Academia.eduAcademia.edu

Figure 6 – uploaded by Alexander Shackman

See full PDFdownloadDownload figure
Fig. 5. Forest plots of the relationship between FM@ and behavioral control (as Pearson’s r) and funnel plots. Here the forest plots are separated by the type of eliciting event and specifically associated EEG activity (A: error/ERN and RT slowing, B: punishment/FRN and switching), as well as the distinction between inter- and intra-individual analysis. C) Funnel plots were used to qualitatively assess the presence of publication bias. These plots display the standard error (y-axis) as a function of the effect size (x- axis). The lines of the funnel represent the range where 95% of points are expected to lie in the absence of publication bias. Asymmetrical deviations around the center line also suggest possible publication bias, especially if there are more points in the lower right corner (small N, large effect) but not in the lower left corner (small N, small effect). There was no qualitative evidence for publication bias in any meta analysis.

Figure 5 Forest plots of the relationship between FM@ and behavioral control (as Pearson’s r) and funnel plots. Here the forest plots are separated by the type of eliciting event and specifically associated EEG activity (A: error/ERN and RT slowing, B: punishment/FRN and switching), as well as the distinction between inter- and intra-individual analysis. C) Funnel plots were used to qualitatively assess the presence of publication bias. These plots display the standard error (y-axis) as a function of the effect size (x- axis). The lines of the funnel represent the range where 95% of points are expected to lie in the absence of publication bias. Asymmetrical deviations around the center line also suggest possible publication bias, especially if there are more points in the lower right corner (small N, large effect) but not in the lower left corner (small N, small effect). There was no qualitative evidence for publication bias in any meta analysis.

Related Figures (5)

