memory (Beck, Locke, Savine, Jimura, & Braver, 2010; Gilbert & Fiez, 2004; Jimura et al., 2010) and decision making (Pochon et al., 2002; Rogers et al., 2004). If the motivational value of a goal is high, the behavior to achieve it needs translating into an optimal cognitive strategy, which involves both behavioral accuracy and neural efficiency. This modulation would involve neurobehavioral adjustment based on proactive/reactive strategies (Braver, 2012; Jimura et al., 2010). However, previous studies have stated that positive incentives may impair cognitive performance by either diminishing behavioral control (e.g., impulsive individuals or drug abuse population) (Padmala & Pessoa, 2010) or impairing cognitive focusing and increasing distractibility (Aarts, Holstein, & Cools, 2011). Particularly, there is very little information available on the conditions that determine interactions between motivation and inhibitory control. As far as we know, very few studies have investigated the interaction between cognition and motivation during a stop signal task (SST) with reward contingencies (Boehler, Hopf, Stoppel, & Krebs, 2012; Boehler, Schevernels, Hopf, Stoppel, & Krebs, 2014; Leotti & Wager, 2010; Padmala & Pessoa, 2010). Padmala and Pessoa (2010) designed an SST in which reward contingencies involved only “go” trials in a blockwise fashion. In behavioral terms, Padmala and Pessoa (2010) observed how participants exhibited longer stop signal reaction times (SSRTs) during reward in relation to the nonreward condition, indicating that it is harder to inhibit their responses under the reward condition. Their neuroimaging findings revealed that a set of brain regions showed reduced activation for successful response inhibition under the reward condition at (1) frontal brain areas like the bilateral inferior frontal gyrus (IFG) and the left precentral gyrus; (2) parietal areas, such as the inferior parietal lobe and the bilateral intraparietal sulcus; and (3) dorsal striatal areas, such as the bilateral putamen. Boehler et al. (2012) designed an SST with randomly intermixed reward-related and reward-unrelated “stop” trials with “go” trials and indicated the type of stop trial by changing the color of the stop signal. Unlike Padmala and Pessoa (2010), Boehler et al. (2012) found that SSRT was reduced for the reward-related stop trials, indicating that it is easier to inhibit their responses under the reward prospect. This same group replicated these behavioral results in a posterior study and tested them inside the scanner (Boehler et al., 2014). They observed that the right insula/IFG and the dorsal anterior cingulate cortex (dACC)/presupplementary motor area (preSMA) displayed enhanced activity during the reward-related

stop trials. Thus, Padmala and Pessoa (2010) observed that response inhibition is harder when reward contingencies favor the opposite “go” response, while Boehler et al. (2012; Boehler et al., 2014) noted that response inhibition can benefit from the prospect of reward in their correct inhibition. Hence, motivational factors influence SSRT during response inhibition and in the implicated neural system. Moreover, this is not the only behavioral influence that we can observe on SSTs, because this task involves preparatory and adjustment processes, which may be observed on trials surrounding the stop trials. Thus, participants can also strategically speed or slow go RTs because speeding go RTs reduces the probability of inhibition, whereas slowing go RTs increases it (Bissett & Logan, 2011). Therefore, we can find two other behavioral adjustments in an SST beyond response inhibition: error prediction (e.g., how go RTs preceding stop trials relate to inhibition accuracy) and postinhibition (e.g., how inhibition accuracy affects RTs on go trials following the stop signal). Individuals differ as to how they change the response strategy under a reward uncertainty condition (Winkler, Hu, & Li, 2013). Thus, previous literature suggests that SR modulates the effects of a motivational context in demanding cognitive situations (Braver, 2012; Jimura et al., 2010; Locke & Braver, 2008), with greater performance enhancement, brain function modulation, and variation in neural and behavioral signatures of the proactive and reactive cognitive control modes, which lead to optimized goal attainment (Braver, 2012). Indeed, SR helps explain the tendency to adopt a proactive control strategy, particularly under cognitive task conditions with a high reward motivational value (Braver, 2012; Jimura et al., 2010). SR reflects the persistency of the reward-triggered behaviors regulated by the reward system (Jimura et al., 2010), which, in turn, becomes involved in the interaction between motivation and cognition (Aarts et al., 2011; Ávila et al., 2011; Engelmann, Damaraju, Padmala, & Pessoa, 2009; Pessoa & Engelmann, 2010; Zink, Pagnoni, Martin-Skurski, Chappelow, & Berns, 2004). So, although SR is expected to mediate approach behaviors rather than response inhibition (Gray & McNaughton, 2000), if response inhibition is modulated by reward, the effects of reward on response inhibition may depend on individuals’ SR. Therefore, we expect SR individual differences to be associated with reward-based adaptations during response inhibition processes. In short, the aims of our study include testing (1) whether the possibility of monetary rewards for correct go trials and stop trials improves performance in both trial types and enhances activation of the brain regions involved in inhibitory control processes (e.g., IFC, SMA, striatum) and (2) whether, in turn, individual differences in SR modulate the rewardrelated effects during SST performance (e.g., SSRT, preparatory and adjustment processes). We predict that (1) reward contingencies will improve performance and enhance brain activity during successful response inhibition and (2)


individual differences in greater SR will show more marked incentive effects on brain responses.

