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When doing nothing is an option: The neural correlates of deciding whether to act or not
NeuroImage 46 (2009) 1187–1193
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NeuroImage j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y n i m g
When doing nothing is an option: The neural correlates of deciding whether to act or not Simone Kühn a,b,⁎, Marcel Brass a a b
Ghent University, Department of Experimental Psychology and Ghent Institute for Functional and Metabolic Imaging, Henri Dunantlaan 2, 9000 Gent, Belgium Department of Psychology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstr. 1A, 04103 Leipzig, Germany
a r t i c l e
i n f o
Article history: Received 9 December 2008 Revised 19 February 2009 Accepted 12 March 2009 Available online 19 March 2009
a b s t r a c t The neural basis of intentionally deciding between different response alternatives has been extensively investigated and associated with the rostral cingulate zone (RCZ). However, from daily experience we know that the decision whether to do something is often prior to the decision what to do. This raises the fundamental question whether the decision to act and the decision not to act can be considered as functionally equivalent. Interestingly, in the legal domain such an equivalence is implicitly assumed by punishing crimes of omission. The aim of the current study was to explicitly test this assumption by comparing the neural representation of intentional actions with intentional non-actions. Our results suggest, that weighing whether to act or not involves similar areas of the brain, namely RCZ and dorsolateral prefrontal cortex, independent of the outcome of this decision. This finding strongly supports the assumption that intentionally not acting can be considered as a mode of action. © 2009 Elsevier Inc. All rights reserved.
Introduction Self-initiated intentional action lies at the heart of human nature. One important precondition for intentional action is the momentum of choice, e.g. the choice whether to do something or not. We are all familiar with the following situation in road traffic: we are driving along a road, we are approaching a traffic light, we spot that it turns yellow. In that moment in time we hesitate and make up our mind whether to accelerate and resume our way or not. Research on decision-making in the voluntary action domain has nearly exclusively focussed on choices between different action alternatives (Cunnington et al., 2006; Lau et al., 2004, 2006; Müller et al., 2007; van Eimeren et al., 2006). Deciding between different response alternatives has been associated with the rostral cingulate zone (RCZ; Debaere et al., 2003; Müller et al., 2007; Walton et al., 2004), pre-supplementary motor area (preSMA; Deiber et al., 1999; Lau et al., 2006), or both RCZ and preSMA (Cunnington et al., 2006; Lau et al., 2004; van Eimeren et al., 2006). The more fundamental decision whether to act or not – as involved in the road traffic example – has been largely neglected. In the legal domain human societies acknowledge non-actions (namely negligence) as intentional acts by considering them as punishable under the precondition of purposefulness. This procedure implicitly assumes that the representation of voluntarily not doing something bears
⁎ Corresponding author. Ghent University, Faculty of Psychology and Educational Sciences, Department of Experimental Psychology, Henri Dunantlaan 2, 9000 Gent, Belgium. E-mail address: [email protected] (S. Kühn). 1053-8119/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2009.03.020
resemblance with the representation of voluntarily doing something. However, to our knowledge this implicit assumption has never been tested scientifically. We set out to compare the brain areas activated during decisions whether to act or not with brain areas involved in choosing between different action alternatives. If the whether decision is associated with brain areas that have been previously associated with the decision between different response alternatives (RCZ and/or preSMA) this would support the assumption that not acting can be regarded as a response option. We used a modified stop task (De Jong et al., 1990, 1995) that bears resemblance to the traffic light example to investigate the intentional decision not to act. In a stop task participants are engaged in a primary response task (comparable to riding on a road), and occasionally and unpredictably are confronted with a signal, that instructs them to inhibit their response to the first stimulus (comparable to a traffic light suddenly changing from green to red). Since participants have to respond most of the time responding is the ‘behavioural default setting’. In order to create a condition of free choice, we modified the classical stop task by introducing a ‘decide’ signal that sometimes occurs instead of the stop signal. This signal indicates that participants can freely choose between executing the prepared action and refraining from it. After those trials participants had to indicate whether they were able to decide or whether they responded impulsively to the primary response stimulus. By subtracting brain areas involved in successful stopping from areas involved in deciding between responding and not responding we obtain a pure measure of cortical involvement in choosing to act or not to act.
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According to the aforementioned considerations that voluntary non-action can be regarded as a mode of action we predict that similar brain areas should be involved in deciding to act and deciding not to act. Methods Participants Nineteen healthy volunteers were recruited from whom we obtained written consent prior to the scanning session. All subjects had normal or corrected-to-normal vision. No subject had a history of neurological, major medical, or psychiatric disorder. The data of two subjects were excluded from the analysis one due to the lack of successful stop trials, the other due to strong movement artifacts. The remaining seventeen subjects (9 women and 8 men; age: mean = 21.6, ranging from 19 to 29) were all right-handed as assessed by a handedness questionnaire (Van Strien, 1992; mean score = 9.6). Behavioral task Stimuli of the primary choice RT task were the uppercase letters M, V, N, and W, displayed in white on a black background. The signal to stop or decide was a change of letter colour (white to pink or white to blue) counterbalanced across subjects. The letters M and V were assigned to the index finger; the letters N and W to the middle finger of the right hand. The trial started with a variable oversampling interval of 0, 500, 1000, or 1500 ms to obtain an interpolated temporal resolution of 500 ms. Then a fixation cross was presented on the centre of the screen for 400 ms. After a blank of 100 ms one of the four letters was presented. In 75% of the trials the letter did not change colour; in the remaining 25% of the trials the white letter stimulus was coloured after a signal delay (SD). The coloured letter remained on the screen until the trial ended after 2000 ms-SD or until a response was given (Fig. 1).
For the first stop and decide trial we used a SD of 300 ms. Afterwards the SD times were continuously modified according to a staircase procedure. If the subject succeeded in withholding the response, the SD increased by 20 ms, making the task more difficult; conversely, if they failed, the SD decreased by 20 ms, making the task easier. In case of decide trials the staircase was treated separately. If the subjects were successful in decide trials and indicated with a button press that they chose their response or no-response intentionally, the staircase was prolonged by 20 ms; if they were not successful and indicated that they did not decide intentionally, it was shortened by 20 ms. The staircase variable was allowed to fluctuate between 20 and 600 ms. This staircase procedure was applied in order to achieve a probability of approximately 0.50 successful and non successful trials (Logan and Cowan, 1984). Participants were instructed to decide equally often to execute or not to execute the response in decide trials. In order to avoid waiting strategies, the primacy of the choice RT task was emphasized in the instruction and the participants were informed that the probability of stopping or deciding would approximate to 0.50, irrespective of whether they were postponing their responses or not. After the so called decide trials, in which the subjects were prompted to choose between performing and not-performing the prepared action, another screen was presented asking whether the subject did have the chance to decide or reacted in the ‘behavioural default setting’ in response to the letter stimulus (“Decided? yes – no” in Dutch: “Beslist? ja – nee”). In response to this request screen subjects had to respond with the index and middle finger of their left hand. In order to allow comparisons with the stop condition and the primary response condition we likewise used request screens after all stop trials (“Stopped? yes – no” in Dutch: “Gestopt? ja – nee”) and after 48 primary response trials (“Fast? yes – no” in Dutch: “Snel? ja – nee”). We additionally included 48 null events, which were pseudorandomly interspersed. The null events were included to compensate the overlap of the blood-oxygenation level-dependent (BOLD) response between adjacent trials.
Fig. 1. A schematic drawing of the different conditions constituting the paradigm.
