The neural correlates and functional integration of cognitive control in a Stroop task
Introduction
The study of the neural bases of executive attention processes has featured prominently in the cognitive neurosciences. Executive or supervisory control refers to the collection of processes that allow us to react flexibly to changing or novel task requirements, by adaptively integrating changes in contextual information and, if necessary, inhibiting and overriding responses that may have previously been associated with successful task performance but are no longer appropriate (e.g., Norman and Shallice, 1986). In recent years, research has focused on dissociating supposed subcomponents of executive processes and their neural substrates, particularly with respect to an emerging model of complementary response conflict monitoring and cognitive control functions of executive attention (Botvinick et al., 1999, Carter et al., 1998, Carter et al., 2000, Casey et al., 2000, Kerns et al., 2004, MacDonald et al., 2000).
According to this prominent model, a response conflict monitoring system continuously parses ongoing information processing for potential response conflict due to interference or “crosstalk” between different processing streams, and this evaluative function is implemented by the (dorsal) anterior cingulate cortex (ACC). When conflict is detected, this model suggests that a cognitive control system, situated in dorsolateral prefrontal cortices (DLPFC), is alerted and subsequently engaged in reducing conflict by biasing information processing in posterior brain regions towards the criteria most relevant to successful task completion (Botvinick et al., 2001).
The behavioral and neuroimaging data on which the conflict monitoring/cognitive control model is based have primarily been derived from the Stroop task (MacLeod, 1991, Stroop, 1935) and the Eriksen Flanker task (Eriksen and Eriksen, 1974), where task-relevant and task-irrelevant stimulus properties are either in conflict with each other or not. For instance, in a typical Stroop paradigm, subjects are required to name the ink color in which a word stimulus is printed, and level of conflict is manipulated by varying the task-irrelevant property of the stimuli (in this case the word-meaning), from conflicting or “incongruent” (e.g., the word RED printed in green ink) to nonconflicting neutral, or “congruent” properties (e.g., the word RED printed in red ink). The Eriksen Flanker task, on the other hand, requires subjects to identify the nature of a central stimulus in a stimulus array (e.g., to indicate the direction of an arrow stimulus pointing left “<”or right “>”), and conflict is manipulated by displaying either incongruent (e.g., >><>>) or congruent flanker stimuli (<<<<<). The interference effects of these conflict manipulations are evident in behavioral measures of reaction times (RT) and error rates, with incongruent trials leading to longer RTs and higher error rates.
Crucially, performance on any given trial is influenced by the context of this trial with respect to conflict levels of preceding trials. The crossing of previous trial type (congruent/incongruent) with current trial type (congruent/incongruent), giving congruent–congruent (CC), congruent–incongruent (CI), incongruent–congruent (IC), and incongruent–incongruent (II) trial pairs, results in an interaction effect. Conflict effects (i.e., differential incongruent versus congruent trial responses) are reduced subsequent to high-conflict (incongruent) trials compared to low-conflict (congruent) trials, the so-called conflict adaptation effect (Gratton et al., 1992). This reduction in conflict stems from the fact that a preceding incongruent trial has opposing effects on responses to incongruent and congruent trials in comparison to responses following congruent trials: Responses to incongruent trials are faster and more accurate, interpreted as reflecting conflict reduction due to cognitive control, whereas congruent trial responses are slower and less accurate, interpreted as reflecting the elimination of a facilitation effect due to cognitive control (Botvinick et al., 1999, Botvinick et al., 2001). It is this interaction between previous and current trial type (“conflict adaptation” or the “Gratton effect”) on which the conflict monitoring/cognitive control model rests (Botvinick et al., 1999, Botvinick et al., 2001, Kerns et al., 2004, Mayr et al., 2003).
Employing Stroop and Eriksen paradigms and contrasting neural responses between conditions of high and low conflict, a number of studies using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have documented the ACC's susceptibility to conflict (Barch et al., 2001, Bench et al., 1993, Botvinick et al., 1999, Carter et al., 1995, Carter et al., 2000, Casey et al., 2000, Durston et al., 2003, Fan et al., 2003, Hazeltine et al., 2003, Kerns et al., 2004, Leung et al., 2000, MacDonald et al., 2000, Milham et al., 2001, Milham et al., 2003, Pardo et al., 1990, Ullsperger and von Cramon, 2001, vanVeen et al., 2001), and the DLPFC's purported role in the implementation of cognitive control (Durston et al., 2003, Kerns et al., 2004, MacDonald et al., 2000, Milham et al., 2001, Milham et al., 2003). However, parsing brain responses related to conflict detection from those related to conflict resolution through cognitive control has not been addressed by the majority of these studies. In view of the conflict adaptation effect, it is evident that to ignore the interaction between previous and current trial types introduces error into the interpretation of current trial interference effects, but only few investigations into the neural bases of conflict detection and resolution have explicitly taken this effect into account (Botvinick et al., 1999, Carter et al., 2000, Casey et al., 2000, Durston et al., 2003, Kerns et al., 2004).