Method Participants Twenty-eight volunteers (23 men and 5 women, of whom 1 was left-handed) participated in this study. Their mean age was 38.89 years old (SD = 10.48; range = 20 – 56), and their average years of education were 11.21 (SD = 2.52; range = 6 – 17). The inclusion criteria to select the sample were (1) no major medical illnesses or DSM IV Axis I disorders, (2) no history of head injury with loss of consciousness not lasting longer than 30 min, and (3) no current use of drugs or psychoactive substances. Each participant completed the Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ; Torrubia, Ávila, Molto, & Caseras, 2001) to obtain a mean SR score of 9.79 (SD = 4.69; range = 3 – 21; men = 10.57, SD = 4.65; woman = 6.2, SD = 3.12), t(26) = 1.99, p = .057. The respective nonparametric test inspected the self-report SR measure (Kolmogorov–Smirnov [K-S], Z = 0.79, p = .57), thus ensuring normality in distribution. The scale showed good reliability (Cronbach’s alpha = .84). All the participants received information about the nature of the research, provided written informed consent prior to participating in the study, and received a monetary award for their participation in accordance with their performance during the task. The institutional Review Board of the Universitat Jaume I (Castellón, east Spain) approved this study. Task design We scanned all the participants during their performance in an SST with reward contingencies (see Fig. 1). Inside the scanner, participants performed two functional runs, each consisting of 170 trials, yielding a total of 340 trials. Of all the trials, 70% were go trials (n = 240), and 30% were stop trials (n = 100). In equal proportions (50%), we divided the go and stop trials into two different conditions given the possibility of obtaining monetary rewards for correct task performance. Each trial began with a fixation point shown for 500 ms, followed by a cue (C) lasting 1 s. For the reward condition (R+), the cue was a circle that informed the participants that correct execution (a fast correct response) on this trial involved a monetary reward of 0.20 euros (R+). For the nonreward condition (R−), the cue was a triangle that informed participants that they would not receive a monetary reward (R−), irrespective of their performance. After the cue and a pseudorandomized variable interval time of 2, 3, or 4 s, we displayed a square with a small circle inside it (target; T) for 1 s. Following the T, a black screen appeared for a

variable interval of 1, 2 or 3 s. Afterward, feedback (F) was presented for 500 ms, according to the potential reward outcome signaled by the cue. Participants saw the message “0.20 €” when they made a successful response or inhibition during R+. Otherwise, they saw the message “0.00€” (see Fig. 1). The interstimulus interval (ISI) was randomized after both the cue and the target presentation, using a variable ISI for both epochs, which allowed a better separation and estimation of the hemodynamic response for the target event of interest. Null events were imposed between trials. The duration of the null events ranged between 1 and 4 s. (mean, 1 s; sampled from the exponential distribution truncated at 4 s). The sequence was selected for its greater efficiency in detecting differences between events (Hagber, Zito, Patria, & Sanes, 2001; Liu, Frank, Wong, & Buxton, 2001). The stop trials and go trials were identical, but after the T, we presented a crossed-out square (stop signal) with a variable stop signal delay (SSD), indicating that participants should withhold their response. The T with the stop signal also had a fixed duration of 1 s. We adjusted the SSD dynamically by adopting a staircase procedure throughout the experiment for both experimental conditions (R + and R −) separately. The staircasing procedure ensured that participants would inhibit their response for approximately 50% of the times (Logan & Cowan, 1984; Logan, Schachar, & Tannock, 1997). The initial SSD for each condition was the median of the SSD obtained in the practice session prior to fMRI scanning. The mean trial duration was 7.30 s, the run duration was 20.71 min, and there was a 2-min rest between both runs. We displayed the accumulated monetary reward earned on a final screen after the participant completed the task. The stimuli presented throughout all the trials were white on a black background with a resolution of 800 × 600 pixels. Before they entered the scanner, we instructed all the participants about the task by reading identical instructions and by playing some demo trials. The instructions explained that the participants had to respond to the target as quickly as they could (go trials) by indicating whether a small circle was in the upper or lower part of a square; and, in some cases, a stop signal might appear, indicating that they had to inhibit their response (stop trials). We warned participants that stopping and going were equally important and that it would not always be possible to stop. We also informed them that a slower response on the go trials would not be considered a correct response. In addition, we told the participants that they would see a figure (a circle or a triangle) before the target, which determined whether they would obtain a reward, or not, for their correct execution. Moreover, we told the participants that they would receive a monetary reward at the end of their participation depending on their performance throughout the task. Thus, their main goal was to win as much money as possible. Inside the scanner, while acquiring the structural T1, the participants completed a practice version of 90 trials to minimize practice effects and to obtain the initial estimated SSDs.