S. Kühn, M. Brass / NeuroImage 46 (2009) 1187–1193 Table 1 Mean RTs (in milliseconds) and standard deviation (SD). Condition
Mean RT
SD
Primary response Failed-to-stop Failed-to-decide Decide-go
558 513 492 1074
67 62 54 351
Our design therefore contained seven different conditions: the primary response, stop, failed-to-stop, decide-go, decide-nogo, failedto-decide and null event condition. All in all the experiment consisted of three blocks with 144 trials each (80 primary response trials without request, 16 primary response trials with request, 16 decide trials, 16 stop trials and 16 null events per block). Before scanning the subjects were trained with one block of 144 trials comparable to the blocks used in the scanner. The data were discarded from further analysis. The experiment in the scanner lasted about 40 min. Scanning procedure Subjects were positioned head first and supine in the bore. Images were collected with a 3 T Magnetom Trio MRI scanner system (Siemens Medical Systems, Erlangen, Germany), using an 8-channel radiofrequency head coil. First, 176 high-resolution anatomical images were acquired using a T1-weighted 3D MPRAGE sequence (TR = 2530 ms, TE = 2.58 ms, TI = 1100ms, acquisition matrix = 176 × 256 × 256, sagittal FOV = 220 mm, flip angle = 7°, voxel size = 0.9 × 0.86 × 0.86 mm3 (resized to 1 × 1 × 1 mm)). Whole brain functional images were collected using a T2⁎-weighted EPI sequence, sensitive to BOLD contrast (TR = 2000 ms, TE = 35
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ms, image matrix =64 × 64, FOV = 224 mm, flip angle = 80°, slice thickness = 3.0 mm, distance factor = 17%, voxel size 3.5 × 3.5 × 3 mm3, 30 axial slices). 390 images were acquired per run. fMRI data pre-processing and main analysis The fMRI data were analysed with statistical parametric mapping, using the SPM5 software (Wellcome Department of Cognitive Neurology, London, UK). The first 4 scans of all EPI series were excluded from the analysis to minimise T1 relaxation artefacts. A mean image for all scan volumes was created, to which individual volumes were spatially realigned by rigid body transformation. The high-resolution structural image was co-registered with the mean image of the EPI series. The structural image was normalised to the Montreal Neurological Institute template. The normalisation parameters were then applied to the EPI images to ensure an anatomically informed normalisation. A commonly applied filter of 8 mm FWHM (full-width at half maximum) was used. The time series data at each voxel were processed using a high-pass filter with a cut-off of 128 s to remove low-frequency drifts. The subject-level statistical analyses were performed using the general linear model (GLM). The main events of interest were the periods after the onset of the white letter. Vectors containing the event onsets were convolved with the canonical haemodynamic response function (HRF) to form the main regressors in the design matrix (the regression model). The vectors were also convolved with the temporal derivatives and the resulting vectors were entered into the model. The statistical parameter estimates were computed separately for each voxel for all columns in the design matrix. Contrast images were constructed from each individual to compare the relevant parameter estimates for the regressors containing the canonical HRF. The group-level random-
Fig. 2. Main contrast of decide vs. stop. Activation map averaged over 17 subjects (p b 0.001, uncorrected, cluster size = 10) mapped onto an anatomical mean image of all subjects. (A) sagittal plane showing activity in the rostral cingulate zone (peak voxel: 4, 21, 35) of the contrast decide-go vs. stop condition; (B) sagittal plane showing activity in the rostral cingulate zone (peak voxel: − 4, 25, 39) of the contrast decide-nogo vs. stop condition; (a) depicts the corresponding percent signal changes for a ROI in RCZ of A U B.
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effects analysis was then performed. One-sample t-test was performed for each voxel of the contrast images. The resulting statistical values were thresholded at p b 0.001 (z N 3.09, uncorrected) with a volume greater than 350 mm3 (10 adjacent voxels; Forman et al., 1995). They were overlaid onto a normalized structural image of a single subject. For the signal change analysis we defined a ROI consisting of the significant voxels of RCZ resulting from the whole-brain contrast of decide-go trials vs. stop trials and/or (I/∩) decide-nogo trials vs. stop trials. Additionally we constructed a ROI consisting of the peak motor cortex voxel of the contrast decide-go vs. decide-nogo condition and its neighbouring voxels. For each subject, region and condition separately the mean percent signal change over a time window of 4–6 s after stimulus onset was calculated and compared by use of paired t-tests. Results Behavioral data Overall participants responded in 3.6% of the trials erroneously. Those trials were excluded from further analysis. Mean reaction times (RTs) of the different response conditions are displayed in Table 1. Paired t-tests reveal a significant difference between RTs in the primary response and decide-go condition (t(16) = − 6.27, p b 0.001). The same holds true for the comparison between the failed-to-decide and decide-go condition ( t(16) = − 6.90, p b 0.0001) indicating that the decision process takes about 500 ms. There is no difference in the SSRT (stop signal reaction time, Logan and Cowan, 1984) for stop and decide trials (290 ms vs. 303 ms, t(16) = 1.23, p = 0.24) confirming the assumptions that decide trials involve a stopping process first and therewith justifying the subtraction of the stop condition in order to extract the pure decision process between acting and not acting. The staircase procedure resulted in a probability of roughly 50% successful decide (47.5%) and successful stop trials (48%). And the participants did conform to the instruction to choose responding and not responding equally often; they decided not to respond in 57% of the successful decide trials. fMRI data First we wanted to investigate which brain areas are involved in deciding to do something and deciding not to do something. Secondly we wanted to see to what extend those brain areas overlap. To address the first point we tested whether successfully deciding to respond after stopping would significantly differ from the condition in which subjects merely stopped successfully. It is crucial to note here that participants in any case need to stop when they receive a decide signal. Otherwise they would not be able to initiate the decision process. The random-effects analysis of the contrast decide-go versus stop condition (contrast A) revealed activation in the rostral cingulate zone (RCZ). Furthermore, there was activation in bilateral insular cortex (extending via the ventrolateral cortex into the right dorsolateral prefrontal cortex (DLPFC)), bilateral globus pallidus, bilateral thalamus and left motor and somatosensory cortex (see Fig. 2A and Table 2). This pattern of activation is consistent with previous research on intentional action (Cunnington et al., 2006; Debaere et al., 2003; Lau et al., 2004; Müller et al., 2007 van Eimeren et al., 2006). In order to investigate the central question of this study, namely which brain areas are involved in the intentional decision not to do something we contrasted the decide-nogo vs. stop condition (contrast B) . This contrast revealed a very similar pattern of brain activation: RCZ, bilateral insular cortex, left and left thalamus and bilateral DLPFC (see Fig. 2B and Table 2). The conjunction of both contrasts comprises
Table 2 Anatomical Location and MNI coordinates with z N 3.09 (p = 0.001, uncorrected). Area A) Decide-go vs. stop condition Rostral cingulated zone Right globus pallidus Right insular cortex (extending over ventrolateral into right dorsolateral prefrontal cortex) Left insular cortex Left globus pallidus Left thalamus Left primary motor cortex, primary somatosensory cortex Right thalamus Visual association cortex (V2) B) Decide-nogo vs. stop condition Left insular cortex Right insular cortex Left thalamus Rostral cingulate zone Left dorsolateral prefrontal cortex Right dorsolateral prefrontal cortex C) Decide-go vs. decide-nogo condition Visual association cortex (V2) Left primary motor cortex, primary somatosensory cortex
BA
Peak coordinates (MNI)
Z-score
Extent
32
4, 21, 35 14, 4, 0 46, 28, − 4
6.23 4.92 4.92
288 50 133
3,4
− 35, 28, − 11 − 14, 4, 0 − 4, − 25, 4 − 35, − 25, 60
4.75 4.40 4.34 4.24
97 20 43 11
18
7, − 14, 4 14, − 95, 4
4.20 3.70
25 41
32 46 9
− 39, 21, − 7 42, 25, − 4 − 7, − 21, 14 − 4, 25, 39 − 46, 35, 21 56, 25, 32
4.86 4.86 4.83 4.59 4.57 4.40
43 64 16 167 55 15
18 4,3,2,1
11, − 91, − 4 − 42, − 25, 67
4.93 4.07
132 50
13
13
13 13
RCZ, bilateral insular cortex, right DLPFC and left thalamus. Nonoverlapping brain areas were primarily related to motor execution. To substantiate our prediction that the decision related activity in deciding to do something and deciding not to do something is virtually the same we performed a signal change analysis in a region of interest (ROI) in the RCZ. Once in a ROI consisting of the RCZ activation in contrast A and B (set union: A ∩ B) and additionally the region where both contrasts show RCZ activity (intersection: A I B). In both ROIs we found significant differences between the failed-to-decide condition and the two successful decide conditions (decide-go: A U B: t(16) = − 4.48, p b 0.001, A I B: t(16) = −4.75, p b 0.001; decide-nogo A ∩ B: t(16) = −5.06, p b 0.001, A I B: t(16) = −5.72, p b 0.001). And additionally, according to the hypothesis that RCZ is involved in both decision processes either resulting in a response or in a no-response we found no significant difference between the decide-go and the decide-nogo condition (A ∩ B: t(16)= 0.24, p = 0.81; A I B: t(16) = −0.09, p = 0.93) (Fig. 2A). A highly similar pattern reveals when performing the same analysis on a ROI in DLPFC/insula. Since the brain regions involved in deciding between responding and not responding overlap to a great extend, we tested the two conditions directly against one another. Because our prediction was that both conditions are rather similar we used the threshold of p b 0.001 (z N 3.09, uncorrected) with a volume greater than 175 mm3 (5 adjacent voxels) in order to detected more subtle differences as well. In the contrast of the decide-nogo vs. decide-go condition we found no significant differences even with a more lenient threshold. For the reversed contrast we found an activation in left primary motor cortex and somatosensory cortex triggered by the overt response with the right hand and visual cortex (Fig. 3C and Table 2). We performed a percent signal change analysis in the motor cortex ROI (Fig. 3b) in order to compare the two conditions in which participants do not respond. As predicted there was a significant difference between the stop and the decide-nogo condition (t(16) =− 2.83, p b 0.05). The fact that the motor cortex activity is higher in the decide-nogo condition supports our
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Fig. 3. Main contrast of decision (decide-go vs. decide-nogo condition). Activation map averaged over 17 subjects (p b 0.001, uncorrected, cluster size = 5) mapped onto an anatomical mean image of all subjects. (C) activity in motor cortex (peak voxel: − 42, − 25, 67); (b) depicts the corresponding percent signal changes for the peak voxel and its direct neighbours in the stop and decide-nogo condition.