The studies that have addressed the conflict adaptation effect, on the other hand, have focused exclusively on identifying neural correlates of response conflict reduction, i.e., brain regions in which activity decreases parallel to behavioral conflict effects (Botvinick et al., 1999, Carter et al., 2000, Casey et al., 2000, Durston et al., 2003, Kerns et al., 2004). This emphasis derived from efforts to resolve whether the role of ACC function in interference tasks is one of response conflict monitoring (e.g., Botvinick et al., 1999, Carter et al., 2000) or selection for action (e.g., Posner and DiGirolamo, 1998). Attempts at identifying brain regions implicated in cognitive control have consisted of examining neural effects of preparing for high- versus low-conflict trials in a cued Stroop paradigm (MacDonald et al., 2000), and of median-split analyses of Stroop trials subsequent to high conflict, associated with either low or high behavioral adjustments in terms of reaction times (Kerns et al., 2004). However, no previous study has specifically investigated the flip-side of conflict reduction in conflict adaptation, namely brain regions that display increased activity with the conflict adaptation interaction effect, paralleling a presumed increase in cognitive control. Furthermore, although it has frequently been suggested that cognitive control is likely implemented by PFC regions affecting the processing in posterior (particularly parietal) brain regions (e.g., Durston et al., 2003), only one previous fMRI study has attempted to assess connectivity measures related to Stroop task performance (Peterson et al., 1999). This study, however, conducted principal components analysis on data from a blocked Stroop paradigm and thus could not measure changes in functional connectivity related to component processes of Stroop performance, such as phasic adjustments in cognitive control.
The current study aimed to further our understanding of executive control mechanisms by identifying neural correlates of conflict adaptation, in particular cortical areas displaying increased activation with reduced conflict, reflecting enhanced cognitive control. In addition, a psychophysiological interaction (PPI) (Friston, 2004, Friston et al., 1997) analysis was employed to reveal changes in the functional interaction between brain regions implementing cognitive control during conflict adaptation. It was hypothesized that neural correlates of cognitive control would be detected in DLPFC, and that these activation foci would display increased functional integration with posterior cortical regions responsible for implementing changes in the focus of sensory and motor processing during conflict adaptation. We designed a simple Stroop paradigm variant that allowed us to examine conflict adaptation effects related to stimulus congruency in the absence of stimulus probability and repetition priming effects. Repetition priming here refers to direct stimulus repetitions that have been shown to be a major potential confounding variable in the interpretation of conflict adaptation effects in the Eriksen flanker task (Mayr et al., 2003) but have not been controlled for in previous studies (for an exception, see Kerns et al., 2004). Our Stroop task entailed equal proportions of CC, CI, IC, and II trial sequences, and none of the CC and II trial pairs contained stimulus repetitions. By excluding error and post-error trials1 from the assessment of behavioral interference effects, and separately modeling the neural correlates of these trials, we furthermore isolated conflict adaptation-related processes from error-related processes.
Section snippets
Subjects
Participants were 14 right-handed native or highly proficient English-speaking volunteers (mean age = 27.4 years, age range = 21–40 years, eight females) with normal or corrected-to-normal vision, who had been screened to exclude participants with previous or current neurological or psychiatric conditions, current medication use, colorblindness, or dyslexia. Participants gave written informed consent in accordance with institutional guidelines.
Paradigm
The Stroop variant employed in this study was a two
Behavioral results
Dependent measures of RT and percent accuracy rates were analyzed in 2 × 2 previous trial type (incongruent vs. congruent) × current trial type (incongruent vs. congruent) factorial mixed-effects ANOVAs. Within each subject, error and post-error trial RTs were excluded from the mean RT estimates, as were RT outlier values (>3 SDs from the mean), resulting in the exclusion of approximately 1.5% of RT data points. Post-error slowing was found to be significant (correct responses RT = 818 ms, SD =
Discussion
Employing a Stroop paradigm that incorporated equal proportions of CC, CI, IC, and II trial pairs, we obtained interference and conflict adaptation effects on RT and accuracy measures, presumably in the absence of repetition priming effects, as the task did not entail any stimulus repetitions on CC or II trial sequences. Event-related fMRI analyses that controlled for error and post-error trial confounds revealed that main effects of current trial conflict (evident in RT and accuracy data) were
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2023, Biological Psychiatry: Cognitive Neuroscience and NeuroimagingCitation Excerpt :Significant symptom-by-conflict interactions emerged for both age-15 anxiety and age-15 irritability (Table 3). Higher anxiety was associated with widespread decreased activity in response to conflict (incongruent vs. congruent trials) across multiple regions implicated in attentional control demand and conflict adaptation (37,38). Regions included the left insula (Figure 1), left middle occipital gyrus, left postcentral gyrus, bilateral fusiform gyrus, right superior occipital gyrus, left middle frontal gyrus, right postcentral gyrus, and left supramarginal gyrus.