Fig. 1 Stop-signal paradigm

fMRI acquisition We acquired blood oxygenation level-dependent (BOLD) fMRI data in a 1.5-T Siemens Avanto (Erlangen, Germany). We helped participants enter the MRI scanner, who occupied a supine position. We immobilized their heads with cushions to reduce motion artifacts. We presented stimuli via MRI-compatible goggles, and we used a response system to control performance during the scanning session (Responsegrips, NordicNeuroLab). We controlled the stimulus presentation with the Presentation software (http://www.neurobs.com). We obtained functional scans using a gradient-echo T2*-weighted echo-planar MR sequence (TR = 2,000 ms; TE = 48 ms; matrix = 64 × 64, voxel size = 3.5 × 3.5 × 4 mm, flip angle = 90°, 4.5-mm thickness, slice gap of 0.5 mm). We acquired 24 interleaved axial slices in parallel to the hippocampi covering the entire brain. Prior to the functional MR sequences, we acquired structural images using a high-resolution T1-weighted sequence with TR/ TE = 2,200/3.84.9 ms, FOV = 224 mm, matrix = 256 × 256 × 160, voxel size = 1×1×1 mm, which facilitated the localization and coregistration of the functional data. fMRI preprocessing We preprocessed and analyzed the data using the SPM8 software package (Statistical Parametric Mapping 8;

Wellcome Department of Imaging Neuroscience; http:// www.fil.ion.ucl.ac.uk/spm), as implemented in MATLAB R2007a (Mathworks, Inc., Natick, MA). Preprocessing included the following steps; (1) slice time correction, (2) realignment of each scan per individual to the first scan to correct motion-related artifacts (movement parameters never exceeded 2 mm of translation or 2° of rotation in any direction for any participant), (3) co-registration, (4) segmentation of each participant’s high resolution anatomical acquisition, and (5) normalization. We carried out normalization in accordance with the Montreal Neurological Institute’s (MNI) template by applying an affine transformation, followed by nonlinear deformation and using the basic functions defined in the SPM program. We applied the computed transformation parameters to all the functional images by interpolating to a final voxel size of 3 × 3 × 3 mm. Subsequently, we spatially smoothed the images with an 8 × 8 × 8 mm (FWHM) Gaussian kernel. Statistical analyses Behavioral analysis on response inhibition Behavioral performance in an SST involves two main variables: percentage of correct inhibitions and the SSRT, which provides an estimate of the inhibitory reaction time. This parameter was estimated by the so-called integration method


(Verbruggen & Logan, 2009a), which has been demonstrated to provide reliable SSRT estimates (Verbruggen, Chambers, & Logan, 2013). Here, the RT during the correct go trials is rankordered, and the RT percentile value corresponding to the percentage of incorrect stop trials is determined on a per participant basis (e.g., 54st percentile of the correct go trials RT distribution for a participant with 54% unsuccessful stop trials). The mean SSD was then subtracted from this value (Boehler et al., 2012). We did the SSRT calculation separately for the nonreward and reward trials. We compared the variables of interest with separated paired t-tests. Finally, we performed the behavioral analyses using the SPSS software package, v.20 (Statistical Package for Social Sciences; SPSS Inc., Chicago, IL). Behavioral analysis on error prediction performance and postinhibition adjustment In order to test the effect of immediate reward contingencies on preparatory and adjustments processes during the SST, we analyzed go RT for trials preceding (preinhibition) and following (postinhibition) the stop trials. Only the correct go trials were taken into account. In order to analyze the error prediction performance effect, we ran a within-subjects ANOVA, including the inhibition accuracy (stop-hit, SH; stop-fail, SF) × current reward condition (preinhibition, GoR+, GoR−) × stop-reward condition (StopR+, StopR−) factors (levels) to test whether the go RT preceding the stop signal presentation predicts inhibition accuracy. Moreover, in order to analyze the effect of postinhibition adjustment, we ran a within-subjects ANOVA, including the inhibition accuracy (SH, SF) × current reward condition (postinhibition GoR+, GoR−) × stop-reward condition (StopR+, StopR−) factors (levels), to test whether inhibition accuracy affects go RT in the trials following the stop signal. fMRI data analysis We performed the statistical analyses following the general lineal model (GLM; Friston et al., 1995). In the first-level analysis, we modeled each participant’s preprocessed time series per event of interest using the hemodynamic response function and its temporal derivate. Thus, we modeled four events of interest: SH for R + and R − (separately) and SF for R + and R − (separately). Moreover, we also modeled the other events in the paradigm: R + cue, R − cue, correct go trials for R + and R − (separately), and a null event type that included all the incorrect go trials and the remaining events that did not undergo modeling—for example, behavioral outcomes (feedback). We modeled all these events as separate regressors in the GLM context. In addition, we removed intrinsic autocorrelations by high-pass filter with a cutoff frequency of 128 Hz, which eliminates low-frequency