assumption that deciding not to do something is more than simply succeeded stopping. Discussion The present study aimed at investigating the neural correlates of voluntarily choosing whether to act or not. Previous studies focussed solely on brain areas involved in voluntary action and action selection between different action alternatives (Cunnington et al., 2006; Lau et al., 2004, 2006; Müller et al., 2007; van Eimeren et al., 2006; Walton et al., 2004). Since we regarded the question what action to perform as secondary to the more fundamental question of whether to perform an action or not we employed a task in which participants had to stop an ongoing action and subsequently voluntarily decide between responding or not. Here the crucial question was whether the brain areas involved in deciding not to do something overlap with the brain areas involved in deciding to do something. In order to compare the neural correlates of voluntarily acting and voluntarily refraining from acting we subtracted brain activity associated with successful stopping. In accordance with the assumption that the decision not to do something is functionally equivalent to the decision to do something, we found a nearly identical pattern of brain activation in both conditions. For voluntary non-actions as well as for voluntary actions we found rostral cingulate zone (RCZ; with a focus in the ACC and extending into dorsal preSMA), insular cortex, left thalamus and right dorsolateral prefrontal cortex (DLPFC) activity. Furthermore, subtracting brain areas involved in voluntary action from those involved in voluntarily not acting yielded no significant differences even with a loosened threshold. At the same time the reversed contrast revealed only left motor cortex and visual activity. The motor cortex activity was clearly expected because the decide response condition involves an overt response of the right hand. The activation in BA 18 on the other hand was not expected but might be due to a
re-inspection of the letter stimulus in order to ensure a correct response when deciding to act; whereas the decision not to respond makes further inspection of the stimulus unnecessary. A region of interest analysis in the activated peak voxel in the motor cortex of the last-mentioned contrast resulted in an interesting difference in percent signal change values between the stop condition and the decide-nogo condition. In both cases no overt response was given and the participants obviously managed to halt the ongoing action. Nevertheless the percent signal change was higher in the condition in which an action would have been adequate and a feasible alternative response option. Therefore we assume that there are no major differences between voluntary action and voluntary non-action because in both cases a representation of the possible movement is maintained. However in the end the action is executed in one condition and not in the other. The role of the RCZ in deciding to do something or not The most extensive activation associated with voluntarily doing something as well as voluntarily not doing something was the frontomedian RCZ activation. ROI analyses in this area demonstrated that there is no difference in the percent signal changes between both conditions. Since what-decisions have been associated with RCZ activity (Cunnington et al., 2006; Debaere et al., 2003; Lau et al., 2004; Müller et al., 2007; van Eimeren et al., 2006) our finding provides strong evidence for the assumption that the voluntary decision not to act can be considered as an equivalent response option. Interestingly, Braver et al. (2001) came to a similar conclusion based on the observation that the inhibition of a prepared response results in a similar degree of ACC activation as the execution of the response. However, in contrast to our study, Braver's study explored inhibition in the context of a Go/NoGo task where inhibition occurs fairly late, namely after the possible response has been prepared. Our results
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extend this finding by demonstrating that very early decisions not to act – that do not require previous response preparation and no subsequent inhibition – are processed as equivalent response options as well. In the literature there is a controversy surrounding the function of medial frontal cortex. Previous studies have implicated medial frontal cortex in conflict monitoring (Botvinick et al., 1999; Carter et al., 1998; Nachev et al., 2005; Ullsperger and von Cramon, 2001) and the control of voluntary action and decision-making (Forstmann et al., 2006, 2008; Lau et al., 2004; Müller et al., 2007; Nachev et al., 2005; Thaler et al., 1995; Walton et al., 2004). It has been argued that conflict arising from competing response alternatives has to be overcome in order to decide for a specific behaviour (Lau et al., 2006). Interpreted in this framework the present findings suggest that conflict can also arise from an intentional non-action. Further research is needed to substantiate this claim. It might for example be interesting to investigate competition between different non-actions (e.g. deciding not to press a right key versus deciding not to press a left key). Our interpretation of the present data suggests that competition between no-response alternative should be functionally equivalent to decisions between response alternatives. Activations outside the fronto-median wall The right DLPFC activation associated with voluntarily choosing to do something, and the bilateral DLPFC activation involved in deciding not to do something has been shown in studies comparing voluntary action with externally triggered action as well (Cunnington et al., 2006; Lau et al., 2004). Lau et al. (2004) propose that DLPFC is involved in attention to the selection of action rather than actual response selection. Various studies demonstrated that random generation tasks likewise elicit DLPFC activation (Frith et al., 1991; Jahanshahi et al., 2000). The involvement of DLPFC in action selection tasks may be explained by the demand to keep track of previous responses which clearly contains a working memory component (Hadland et al., 2001). The fact that we find pronounced bilateral DLPFC activation in decisions not to respond emphasizes our reasoning that choosing whether to act or not involves a selection between two response options, although one of them lacks the overt component. Additionally to the DLPFC we also found other brain areas typically activated in cognitive control tasks, namely insula cortex, thalamus and basal ganglia. In deciding to act as well as deciding not to act we found bilateral activation of the insula cortex. Dorsal anterior insula activation has been reported in various response inhibition studies (Aron and Poldrack, 2006; Garavan et al., 1999; Wager et al., 2005). Insula activations occur frequently in context of cognitive tasks but are often not explicitly discussed. Nevertheless there is evidence indicating that anterior insula is associated with concentration and “cognitive effort” (Allen et al., 2007). In our task subjects might consider it as effortful and straining to reinitiate an action that has been inhibited just before or to decide not to do something after it has already been inhibited. Additionally anterior insula appears to be related to risk taking and therefore involved in intentional action as well. It may provide a “gut” feeling in decision-making (Paulus et al., 2005; Zysset et al., 2006) which may be mediated by autonomic states. The thalamus and basal ganglia are known to play a central role in behavioural action selection (Humphries and Gurney, 2002; Humphries et al., 2006; Redgrave et al., 1999). According to the action selection hypothesis basal ganglia poses the adjudicator of which of the potential actions that the cortex might be planning actually gets executed (Gurney et al., 2001). In particular Frank (2006) uses a neural network to simulate basal ganglia function in Go/NoGo tasks by modelling not only a Go (direct) pathway, but also a separate and competing NoGo (indirect) pathway. This structure of two simulta-
