Abstract Anterior cingulate and medial frontal cortex (dACC/mFC) response to negative feedback represents the actions of a generalized error-monitoring system critical for the management of goal-directed behavior. Magnitude of dACC/mFC response to negative feedback correlates with levels of post-feedback behavioral change, and with proficiency of operant learning processes. With this in mind, it follows that an ability to alter dACC/mFC response to negative feedback may lead to representative changes in operant learning proficiency. To this end, the present study investigated the extent to which healthy individuals would show modulation of their dACC/mFC response when instructed to try to either maximize or minimize their neural response to the presentation of contingent negative feedback. Participants performed multiple runs of a standard time-estimation task, during which they received feedback regarding their ability to accurately estimate a one-second duration. On Watch runs, participants were simply instructed to try to estimate as closely as possible the one second duration. On Increase and Decrease runs, participants performed the same task, but were instructed to “try to increase [decrease] their brain's response every time they received negative feedback”. Results indicated that participants showed changes in dACC/mFC response under these differing instructional conditions: dACC/mFC activity following negative feedback was higher in the Increase condition, and dACC activity trended lower in the Decrease condition, compared to the Watch condition. Moreover, dACC activity correlated with post-feedback performance adjustments, and these adjustments were highest in the Increase condition. Potential implications for neuromodulation and facilitated learning are discussed.

Citation: Shane MS, Weywadt CR (2014) Voluntary Modulation of Anterior Cingulate Response to Negative Feedback. PLoS ONE 9(11): e107322. https://doi.org/10.1371/journal.pone.0107322 Editor: Emmanuel Andreas Stamatakis, University Of Cambridge, United Kingdom Received: December 23, 2013; Accepted: August 15, 2014; Published: November 6, 2014 Copyright: © 2014 Shane, Weywadt. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This project has been supported by an internal award by The Mind Research Network to MSS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction The ability to adaptively manage goal-directed behavior requires a consistent monitoring for, and adjustment to, indication of error in performed actions. To this end, electrophysiological and functional magnetic resonance imaging (fMRI) work have converged to demonstrate important neural signatures that appear sensitive to the receipt of feedback indicating goal-directed error [1]–[4]. This work originated with the identification of the feedback error-related negativity (fERN) – a unique electrophysiological signature that occurs reliably between 250–400 ms following the receipt of feedback indicating goal-directed error [1]–[2]. Source localization work has subsequently identified the likely source of this activity to the dorsal anterior cingulate cortex (dACC) and/or adjacent medial frontal cortex (mFC), and convergent fMRI work has confirmed a central role for dACC/mFC in the detection of negative feedback [3]–[4].Contemporary models of reinforcement learning now stress that this dACC/mFC response likely reflects the generation of a critical neural reward-prediction error signal that indicates when outcomes do not occur as expected [5]. Indeed, several studies have reported that individuals with greater amplitude fERNs and/or increased dACC/mFC response to negative feedback show more adaptive post-feedback performance adjustment and/or greater operant learning proficiency [6]–[8]. This has led researchers to posit that dACC/mFC response to negative feedback represents not only a feedback detection system, but also a critical component for the adaptive selection and guidance of subsequent corrective behavior [9]–[10]. A burgeoning area of work has become interested in the extent to which brain activity may be responsive to adaptive modulation. Emerging technologies including transcranial direct current stimulation (TDCS) [11], transcranial magnetic stimulation (TMS) [12], and real-time functional magnetic resonance imaging (rt-fMRI) [13] have all shown potential for facilitating changes in specific neural firing patterns. More basic even, is complimentary work on “emotion regulation”, which has demonstrated that participants are capable of engendering changes in neural responses, simply through voluntarily-initiating up- or down-modulation of reactivity to presented stimuli [14]–[16]. In a standard emotion regulation paradigm, participants are shown a variety of emotionally-valent pictorial stimuli, and are simply asked to try to intentionally increase or decrease their responsivity during stimulus processing. Evidence suggests that participants are quite capable of voluntarily modulating their neural responses in this fashion. Moreover, several studies have reported important relationships between voluntarily-induced modulation of neural responses, and subsequent changes in either behavior [17]–[18] or experience [19]–[20]. Thus, the ability to voluntarily modulate neural reactivity may be more than academic, and may offer a variety of useful real-world applications. One such application could be the ability to foster improved sensitivity to information indicating error in goal-directed behavior. Increased sensitivity to error could promote improvements in cognitive control and/or operant learning proficiency. Decreased sensitivity to error could, in turn, prove adaptive for individuals with characteristic hypersensitivity to error (e.g., high-anxious populations) [21]. With this in mind, the present study sought to evaluate the extent to which participants could modulate their neural responses following the receipt of negative feedback within a standard time-estimation task (within which they received veridical positive or negative feedback indicating the accuracy of their attempts to estimate a one-second duration). Closely mirroring the technique employed in studies on emotion-regulation, each participant performed this task under three instructional conditions. In a Watch condition, participants performed the task normally, with standard instructions to estimate the one-second duration as accurately as possible. In Increase and Decrease conditions, participants were instructed to perform the same task, but to “try to increase [decrease] your brain's response” as much as possible following the receipt of negative feedback. In this way, the study afforded a careful within-subject evaluation of the extent to which individuals could voluntarily modulate their neural responses to the presentation of contingent negative feedback. Of particular interest were changes in participants' dACC/mFC response, given its well-established involvement in error-detection and action-monitoring. In addition, we sought to evaluate the relationship between dACC/mFC activity and post-feedback performance adjustments by evaluating changes in estimation attempts from trial n to trial n+1. We hypothesized that our sample of healthy individuals would show changes in dACC/mFC response following negative feedback in the direction instructed. Moreover, we hypothesized that these changes in neural response would be related to the magnitude of participants' post-feedback performance adjustments.