components. We included the motion parameters of each participant’s realignment correction in the model as “nuisance” variables. Given the objective of our study, we generated statistical contrasts of interest to obtain brain activation for the correct and incorrect responses to the target, the SH, and the SF during both conditions separately. To obtain these brain activations, we computed five contrast images: SH for R + versus baseline, SH for R−versus baseline, SF for R + versus baseline, SF for R— versus baseline, and SH for R+ and R − versus baseline, for each participant. The reference baseline for the SH and the SF under R + and R − was the same, the brain’s response to the correct go trials under both experimental conditions (R + and R−). We used those contrasts images obtained from the first-level analysis in a second-level random effects analysis to test the effects of interest in a withinsubjects ANOVA, including the inhibition accuracy (SH, SF) × stop-reward condition (StopR+, StopR−) factors (levels). Region-of-interest analysis First, in order to ensure the effect of inhibition, we defined a one-sample t-test across all the participants by directly contrasting SH>Go, irrespectively of the motivational condition (Aron & Poldrack, 2006; Xue, Aron, & Poldrack, 2008). Moreover, in order to analyze the error-related effect, we defined a one-sample t-test across all the participants by contrasting SF>Go, irrespective of the motivational condition, in order to isolate error-related activation (Ide & Li, 2011b). Finally, for the interaction effect between the cognitive and motivational processes, we used a repeated measures ANOVA with the inhibition accuracy (SH; SF) and stopreward condition (StopR+, StopR−) factors (levels) (Padmala & Pessoa, 2010). In all the analyses, gender was included as a nuisance covariate, given that previous studies reported gender differences in the SR scores (Caseras, Ávila, & Torrubia, 2003; Li, Huang, Lin, & Sun, 2007), which may affect reactivity to the reinforcing component of our task design. The focal point of our analysis on regions of interest (ROIs) was twofold: to focus the analysis on the brain regions previously related with the response inhibition and errorrelated processes and to maximize statistical power. For these purposes, we used an anatomically defined ROIs analysis based on a previous quantitative meta-analysis, which identified the main brain areas engaged by the SST (Swick, Ashley, & Turken, 2011); indeed, previous fMRI studies that investigated the interaction between inhibition and motivation have shown most of them (Boehler et al., 2014; Padmala & Pessoa, 2010). Specifically, the ROIs masks were the right IFG, right middle frontal gyrus, left superior frontal gyrus, right medial frontal gyrus, right cingulate gyrus, bilateral insula, right inferior parietal lobule, bilateral superior parietal lobule, right


precentral gyrus (SMA), left putamen and right thalamus, following Swick et al. We also predefined the right preSMA as an ROI given its implication in stop inhibition (Aron & Poldrack, 2006), the right caudate as a brain area involved in the frontostriatal loops implicated in the cognitive control of motor behavior (Chevrier, Noseworthy, & Schachar, 2007; Li et al., 2008a), the right STN (Subthalamic Nucleus) given its involvement in the fast blocking of go response execution (Aron & Poldrack, 2006), and the cerebellum, which has been found to be involved in conflict (Ide & Li, 2011b). For all these analyses, we thresholded the functional effects at a voxel-wise and a cluster-wise corrected level (FWE at p < .05). We drew the ROIs masks by Automatic Atlas Labeling from the WFUPickAtlas (Maldjian, Laurienti, Kraft, & Burdette, 2003) using a 5-mm-radius sphere (following the similar ROI definition parameters in Padmala & Pessoa, 2010) centered at the peak voxel of each cluster, as indicated in previously reported studies. Correlation analysis with SR scores Correlation analyses were performed in order to study the expected modulation of individual differences on SR over cognitive motivational interactions. Individual SR scores were correlated with the SST behavioral performance (e.g., SSRT, go RT, go and stop errors), error prediction, and postinhibition adjustment behavioral data. We also analyzed the modulator effects of SR on brain activity by correlating the individuals’ SR scores and the mean value of activity in specific ROIs. For each participant, we calculated the mean value of the parameter estimates extracted from active voxels during the interaction effect within the ROIs. Finally, these values were included in a bivariate correlation with SR scores. We carried out these analyses using SPSS v.20 (SPSS Inc., Chicago, IL).

Table 1 Means (standard deviations) of behavioral variables of interest in reward and nonreward conditions Nonreward

Reward

Go RT (ms)* Inhibition rate (%) SSD (ms) SSRT (ms)*

679.34 (90.85) 51.71 (2.71) 494.35 (102.56) 179.23 (59.35)

664.44 (85.07) 52.93 (2.80) 501.66 (107.21) 151.43 (69.35)

Unsuccessful RT (ms) Go error rate (%)

607.32 (89.73) 7.50 (5.08)

615.99 (94.08) 6.41 (5.15)

Note. RT, reaction time; ms, milliseconds; SSD, stop signal delay; SSRT, stop signal reaction time * Significant differences between experimental conditions (p < ,001)

significant differences for SSD between the R+and R−conditions, t(27) = −1.22, p > .1. Finally under both conditions, the RT for SF was shorter than those for the correct go trials [R−, t(27) = 13.17, p < .001; R+, t(27) = 9.1, p < .001], which is in line with race model predictions (Logan & Cowan, 1984). Error prediction performance and postinhibition adjustment

Results

The ANOVA on error prediction performance showed a current reward condition × the stop-reward condition interaction, F(1, 27) = 13.67, p < .01, and a triple interaction of current reward condition, stop-reward condition, and inhibition accuracy, F(1, 27) = 6.13, p > .05 (see Supplementary Materials Table 1, Table 2, and figure). Thus, preinhibition go RT predicted inhibition accuracy in accordance with the reward condition of the current go trial and the reward condition under which SH or SF took place. GoR + was faster than GoR − before SF during StopR+, t(27) = 3.28, p < .01, but this pattern reversed before SF during StopR−, t(27) = 2.32, p < .05. Moreover, the postinhibition adjustment showed a main current reward condition effect, F(1, 27) = 4.95, p < .05, and an inhibition accuracy × current reward condition interaction effect, F(1, 27) = 5.46, p < .05, which involved significant RT slowing for the GoR − trials after SF, but not for GoR + (see Fig. 2).