neously active pathways resembles our notion that voluntary nonactions compete with voluntary actions. De Jong and Paans (2007) demonstrate pallidum and thalamus activation for movements with less fingers suggesting active inhibition of the unwanted synergist finger movements. This principle of selection by suppression, in our case possibly suppression of the competing no-response substantiates our claim that voluntary non-actions should be regarded as a special case of action. A possible alternative explanation for the similarity of brain areas involved in the decision to act and the decision not to act might be that the two conditions involve a similar degree of prolonged inhibition necessary to come to a decision. In order to test which brain areas are related to inhibition we called in the contrast stop vs. failed-to-stop, which reveals an activation in the dorsal preSMA (0, 6, 57). Therefore it might well be that the preSMA part of our RCZ activation is involved in prolonged inhibition, but this leaves our interpretation of the function of the more anterior part of the RCZ unaffected. Another alternative explanation could be that the increased cognitive effort due to task complexity may have induced variations obscuring possible differences. Interestingly, in a recent ERP study that used a slightly different design we also found a high similarity between voluntary action and voluntary non-action while avoiding high task complexity (Kuehn et al., 2009). Given this multimethodological evidence in favour of the hypothesis that voluntary actions and voluntary non-actions show a high degree of similarity on the functional level, we think that this alternative explanation is not very plausible. Early versus late ‘whether’ In a recent model of intentional action Brass and Haggard (2008) distinguished a ‘what’, a ‘when’ and a ‘whether’ component of intentional action. While the what component is equivalent to the what decision investigated in the present study it is crucial to distinguish their ‘whether’ component from the intentional decision not to act investigated in the present study. Contrary to our definition of a whether decision as an unbiased choice between two options: to respond or not to respond, Brass and Haggard (2007, 2008) defined the whether component as the final decision to execute an action or not after a response is already thoroughly prepared. This whether process which bears resemblance with a veto has been associated with a brain area more anterior to RCZ (dorso-frontomedian cortex, BA 9; Brass and Haggard, 2007; Kühn, Haggard & Brass, in press). To dissolve this ambiguity we would like to differentiate between an early and late whether component. The early whether consists of the fundamental choice whether to engage in an action at all, whereas the late whether component comprises a gating process able to veto an action that has been selected in the what component. Since the early whether component is on a neural level equivalent with the what component we conclude that the decision not to respond is processed as a thorough response alternative. This nicely confirms our proposition to consider not acting as a mode of action. Legal and psychological implications These findings have implications in the legal and psychological domain. Most juridical systems recognize intentional non-action – namely negligence – as intentional acts by regarding it in principle as culpable. Under the label of “crimes of omission” a failure to act can be regarded as an actus reus (guilty act). This concept takes for granted that human beings do represent what they choose not to do and can therefore be accused of a mens rea (guilty mind) when omitting an action voluntarily. On a surface level this contradicts with findings in research about repentance in social psychology. According to the “actor effect” (Kahneman and Tversky, 1982) or “omission bias”-effect (Spranca
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et al., 1991; Ritov and Baron, 1990) people think that a decision was worse if the outcome resulted from an action than if it resulted from inaction. To explain these findings it has been suggested that, actions are more salient and more often used to infer one's own and others' attitudes (Fazio et al., 1982). However these findings mostly originate from judgements out of a third person perspective. Given that human beings usually evaluate their decisions from a first person perspective they probably regard their choices to act and not to act in equal measure as intentional decisions. Conclusion Our results indicate that the brain areas involved in representation of voluntary action and voluntary non-action are virtually the same. Moreover, choosing whether to act or not to act shows a similar neural signature as selecting between different overt response options. This constitutes first evidence in favour of the current judicial procedure to regard voluntary non-actions as punishable acts. Acknowledgment The work was supported by the dissertation grant of the Studienstiftung des Deutschen Volkes awarded to the first author. References Allen, M.D., Bilger, E.D., Larsen, J., Goodrich-Hunsaker, N.J., Hopkins, R.O., 2007. Functional neuroimaging evidence for high cognitive effort of the word memory test in the absence of external incentives. Brain Inj. 21 (13–14), 1425–1428. Aron, A.R., Poldrack, R.A., 2006. Cortical and subcortical contributions to stop signal response inhibition: role of the subthalamic nucleus. J. Neurosci. 26 (9), 2424–2433. Botvinick, M., Nystrom, L.E., Fissell, K., Carter, C.S., Cohen, J.D., 1999. Conflict monitoring versus selection-for-action in anterior cingulated cortex. Nature 402, 179–181. Brass, M., Haggard, P., 2007. To do or not to do: the neural signature of self-control. J. Neurosci. 27 (34), 9141–9145. Brass, M., Haggard, P., 2008. The what, when, whether (WWW) model of intentional action. Neuroscientist 14 (4), 319–325. Braver, T.S., Barch, D.M., Gray, J.R., Molfese, D.L., Snyder, A., 2001. Anterior cingulate cortex and response conflict: effects of frequency, inhibition and errors. Cereb. Cortex 11, 825–836. Carter, C.S., Braver, T.S., Barch, D.M., Botvinick, M.M., Noll, D., Cohen, J.D., 1998. Anterior cingulated cortex, error detection and the online monitoring of performance. Science 280, 747–749. Cunnington, R., Windischberger, C., Robinson, S., Moser, E., 2006. The selection of intended actions and the observation of others' actions: a time-resolved fMRI study. NeuroImage 29 (4), 1294–1302. Debaere, F., Wenderoth, N., Sunaert, S., Van Hecke, P., Swinnen, S.P., 2003. Internal vs external generation of movements: differential neural pathways involved in bimanual coordination performed in the presence or absence of augmented visual feedback. NeuroImage 19 (3), 764–776. Deiber, M.P., Honda, M., Ibanez, V., Sadato, N., Hallett, M., 1999. Mesial motor areas in self-initiated versus externally triggered movements examined with fMRI: effect of movement type and rate. J. Neurophysiol. 81 (6), 3065–3077. De Jong, B.M., Paans, A.M.J., 2007. Medial versus lateral prefrontal dissociation in movement selection and inhibitory control. Brain Res. 1132, 139–147. De Jong, R., Coles, M.G.H., Gratton, G., Logan, G.D., 1990. In search of the point of no return: the control of response processes. J. Exp. Psychol. Hum. Percept. Perform. 16 (1), 164–182. De Jong, R., Coles, M.G.H., Logan, G., 1995. Strategies and mechanisms in nonselective and selective inhibitory motor control. J. Exp. Psychol. Hum. Percept. Perform. 21 (3), 498–511.