Method Participants Eighteen healthy individuals (8 females) were recruited through advertisements posted on The University of New Mexico campus. Age ranged from 18 to 44 (M = 25.00, SD = 6.27). Time Estimation Task The time estimation task (depicted graphically in Figure 1) required that participants attempt to estimate as accurately as possible a one-second duration. Each participant performed 5 practice trials, followed by 60 experimental trials, all of which were similarly designed, and modeled after previous work [4]. Each trial began with a large asterisk presented on-screen for 1000 ms. Participants were informed that they should wait for the asterisk to disappear, and then try to press a button with their right index finger exactly 1000 ms after the asterisk's offset. Following their button press was a randomly jittered interval (2000 ms, 3500 ms, or 5000 ms) to improve deconvolution from the standard hemodynamic response curve. Finally, participants received feedback regarding the accuracy of their estimate attempt. This feedback came in one of two varieties: on informative feedback trials, participants received either a plus sign (‘+’) or a minus sign (‘-‘), which indicated whether their estimate was accurate or inaccurate on that trial. On uninformative feedback trials, participants received only a question mark (‘?’), regardless of whether their estimate was accurate or inaccurate. These question mark trials were presented on exactly 50% of all accurate trials and 50% of all inaccurate trials. Thus four different trial types were possible (Informed-Accurate, Informed-Inaccurate, Uninformed-Accurate, Uninformed-Inaccurate), and each occurred with near-equal frequency. The uninformative trials may appear cumbersome, but constituted a critical component of the study design: because participants' actual estimation accuracy could be matched across informative and uninformative trials, a direct comparison of these trials afforded a careful control for well-established effects of outcome anticipation on neural responses to feedback stimuli [5], [22] (see Data Analytic Strategy section below for additional discussion within the present paper). Following a second jittered interval (2000 ms, 3500 ms, or 5000 ms) the next trial began. PPT PowerPoint slide

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larger image TIFF original image Download: Figure 1. Task details and trial type organization. https://doi.org/10.1371/journal.pone.0107322.g001 Participants' estimates were deemed accurate if they fell within a specified window surrounding 1000 ms. The initial window was set at ±250 ms; thus time estimates between 750 ms and 1250 ms received accurate feedback, and estimates that fell outside this window received inaccurate feedback. To ensure that participants received an equal number of accurate/inaccurate feedback trials, an adaptive algorithm was employed, such that the window of accuracy increased by 30 ms following each accurate estimate, and decreased by 30 ms following each inaccurate estimate. As in previous research, this algorithm allowed for the nearly equal presentation of positive (51.5% of all trials) and negative (48.5% of all trials) feedback trials in a manner that was undetectable by participants. Procedure After providing informed consent, participants completed 5 practice trials to familiarize themselves with the task. Following these practice trials, participants performed six separate 60-trial runs of the time-estimation task described above (two Watch runs, two Increase runs, two Decrease runs). Thus, participants completed 120 Watch trials, 120 Increase trials, and 120 Decrease trials of the task over the course of a single one-hour MRI session. All participants performed the two Watch runs first, to establish their baseline neural response to the positive and negative feedback, followed by the Increase and Decrease runs, which were presented in counter-balanced order. On Watch runs, participants were instructed to press a button as soon as they believed a one-second duration had expired following the offset of the presented asterisk. They were informed that they would receive accuracy/inaccuracy feedback, however no additional instructions were provided regarding how to process that feedback. On Increase and Decrease runs, participants were again instructed to estimate this one-second duration, but were explicitly instructed to “try to increase [decrease] your brain's response every time you receive negative feedback”. Participants were not provided with instructions for how to accomplish this neuromodulation; rather, they were told that the intent of the study was to determine whether they could accomplish this by their own devices. Ethics Statement All procedures were approved by The University of New Mexico Internal Review Board, and were in accordance with the provisions of the World Medical Association Declaration of Helsinki. All participants provided written informed consent to participate in the study. Data Acquisition All fMRI data collection was performed using a Siemens TIM Trio 3 Tesla MRI system. Images were presented with a JVC DLA Multimedia projector (Model DLA-SX200-NLG) using E-Prime 2.0 software [23]. Thirty-two