Fig. 1. The Adaptive Control Hypothesis (TACH). In humans and other primates, the rostral cingulate (architectonic areas 24, 25, 32 and 33)— a thick belt of cortex encircling the rostral corpus callosum — is among the most prominent features on the mesial surface of the brain. Much of the constituent gray matter lies buried within the cingulate sulci. (A) The four major subdivisions of the human rostral cingulate. Supracallosal cingulate is designated the midcingulate cortex (MCC) and is divided into anterior (aMCC; green) and posterior (pMCC; magenta) subdivisions. Cingulate territory lying anterior and ventral to the corpus callosum is designated the anterior cingulate cortex (ACC) and is approximately divided into pregenual (pgACC; orange) and subgenual (sgACC; blue) subdivisions by the coronal plane at the anterior tip of the genu. (B) Negative affect, pain and cognitive control activate a common region within aMCC. This map depicts the results of a coordinate-based meta-analysis (CBMA) of 380 activation foci (192 experiments involving >3000 subjects). The upper panel shows thresholded activation likelihood estimate maps for each domain. The lower panel depicts the region of three- way overlap within aMct (areas 32’, a24b’/c’). (C) The MCC harbors somatotopically-organized premotor areas. Shown here are provisional locations of the cingulate premotor areas, the rostral and caudal cingulate zones (RCZ, CCZ). Somatotopy in RCZ and CCZ are based on human imaging studies. The cluster identified by the meta- analysis corresponds to the location of RCZ. The abundant projections from aMCC to motor centers would permit it to use information about punishment, feedback and other aversive reinforcers to optimize aversively-motivated instrumental actions. This stands in contrast with other cortical regions, such as the OFC and insula, that lack strong ties with motor centers. (D) Subcortical connnectivity of the rhesus homologue to human RCZ. This area receives substantial inputs from the spinothalamic system, which relays nociceptive information from the periphery to RCZ via the mediodorsal nucleus of the thalamus. Dopaminergic inputs to RCZ arise from the substantia nigra and, to a lesser extent, the ventral tegmental area. RCZ projects to the ventral striatum, including the core region of nucleus accumbens, and has robust reciprocal connections with the lateral basal nucleus of the amygdala. Dotted arrows indicate reciprocal connections. (E) The Adaptive Control Hypothesis (TACH). We have previously argued that MCC implements adaptive control by integrating information about punishment arriving from subcortical regions (Panel D), insula, orbitofrontal cortex (OFC) and elsewhere in order to bias responding in situations where the optimal course of action is uncertain or entails competition between alternative courses. Control signals generated in aMCC and directed at the amygdala or periaqueductal gray (PAG) might serve to resolve conflict between passive and active defensive behaviors. Another possibility is that aMCC directly biases aversively-motivated actions through its connections with motor centers, but indirectly biases selective attention through its connections with the frontoparietal network. It is also possible that these different mechanisms are functionally segregated at a finer level of resolution (e.g., intermingled networks) or are organized along overlapping gradients within MCC. Abbreviations: anterior cingulate cortex (ACC), caudal cingulate zone (CCZ), midcingulate cortex (MCC), orbitofrontal cortex (OFC), periaqueductal gray (PAG), rostral cingulate zone (RCZ), substantia nigra (SN), ventral tegmental area (VTA). Panels A-D adapted from (Shackman et al., 2011). »
Fig. 1. The Adaptive Control Hypothesis (TACH). In humans and other primates, the rostral cingulate (architectonic areas 24, 25, 32 and 33)— a thick belt of cortex encircling the rostral corpus callosum — is among the most prominent features on the mesial surface of the brain. Much of the constituent gray matter lies buried within the cingulate sulci. (A) The four major subdivisions of the human rostral cingulate. Supracallosal cingulate is designated the midcingulate cortex (MCC) and is divided into anterior (aMCC; green) and posterior (pMCC; magenta) subdivisions. Cingulate territory lying anterior and ventral to the corpus callosum is designated the anterior cingulate cortex (ACC) and is approximately divided into pregenual (pgACC; orange) and subgenual (sgACC; blue) subdivisions by the coronal plane at the anterior tip of the genu. (B) Negative affect, pain and cognitive control activate a common region within aMCC. This map depicts the results of a coordinate-based meta-analysis (CBMA) of 380 activation foci (192 experiments involving >3000 subjects). The upper panel shows thresholded activation likelihood estimate maps for each domain. The lower panel depicts the region of three- way overlap within aMct (areas 32’, a24b’/c’). (C) The MCC harbors somatotopically-organized premotor areas. Shown here are provisional locations of the cingulate premotor areas, the rostral and caudal cingulate zones (RCZ, CCZ). Somatotopy in RCZ and CCZ are based on human imaging studies. The cluster identified by the meta- analysis corresponds to the location of RCZ. The abundant projections from aMCC to motor centers would permit it to use information about punishment, feedback and other aversive reinforcers to optimize aversively-motivated instrumental actions. This stands in contrast with other cortical regions, such as the OFC and insula, that lack strong ties with motor centers. (D) Subcortical connnectivity of the rhesus homologue to human RCZ. This area receives substantial inputs from the spinothalamic system, which relays nociceptive information from the periphery to RCZ via the mediodorsal nucleus of the thalamus. Dopaminergic inputs to RCZ arise from the substantia nigra and, to a lesser extent, the ventral tegmental area. RCZ projects to the ventral striatum, including the core region of nucleus accumbens, and has robust reciprocal connections with the lateral basal nucleus of the amygdala. Dotted arrows indicate reciprocal connections. (E) The Adaptive Control Hypothesis (TACH). We have previously argued that MCC implements adaptive control by integrating information about punishment arriving from subcortical regions (Panel D), insula, orbitofrontal cortex (OFC) and elsewhere in order to bias responding in situations where the optimal course of action is uncertain or entails competition between alternative courses. Control signals generated in aMCC and directed at the amygdala or periaqueductal gray (PAG) might serve to resolve conflict between passive and active defensive behaviors. Another possibility is that aMCC directly biases aversively-motivated actions through its connections with motor centers, but indirectly biases selective attention through its connections with the frontoparietal network. It is also possible that these different mechanisms are functionally segregated at a finer level of resolution (e.g., intermingled networks) or are organized along overlapping gradients within MCC. Abbreviations: anterior cingulate cortex (ACC), caudal cingulate zone (CCZ), midcingulate cortex (MCC), orbitofrontal cortex (OFC), periaqueductal gray (PAG), rostral cingulate zone (RCZ), substantia nigra (SN), ventral tegmental area (VTA). Panels A-D adapted from (Shackman et al., 2011). »