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Frith, C.D., Friston, K., Liddle, P.F., Frackowiak, R.S., 1991. Willed action and the prefrontal cortex in man: a study with PET. Proc. R. Soc. London B Biol. Sci. 244, 241–246. Gurney, K., Prescott, T.J., Redgrave, P., 2001. A computational model of action selection in the basal ganglia. Biol. Cybern. 84 (6), 411–423. Hadland, K.A., Rushworth, M.F.S., Passingham, R.E., Jahanshahi, M., Rothwell, J.C., 2001. Interference with performance of a response selection task that has no working memory component: an rTMS comparison of the dorsolateral prefrontal and medial frontal cortex. J. Cogn. Neurosci. 13 (8), 1097–1108. Humphries, M.D., Gurney, K., 2002. The role of intra-thalamic and thalamocortical circuits in action selection. Network 13, 131–156. Humphries, M.D., Stewart, R.D., Gurney, K.N., 2006. A physiologically plausible model of action selection and oscillatory activity in the basal ganglia. J. Neurosci. 26 (50), 12921–12942. Jahanshahi, M., Dirnberger, G., Fuller, R., Frith, C.D., 2000. The role of the dorsolateral prefrontal cortex in random number generation: a study with positron emission tomography. NeuroImage 12, 713–725. Kahneman, D., Tversky, A., 1982. The psychology of preferences. Sci. Am. 246, 136–142. Kuehn, S., Gevers, W., Brass, M., 2009. The neural correlates of intending not to do something. J. Neurophysiol, 101, 1913–1920. Kühn, S., Haggard, P., Brass, M. (in press). Intentional inhibition: How the "veto-area" exerts control. Human Brain Mapping. Lau, H.C., Rogers, R.D., Ramnani, N., Passingham, R.E., 2004. Willed action and attention to the selection of action. NeuroImage 21 (4), 1407–1415. Lau, H., Rogers, R.D., Passingham, R.E., 2006. Dissociating response selection and conflict in the medial frontal surface. NeuroImage 29 (2), 446–451. Logan, G.D., Cowan, W.B., 1984. On the ability to inhibit thought and action: a theory of an act of control. Psychol. Rev. 91 (3), 295–327. 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NeuroImage j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y n i m g
When doing nothing is an option: The neural correlates of deciding whether to act or not Simone Kühn a,b,⁎, Marcel Brass a a b
Ghent University, Department of Experimental Psychology and Ghent Institute for Functional and Metabolic Imaging, Henri Dunantlaan 2, 9000 Gent, Belgium Department of Psychology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstr. 1A, 04103 Leipzig, Germany
a r t i c l e
i n f o
Article history: Received 9 December 2008 Revised 19 February 2009 Accepted 12 March 2009 Available online 19 March 2009
a b s t r a c t The neural basis of intentionally deciding between different response alternatives has been extensively investigated and associated with the rostral cingulate zone (RCZ). However, from daily experience we know that the decision whether to do something is often prior to the decision what to do. This raises the fundamental question whether the decision to act and the decision not to act can be considered as functionally equivalent. Interestingly, in the legal domain such an equivalence is implicitly assumed by punishing crimes of omission. The aim of the current study was to explicitly test this assumption by comparing the neural representation of intentional actions with intentional non-actions. Our results suggest, that weighing whether to act or not involves similar areas of the brain, namely RCZ and dorsolateral prefrontal cortex, independent of the outcome of this decision. This finding strongly supports the assumption that intentionally not acting can be considered as a mode of action. © 2009 Elsevier Inc. All rights reserved.
Introduction Self-initiated intentional action lies at the heart of human nature. One important precondition for intentional action is the momentum of choice, e.g. the choice whether to do something or not. We are all familiar with the following situation in road traffic: we are driving along a road, we are approaching a traffic light, we spot that it turns yellow. In that moment in time we hesitate and make up our mind whether to accelerate and resume our way or not. Research on decision-making in the voluntary action domain has nearly exclusively focussed on choices between different action alternatives (Cunnington et al., 2006; Lau et al., 2004, 2006; Müller et al., 2007; van Eimeren et al., 2006). Deciding between different response alternatives has been associated with the rostral cingulate zone (RCZ; Debaere et al., 2003; Müller et al., 2007; Walton et al., 2004), pre-supplementary motor area (preSMA; Deiber et al., 1999; Lau et al., 2006), or both RCZ and preSMA (Cunnington et al., 2006; Lau et al., 2004; van Eimeren et al., 2006). The more fundamental decision whether to act or not – as involved in the road traffic example – has been largely neglected. In the legal domain human societies acknowledge non-actions (namely negligence) as intentional acts by considering them as punishable under the precondition of purposefulness. This procedure implicitly assumes that the representation of voluntarily not doing something bears
⁎ Corresponding author. Ghent University, Faculty of Psychology and Educational Sciences, Department of Experimental Psychology, Henri Dunantlaan 2, 9000 Gent, Belgium. E-mail address: [email protected] (S. Kühn). 1053-8119/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2009.03.020
resemblance with the representation of voluntarily doing something. However, to our knowledge this implicit assumption has never been tested scientifically. We set out to compare the brain areas activated during decisions whether to act or not with brain areas involved in choosing between different action alternatives. If the whether decision is associated with brain areas that have been previously associated with the decision between different response alternatives (RCZ and/or preSMA) this would support the assumption that not acting can be regarded as a response option. We used a modified stop task (De Jong et al., 1990, 1995) that bears resemblance to the traffic light example to investigate the intentional decision not to act. In a stop task participants are engaged in a primary response task (comparable to riding on a road), and occasionally and unpredictably are confronted with a signal, that instructs them to inhibit their response to the first stimulus (comparable to a traffic light suddenly changing from green to red). Since participants have to respond most of the time responding is the ‘behavioural default setting’. In order to create a condition of free choice, we modified the classical stop task by introducing a ‘decide’ signal that sometimes occurs instead of the stop signal. This signal indicates that participants can freely choose between executing the prepared action and refraining from it. After those trials participants had to indicate whether they were able to decide or whether they responded impulsively to the primary response stimulus. By subtracting brain areas involved in successful stopping from areas involved in deciding between responding and not responding we obtain a pure measure of cortical involvement in choosing to act or not to act.
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According to the aforementioned considerations that voluntary non-action can be regarded as a mode of action we predict that similar brain areas should be involved in deciding to act and deciding not to act. Methods Participants Nineteen healthy volunteers were recruited from whom we obtained written consent prior to the scanning session. All subjects had normal or corrected-to-normal vision. No subject had a history of neurological, major medical, or psychiatric disorder. The data of two subjects were excluded from the analysis one due to the lack of successful stop trials, the other due to strong movement artifacts. The remaining seventeen subjects (9 women and 8 men; age: mean = 21.6, ranging from 19 to 29) were all right-handed as assessed by a handedness questionnaire (Van Strien, 1992; mean score = 9.6). Behavioral task Stimuli of the primary choice RT task were the uppercase letters M, V, N, and W, displayed in white on a black background. The signal to stop or decide was a change of letter colour (white to pink or white to blue) counterbalanced across subjects. The letters M and V were assigned to the index finger; the letters N and W to the middle finger of the right hand. The trial started with a variable oversampling interval of 0, 500, 1000, or 1500 ms to obtain an interpolated temporal resolution of 500 ms. Then a fixation cross was presented on the centre of the screen for 400 ms. After a blank of 100 ms one of the four letters was presented. In 75% of the trials the letter did not change colour; in the remaining 25% of the trials the white letter stimulus was coloured after a signal delay (SD). The coloured letter remained on the screen until the trial ended after 2000 ms-SD or until a response was given (Fig. 1).
For the first stop and decide trial we used a SD of 300 ms. Afterwards the SD times were continuously modified according to a staircase procedure. If the subject succeeded in withholding the response, the SD increased by 20 ms, making the task more difficult; conversely, if they failed, the SD decreased by 20 ms, making the task easier. In case of decide trials the staircase was treated separately. If the subjects were successful in decide trials and indicated with a button press that they chose their response or no-response intentionally, the staircase was prolonged by 20 ms; if they were not successful and indicated that they did not decide intentionally, it was shortened by 20 ms. The staircase variable was allowed to fluctuate between 20 and 600 ms. This staircase procedure was applied in order to achieve a probability of approximately 0.50 successful and non successful trials (Logan and Cowan, 1984). Participants were instructed to decide equally often to execute or not to execute the response in decide trials. In order to avoid waiting strategies, the primacy of the choice RT task was emphasized in the instruction and the participants were informed that the probability of stopping or deciding would approximate to 0.50, irrespective of whether they were postponing their responses or not. After the so called decide trials, in which the subjects were prompted to choose between performing and not-performing the prepared action, another screen was presented asking whether the subject did have the chance to decide or reacted in the ‘behavioural default setting’ in response to the letter stimulus (“Decided? yes – no” in Dutch: “Beslist? ja – nee”). In response to this request screen subjects had to respond with the index and middle finger of their left hand. In order to allow comparisons with the stop condition and the primary response condition we likewise used request screens after all stop trials (“Stopped? yes – no” in Dutch: “Gestopt? ja – nee”) and after 48 primary response trials (“Fast? yes – no” in Dutch: “Snel? ja – nee”). We additionally included 48 null events, which were pseudorandomly interspersed. The null events were included to compensate the overlap of the blood-oxygenation level-dependent (BOLD) response between adjacent trials.