Behavioral results

Functional results

Between experimental conditions

We proved the inhibition effect across the set of ROIs (see above). Consistent with a growing body of literature, we observed one main effect of inhibition throughout the frontal and parietal regions (see Table 2), which included the right IFG, bilateral insula/inferior frontal cortex, right precentral/ SMA, right medial frontal gyrus, left superior frontal gyrus, right inferior parietal lobule, and bilateral superior parietal lobule (FWE at p < .05). The analysis of the error-related effect (SF>Go) evoked greater activation across several ROIs (see Table 2): the right cingulated gyrus, right medial frontal gyrus, right insula/

Table 1 summarizes the behavioral results. The staircase procedure guarantees that performance does not differ between conditions; thus, there were no differences (p > .1) in terms of inhibition accuracy. For the go correct trials, we observed significant differences in RT, which were shorter during R+, t(27) = 4.47, p < .001. For the stop trials, the SSRT was significantly shorter during R+, t(27) = 4.19, p < .001, which means that it was easier for our participants to inhibit their behavioral response during R+. However, we found no


Fig. 2 Reaction times for error-prediction performance (a) and post-inhibition adjustment (b). Figure demonstrate how reward modulated the postinhibition adjustment effect (see Suplementary Materials)

Table 2 Functional inhibition and interactional effects (FWE; p < .05) ROI Analyses

Brain Region

Brodmann Area

MNI Coordinates

Volume (mm3)

Z Score

Inhibition effect

Right inferior frontal gyrus Right insula/ inferior frontal cortex Left insula/ inferior frontal cortex Right inferior parietal lobule Right superior parietal lobule Left superior parietal lobule Right medial frontal gyrus Right precentral gyrus (SMA) Left superior frontal gyrus Right cingulated gyrus Right medial frontal gyrus Right insula/ inferior frontal cortex Left insula/ inferior frontal cortex Right inferior parietal lobule

48 47 48 42 7 7 32 6 46 23 32 47 48 42

45 36 −36 60 27 −27 3 39 −39 0 6 39 −39 63

14 20 17 −46 −64 −67 17 5 35 −22 14 20 11 −43

28 1 −2 25 43 46 49 40 34 31 52 1 1 28

594 81 270 864 810 756 54 621 27 324 864 297 756 864

3.78 3.4 4.6 5.27 5.08 4.17 2.98 4.19 2.47 3.51 4.61 3.02 4.35 5.57

Right pre-SMA/ACC Right precentral gyrus (SMA) Right thalamus/subthalamic nucleus Right cingulated gyrus Right inferior frontal gyrus Right insula/ inferior frontal cortex Left insula/ inferior frontal cortex Left putamen Right inferior parietal lobule Right pre-SMA/ACC Right superior parietal lobule Right medial frontal gyrus Right precentral gyrus (SMA) Left superior frontal gyrus Left insula/ inferior frontal cortex Right pre-SMA/ACC Right medial frontal gyrus

32 6

0 42 9 3 42 36 −36 −12 60 3 30 3 39 −36 −39 0 3

11 2 −22 −31 8 20 11 8 −43 14 −58 14 5 41 11 11 11

55 43 1 28 25 7 1 7 31 46 43 52 40 34 4 49 43

405 189 783 513 405 756 756 108 729 756 432 864 540 459 432 459 756

4.15 3 3.76 3.13 3.03 4.2 4.28 3.21 4.38 5.18 3.5 5.29 3.34 3.36 3.74 3.55 4.52

Error response effect

Interaction effect

Error response effect on R − condition

23 48 48 48 48 32 7 32 6 46 48 32 32

Note. SMA, supplementary motor area; ACC, anterior cingulate cortex; ROI, region of interest; MNI, Montreal Neurologic Institute.


Fig. 3 Regions of interest showing a cognitive motivational interaction (a,b,c,d) and the scatter plots of those regions in which their interaction effects were associted with interindividual differences in SR (B, D)


