Uncertainty and Cognitive Control

Cognitive control

Cognitive control, also often referred to as executive function, usually denotes a category of processes that are implemented when automatic schemata are not sufficient for successful adaptation to the environment. These processes tend to be conscious², attention-demanding, and they involve a flexible coordination of several cognitive processes in order to attain a specific goal (Atkinson and Shiffrin, 1968; Baddeley, 1986, 2003; Norman and Shallice, 1986; Baddeley and Della Sala, 1996; Miller and Cohen, 2001; Braver et al., 2007).

For instance, experienced car drivers utilize a number of over-trained automatic schemata on a daily basis (e.g., how to negotiate roundabouts and crossroads, how to operate the gearbox, etc.). However, if US or continental European drivers have to drive in a right-hand drive country (e.g., UK or Australia), then these automatic schemata are no longer adapted and a series of conscious, attention-demanding processes have to be implemented on a sustained basis. The driver will have to actively inhibit previously learned automatic behaviors and might have to consistently maintain the new traffic rules that have to be followed in working memory (WM). This example illustrates a typical implementation of at least two canonical cognitive control processes: inhibition of dominant responses and active maintenance of relevant information in WM stores (Miyake, 2001; Braver and Ruge, 2006). It is easy to imagine other cognitive control tasks that these drivers might have to carry out: dual tasks, controlled encoding, and retrieval from episodic memory of new driving rules, updating of rules in WM, etc.

Research on cognitive control has flourished in the last 25 years, and enormous progress has been attained in characterizing different subprocesses or dimensions of cognitive control (Miyake, 2001; Braver et al., 2007), as well as their neural substrates (e.g., Botvinick et al., 2001; Miller and Cohen, 2001; Braver and Ruge, 2006; Sakai, 2008). The question of what “triggers” cognitive control implementation, i.e., what determines that a system has to switch from an automatic toward a controlled processing mode, has remained for a long time unexplored and relegated to a “black box” (Baddeley, 2003). However, a relatively recent trend of research has explored the hypothesis that specific systems are devoted to the detection of the “need for control.” Specifically, a number of models have suggested the existence of brain systems devoted to monitoring the need for control and responsible for sending relevant “trigger” signals to other systems responsible for implementing control processes (Botvinick et al., 2001; Davis and Whalen, 2001; Sander et al., 2003; Kerns et al., 2004; Schaefer et al., 2006; Yeung and Cohen, 2006; Schaefer and Gray, 2007). A number of neuroimaging studies have provided clues on the neural network involved in these functions [see below the discussion about the anterior cingulate cortex (ACC), orbital frontal cortex (OFC), and amygdala].

Several hypotheses have been provided regarding the exact contextual conditions that would signal the need for control: detection of errors (Yeung and Cohen, 2006; Braver et al., 2007), changes in the motivational value (or goal-relevance) of the environment (Sander et al., 2003; Schaefer et al., 2006; Baddeley, 2007), conflict between competing responses (Botvinick et al., 2001), and changes in the prediction of error likelihood (Brown and Braver, 2005). Importantly, the common feature of these hypotheses is a conflict between a current representation used to guide behavior (and often accessed in an automatic mode) and environmental conditions in which expected outcomes are unlikely to be optimally achieved.

Dorsolateral prefrontal cortex

Traditionally, the dorsolateral prefrontal cortex (DLPFC) is thought to play a crucial role in WM and in the active maintenance of current task goals (Goldman-Rakic, 1995). Extensive neuroimaging and neuropsychological evidence shows that the DLPFC is strongly implicated in cognitive control tasks. For instance, it is involved in the retention of information during WM tasks and may store active representations that are necessary for adaptive decision-making performance (see Curtis and D’Esposito, 2003).The DLPFC has also been implicated in response selection in the presence of interfering or conflicting information (Rowe et al., 2000; Botvinick et al., 2001; Hadland et al., 2001; Schumacher and D’Esposito, 2002; Schumacher et al., 2003), control adjustments (Durston et al., 2003), the formulation and execution of plans of action (Fuster et al., 2000), top-down attentional control (Milham et al., 2003), and in maintaining attentional demands of the task (MacDonald et al., 2000). In addition, the DLPFC is also involved in task switching (Hyafil et al., 2009; Savine and Braver, 2010). Overall, several studies and models suggest that the DLPFC is involved in the implementation of control processes in order to resolve conflict, in contrast with other areas thought to be involved in the detection of conflict (Carter and Van Veen, 2007). This interpretation would be consistent with the fact that the DLPFC has preferential connections with motor system structures which may be central to how the PFC exerts control over behavior (Miller and Cohen, 2001). A wealth of additional studies have explored the links between the DLPFC and cognitive control, and reviewing them would be beyond the scope of this article. We refer the readers to authoritative reviews on this topic for more details (Miller and Cohen, 2001; Braver and Ruge, 2006; Carter and Van Veen, 2007; Mansouri et al., 2009).