axial slices covering the whole brain (3.5 mm) were collected using a gradient echo-planar pulse sequence (TR = 2000 ms, TE 29 ms, FA = 65, FOV: 24×24 cm, 64×64 matrix, 3.44 mm×3.44 mm in plane resolution, flip angle: 75°). All preprocessing and GLM-based statistical analyses of data were carried out using Statistical Parametric Mapping 5 (SPM5) as described below. Functional images were reconstructed offline and reoriented to approximately the anterior commissure/posterior commissure (AC/PC) plane. Functional image runs were motion corrected using an algorithm unbiased by local signal changes (INRIAlign) [24] as implemented in SPM5. No participants showed head movements in excess of 5 mm, and thus all 18 participants were retained for the analyses reported below. A mean functional image volume was constructed for each run from the realigned image volumes. The mean EPI image was normalized to the EPI template. The spatial transformation into standard MNI space was determined using a tailored algorithm with both linear and nonlinear components [25]. The normalization parameters determined for the mean functional volume were then applied to the corresponding functional image volumes for each participant. The normalized functional images were smoothed with a 9 mm full width at half-maximum (FWHM) Gaussian filter. A high-pass filter (cutoff period 116 hz) was applied to remove any low-frequency confounds. A latency variation amplitude-correction method was used to provide a more accurate estimate of hemodynamic response for each condition [26]. Data Analysis Individual participant data was analyzed using a mixed-effects event-related model in SPM5. The asterisk cue, the participant's response (accurate, inaccurate), and the feedback presentation (Informed-Accurate, Uninformed-Accurate, Informed-Inaccurate, Uninformed-Inaccurate) were each modeled as separate events. The primary event of interest, feedback presentation, was modeled with a standard hemodynamic response function with 2 s duration. Contrast images corresponding to Informative-Accurate, Informative-Inaccurate, Uninformative-Accurate and Uninformative-Inaccurate trials were computed separately for each of the Watch, Increase and Decrease conditions, and compared against each condition's implicit baseline using the general linear model. Group analyses utilized a random effects ‘flexible factorial' approach in SPM5 to create a 3 (Instruction: Watch, Increase, Decrease) ×4 (Feedback: Informed-Accurate, Informed-Inaccurate, Uninformed-Accurate, Uninformed-Inaccurate) within-group ANOVA at the second level. Evaluation of higher-order main effects and interactions were followed by t-contrasts, guided by a priori hypotheses, which focused on comparing the Informed-Inaccurate > Uninformed-Inaccurate contrast across each of the Watch, Increase and Decrease conditions. Activity in the Watch condition was used as a representation of participants' neural responses upon receipt of Informative-Inaccurate feedback under standard “passive-viewing” conditions. Activity in the Increase and Decrease conditions, in turn, was used as a representation of the extent to which participants could follow the instruction to voluntarily increase or decrease their neural activity following receipt of the same Informed-Inaccurate feedback. Analyses thus evaluated Informed-Inaccurate > Uninformed-Inaccurate BOLD response within each of the three instruction conditions, and also evaluated the extent to which Informed-Inaccurate activity in the Increase and Decrease conditions differed from activity in the Watch condition. For authenticity, we also report results for the Informed-Inaccurate > Informed-Accurate contrast; however, we primarily focus on the Informed-Inaccurate > Uninformed-Inaccurate contrast because we believe this contrast affords a more careful control for the participants' actual estimation accuracy. Increased control of this nature may generally be viewed as advantageous, but may be particularly important in the present context, given the well-established reciprocality of neural responses during the expectation and presentation phases of negative feedback processing [5], [22]. Because of this reciprocality, neural responses during the presentation-phase of negative feedback processing have been shown to attenuate if participants have formed a previous expectation that such feedback is likely. In the Informed-Inaccurate > Informed-Accurate contrast the accuracy of participants' estimates (and, by proxy, their expectation of accurate or inaccurate feedback) is likely to vary. In contrast, the Informed-Inaccurate > Uninformed-Inaccurate contrast afforded a careful matching of participants' actual estimation accuracy, and thus minimized the likelihood that expectation-related effects would complicate the data. All data were intensity-thresholded at p<.001, with a cluster-size correction undertaken via AlphaSim to equate to a family wise error (FWE) rate of p<.05 (k = 19). In addition, two 10 mm regions of interest (ROIs) spheres were constructed, within dACC (central coordinate: x = 9, y = 30, z = 27) and mFC (central coordinate: x = 6, y = 15, z = 57), to allow for optimal evaluation of activity within regions with primary involvement in error-monitoring. Coordinates for these ROIs were arrived at by averaging coordinates obtained through an instructed sampling of the relevant literature on error-monitoring responses to negative feedback [27], [28], and were thresholded at p<.05, FWE-svc. Voluntarily-induced modulation and post-feedback performance adjustments. To evaluate the extent to which voluntarily-induced changes in neural response to negative feedback would influence performance adjustments on the following trial, we calculated ‘estimation change scores' for each participant on a trial-by-trial basis, by calculating the absolute value of (participants' estimation time on trial n) - (participant's estimation time on trial n+1). For instance, if a participant's time estimates on successive trials were: 1250 ms, 950 ms, and 1400 ms, then two change scores could be calculated as follows: Note that by using absolute values, change scores were not sensitive to the direction of change. This was deemed appropriate given that the negative feedback did not provide participants with directional information. Three mean change scores were calculated, representing the extent to which participants adjusted their estimation attempt on trials following Informed-Inaccurate feedback in each of the Watch, Increase and Decrease instruction conditions. These estimation change scores were then interrogated in two complimentary ways. First, change scores were entered into SPSS and evaluated via one-way ANOVA to identify behavioral differences in adjustment magnitude across the three instruction conditions. Second, estimation change scores were entered as a first-level parametric modulator (tied to the feedback presentation) in SPM5, and then interrogated via a 3 (Instructions: Watch, Increase, Decrease) ×4 (Feedback: Informed-Accurate, Informed-Inaccurate, Uninformed-Accurate, Uninformed-Inaccurate) “flexible-factorial” ANOVA, to afford whole-brain (p<.05, FWE, cluster-corrected via AlphaSim (k = 19)) and ROI (p<.05, FWE-svc) evaluations of neural activity that varied with estimation change scores on a trial-by-trial basis. As before, higher-level main effect and interaction analyses were followed by planned comparisons capable of targeting instruction-related differences across each of the Informed-Inaccurate > Uninformed-Inaccurate contrast images.

Discussion The present study was designed to evaluate the extent to which participants could undertake voluntary up- and/or down-modulation of error-related neural activity in response to the presentation of negative feedback. To this end, participants were asked to perform a simple time-estimation task under a standard Watch condition, and under Increase and Decrease conditions during which they were asked to intentionally enhance or reduce their brain's response following the presentation of the negative feedback. Despite being given no guidance as to how to accomplish this neuromodulation, participants showed considerable capacity for altering their neural responses in the instructed direction. Activity within a large cluster that spanned across hypothesized regions increased in the Increase condition; and a more specific sub-cluster within dACC trended towards a significant reduction in the Decrease condition. To our knowledge, this is the first study to demonstrate that neural activity underlying the processing of error-related information may be modulated through such voluntary efforts (but see [30] for evidence that one's emotional response to error can be regulated). This finding may be interpreted within the context of a growing literature suggesting that humans may have the capacity to invoke substantive influence over the nature of their neural responses to specific stimulus types, including emotionally-valent pictures [15], [31], pain [32], stimuli that evoke craving [33], and during the processing of happy memories [34]. The ability to modulate neural activity to negative feedback may have particularly practical implications, however. The magnitude of this dACC response is believed to index the activity of a generalized error-monitoring system important for the adaptive selection and guidance of subsequent corrective behavior [6]–[8]. If this is true, then up-regulation of this dACC response could facilitate one's ability to learn through trial-and-error, and to manage goal-directed behavior in the face of changing environmental demands. The results of the present study support this hypothesis: magnitude of dACC response following negative feedback correlated with the extent to which participants adjusted their estimation attempts on the following trial (in the Increase and Watch conditions, at least). Moreover, estimation change scores correlated with dACC response across the Watch and Increase conditions, and were higher in the Increase condition compared to the Watch and Decrease conditions. While these results did not translate to the Decrease condition, they nonetheless suggest that participants' voluntary modulation of their neural response to the negative feedback also influenced the extent to which they adjusted their post-feedback behavior. The possibility that more prolonged neuromodulatory training could serve to facilitate operant learning proficiency is an intriguing possibility that future research could further consider (see [35]). It is also relevant to note that differential responsivity to error has been shown characteristic of a variety of personality characteristics related to clinical and subclinical states. Individuals with low levels of inhibitory control, including those with substance abuse disorders and attention hyperactivity deficit disorder (ADHD) have, for instance, been characterized with a hypoactive dACC response to error-related information [36], [37]. Individuals characterized by heightened levels of anxiety have, in turn, been characterized with increased dACC response to error-related information [21], [38], The extent to which these individual differences would themselves influence the capacity to successfully undertake dACC neuromodulation to error remains an open question, but one that future research may do well to consider. It should also be noted, however, that participants in the present study showed dACC reductions in the Decrease condition that only reached trend levels of significance – while this may be the result of the small sample size of the present study, it also may deem consideration of therapeutic benefits of dACC down-regulation somewhat premature. It is important to note that our two primary regions of interest showed somewhat different patterns of activity across the three instruction conditions. Activity in dACC showed the hypothesized pattern: activity increased in the Increase condition, and decreased in the Decrease condition, compared to the Watch condition. However, activity in mFC, as well as in insular and inferior frontal regions, showed significant increases in both the Increase and Decrease conditions. A simple interpretation of this data could be that participants were capable of modulating their dACC response to the presentation of negative feedback, but that this capacity did not extend to adjacent mFC regions. However, such interpretation would ignore the extent to which participants' modulation attempts themselves required the recruitment of frontoparietal resources towards the initiation of top-down effortful control [39], [40]. Indeed, we may anticipate increased control-related recruitment in both the Increase and Decrease conditions, as participants attempt to modulate their neural reactivity in the instructed direction. In this context, the increased mFC activity seen across Increase and Decrease conditions may not imply decreased modulation capacity, but rather increased recruitment of regulatory resources. To separate activity associated with each process, we undertook a direct comparison of post-error feedback activity in the Increase and Decrease conditions (both of which should have required similar recruitment of effortful control processes). This contrast identified increased activity in both dACC and mFC in the Increase condition. We interpret this as evidence of successful modulation of dACC/mFC response, even after parsing activity associated with the initiation of resource-intensive top-down control processes. This study is not without its limitations. First, our sample size is relatively modest, which may have challenged our ability to identify small- or medium-sized effects. This may be particularly relevant when interpreting the results from the Decrease condition, where dACC reductions only reached trend significance levels. Future research would do well to replicate this effect within a larger sample, at which point more conclusive evidence for the capacity to down-modulate error-related responses may be acquired. Second, we once again acknowledge the challenges associated with trying to distinguish between generative versus regulatory processes. This is an oft-acknowledged challenge [17] that characterizes the majority of emotion-regulation work, and the present study is no exception. The time-estimation paradigm was not designed to explicitly leverage the ability to distinguish generative versus regulatory processing; however, direct contrast of the Increase and Decrease conditions provided some useful insights. Future work specifically designed to isolate the heavily overlapping processes would greatly benefit the field. To summarize, our results expand on work undertaken within the "emotion regulation" literature, and demonstrate that individuals have the capacity for voluntary modulation of neural activity underlying a more cognitively-mediated error-monitoring process. This synthesizes well with growing work indicating the plasticity of neural structure and function, and highlights the fact that such plasticity is not necessarily reliant on emerging high-tech methodologies such as TMS, TDCS or rt-fMRI. It is frequently assumed that both automatic and controlled emotion regulation strategies serve adaptive (and/or maladaptive) self-regulatory functions; the extent to which cognitive regulation strategies also serve such functions has received less, and perhaps less-than-warranted, attention.

Author Contributions Conceived and designed the experiments: MSS. Performed the experiments: CRW MSS. Analyzed the data: MSS CRW. Contributed reagents/materials/analysis tools: MSS CRW. Wrote the paper: MSS CRW.