inferior frontal cortex, left insula/inferior frontal cortex, right inferior parietal lobule, right pre-SMA/ACC, right precentral gyrus (SMA), right thalamus/STN (FWE at p < .05). Inhibition accuracy by the reward condition showed a significant interaction in a set of frontal and parietal regions, in addition to the subcortical ones (see Table 2; Fig. 3). These areas included the right cingulated gyrus, right inferior parietal lobule, right superior parietal lobule, right precentral gyrus (SMA), right pre-SMA/ACC, bilateral insula, right IFG, right medial frontal gyrus, left superior frontal gyrus, and left putamen (FWE at p < .05). We find three kinds of interactions (see Fig. 2). (1) The right precentral gyrus (SMA) and the right superior parietal lobule showed reduced activation during SF in relation to SH in R−, but this reduction was more marked during R+. (2) The right pre-SMA/ACC, the bilateral insula/inferior frontal cortex, and the putamen showed a reversed pattern for R − if compared with R+; that is, while R − had an activation effect during SF in relation to SH, R + had a deactivation effect on SF. (3) The right IFG and the right inferior parietal lobule showed that R − had a similar effect on both SF and SH, but a reverse pattern of activation was noted during SF in relation to SH under R+. In general, R + incremented the functional differences between SH and SF (always SH > SF). This pattern may be associated with an opposite effect of reward anticipation over brain activation in accordance with inhibition accuracy by increasing differential responses (e.g., precentral gyrus, superior and inferior parietal) or by reversing the pattern for SH and SF (e.g., pre-SMA/ACC, bilateral insula). The IFG appeared to be engaged by both the go and stop responses for SH and SF, and reward increased the differential responses. The error-related effect by the reward condition interaction did not show any main effect (SF > SH) given the cognitive motivational interaction. However, the single contrast SF > SH under the R − condition (following Ide & Li, 2011a; Li et al. 2008b reported specific error-related activation at the left insula/inferior frontal cortex, the right pre-SMA/ACC, and the right medial frontal gyrus (FWE at p < .05). Correlation analysis with SR scores SR scores did not correlate with SST behavioral performance variables such as SSRT, go RT, and go or stop errors. Likewise, SR correlated not with postinhibition adjustment, but with error prediction performance. Thus, SR correlated positively with the go RT difference between preinhibition reward conditions (GoR + > GoR−) previous to nonrewarded SF trials, r(28) = .34, p < .05. At the brain level, the correlation analysis showed a significant positive correlation between the right insula/

inferior frontal cortex and left putamen ROI activation and the SR scores during inhibition in the R + context—that is, when rewarding SH, r = .424 and .430, p < .05, respectively (see Fig. 2).

Discussion and conclusions Our study reveals how motivational contingencies can determine the neurobehavioral modulation of inhibitory control processes. Correct response inhibition with the reward contingencies possibility improved behavioral performance and enhanced brain response in those regions commonly observed during inhibitory control processes. Furthermore, brain activation during cognitive motivational interactions was associated with individual differences in SR. Therefore, cognitive and motivational processes interact in the brain during inhibitory control (Aarts et al., 2011; Boehler et al., 2014; Engelmann et al., 2009; Locke & Braver, 2008; Padmala & Pessoa, 2010, 2011). Moreover, the interindividual differences in SR, these being the affective-related traits associated with SR (Ávila et al., 2011; Ávila & Parcet, 2001; Jimura et al., 2010), modulate this neurobehavioral interaction. The detailed analysis of methodological manipulations may involve a solution for the apparent lack of agreement reached for the behavioral and brain effects of monetary reward contingencies on inhibitory control (Beck et al., 2010; Boehler et al., 2012; Boehler et al., 2014; Padmala & Pessoa, 2010, 2011; Pochon et al., 2002; Rogers et al., 2004; Stoppel et al., 2011). By using an SST, our results can be compared with those obtained by Padmala and Pessoa (2010), and Boehler et al., (2012, 2014). In our study, the effects of anticipating reward contingencies, depending on both the go and stop performances, facilitated both processes. Boehler et al., (2012, 2014 observed a facilitation of the stop processes by rewarding only the stop trials. However, the Padmala and Pessoa (2010) design worsened stop performance by rewarding go performance. Boehler et al. (2012) suggested that the schedule of reward contingencies can independently modulate the stop and go processes. Our results add the reward modulation effect to both the stop and go processes in an intermixed design that attempts to avoid the strategic factors related to go stimulus processing; that is, shorter go RTs and SSRTs during the reward condition contrast with the observed strategic influence of slowing of go RT, yielding shorter SSRTs (Leotti & Wager, 2010; Verbruggen et al., 2013). The change in the reward contingencies and anticipation of reward chance may explain these differences between studies. We cued the trials that offered the possibility of obtaining a reward, while Boehler et al., (2012, 2014) signaled this possibility by changing the color of the stop signal, which appears at the same time as the response that must be


inhibited. After taking into account Boehler et al., (2012, 2014) and our study, we suggest that reward can enhance both reactive and proactive control. Particularly, our results confirm the theoretical approach that suggests that adopting a proactive control strategy under reward contingencies improves goal attainment (Braver, 2012). In this sense, Jimura et al. (2010) stated that the adoption of a proactive control strategy involves preparatory maintenance and updating task goals, which facilitate performance in rewarding contexts (e.g., improvement on nonrewarded trials in a reward context). Our results involve performance facilitation on trials involving reward contingencies, as compared with those that do no. However, it should be noticed that it is not possible to test the contextual effects with our study given the absence of a “neutral context,” because our paradigm parallels the rewarding context defined in the study of Jimura et al., (2010). Therefore, we should restrict the conclusions we draw herein to the immediacy of trial contingencies that differentiate our experimental conditions. From our ROI analysis approach, we can ensure that the distributed system of the bilateral cortical and subcortical regions subserves the brain processes involved in response inhibition during the SST in our study and in previous ones (Swick et al., 2011). However, our results do not provide evidence to consider these regions to relate only with inhibitory processes. Regions such the IFC, insula, pre-SMA, and ACC are nodes of the salience network that are linked to attentional processes beyond inhibitory ones during SST performance. As far as we know, the salience network as a whole has not been engaged in cognitive control during SST (Zhang & Li, 2012), although its nodes are functional and structurally connected to other networks that are directly related to the attentional and inhibitory processes involved while performing this paradigm (Boehler et al., 2014; Bonnelle et al., 2012; Zhang & Li, 2012). In broader terms, our study shows that the cognitive system required by response inhibition is influenced by motivational factors. By replicating Boehler et al. (2014), we observed that, unlike in Padmala and Pessoa (2010), reward contingencies enhance the activation of this system. Our results suggest a proactive cognitive control strategy for the adaptation of a proactive inhibitory control toward better outcomes (Aron, 2011). Two regions, the right IFC and the pre-SMA, appear to work together to intercept a go process via the striatum (Aron, 2011), although both areas would play different roles in stop signal inhibition. Concretely, IFC has been shown to detect the less frequent and behaviorally relevant stop signal because it demands a change of response (Chao, Luo, Chang, & Li, 2009; Duann, Ide, Luo, & Li, 2009). Chao et al. observed greater activity in the preSMA associated with a short SSRT, while the IFC did not differentiate between a short and a long SSRT. These results suggest an attentional role of the IFC during SST, while preSMA plays a direct role in inhibitory control (Chao et al.,

2009; Duann et al., 2009). In fact, our results support the possibility of preparing this brain network for proactive stopping (Chikazoe, 2010; Jahfari, Stinear, Claffey, Verbruggen, & Aron, 2010; Vink et al., 2005; Zandbelt et al., 2008). The striatum seems to relate more to proactive than to reactive stopping (Boehler et al., 2014; Vink et al., 2005). Some works have specifically demonstrated the putamen’s implication in response inhibition (Chambers, Garavan & Bellgrove, 2009; Chao et al., 2009; Padmala & Pessoa, 2010), in motivation (Padmala & Pessoa, 2010; Schultz, Tremblay, & Hollerman, 2000), and in the interaction of inhibition and motivational processes (Boehler et al., 2014; Padmala & Pessoa, 2010). Our interaction effects in these brain regions might determine a more pronounced functional engagement of these areas in order to accomplish accurate response inhibition for ensuring reward outcomes. Similarly, it is feasible to suggest that when reward contingencies are available, increased parietal activation is due to participants paying more attention to the stop stimuli in order to display accuracy (Corbetta & Shulman, 2002; Padmala & Pessoa, 2010). The analysis of the error-related effect showed overlapped activations between SH>Go and SF>Go at the bilateral insula/ inferior frontal cortex, the right precentral gyrus (SMA), and the right medial frontal gyrus, reflecting the more salient effect of the stop trials than of the go trials (Ide & Li, 2011a; Li et al., 2008b). However, the error-related effect during the stop trials (SF>SH) specifically activated the left insula/inferior frontal cortex, the right pre-SMA/ACC, and the right medial frontal gyrus in the absence of reward contingencies, which is in agreement with earlier studies implicating these structures in error detection and feedback processing (Hendrick, Ide, Luo, & Li, 2010; Ide & Li, 2011a, b; Winkler et al., 2013). Yet these effects reversed under reward contingencies, as reflected by the cognitive–motivational interaction. Thus, the possibility of reward contingency reducing brain reactivity to error detection may favor better response inhibition (e.g., lower SSRT) and task performance (e.g., shorter go RT), which suggests better monitoring of task demands. The possibility of reward contingencies for go and stop performance may proactively adjust the response strategies reflecting an optimal balance between conflicting demands of the go and stop tasks (Verbruggen & Logan, 2008b). Future studies may test how the effects of reward contingencies competing with task goals (Padmala & Pessoa, 2010; Pessoa, 2009) may subserve an explanation for reward-dependent disinhibition disorders, such as addiction. In agreement with previous studies (Bisset & Logan, 2011; Boehler et al., 2009; Chevrier & Schachar, 2010; Li et al., 2008a; Verbruggen & Logan, 2009), we found that error prediction performance adapts to the competing stopping and going demands in the SST. Rewarded go RT prior to stop trials tended to be shorter before rewarded SF, which follows the main interaction effect of reward on go RT during SST.


Unexpectedly, we observed that this tendency depended on the reward condition under which inhibition took place; that is, go RT during GoR − trials was longer when preceding nonrewarded SF. This result suggests that performance immediately preceding inhibition does not predict performance accuracy (e.g., SF), irrespectively of reward contingencies that influence task performance during go and stop trials (Boehler et al., 2012). Therefore, the cognitive control strategy may influence future performance, depending on the reward contingencies of future events. Otherwise, we did not find any significant activation that paralleled this behavioral interaction at the brain level. As far as we know, Ide, Shenoy, Yu, and Li (2013) is the only study that has reported the dorsal ACC as a signed error-related structure in a Bayesian ideal observer model to predict trial-by-trial probabilistic expectation of response errors during the SST across go and stop trials (Ide et al., 2013). On the other hand, the go RT immediately posterior to the stop signal on the SF trials was significantly longer than on the SH trials, reflecting postinhibition adjustment. This result suggests a change in the control strategy. The participants slowed down responses to the target in order to acquire the SH after an SF, which has also been interpreted as proactive slowing in anticipation of stop signals (Bisset & Logan, 2011). Accordingly, some previous works in the literature show this postinhibition adjustment to be greater after SF (Schachar et al., 2004), while others indicate that it was greater after SH (Emeric et al, 2007; but see also Verbruggen & Logan, 2008a). The observed postinhibition slowing can involve a reactivation of the goal (inhibit), rather than the continuous maintenance of such information, which may be a disadvantageous strategy (Braver, 2012). In our study, postinhibition adjustment disappears given the possibility of reward contingencies; that is, the RTs for the posterior go trials to the stop signals were not significantly longer during the SF trials versus the SH trials. The reinforcement cue effects on postinhibition adjustments may be considered to extend the effects of reward outcomes in conflict monitoring (Braem et al., 2012; van Steenbergen, Band, & Hommel, 2009) to reward anticipation effects. In particular, the participants seem to change response strategies proactively according to task performance (e.g., SF) and contextual cues (e.g., R+). Previous reports have shown posterror performance to be associated with the activation of prefrontal cortical regions (Li, Chao, & Lee, 2009; MarcoPallarés, Camara, Münte, & Rodríguez-Fornells, 2008). However, we found no significant activation to parallel the observed postinhibition adjustment interaction at the brain level. Taking together the behavioral results obtained during error-prediction and postinhibition task performance, we suggest that the choice of a control strategy depends on current contextual cues, because both the reactive and proactive cognitive controls offered complementary advantages and limitations, and successful cognitive control probably depends on a mixture of both strategies (Braver, 2012); that is, a particular

behavior can be proactive in a particular context, but not in another, which implies that executive functions must be flexible enough to adapt to the context. This notion supports the fact that many clinical and nonclinical groups, including impulsive individuals, attention deficit hyperactivity disorder, obsessive–compulsive disorder, and drug abuse populations, exhibit impairments when performing executive functions (Padmala & Pessoa, 2010). We also found that individual differences in SR specifically modulate the cognitive motivational interaction effect to show greater IFG and striatum (putamen) activity in high rewardsensitive individuals during correct response inhibition with the possibility of reward contingencies. Therefore, our results suggest an association between the SR trait and neural adjustment for proactive versus reactive control in an SST. Previous findings on manipulating reward contexts, which involved different tasks and cognitive domains, have shown similar effects (Jimura et al. (2010); Locke & Braver, 2008). Since the individuals who obtained higher SR scale scores show more incentive motivation (Ávila, Parcet, & BarrosLoscertales, 2008), our interpretation is that reward approach-related behaviors can determine the adoption of cognitive strategies, which lead to the best outcome. As Jimura et al. (2010) stated, highly reward-sensitive individuals can estimate successful behavioral performance, which is especially valuable in those contexts that show its association with reward attainment. Thus, under these conditions, we can expect high-scoring SR participants to preferentially show motivation to adopt a proactive cognitive control strategy with a view to optimizing their behavioral outcomes. However, our conclusions are limited, given the lack of SR-related behavioral effects during SST performance. SR merely correlated with the RT difference (GoR+>GoR−) between the go trials preceding nonrewarded incorrect inhibition. Interestingly, greater SR predicted a more marked dissociation between the rewarded and nonrewarded go behavior preceding those trials. As far as we know, no previous reports on SR and errorprediction have been published. Finally, our results should be cautiously considered, given the weakness of the behavioral effects associated with SR. Our methodological approach is not without its limitations. We assumed correct go trials as an explicit baseline in the firstlevel analyses because we obtained all the contrast images relating to stop trials by subtracting all the correct go trials, while several studies did not directly model the correct go trials (Chamberlain et al., 2009; Rubia, Smith, Brammer, & Taylor, 2003; Rubia, Smith, Taylor, & Brammer, 2007). Furthermore, the sex distribution between males and females is not equal, although the main results are still significant when we regress out gender effects, as previously noticed, or even if we exclude the five women from the analysis. Finally, the experimental task design may explain the lack of


performance effects during the SST in relation to the SR scores—for example, given the timing parameters between the go and stop trials. Thus, we suggest that a more demanding task performance (e.g., faster sequence of the go and stop events) may favor SR-related behavioral effects. In conclusion, the reward value of behavioral goals can facilitate cognitive processes and enhance the brain activation required for goal achievement. Intermittent cued rewards may facilitate a proactive cognitive control strategy to enable neurobehavioral optimization. Thus, the behavioral performance enhancement observed in an SST involves reward expectation, given the significant influence of motivational cues on brain activity during inhibitory control. The brain regions involved in such a task display more activation when rewarding correct inhibition with a view to improving performance. In addition, the SR, a personality trait that defines individual differences in participants’ sensitivity and reactivity to appetitive stimuli, seems to modulate this effect at the brain level. Reward cues seem to influence error prediction and postinhibition adjustments, which suggests changes in the cognitive control strategies in accordance with the possibilities of reward attainment during the SST.

Acknowledgements This research has been supported by Grants PSI2012-33054 from the Spanish Ministry of Economy and Competitiveness, GV/2012/042 from the Generalitat Valenciana, and 2011I040 from the Spanish National Drug Strategy.

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