Fig. 1. A schematic drawing of the different conditions constituting the paradigm.
S. Kühn, M. Brass / NeuroImage 46 (2009) 1187–1193 Table 1 Mean RTs (in milliseconds) and standard deviation (SD). Condition
Mean RT
SD
Primary response Failed-to-stop Failed-to-decide Decide-go
558 513 492 1074
67 62 54 351
Our design therefore contained seven different conditions: the primary response, stop, failed-to-stop, decide-go, decide-nogo, failedto-decide and null event condition. All in all the experiment consisted of three blocks with 144 trials each (80 primary response trials without request, 16 primary response trials with request, 16 decide trials, 16 stop trials and 16 null events per block). Before scanning the subjects were trained with one block of 144 trials comparable to the blocks used in the scanner. The data were discarded from further analysis. The experiment in the scanner lasted about 40 min. Scanning procedure Subjects were positioned head first and supine in the bore. Images were collected with a 3 T Magnetom Trio MRI scanner system (Siemens Medical Systems, Erlangen, Germany), using an 8-channel radiofrequency head coil. First, 176 high-resolution anatomical images were acquired using a T1-weighted 3D MPRAGE sequence (TR = 2530 ms, TE = 2.58 ms, TI = 1100ms, acquisition matrix = 176 × 256 × 256, sagittal FOV = 220 mm, flip angle = 7°, voxel size = 0.9 × 0.86 × 0.86 mm3 (resized to 1 × 1 × 1 mm)). Whole brain functional images were collected using a T2⁎-weighted EPI sequence, sensitive to BOLD contrast (TR = 2000 ms, TE = 35
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ms, image matrix =64 × 64, FOV = 224 mm, flip angle = 80°, slice thickness = 3.0 mm, distance factor = 17%, voxel size 3.5 × 3.5 × 3 mm3, 30 axial slices). 390 images were acquired per run. fMRI data pre-processing and main analysis The fMRI data were analysed with statistical parametric mapping, using the SPM5 software (Wellcome Department of Cognitive Neurology, London, UK). The first 4 scans of all EPI series were excluded from the analysis to minimise T1 relaxation artefacts. A mean image for all scan volumes was created, to which individual volumes were spatially realigned by rigid body transformation. The high-resolution structural image was co-registered with the mean image of the EPI series. The structural image was normalised to the Montreal Neurological Institute template. The normalisation parameters were then applied to the EPI images to ensure an anatomically informed normalisation. A commonly applied filter of 8 mm FWHM (full-width at half maximum) was used. The time series data at each voxel were processed using a high-pass filter with a cut-off of 128 s to remove low-frequency drifts. The subject-level statistical analyses were performed using the general linear model (GLM). The main events of interest were the periods after the onset of the white letter. Vectors containing the event onsets were convolved with the canonical haemodynamic response function (HRF) to form the main regressors in the design matrix (the regression model). The vectors were also convolved with the temporal derivatives and the resulting vectors were entered into the model. The statistical parameter estimates were computed separately for each voxel for all columns in the design matrix. Contrast images were constructed from each individual to compare the relevant parameter estimates for the regressors containing the canonical HRF. The group-level random-
Fig. 2. Main contrast of decide vs. stop. Activation map averaged over 17 subjects (p b 0.001, uncorrected, cluster size = 10) mapped onto an anatomical mean image of all subjects. (A) sagittal plane showing activity in the rostral cingulate zone (peak voxel: 4, 21, 35) of the contrast decide-go vs. stop condition; (B) sagittal plane showing activity in the rostral cingulate zone (peak voxel: − 4, 25, 39) of the contrast decide-nogo vs. stop condition; (a) depicts the corresponding percent signal changes for a ROI in RCZ of A U B.
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effects analysis was then performed. One-sample t-test was performed for each voxel of the contrast images. The resulting statistical values were thresholded at p b 0.001 (z N 3.09, uncorrected) with a volume greater than 350 mm3 (10 adjacent voxels; Forman et al., 1995). They were overlaid onto a normalized structural image of a single subject. For the signal change analysis we defined a ROI consisting of the significant voxels of RCZ resulting from the whole-brain contrast of decide-go trials vs. stop trials and/or (I/∩) decide-nogo trials vs. stop trials. Additionally we constructed a ROI consisting of the peak motor cortex voxel of the contrast decide-go vs. decide-nogo condition and its neighbouring voxels. For each subject, region and condition separately the mean percent signal change over a time window of 4–6 s after stimulus onset was calculated and compared by use of paired t-tests. Results Behavioral data Overall participants responded in 3.6% of the trials erroneously. Those trials were excluded from further analysis. Mean reaction times (RTs) of the different response conditions are displayed in Table 1. Paired t-tests reveal a significant difference between RTs in the primary response and decide-go condition (t(16) = − 6.27, p b 0.001). The same holds true for the comparison between the failed-to-decide and decide-go condition ( t(16) = − 6.90, p b 0.0001) indicating that the decision process takes about 500 ms. There is no difference in the SSRT (stop signal reaction time, Logan and Cowan, 1984) for stop and decide trials (290 ms vs. 303 ms, t(16) = 1.23, p = 0.24) confirming the assumptions that decide trials involve a stopping process first and therewith justifying the subtraction of the stop condition in order to extract the pure decision process between acting and not acting. The staircase procedure resulted in a probability of roughly 50% successful decide (47.5%) and successful stop trials (48%). And the participants did conform to the instruction to choose responding and not responding equally often; they decided not to respond in 57% of the successful decide trials. fMRI data First we wanted to investigate which brain areas are involved in deciding to do something and deciding not to do something. Secondly we wanted to see to what extend those brain areas overlap. To address the first point we tested whether successfully deciding to respond after stopping would significantly differ from the condition in which subjects merely stopped successfully. It is crucial to note here that participants in any case need to stop when they receive a decide signal. Otherwise they would not be able to initiate the decision process. The random-effects analysis of the contrast decide-go versus stop condition (contrast A) revealed activation in the rostral cingulate zone (RCZ). Furthermore, there was activation in bilateral insular cortex (extending via the ventrolateral cortex into the right dorsolateral prefrontal cortex (DLPFC)), bilateral globus pallidus, bilateral thalamus and left motor and somatosensory cortex (see Fig. 2A and Table 2). This pattern of activation is consistent with previous research on intentional action (Cunnington et al., 2006; Debaere et al., 2003; Lau et al., 2004; Müller et al., 2007 van Eimeren et al., 2006). In order to investigate the central question of this study, namely which brain areas are involved in the intentional decision not to do something we contrasted the decide-nogo vs. stop condition (contrast B) . This contrast revealed a very similar pattern of brain activation: RCZ, bilateral insular cortex, left and left thalamus and bilateral DLPFC (see Fig. 2B and Table 2). The conjunction of both contrasts comprises
Table 2 Anatomical Location and MNI coordinates with z N 3.09 (p = 0.001, uncorrected). Area A) Decide-go vs. stop condition Rostral cingulated zone Right globus pallidus Right insular cortex (extending over ventrolateral into right dorsolateral prefrontal cortex) Left insular cortex Left globus pallidus Left thalamus Left primary motor cortex, primary somatosensory cortex Right thalamus Visual association cortex (V2) B) Decide-nogo vs. stop condition Left insular cortex Right insular cortex Left thalamus Rostral cingulate zone Left dorsolateral prefrontal cortex Right dorsolateral prefrontal cortex C) Decide-go vs. decide-nogo condition Visual association cortex (V2) Left primary motor cortex, primary somatosensory cortex
BA
Peak coordinates (MNI)
Z-score
Extent
32
4, 21, 35 14, 4, 0 46, 28, − 4
6.23 4.92 4.92
288 50 133
3,4
− 35, 28, − 11 − 14, 4, 0 − 4, − 25, 4 − 35, − 25, 60
4.75 4.40 4.34 4.24
97 20 43 11
18
7, − 14, 4 14, − 95, 4
4.20 3.70
25 41
32 46 9
− 39, 21, − 7 42, 25, − 4 − 7, − 21, 14 − 4, 25, 39 − 46, 35, 21 56, 25, 32
4.86 4.86 4.83 4.59 4.57 4.40
43 64 16 167 55 15
18 4,3,2,1
11, − 91, − 4 − 42, − 25, 67
4.93 4.07
132 50
13
13
13 13
RCZ, bilateral insular cortex, right DLPFC and left thalamus. Nonoverlapping brain areas were primarily related to motor execution. To substantiate our prediction that the decision related activity in deciding to do something and deciding not to do something is virtually the same we performed a signal change analysis in a region of interest (ROI) in the RCZ. Once in a ROI consisting of the RCZ activation in contrast A and B (set union: A ∩ B) and additionally the region where both contrasts show RCZ activity (intersection: A I B). In both ROIs we found significant differences between the failed-to-decide condition and the two successful decide conditions (decide-go: A U B: t(16) = − 4.48, p b 0.001, A I B: t(16) = −4.75, p b 0.001; decide-nogo A ∩ B: t(16) = −5.06, p b 0.001, A I B: t(16) = −5.72, p b 0.001). And additionally, according to the hypothesis that RCZ is involved in both decision processes either resulting in a response or in a no-response we found no significant difference between the decide-go and the decide-nogo condition (A ∩ B: t(16)= 0.24, p = 0.81; A I B: t(16) = −0.09, p = 0.93) (Fig. 2A). A highly similar pattern reveals when performing the same analysis on a ROI in DLPFC/insula. Since the brain regions involved in deciding between responding and not responding overlap to a great extend, we tested the two conditions directly against one another. Because our prediction was that both conditions are rather similar we used the threshold of p b 0.001 (z N 3.09, uncorrected) with a volume greater than 175 mm3 (5 adjacent voxels) in order to detected more subtle differences as well. In the contrast of the decide-nogo vs. decide-go condition we found no significant differences even with a more lenient threshold. For the reversed contrast we found an activation in left primary motor cortex and somatosensory cortex triggered by the overt response with the right hand and visual cortex (Fig. 3C and Table 2). We performed a percent signal change analysis in the motor cortex ROI (Fig. 3b) in order to compare the two conditions in which participants do not respond. As predicted there was a significant difference between the stop and the decide-nogo condition (t(16) =− 2.83, p b 0.05). The fact that the motor cortex activity is higher in the decide-nogo condition supports our
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Fig. 3. Main contrast of decision (decide-go vs. decide-nogo condition). Activation map averaged over 17 subjects (p b 0.001, uncorrected, cluster size = 5) mapped onto an anatomical mean image of all subjects. (C) activity in motor cortex (peak voxel: − 42, − 25, 67); (b) depicts the corresponding percent signal changes for the peak voxel and its direct neighbours in the stop and decide-nogo condition.
assumption that deciding not to do something is more than simply succeeded stopping. Discussion The present study aimed at investigating the neural correlates of voluntarily choosing whether to act or not. Previous studies focussed solely on brain areas involved in voluntary action and action selection between different action alternatives (Cunnington et al., 2006; Lau et al., 2004, 2006; Müller et al., 2007; van Eimeren et al., 2006; Walton et al., 2004). Since we regarded the question what action to perform as secondary to the more fundamental question of whether to perform an action or not we employed a task in which participants had to stop an ongoing action and subsequently voluntarily decide between responding or not. Here the crucial question was whether the brain areas involved in deciding not to do something overlap with the brain areas involved in deciding to do something. In order to compare the neural correlates of voluntarily acting and voluntarily refraining from acting we subtracted brain activity associated with successful stopping. In accordance with the assumption that the decision not to do something is functionally equivalent to the decision to do something, we found a nearly identical pattern of brain activation in both conditions. For voluntary non-actions as well as for voluntary actions we found rostral cingulate zone (RCZ; with a focus in the ACC and extending into dorsal preSMA), insular cortex, left thalamus and right dorsolateral prefrontal cortex (DLPFC) activity. Furthermore, subtracting brain areas involved in voluntary action from those involved in voluntarily not acting yielded no significant differences even with a loosened threshold. At the same time the reversed contrast revealed only left motor cortex and visual activity. The motor cortex activity was clearly expected because the decide response condition involves an overt response of the right hand. The activation in BA 18 on the other hand was not expected but might be due to a
re-inspection of the letter stimulus in order to ensure a correct response when deciding to act; whereas the decision not to respond makes further inspection of the stimulus unnecessary. A region of interest analysis in the activated peak voxel in the motor cortex of the last-mentioned contrast resulted in an interesting difference in percent signal change values between the stop condition and the decide-nogo condition. In both cases no overt response was given and the participants obviously managed to halt the ongoing action. Nevertheless the percent signal change was higher in the condition in which an action would have been adequate and a feasible alternative response option. Therefore we assume that there are no major differences between voluntary action and voluntary non-action because in both cases a representation of the possible movement is maintained. However in the end the action is executed in one condition and not in the other. The role of the RCZ in deciding to do something or not The most extensive activation associated with voluntarily doing something as well as voluntarily not doing something was the frontomedian RCZ activation. ROI analyses in this area demonstrated that there is no difference in the percent signal changes between both conditions. Since what-decisions have been associated with RCZ activity (Cunnington et al., 2006; Debaere et al., 2003; Lau et al., 2004; Müller et al., 2007; van Eimeren et al., 2006) our finding provides strong evidence for the assumption that the voluntary decision not to act can be considered as an equivalent response option. Interestingly, Braver et al. (2001) came to a similar conclusion based on the observation that the inhibition of a prepared response results in a similar degree of ACC activation as the execution of the response. However, in contrast to our study, Braver's study explored inhibition in the context of a Go/NoGo task where inhibition occurs fairly late, namely after the possible response has been prepared. Our results
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extend this finding by demonstrating that very early decisions not to act – that do not require previous response preparation and no subsequent inhibition – are processed as equivalent response options as well. In the literature there is a controversy surrounding the function of medial frontal cortex. Previous studies have implicated medial frontal cortex in conflict monitoring (Botvinick et al., 1999; Carter et al., 1998; Nachev et al., 2005; Ullsperger and von Cramon, 2001) and the control of voluntary action and decision-making (Forstmann et al., 2006, 2008; Lau et al., 2004; Müller et al., 2007; Nachev et al., 2005; Thaler et al., 1995; Walton et al., 2004). It has been argued that conflict arising from competing response alternatives has to be overcome in order to decide for a specific behaviour (Lau et al., 2006). Interpreted in this framework the present findings suggest that conflict can also arise from an intentional non-action. Further research is needed to substantiate this claim. It might for example be interesting to investigate competition between different non-actions (e.g. deciding not to press a right key versus deciding not to press a left key). Our interpretation of the present data suggests that competition between no-response alternative should be functionally equivalent to decisions between response alternatives. Activations outside the fronto-median wall The right DLPFC activation associated with voluntarily choosing to do something, and the bilateral DLPFC activation involved in deciding not to do something has been shown in studies comparing voluntary action with externally triggered action as well (Cunnington et al., 2006; Lau et al., 2004). Lau et al. (2004) propose that DLPFC is involved in attention to the selection of action rather than actual response selection. Various studies demonstrated that random generation tasks likewise elicit DLPFC activation (Frith et al., 1991; Jahanshahi et al., 2000). The involvement of DLPFC in action selection tasks may be explained by the demand to keep track of previous responses which clearly contains a working memory component (Hadland et al., 2001). The fact that we find pronounced bilateral DLPFC activation in decisions not to respond emphasizes our reasoning that choosing whether to act or not involves a selection between two response options, although one of them lacks the overt component. Additionally to the DLPFC we also found other brain areas typically activated in cognitive control tasks, namely insula cortex, thalamus and basal ganglia. In deciding to act as well as deciding not to act we found bilateral activation of the insula cortex. Dorsal anterior insula activation has been reported in various response inhibition studies (Aron and Poldrack, 2006; Garavan et al., 1999; Wager et al., 2005). Insula activations occur frequently in context of cognitive tasks but are often not explicitly discussed. Nevertheless there is evidence indicating that anterior insula is associated with concentration and “cognitive effort” (Allen et al., 2007). In our task subjects might consider it as effortful and straining to reinitiate an action that has been inhibited just before or to decide not to do something after it has already been inhibited. Additionally anterior insula appears to be related to risk taking and therefore involved in intentional action as well. It may provide a “gut” feeling in decision-making (Paulus et al., 2005; Zysset et al., 2006) which may be mediated by autonomic states. The thalamus and basal ganglia are known to play a central role in behavioural action selection (Humphries and Gurney, 2002; Humphries et al., 2006; Redgrave et al., 1999). According to the action selection hypothesis basal ganglia poses the adjudicator of which of the potential actions that the cortex might be planning actually gets executed (Gurney et al., 2001). In particular Frank (2006) uses a neural network to simulate basal ganglia function in Go/NoGo tasks by modelling not only a Go (direct) pathway, but also a separate and competing NoGo (indirect) pathway. This structure of two simulta-
neously active pathways resembles our notion that voluntary nonactions compete with voluntary actions. De Jong and Paans (2007) demonstrate pallidum and thalamus activation for movements with less fingers suggesting active inhibition of the unwanted synergist finger movements. This principle of selection by suppression, in our case possibly suppression of the competing no-response substantiates our claim that voluntary non-actions should be regarded as a special case of action. A possible alternative explanation for the similarity of brain areas involved in the decision to act and the decision not to act might be that the two conditions involve a similar degree of prolonged inhibition necessary to come to a decision. In order to test which brain areas are related to inhibition we called in the contrast stop vs. failed-to-stop, which reveals an activation in the dorsal preSMA (0, 6, 57). Therefore it might well be that the preSMA part of our RCZ activation is involved in prolonged inhibition, but this leaves our interpretation of the function of the more anterior part of the RCZ unaffected. Another alternative explanation could be that the increased cognitive effort due to task complexity may have induced variations obscuring possible differences. Interestingly, in a recent ERP study that used a slightly different design we also found a high similarity between voluntary action and voluntary non-action while avoiding high task complexity (Kuehn et al., 2009). Given this multimethodological evidence in favour of the hypothesis that voluntary actions and voluntary non-actions show a high degree of similarity on the functional level, we think that this alternative explanation is not very plausible. Early versus late ‘whether’ In a recent model of intentional action Brass and Haggard (2008) distinguished a ‘what’, a ‘when’ and a ‘whether’ component of intentional action. While the what component is equivalent to the what decision investigated in the present study it is crucial to distinguish their ‘whether’ component from the intentional decision not to act investigated in the present study. Contrary to our definition of a whether decision as an unbiased choice between two options: to respond or not to respond, Brass and Haggard (2007, 2008) defined the whether component as the final decision to execute an action or not after a response is already thoroughly prepared. This whether process which bears resemblance with a veto has been associated with a brain area more anterior to RCZ (dorso-frontomedian cortex, BA 9; Brass and Haggard, 2007; Kühn, Haggard & Brass, in press). To dissolve this ambiguity we would like to differentiate between an early and late whether component. The early whether consists of the fundamental choice whether to engage in an action at all, whereas the late whether component comprises a gating process able to veto an action that has been selected in the what component. Since the early whether component is on a neural level equivalent with the what component we conclude that the decision not to respond is processed as a thorough response alternative. This nicely confirms our proposition to consider not acting as a mode of action. Legal and psychological implications These findings have implications in the legal and psychological domain. Most juridical systems recognize intentional non-action – namely negligence – as intentional acts by regarding it in principle as culpable. Under the label of “crimes of omission” a failure to act can be regarded as an actus reus (guilty act). This concept takes for granted that human beings do represent what they choose not to do and can therefore be accused of a mens rea (guilty mind) when omitting an action voluntarily. On a surface level this contradicts with findings in research about repentance in social psychology. According to the “actor effect” (Kahneman and Tversky, 1982) or “omission bias”-effect (Spranca
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et al., 1991; Ritov and Baron, 1990) people think that a decision was worse if the outcome resulted from an action than if it resulted from inaction. To explain these findings it has been suggested that, actions are more salient and more often used to infer one's own and others' attitudes (Fazio et al., 1982). However these findings mostly originate from judgements out of a third person perspective. Given that human beings usually evaluate their decisions from a first person perspective they probably regard their choices to act and not to act in equal measure as intentional decisions. Conclusion Our results indicate that the brain areas involved in representation of voluntary action and voluntary non-action are virtually the same. Moreover, choosing whether to act or not to act shows a similar neural signature as selecting between different overt response options. This constitutes first evidence in favour of the current judicial procedure to regard voluntary non-actions as punishable acts. Acknowledgment The work was supported by the dissertation grant of the Studienstiftung des Deutschen Volkes awarded to the first author. References Allen, M.D., Bilger, E.D., Larsen, J., Goodrich-Hunsaker, N.J., Hopkins, R.O., 2007. Functional neuroimaging evidence for high cognitive effort of the word memory test in the absence of external incentives. Brain Inj. 21 (13–14), 1425–1428. Aron, A.R., Poldrack, R.A., 2006. Cortical and subcortical contributions to stop signal response inhibition: role of the subthalamic nucleus. J. Neurosci. 26 (9), 2424–2433. Botvinick, M., Nystrom, L.E., Fissell, K., Carter, C.S., Cohen, J.D., 1999. Conflict monitoring versus selection-for-action in anterior cingulated cortex. Nature 402, 179–181. Brass, M., Haggard, P., 2007. To do or not to do: the neural signature of self-control. J. Neurosci. 27 (34), 9141–9145. Brass, M., Haggard, P., 2008. The what, when, whether (WWW) model of intentional action. Neuroscientist 14 (4), 319–325. Braver, T.S., Barch, D.M., Gray, J.R., Molfese, D.L., Snyder, A., 2001. Anterior cingulate cortex and response conflict: effects of frequency, inhibition and errors. Cereb. Cortex 11, 825–836. Carter, C.S., Braver, T.S., Barch, D.M., Botvinick, M.M., Noll, D., Cohen, J.D., 1998. Anterior cingulated cortex, error detection and the online monitoring of performance. Science 280, 747–749. Cunnington, R., Windischberger, C., Robinson, S., Moser, E., 2006. The selection of intended actions and the observation of others' actions: a time-resolved fMRI study. NeuroImage 29 (4), 1294–1302. Debaere, F., Wenderoth, N., Sunaert, S., Van Hecke, P., Swinnen, S.P., 2003. Internal vs external generation of movements: differential neural pathways involved in bimanual coordination performed in the presence or absence of augmented visual feedback. NeuroImage 19 (3), 764–776. Deiber, M.P., Honda, M., Ibanez, V., Sadato, N., Hallett, M., 1999. Mesial motor areas in self-initiated versus externally triggered movements examined with fMRI: effect of movement type and rate. J. Neurophysiol. 81 (6), 3065–3077. De Jong, B.M., Paans, A.M.J., 2007. Medial versus lateral prefrontal dissociation in movement selection and inhibitory control. Brain Res. 1132, 139–147. De Jong, R., Coles, M.G.H., Gratton, G., Logan, G.D., 1990. In search of the point of no return: the control of response processes. J. Exp. Psychol. Hum. Percept. Perform. 16 (1), 164–182. De Jong, R., Coles, M.G.H., Logan, G., 1995. Strategies and mechanisms in nonselective and selective inhibitory motor control. J. Exp. Psychol. Hum. Percept. Perform. 21 (3), 498–511.
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