Recent research suggests that the DLPFC may also play an important role in decision-making under uncertainty. Particularly, Huettel et al. (2005) found increased hemodynamic activity in the DLPFC during decision-making under uncertainty. The authors presented participants with eight different types of stimuli (varying amounts of triangles and circles). Participants were asked to make high or low confidence judgments about the likelihood of the feedback stimulus being the same as their selection. If the stimuli included all triangles or all circles, the chance of feedback stimuli having the same shape would be 80%, thus the uncertainty would be low at 20%. These authors varied the proportion of shapes presented as stimuli so that selections could be made in 20, 27.5, 35, 42.5, or 50% uncertainty. As uncertainty increased, so did activation in the DLPFC; which they suggest reflects the DLPFC’s role in uncertainty resolution in the form of short-term modification of stimulus–response contingencies, a process that might rely on high level cognitive control processes (Huettel et al., 2005). These representations of stimulus–response contingencies are thought to guide our everyday decisions through underlying rules in which we learn that a specific association between a stimulus (S) and a response (R) is linked with a positive or negative outcome (O) (De Wit and Dickinson, 2009). Interestingly, evidence from probabilistic learning tasks such as the weather prediction task (Gluck and Bower, 1988; Gluck et al., 2002) suggests that the involvement of the DLPFC in probabilistic learning might reflect implicit, non-declarative processes rather than conscious control processes (Poldrack et al., 1999; Weickert et al., 2002). However, recent evidence indicates that the DLPFC is not necessary to non-declarative learning in these tasks (Rushby et al., 2011).

Overall, these findings suggest a role for the DLPFC in modifying S–R–O contingencies to enable adaptive decision-making in the face of uncertainty and that activation in the DLPFC is dependent on predictability of S–R–O contingencies (Paulus et al., 2002).Therefore, predictability and thus certainty, may be represented in the DLPFC through maintaining representations of the reinforcement history (previously encountered S–R–O rules) to select an “optimal strategy” (Paulus et al., 2002). It could therefore be speculated that the DLPFC is involved in the active maintenance and manipulation in WM of a history of past contingencies in order to facilitate the prediction of future outcomes. Nevertheless, there is some inconsistency across studies with evidence indicating that the DLPFC activation increases as decision stimuli become more unexpected (Huettel et al., 2002) and others that suggest that the DLPFC is more active during low error rates, i.e., greater predictability (Paulus et al., 2002).

Posterior parietal cortex

Parietal areas and the posterior parietal cortex (PPC) in particular have often been associated to cognitive control tasks (Braver and Ruge, 2006). The PPC has been implicated in an array of cognitive control processes including task switching (Sohn et al., 2000) and movement planning (Snyder et al., 1997). The PPC is also thought to be important for sensory–motor integration that might encode movement intentions (Andersen and Buneo, 2002). Indeed, neurons within the PPC of non-human primates provide codes for decision variables such as probability and expected value when making a decision between potential movement intentions (Platt and Glimcher, 1999).

Along with the DLPFC, Huettel et al. (2005) also found that the resolution of uncertainty was linked with the PPC. These authors suggest that the PPC may also be involved in short-term response selection processes such as modification of S–R–O contingencies during uncertainty. More long term resolution of uncertainty may rely on S–R–O contingency development, i.e., learning adaptive S–R–O rules which might be mediated by areas of the medial frontal cortex (Huettel et al., 2005). Indeed, Volz et al. (2003, 2004) also demonstrated medial frontal cortex and PPC activation increases with increasing uncertainty. In their study, Volz et al. (2003) showed participants pairs of pictures showing comic figures which were systematically associated with a particular reward probability (either 0.6, 0.7, 0.8, 0.9, or 1.0), and these associations were consistent throughout the experiment. As uncertainty increased, posterior parietal areas known to underlie WM functions also increased. It has been suggested that this type of result could reflect the role of the PPC in actively maintaining in WM all S–R–O rules that are valid in an experiment (Bunge et al., 2002).

Anterior cingulate cortex

The Anterior Cingulate Cortex (ACC) is one of the most prominent neural substrates identified in cognitive control processing (see Botvinick et al., 2001; Braver and Ruge, 2006). For instance, the ACC has been found to be involved in many classical cognitive control tasks such as the Stroop task (Pardo et al., 1990), the Flanker task (Botvinick et al., 1999), N-back task (Harvey et al., 2005), and stop-signal tasks (Brown and Braver, 2005), amongst others. Evidence also suggests that the ACC is involved in monitoring cognitive conflicts (MacDonald et al., 2000; Botvinick et al., 2001) and error detection (Ullsperger and Von Cramon, 2003). Certain electrophysiological components known to be linked to cognitive control (and to conflict monitoring in particular) are thought to be generated by ACC sources. For instance, the N2 and the Error-related negativity (ERN/Ne) event-related brain potentials (ERP) components are thought to originate in the ACC (Dehaene et al., 1994; Dikman and Allen, 2000; Gehring and Knight, 2000; Debener et al., 2005) and are likely to reflect the cognitive demands of situations involving a high level of conflict between competing responses (Yeung and Nieuwenhuis, 2009). Importantly, the ACC is widely thought to be part of a network of brain structures specialized in the detection of environmental conditions signaling the need for the implementation of cognitive control, and responsible for sending “triggers” to other systems specialized in the actual implementation of control (Botvinick et al., 2001; Kerns et al., 2004; Brown and Braver, 2005).

Activity in the ACC has also been demonstrated to correlate with levels of uncertainty (Stern et al., 2010). In their task, Stern et al. (2010) showed participants four sequential draws of cards taken from two decks. Deck A which contained 80% red cards and 20% blue cards and Deck B which contained 20% red cards and 80% blue cards. Participants had to identify the deck from which the cards had been drawn. Uncertainty was manipulated by the frequency of cards presented with the same color in the sequence of four cards presented to the participants. For instance, four blue cards was the highest certainty condition, whereas a combination of two red and two blue cards was associated to maximum uncertainty. The authors found that increased uncertainty was associated with increases in ACC activity. According to the authors, the ACC was specifically involved in “evidence accumulation” whereas the OFC was involved in the execution of the decision following the four sequential draws of cards (Stern et al., 2010). Increased activity in the ACC has also been found to be related to outcome uncertainty and uncertainty-related physiological arousal (Critchley et al., 2001). In their study, Critchley et al. (2001) gave participants a two choice decision-making task, in which a cue (a card) was predictive of the value of a “feedback card.” Participants had to decide from the initial card if the feedback card would be higher or lower than the initial card. Uncertainty was manipulated by the degree to which the initial card was a reliable predictor of the feedback card. The participants then experienced a delay period before the outcome (gain/loss) was presented. Critchley et al. (2001) found that the ACC was modulated by both outcome uncertainty and anticipatory delay. In addition, the authors found a distinct region of the ACC that was commonly modulated by both uncertainty and physiological arousal.

The ACC has also been associated with hypothesis testing in uncertain contexts (Elliott and Dolan, 1998). Specifically, Elliott and Dolan (1998) presented participants with a series of pairs of checkerboards with different spatial configurations of black and white squares in which participants were asked to try to work out a rule governing which of each pair was correct (feedback was set at an uncertain 50% correct level). The task requirement was to generate a hypothesis (i.e., top left-hand corner filled) and then test and update it based on information about which checkerboard was correct. Hypothesis testing and making a choice was associated with different foci within the anterior cingulate. Whilst the dorsal ACC was involved in complex hypothesis testing, the ventral ACC was associated with implementing a choice. This suggests different roles of the ACC in purely cognitive aspects of processing (hypothesis testing) and evaluative processes related to the emotional consequences of a choice (Elliott and Dolan, 1998.) Indeed, the ventral region of the ACC is interconnected with limbic regions such as the amygdala which is widely thought to underlie emotional processes (Bracht et al., 2009).

Finally, the ACC has also been linked to volatility, i.e., uncertainty created by frequent changes in S–R–O contingencies (Behrens et al., 2007). In Behrens et al.’s (2007) study, subjects carried out a one-armed bandit task in which they had to choose between blue and green stimuli. Subjects underwent trials where the probability of a blue outcome was 75% (a certain/stable environment) and trials where reward probabilities switched between 80% blue and 80% green every 30 or 40 trials (an uncertain/volatile environment). Behrens et al.’s (2007) results suggest that ACC activity might reflect a Bayesian estimate of the environment’s volatility during a monitoring stage, i.e., when outcomes are being evaluated in order to regulate current beliefs about the underlying stimulus–response contingencies of the environment. The model of Behrens et al. (2007) also suggests that the ACC might encode how much influence feedback should give to subsequent decisions, with more recent outcomes being more salient in uncertain contexts (Rushworth and Behrens, 2008).

The available findings about the involvement of the ACC in decision-making under uncertainty and in cognitive control tasks provide an interesting parallel. On the one hand, studies and models from the realm of cognitive control research suggest that the ACC might be involved in the detection of the contextual conditions signaling the need for control. On the other hand, data from the field of decision-making processes suggest that the ACC might be involved in the computation of the level of uncertainty of current SRO representations (Behrens et al., 2007). An integration of these findings could tentatively suggest that both traditions of research are tapping overlapping processes, in the sense that uncertainty could be easily seen as a condition signaling the need for control. However, results from the field of decision-making under uncertainty (and in particular the study from Behrens et al., 2007) might be providing a more formal description of the computational nature of one of the potential mechanisms that can trigger the implementation of cognitive control. Nevertheless, at this stage, these considerations remain hypothetical, as there is to our knowledge no evidence that clearly demonstrates that the estimation of uncertainty, as described by Behrens et al. (2007), necessarily lead to the implementation of cognitive control, or that the adaptation to uncertainty in these studies relied on processes other than implicit learning.

Orbito-frontal cortex

The Orbito-Frontal Cortex (OFC) has classically been linked to the perception of reward and punishment feedback, including anticipation and receipt of feedback, and is usually thought to be involved in the representation of stimulus–reward relationships (Rolls, 1996; O’Doherty et al., 2003). The OFC has also become a brain area of interest for cognitive control when it was discovered that this region not only detects valenced outcomes but it also probably uses this information to bias future behavioral choices (O’Doherty et al., 2001, 2003). Therefore, it has been suggested that the OFC might also be involved in processing environmental conditions signaling the need for behavioral control (O’Doherty et al., 2003; Braver and Ruge, 2006).

More recently, the OFC has also been shown to differentiate between different levels of uncertainty (Hsu et al., 2005). In this study, Hsu et al. (2005) manipulated uncertainty by creating situations in which participants had to make choices under ambiguity. For instance, one of the conditions included placing a bet on the color of a card (e.g., blue or red) to be drawn from a deck without knowing the actual proportions of blue and red cards of the deck. In control conditions, relevant information was available prior to the choice (i.e., participants were told the exact proportions of red and blue cards in the deck). They found that the level of uncertainty correlated with activation of the OFC and the amygdala. These authors suggest that a network involving the OFC and the amygdala would reflect the operations of a “vigilance”/evaluation system dedicated to evaluate uncertainty and bias behavioral adaptation as a function of uncertainty levels. They further support this idea through a behavioral study on patients with OFC lesions, finding that OFC lesions impair the ability to distinguish between degrees of uncertainty (Hsu et al., 2005).

Related results suggest that the OFC enables acquisition of S–R–O contingencies, whereby patients with OFC lesions show deficits in shifting behavior when the underlying S–R–O rule is volatile (Rolls, 2000). This is consistent with data showing that OFC activation is associated with hypothesis generation of S–R–O rules (Goel and Dolan, 2000) and the detection of change in reward contingencies (O’Doherty et al., 2003). In addition, a number of other studies have also shown that brain activity in the OFC increases with increasing outcome uncertainty (Critchley et al., 2001) and reward uncertainty (Tobler et al., 2006). Finally, activity in the OFC also increases as the probabilistic stimulus–response contingencies become more complex, suggesting that the OFC may reflect additional requirements of dealing with uncertainty (Elliott et al., 1999). However, Stern et al. (2010) suggest that activation of the OFC by uncertainty is absent when there is no risk, suggesting that the OFC does not detect cognitive uncertainty per se, but instead responds to uncertainty only when there are incentive consequences.

In summary, available research clearly shows that the OFC is sensitive to several types of manipulation of uncertainty. It is unclear at this stage as to what specific role the OFC plays in the processing of uncertainty. However, research in the field of cognitive control would indicate that the OFC might be involved in tracking valenced information signaling the need for behavioral control. It could therefore be speculated that the OFC detects several forms of outcome uncertainty because it equates them to motivationally relevant contextual cues reflecting the need for control.

Amygdala

The amygdala is widely known to be associated to a series of emotional processes, and it has been famously associated to fear and anxiety (LeDoux, 1996), although it is now known that the amygdala is also involved in a wider array of affective processes including positive affect (Zald, 2003). A recent research trend has also found evidence that the amygdala plays an important role in cognitive control. For instance, Schaefer et al. (2006) have shown that increases in amygdala activity significantly predicted an improvement of performance in a canonical executive task, the N-back WM task, across two different experiments. More recent studies have found similar evidence of a relationship between the amygdala and cognitive control processes (Nishijo et al., 2008; Ousdal et al., 2008; Yun et al., 2010; Chiew and Braver, 2011), which adds to earlier evidence in both human and animal research (for a review, see Schaefer and Gray, 2007). A common theory to account for the role of the amygdala in cognitive control posits that the amygdala is responsible for evaluating changes in the motivational value (or goal-relevance) of the environment, and when these changes exceed a certain threshold, biasing signals are sent to cognitive and motor systems in order to facilitate adaptation (Davis and Whalen, 2001; Sander et al., 2003; Schaefer et al., 2006; Schaefer and Gray, 2007). It is thought that this mechanism can be involved in the implementation of cognitive control, but also in the generation of emotional responses (Schaefer et al., 2006).

As indicated by the findings of Hsu et al. (2005) described above, the amygdala also appears to be linked to the evaluation of contextual uncertainty. Similar conclusions have been reached in animal studies (for a review, see Rosen and Donley, 2006). In particular, Rosen and Donley (2006) suggest that the amygdala may be particularly linked to uncertain situations characterized by unexpected changes in the environment, which would relate to the concept of unexpected uncertainty (Yu and Dayan, 2005), and to the construct of rule volatility (Behrens et al., 2007). The amygdala is also activated under internally driven uncertainty, i.e., self reported levels of uncertainty (Zaretsky et al., 2010), which suggests that the amygdala is activated when sufficient data regarding a potential threat in the surroundings is lacking (Whalen et al., 1998; Zaretsky et al., 2010). For instance, Zaretsky et al. (2010) asked participants to report their perceived certainty regarding the level of danger associated to ambiguous face stimuli. They found that amygdala activity increases were associated with an increase in perceived uncertainty. Consistent with this idea, Davis and Whalen (2001) have suggested a model in which the amygdala is responsible for increasing levels of vigilance in response to uncertain stimuli or situations. This increase in vigilance levels would bias a series of control systems which would in turn facilitate general adaptation to the initial uncertain context.

The similarities between models of the role of the amygdala in uncertainty and models of the role of the amygdala in cognitive control are obvious. In both cases, fundamental changes in the goal-relevance of a situation are thought to trigger a response that signals the need to implement a controlled adaptive response. This mechanism shares similarities with the role often attributed to the ACC in cognitive control and reinforces the thesis that uncertainty plays a significant potential role in “triggering” the implementation of cognitive control.

General anxiety disorder

General anxiety disorder is a disorder characterized by excessive and persistent worrying (Dugas et al., 1998) and has been used to highlight the importance of uncertainty in psychiatric disorders (Paulus, 2007). IOU has been postulated to play a central role in anxiety (Krohne, 1989). For instance, measures of IOU can differentiate between clinical and healthy populations in GAD (Buhr and Dugas, 2002) and can predict changes in anxious symptoms (Dugas et al., 2009). Importantly, high worriers show a clear deficit in decision-making under uncertain situations with longer reaction times (see Ladouceur et al., 2000) and more information seeking behavior (Gibbs-Gallagher et al., 2003). Further, IOU has been associated with greater recall and threatening interpretations of ambiguous information (Dugas et al., 2005) and greater attempts to reduce uncertainty (Ladouceur et al., 2000). Worriers take longer to make category judgments than non-worriers, particularly when confronted with uncertain (ambiguous) stimuli (Metzger et al., 1990; Tallis et al., 1991). Raghunathan and Pham (1999) found that anxious decision makers preferred to choose high probability rewards linked to low monetary gains instead of more infrequent but much larger gains. This finding illustrates the tendency of anxious individuals to opt for safer and less uncertain choices, even at a significant cost.

Schizophrenia

Patients with psychosis, or at risk of psychosis, display a difficulty in tolerating uncertainty (e.g., Broome et al., 2007). Strauss et al. (2011) claim that reduced reward-seeking behavior in Schizophrenia is critically related to the extent to which patients make exploratory choices when they are uncertain. “Jumping to conclusions” (JTC), a frequent symptom in schizophrenia, has also been found to be associated with the construct of IOU (Freeman et al., 2008). Moritz et al. (2011) have suggested the idea that JTC in schizophrenic patients may contribute to well-being since quick decision-making decreases doubt and uncertainty. Indeed, JTC may be more evident in individuals who find it difficult to tolerate ambiguity (Colbert and Peters, 2002). This suggests a need to reduce uncertainty in schizophrenia, similar to that seen in other psychopathologies. Interestingly, a similar phenomenon is associated to IOU in GAD individuals. High IOU was indeed associated with faster decision-making in anxious individuals when subjects were faced with an outcome delay and thus waiting in a state of uncertainty (Luhmann et al., 2011). Further, an important theory of uncertainty processing (Yu and Dayan, 2005), has been linked to delusions and hallucinations associated with schizophrenia. Specifically, Patel et al. (2010) have suggested that hallucinations may be linked to neurotransmitters involved in the interaction between top-down and bottom-up processing where hallucinations could be experienced due to over-processing of top-down signals resulting in uncertainty in information processing.

Obsessive–compulsive disorder

One of the characteristics of OCD is pathological doubt, which is most clearly evident among patients with checking rituals (Rachman and Hodgson, 1980; Rasmussen and Eisen, 1992). Interestingly, uncertainty extends to long term memory of past events, as OCD individuals tend to be uncertain about their memory for checked events (Van Den Hout and Kindt, 2004). More directly, Tolin et al. (2003) found that repeating rituals were associated with IOU. IOU in OCD patients may reflect the belief on the part of OCD patients that they lack sufficient coping or problem solving skills to effectively manage threatening situations that have the potential to evoke discomfort and negative emotionality (Holaway et al., 2006). Many of the traits associated with OCD can be viewed as the products of control exerted within the emotional, interpersonal, cognitive, and behavioral domains (Gibbs-Gallagher et al., 2003). Difficulty in decision-making in OCD is characterized by a cautious approach to information categorization, frequent requests for information repetition, and doubts about the correctness of decisions (Obsessive Compulsive Cognitions Working Group, 1997).

Cognitive control and psychopathology

Importantly, patients with these psychopathological disorders not only show an IOU but also exhibit deficits in classical cognitive control tasks. For instance, OCD patients usually show an impaired performance on tests of executive function. Indeed, OCD patients show performance deficits on measures of response inhibition (Aycicegi et al., 2003). Particularly, OCD subjects exhibit higher interference costs in the Stroop task (Hartston and Swerdlow, 1999), making significantly more errors and slower reaction times on the interference trials of the Stroop test (Martinot et al., 1990; Bannon et al., 2002). It has been suggested that OCD subjects exhibit deficits in behavioral and cognitive inhibition (Enright and Beech, 1990; Rosenberg et al., 1997), which might underlie the repetitive symptomatic behaviors of the disorder, such as compulsions and obsessions (Bannon et al., 2002). Deficits among patients with OCD may also be associated with co-morbidity with depressive symptoms and schizotypal personality features (Aycicegi et al., 2003). Schizophrenia is also usually characterized by deficits in executive function (Liddle and Morris, 1991). For instance, schizophrenic patients perform worse in NoGo conditions in a Go/NoGo tasks suggesting a failure of response inhibition in schizophrenic patients (Weisbrod et al., 2000). Deficits in cognitive control have also been observed in patients with high anxiety (Wood et al., 2001). For instance, these deficits include dysfunctional response inhibition (Gernsbacher et al., 1990) with more attention paid to threatening distracters in the Stroop task (Mathews et al., 1990). High levels of anxiety have also been shown to limit WM capacity (Eysenck and Calvo, 1992).

Alexandre Schaefer is supported by the UK Biotechnology and Biological Sciences Research Council (BBSRC) and Amy R. Bland is supported by the Economic and Social Research Council (ESRC).