Fig. 2. A variety of events indicating a need for control are associated with a similar neuroelectrical signature in the theta band (~4-8 Hz) over mid-frontal sites. Rows depict different components of the event-related electrophysiological signal, columns show different events associated with increased demands for adaptive control. (A) Event-related potential (ERP) components in the time-domain. N2: an ERP component evoked by exogenous cues of novelty or conflict. Feedback Related Negativity (FRN): An N2-like component evoked by exogenous feedback signaling loss or punishment. Error Related Negativity (ERN): A massive ERP component evoked by commission errors. While these ERP components (i.e., peaks and troughs in the wave) are related to learning and adaptive control, they represent a small fraction of ongoing neural dynamics: signal averaging in the time-domain imposes a substantial reduction in potentially meaningful information. (B) The full spectral dynamics of event-related neuroelectrical activity depicted in time-frequency plots. Here, significant increases in power to conflict, punishment and error are outlined in black, revealing a common feature in the theta band (~4-8 Hz). (C) Scalp topography of event-related theta activity. The distribution of theta power bursts is consistently maximal over the frontal midline. Data and statistical tests from (Cavanagh et al., 2012b).
Fig. 2. A variety of events indicating a need for control are associated with a similar neuroelectrical signature in the theta band (~4-8 Hz) over mid-frontal sites. Rows depict different components of the event-related electrophysiological signal, columns show different events associated with increased demands for adaptive control. (A) Event-related potential (ERP) components in the time-domain. N2: an ERP component evoked by exogenous cues of novelty or conflict. Feedback Related Negativity (FRN): An N2-like component evoked by exogenous feedback signaling loss or punishment. Error Related Negativity (ERN): A massive ERP component evoked by commission errors. While these ERP components (i.e., peaks and troughs in the wave) are related to learning and adaptive control, they represent a small fraction of ongoing neural dynamics: signal averaging in the time-domain imposes a substantial reduction in potentially meaningful information. (B) The full spectral dynamics of event-related neuroelectrical activity depicted in time-frequency plots. Here, significant increases in power to conflict, punishment and error are outlined in black, revealing a common feature in the theta band (~4-8 Hz). (C) Scalp topography of event-related theta activity. The distribution of theta power bursts is consistently maximal over the frontal midline. Data and statistical tests from (Cavanagh et al., 2012b).
Fig. 3. FM@ is consistently related to dispositional anxiety and predicts aversively- motivated behavioral adjustments. (A) Individuals characterized by greater dispo- sitional anxiety show larger FMO signals in response to conflict, punishment, and error. There was not a significant difference between response-locked error signals (filled circles) and cue-locked signals of punishment (empty circles) or conflict (filled diamonds). (B and C) Larger control signals predict a more cautious or inhibited response set following punishment or errors. (B) Larger error-related FM@ signals predict greater post-error slowing on the subsequent trial. This was observed both inter-individually (filled circles: individuals with larger error signals showed increased behavioral adjustments) and intra-individually (empty circles: trial-to-trial differences in control signals predicted proportional variation in post- error slowing) analyses. There was a significant moderating effect of this level of analysis, where intra-individual studies had a significantly larger relationship between error signals and response slowing (z-test = 2.02, p < .05). (C) Larger FMO responses to worse-than-expected feedback predict an increased probability of switching to the alternative among studies employing both inter-individual (filled circles) and intra-individual (empty circles) analytic strategies. The empty circle outlined by a dashed box indicates a study where feedback must be integrated over time and rapid switching would be maladaptive, this study was excluded a priori from this meta anlaysis on these grounds. Statistics were determined using random-effects meta-analyses (k: number of studies; n: number of participants). Error bars depict the random-effects estimate of the brain-behavior correlations (+95% CI). Each circle is centered on the correlation of each study, with the size of the circle scaled by the sample size (10*log10(N)). Larger numbers indicate a larger absolute relationship (i.e., the sign of the voltage potential is not taken into account).
Fig. 3. FM@ is consistently related to dispositional anxiety and predicts aversively- motivated behavioral adjustments. (A) Individuals characterized by greater dispo- sitional anxiety show larger FMO signals in response to conflict, punishment, and error. There was not a significant difference between response-locked error signals (filled circles) and cue-locked signals of punishment (empty circles) or conflict (filled diamonds). (B and C) Larger control signals predict a more cautious or inhibited response set following punishment or errors. (B) Larger error-related FM@ signals predict greater post-error slowing on the subsequent trial. This was observed both inter-individually (filled circles: individuals with larger error signals showed increased behavioral adjustments) and intra-individually (empty circles: trial-to-trial differences in control signals predicted proportional variation in post- error slowing) analyses. There was a significant moderating effect of this level of analysis, where intra-individual studies had a significantly larger relationship between error signals and response slowing (z-test = 2.02, p < .05). (C) Larger FMO responses to worse-than-expected feedback predict an increased probability of switching to the alternative among studies employing both inter-individual (filled circles) and intra-individual (empty circles) analytic strategies. The empty circle outlined by a dashed box indicates a study where feedback must be integrated over time and rapid switching would be maladaptive, this study was excluded a priori from this meta anlaysis on these grounds. Statistics were determined using random-effects meta-analyses (k: number of studies; n: number of participants). Error bars depict the random-effects estimate of the brain-behavior correlations (+95% CI). Each circle is centered on the correlation of each study, with the size of the circle scaled by the sample size (10*log10(N)). Larger numbers indicate a larger absolute relationship (i.e., the sign of the voltage potential is not taken into account).
Fig. 4. Forest plots of the relationship between FM@ and trait anxiety (as Pearson’s r). Forest plots display each study included in the meta-analysis in descending order of sample size (also indicated by the size of the box). Here the forest plots are separated by the type of eliciting event and specifically associated ERP component (A: error/ERN, B: punishment/FRN, C: conflict/N2). Psychometric instruments used to assess dispositional anxiety are detailed on the right of each plot.
Fig. 4. Forest plots of the relationship between FM@ and trait anxiety (as Pearson’s r). Forest plots display each study included in the meta-analysis in descending order of sample size (also indicated by the size of the box). Here the forest plots are separated by the type of eliciting event and specifically associated ERP component (A: error/ERN, B: punishment/FRN, C: conflict/N2). Psychometric instruments used to assess dispositional anxiety are detailed on the right of each plot.
Related topics:
PsychologyBiological PsychologyClinical PsychologyCognitive Behavioral TherapyCognitive PsychologyCognitive SciencePersonality PsychologyPositive PsychologyPsychoanalysisSocial Psychology
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved