Posted comment on ´Default mode contributions to automated information processing` by D. Vatansever, D.K. Menon and E.A. Stamatakis and published in PNAS October 23rd 2017 doi 10.1073/pnas.1710521114
Vatansever, Menon and Stamatakis investigated the role of the Default Mode Network (DMN) in circumstances they defined as requiring automated decision-making ie. routine, predictable challenges that require fast and accurate responses. The authors of the article found that the DMN exhibited greater activity and connectivity between the hippocampus, parahippocampus and primary visual cortex areas in these circumstances and suggested that the DMN played an ´autopilot` role. This confirmed the findings of others.
In their study, Vatansever, Menon and Stamatakis used the cognitive flexibility task the Wisconsin Card Sorting Task (WCST). They took 28 subjects aged between 22 and 34 and presented them with 4 permanent reference cards and one alternating target card taken from a pool of 60 cards. The test participants had to sort the given card to one of the 4 reference cards according to a set of rules which included colour, shape, number and similarity. The participants were not told of the sorting criteria before the test and had to work it out for themselves as the test progressed. Each participant took part in 10 trials consisting of 4 blocks for each of the 4 rules. Feedback was given including indicating choice accuracy so the participants could work out the rule for each block. The results were attributed to two phases: the first known as the ´acquisition phase` where the rules were learnt by trial and error and the second phase the ´application phase` for the rest of the block when the correct responses had been learnt. Brain area activity was assessed using MRI.
Vatansever and colleagues assumed that their results would show that compared to the control condition, the percentage of correct answers in the task condition would be lower and they assumed that the participants` performances would be worse in the acquisition phase compared to the later application phase. Their assumptions were found to be correct with accuracy around 92% in the acquisition phase compared to about 99% in the application phase. They also predicted correctly that there would be a longer latency for the correct responses during the initial acquisition phase and this was also shown to occur with the control condition.
Brain area involvement during their task was also investigated by Vatansever, Menon and Stamatakis with emphasis placed on the activity of the frontoparietal, dorsal attention, cingulo-opercular, salience and visual networks. The authors assumed that DMN regions would be more active in the application phase rather than the acquisition phase because the task would demand at this time greater access to learnt, memory-based information in order to bring about the fast, correct responses. Connectivity of the areas would also reflect the different demands on the systems required at that time eg. perception. Vatansever and colleagues found that their experiments supported their hypotheses. Their MRI studies showed a highly symmetrical bilateral set of frontoparietal, insular, subcortical, and cerebellar brain regions more active in the acquisition phase compared to the application phase. This activity was associated with frontoparietal, dorsal attention, cingulo-opercular, salience and visual networks and supported previous work by others who found that these areas were involved in the successful performance of WCST tasks. Greater activity was found in the application phase in the regions associated with the DMN plus others (eg. ventromedial PFC, somatomotor networks and posterior cingulate cortex plus ventral anterior cingulate cortex, medial temporal lobe structures such as hippocampus, parahippocampal gyrus, right amygdala, superior and middle temporal gyri and the left middle occipital gyrus). A further seed-based connectivity experiment using a seed placed in the left posterior cingulate cortex/precuneus (PCC/PCUN) region showed that the two phases of task produced slight differences in levels of activity within the DMN areas ie. during the application phase there was greater activity in the PCC/PCUN, ventromedial prefrontal cortex, and left angular gyrus areas, but reduced connectivity with the bilateral insular gyri and right presupplementary motor area. The authors attributed the differences in brain activity observed to the different cognitive demands placed on the systems occurring during the course of the test. For example in the learning phase there would be low DMN activity and then the repetition phase when the rules are learnt, there is increased activity of the areas associated with the DMN.
Vatansever, Menon and Stamatakis also investigated the role of the dorsal attentional system (DAN) during their experimental task and its interrelationship with the DMN. The authors found that DAN activity was negatively correlated with that of DMN during resting state conditions. During both phases of the experiment the extensive DAN network encompassed activity of the frontal eye field (FEF – the seed of the seed-based connectivity experiment) and the inferior parietal lobe. However, differences were again observed between the acquisition phase and application phase with increased negative correlation with those regions commonly linked to the DMN in the latter phase to the former. Activity of the middle/superior temporal area and inferior/superior parietal gyri was observed to be lower in the acquisition phase compared with the later application phase. Accordingly, these observations were attributed to the alterations in functional connectivity in response to demands on the system for the two phases of the task and provided evidence that the task demanded changes in activity and connectivity not only for the DMN, but the attentional network too.
The authors continued their investigation into the activities of the DMN and DAN networks correlating them to behavioural performance measured through reaction times. It was assumed that DMN activity was associated with memory-based, automated decision-making and the DAN network with controlled, effort-requiring informational processing. An investigation into DAN connectivity and reaction time found that there was greater activity between the FEF and somatomotor regions (precentral and post central gyrus and paracentral lobe) in the acquisition phase which correlated with faster reaction times and better performance. This supported the view that connectivity between the FEF and precentral gyrus controls the saccades when processing visual information in visual searches and extraction of information during the rule-making acquisition phase. No correlation was found in the application phase. In the case of the DMN, no correlation was found between the PCC/PCUN with the parahippocampus, hippocampus, amygdala, primary visual cortices areas in the acquisition phase, but greater connectivity of the PCC/PCUN with these areas correlated with faster reaction times and better performance in the application phase. This supported again the view that the medial temporal lobe plays a role in context specific, memory based information processing and the visual nature of task during the acquisition phase whilst participants utilise learnt responses in the application phase.
Vatansever, Menon and Stamatakis concluded that their experiments supported the view that the DMN contributes to spontaneous internal thoughts during the brain`s state of rest, but greater connectivity and activity of the DMN areas is observed when individuals are required to access memory stores for a task. In the case of their experimental task then the DMN showed greater activity during the task`s application phase ie. during automated information processing than the acquisition phase. This was explained as automated information processing allowing individuals to use their own internal models of the world previously gained through experience to interpret their surroundings. This led the authors to describe the action of the DMN being in ´autopilot mode` in the application mode compared to ´manual mode` when the DMN failed to predict the current environment in the acquisition phase. This also provided an explanation as to why the DMN areas have ongoing activity when the brain is at rest and why it is active in certain situations where social interactions are important (eg. theory of mind, intuition, creativity and conscious sense of self).
The DMN`s cognitive functionality was attributed by the authors to be due to its extensive connectivity to the rest of brain which provided then a common workspace for the convergence of information from external sources and access to memory-based information. Therefore, their experimental results were interpreted as that the increased activity and connectivity of the DMN in the application phase of WCST task indicated the network`s ability to integrate memory-based information in order for fast automated decision-making, whereas the novel unpredictable situation of the acquisition phase demanded further attention and perception before the decisions were made. This would involve other networks eg. perceptual system and was observed with the roles played by the DMN areas and DAN areas during the two phases. Vatansever, Menon and Stamatakis went on to say that the differential involvement of the two networks may not then be described as just dependent on internally or externally directed cognition, but instead be described in addition by their dependency on the predictability of the environmental demands requiring either a memory-based (learnt) or perception-based (novel) response. Their idea of duality in decision-making system under varying levels of predictability in the environment supports the views of others. For example: Norman and Shallice argued for stored schemas that automatically take over processing in familiar contexts with the attentional system playing an intentional inhibitory role when the rules change; and Kahneman and Tversky`s work where they promoted a two system view with System 1 required for automatic decision-making to provide fast, best guesses and System 2 for calculated effort-requiring decisions.
Vatansever, Menon and Stamatakis concluded their article by saying that future studies were necessary to investigate the potential role of DMN in the formation of habitual behaviour in decision-making and to investigate its potential deficiency in cognitive disorders such as addiction, obsessive-compulsive disorder or clinical depression.
What makes this article so interesting is that it describes the relationship between two types of decision-making relative to a specific task and how the activities of the brain areas connected to the Default Mode network change relative to this task and the decision-making system in play. The results mean that we have to find an explanation as to why in the acquisition phase of a learning task and assumed to be where there is highly demanding cognitive processing this phase elicits lower activity in these DMN areas. Conversely, we also have to provide an explanation as to why in the application phase of the task these same brain areas elicit higher activity and connectivity at a point when cognitive demands are lower.
In order to do this we first have to look at what is going on in decision-making and what demands this places on brain functioning and hence, brain area activity. We can assume that the dual system of decision-making given by others is correct and that the task of card sorting used in Vantansever, Menon and Stamatakis`s experiments demonstrates to a large extent the difference between the two systems: the acquisition phase being System 2 which is slower, sequential and requiring central executive participation; and the application phase demonstrating to a certain extent System 1 type decision-making which is rapid, parallel and automatic. The actual stages of the decision-making process between the two phases remain relatively the same, but there are notable differences in the early stage and goal matching stage. Both the acquisition phase and application phase begin with the first stage of ´purpose and input` which equate to the individual knowing the ultimate goal of the task which has been given to him by the experimenters. The individual knows he has to perceive the card, identify its features and has to match it to one of the reference ones lying before him. The only difference is that in the acquisition phase the exact detail ie. the sorting criteria is not known. Experience tells him that there is no ´magic answer` available saying on which reference card he has to place his sample card since he has no previous examples to access. The only previous experience relevant to him at this stage is the awareness that the problem requires working out and this could take several steps. This is the difference in this stage of the decision-making process between the two phases since in the application phase the target sorting feature is known and the ´purpose` is defined as card matching according to a particular feature compared to the acquisition phase where the ´purpose` is essentially ´to find the sorting criteria`. Although most neurochemical systems are the same in this initial stage of the process, eg. visual input, perception, sensory memory store formation, both the attentional and consciousness systems demonstrate lower involvement in the later application phase due to the reduced demands placed on them since the purpose of the task has been identified and only matching is required.
This difference in cognitive demand between acquisition phase and application phase becomes even more apparent in the next stage of the decision-making process which is the ´solutions` stage where problem solving strategy has to be employed so that a decision can be made. In the acquisition phase this is highly demanding and it involves the perception of the real-time situation as where we are (eg. card in hand) and projection into the future of where we want to be (eg. placing the card on the correct reference card). From a neurochemical point of view it requires the formation of a short term memory store (termed input neuronal cell assembly, iNCA) representing the card in the hand and a purpose tNCA (transitional neuronal cell assembly) representing the unknown single sorting characteristic highlighted in the form of a card. (The rest of the card characteristics can be ignored and basically the visual search strategy centers solely on the required feature.)The purpose tNCA is formulated by applying a problem-solving strategy evoked initially from experience. Individuals have an array of strategies they use to make decisions eg. some people look at the good points or bad points of a decision (Plus versus Minus), or some look at the consequences of a decision (Cause and Effect) and it is the application of the optimal strategy which improves the chances of the correct decision being made. In the case of this experiment it is clear that the optimal strategy is the trial and error method for the initial acquisition stage so that the participants can work out what the target sorting feature is. However, other tasks would require more complicated strategies perhaps requiring a change during the process and may require the involvement of the emotional system and value comparisons. Naturally, the application phase does not require this cognitive processing stage since the purpose tNCA is already formed representing the target sorting feature and therefore, in this phase only the visual input in the form of a temporary sensory memory store has to be matched against the purpose tNCA. The demands on awareness and attentional systems again are lower in this phase due to the sorting feature being known and hence, the stage becomes essentially automatic and to some extent subconscious.
From a neurochemical perspective the choice of strategy and making the decision are complex mechanisms and require the simultaneous functioning of multiple cognitive systems eg. working memory, error monitoring and emotional system. It is only the nature of the experiment described here that makes this stage relatively easy to follow. With reference to the DMN network, this stage requires the functioning of the higher order brain areas such as the prefrontal cortex and orbitofrontal cortex in the calculation of reward and value and a requirement for dopamine activity which shows some overlap between DMN connectivity and decision-making.
In the next stage of the decision-making process, the action is carried out which in the case of Vatansever, Menon and Stamatakis` experiment is the laying of the card on the correct reference card matching the target feature. The decision as to which reference card this is has been estimated in the first stages of the acquisition phase according to the trial and error strategy employed, but in the application phase the action is definitive and carried out according to the matching of features. The sensorimotor control is the same in both phases and involves the parietal cortex, basal ganglia and motor cortical areas, but the speed of action in the application phase may be increased. This is likely due to unconscious processing of the card`s features being faster than conscious processing so the action has begun before conscious awareness exists that it should be carried out. This is not new and we are all aware of situations where we have already begun some movement before we actually think we should move eg. trying to catch a falling glass. The demands on the attentional system are also reduced in this stage in the application phase and therefore, distraction or divided attention may force errors in movement.
In the case of trial and error learning, the final stage of decision-making is especially important and the application phase would take longer to reach (if it all) if it was not available. Learning which feature the cards should be sorted by is achieved using this strategy by guessing in the initial stages and receiving immediate feedback as to whether the choice was correct or incorrect. Biochemically, the iNCA formed from the given card is temporarily matched to the generic purpose tNCA of the reference card. Complex conscious decisions are made by matching cell assembly firing based on risk (the value of the decision plays a role), strength (a factor of how often the representation or features of the representation are observed – termed frequency) or similarity (how much the firing overlaps between the two assemblies due to matching characteristics and hence, how much stronger it is). When positive feedback is given ie. the choice of reference card was correct then comparison of the cell assemblies occurs according to how much overlap there is (ie. similarity of features) and the feature representing the strongest firing becomes the temporary ´ target feature`. The initial stages mean that correct and incorrect decisions are made and so the individual uses the feedback given to hone the number of sorting criteria contenders by monitoring the overlapping representations of the cards put before him by looking for similarity when he is told that the decision was correct (described as a form of reframing). This carries on until the iNCA formed matches the target/purpose tNCA which consists of one feature only. At this point the acquisition phase is completed and the application phase begins. Feedback in the application phase is not as important since the individual knows the pattern to be followed and therefore, it is regarded more as a measurement of personal performance. As given before, errors in decision-making can occur due to distraction for example, but monitoring for errors occurs subconsciously. With reference to the DMN, error monitoring is attributed to anterior cingulate cortex (ACC) functioning which is located near to and has high connectivity with the posterior cingulate cortex (PCC)known to play a strong role in the DMN network.
Therefore, we have seen that certain brain areas linked to DMN network functioning also play roles in the standard decision-making process and hence, changes in area activity would be expected as the demands of the decision-making process alter with the task at hand. We have already given in the description of the decision-making process above examples of the roles some of the brain areas which are linked to the DMN network play, but their functioning goes beyond the simple task used by these experimenters. For example, there is the post-cingulate cortex (PCC) with its differing functions according to location with the dorsal part responsible for involuntary awareness and arousal and the ventral part, SELF, cognition and thoughts and the frontal eye fields which are shown to have error-related activity and thought to respond locally leading on to the more general response elicited by the ACC. The parietal cortex is another area which is shown to be involved in the decision of movement or non-movement, but is also capable of temporarily storing, maintaining and manipulating information in the working memory (important in the formation of the temporary purpose and input neuronal cell assemblies) through either a cortico-cortico pathway or a subcortical pathway (supports flexible updating of working memory content). The medial prefrontal cortex (mPFC) is also important where activity in the decision-making process is modulated to upcoming action values and its activity determines behavior, but not reward and the more important ventral medial portion (orbitofrontal cortex – OFC. This is widely recognized as being involved in the System 1 decision-making system and important for the computation of the subjective value of events through organization by an anterior-posterior gradient corresponding to secondary versus primary rewards. Also individual differences in the degree of model-based control are attributed to the structural integrity of the white matter tracts leading from this area to the striatum. The temporal lobe also contributes with its hippocampal involvement (binding and relay station role for information) and theta oscillations over the participating connected areas important for the perception and processing of different parts of the information eg. features and values during non-spatial decisions.
However, decision-making also requires the activity of a number of areas which are not considered to be part of the DMN network. We have already mentioned the ACC which lies next to the PCC which is known to have DMN related activity. The ACC plays a role in detecting when strategic control is required. This includes autonomic regulation such as pain perception. It also has been shown to have an increased level activity before decisions are made in certain situations and this observation indicates its role in learning the value of actions and guiding voluntary choices based on past experiences. This evaluation process in decision-making involves weighing costs against benefits and this function has been linked to dorsal ACC activity and the striatum. Other areas are also involved in decision-making that lie in the vicinity of DMN networking areas. For example, other parts of the PFC such as the left side which is thought to be involved in planning, the right inferior area shown to be involved in System 2 decision-making and the lateral area thought to play a role in strategic control. However, an important area not part of the DMN network, but which has significant activity in decision-making is the dorsolateral PFC. This area is known for its involvement in plan generation (the right side) and plan execution (the left) with neurons in this area shown to encode a diverse array of signals relating to both task relevant and irrelevant features with only the former being encoded simultaneously with choice signals. Basal ganglia areas are also shown to be involved in the decision-making process, but not considered part of the DMN network. The striatum in particular is important with the strength of the cortico-striatal pathway with its dopamine dependent plasticity determining effectiveness of the decision-making process. Its role is related to the trade-off between computational simplicity and flexibility and the efficiency of using experience and involves competition between it and the prefrontal cortex. Connectivity between the striatum and hippocampus is associated with prediction and anticipation of reward. This link between basal ganglia functioning and reward in decision-making is further strengthened by the actions of the amygdala. The amygdala with the OFC area have both been shown to have altered activity in abnormal decision-making involving the risk of punishment. Lesions of the basal amygdala were linked with the increased choice of large risky rewards, but did not impair sensitivity to punishment whereas lesions of the OFC decreased risk taking. Stress which has been shown to impair the biasness towards larger rewards was shown to be blocked by temporary inactivation of the amygdala which reinforces the view that the amygdala plays an important role in the assessment of reward and risk in relation to decision-making.
Therefore, we have demonstrated that the process of decision-making requires a multitude of different brain areas some of which are said to be part of the DMN network and some not. Our next question is does the pattern of DMN functioning during the decision-making task given follow the pattern of other cognitive systems? Vatansever, Menon and Stamatakis showed that DMN activity and connectivity was decreased in the acquisition phase (termed ´manual mode`) and was increased during the application phase (termed ´autopilot` mode). This appears not to concur with what is thought to be happening in other brain systems. For example, working memory, attentional system and conscious awareness all appear to have placed on them a greater demand during the acquisition phase of this particular decision-making task and a lower demand in the application phase. This is in accordance with the progress of the decision-making task. For example, there is complex/high demand in the acquisition phase when there is no simple answer, a strategy is required where feedback is important and a method for comparing option and target on the power of strength of firing of complimentary features (´manual mode`). This is compared to the application phase where there is simple decision-making (does it match or not?) and achieved through previous experience and occurring primarily through unconscious processing or low level cognitive demand. This phase is more attributable to the ´autopilot mode` and lower demands on the working memory, attentional and consciousness systems. In the case of the visual system, memory mechanisms and motor control the demands on these systems appear to be the same independent of experimental phase and cognitive demand.
So, since the DMN network shows opposite activity and connectivity levels to the other systems in play in the decision-making task, what can we conclude about what the DMN is and what it does? (Before we go further though we should rule out the possibility that the DMN network is a figment of experimental procedures. Researchers have shown that there is a definitive group of brain areas connected by vast white matter tracts and these areas show the highest blood flow when the brain is considered at rest as expected. However, the DMN network areas are multifunctional.) Regarding the function of the DMN network, researchers follow two modes of thought. The consciousness theorists relate its action to the resting state of the brain ie. when brain functioning is not directed at any particular task and hence, when conscious awareness is likely to be low. In this way according to the consciousness theorists the DMN network then has ´no function`, just exists under particular conditions. When we become aware of it then this pushes the functionality of the brain to the higher level of cognitive thinking and hence, the brain is no longer considered as ´resting`.
The second school of thought comes from the researchers who hypothesise that the activity of the DMN network is linked to self-related cognition eg. thinking about the past, future and the SELF; bodily state monitoring eg. heart beat; and autonomic regulation eg. breathing. If this is the case, then the DMN network may fill the role of ´monitor` of the SELF considered as both cognitive and physiological. We are not aware of this monitoring with subconscious processing leading to conscious processing if asked from an external source (eg. ´How are you feeling?`) or forced (eg. by a change of circumstances). This is possible since the dorsal PCC which is a participant of the DMN network is equated with involuntary awareness and arousal. However, it is unlikely since the DMN network is linked to one system where monitoring would be important and that is the pain system. This view is supported because the brain areas involved in the pain response eg. ACC, dorsolateral PFC and somatosensory cortex are not part of the DMN network and acupuncture shows a decrease in connectivity and activity of the DMN areas even though increased self-awareness and decreased pain response occurs. The non-involvement of the DMN however, could be explained by the pain system producing its own necessary fast responses.
Therefore, what can we conclude about the DMN network`s function? Well, it is probably a ´real` network which probably functions according to both views expressed above: resting state activity involvement and subconscious ´monitor` of the cognitive and physiological SELF. It is probably what keeps the brain in a state of readiness which would be vital to survival in a changing environment. Therefore, resting state would mean ´state of readiness` and not ´rest/no activity`. This view is supported by the evidence that shows that switching off neuronal firing activity induces adverse effects on neuronal cell physiology such as protein breakdown and even eventually apoptosis. By keeping areas that are highly connected eg. dorsal and ventral PCC, medial PFC, hippocampus and thalamus functioning even when there is no cognitive task requiring higher order area activity then the neuronal cells and neuronal pathways are always working to some extent and cell replenishment and renewal is constantly occurring.
As seen with Vatansever and colleagues experiments, the overall activity of the DMN network relates to the tasks underhand being active to keep the brain in a ´state of readiness` and deactivated when the higher order brain areas and systems such as working memory and attention are placed under high demand by for example a directed task. The network ´switches on` a fraction of a second after such a task is completed and the level of the network`s activity has been observed to be related to its previous experience and status (eg. activity was seen to have changed after a prior task that required the formation of memories). This confirms that the overall status of the network is constantly readjusting and fits in with the view that the DMN network represents a monitoring ´readiness` state. DMN activity and deactivation is also reflected in certain conditions and states. For example, in the sleep stages where Stage 1 and the synchrony of thoughts means that an increase in DMN activity is expected and this is observed as the individual slips into sleep. However, in the later stages of sleep there is greater connectivity of the higher brain areas as synchronized firing occurs in order to form long-term memories then a decrease in DMN network activity is expected and this is also observed. Another clear example of the relationship between activity and low demand on higher order functioning is in the case of REM sleep and Alzheimer disease. In both of these cases, chaotic neuronal firing and connectivity leads to problems building coherent neuronal cell assemblies. This would be expected to lead to decreased DMN activity and this is observed. Problems with pathway connectivity between the PCC and mPFC areas also lead to decreased DMN activity and this is observed in autism where problems with social skills and poor empathy occur. However, the explanations for other examples of reported changes in DMN activity are not so simple. In the case of acupuncture, there is no goal directed task and one would expect if the theorists are correct that there is an improvement of status through meridian energy adjustment and also maybe pain responses. Therefore, an increase in DMN activity would be expected. However, studies show a decrease occurs and this could possibly be explained by the experimental conditions. For example, it could be that positive changes in concentration and awareness on treatment affect the network activity. Another example is depression where the individual is likely to suffer from misinterpretation of the status of the cognitive and physiological SELF and personal values but active cognitive thinking relating to the SELF still occurs. In this case, a decrease in activity of the DMN network would be expected, but instead an increase is observed. This possibly could be explained by the network reflecting the lower level of higher order cognitive thought taking place due to lack of interest by the individual, or by the individual blocking thoughts relating to the SELF.
Therefore, we can conclude that the DMN network is a probably a network of brain areas which function as a ´subconscious monitor` of the cognitive and physiological SELF and neurochemically maintains the brain in a state of readiness when there are no demands placed on the higher brain orders. This activity, which is not related to directed task or thought, prevents the breakdown of neuronal pathway firing and connectivity due to inactivity and keeps the brain areas functioning to some extent. High cognitive demands lead to the activity of this network being reduced in comparison so that available energy can be used for the higher functions. Further studies on the DMN network are important since it is clear that both sets of systems are required for a balanced, correctly functioning brain and it is possible that whilst research attention is centered on the more popular higher cognitive functions of the brain, the key to solving the problem of cognitive disorders such as Alzheimer`s disease or autism may actually lie in the brain`s ´quiet period`.
Since we`re talking about the topic …………..
……… work by Goh showed that aging leads to compromised activity in the frontal, striatal, and medial temporal areas having an effect on the reward system, impeding accurate value representation and feedback processing all critical for optimal decision making. It has also been shown that increased ventromedial PFC activity is positively associated with cognitive performance in older adults. If the experiment given here was repeated using older and elderly participants would we see changes in the DMN network activity not only in overall level of activity, but also in its timing in relation to when the test shifts from acquisition phase to application phase?
……can we assume that if the sorting criteria were two features and not just one, the acquisition phase would be longer and that one feature would take priority over the other? If a distraction was introduced, would this be even more detrimental to the decision-making process and we might see no increase in DMN activity in the application phase since the attentional system would be subject to higher demand?
…… would the DMN network ever demonstrate increased connectivity if the experiment given here was repeated but with a risk of punishment if the incorrect card was chosen?
……experiments have shown that when subjects are asked to report the times of their own decisions, there is a degree of inaccuracy on the times given. Imaging studies have shown brain activity correlates with the decision to move before a person reports that decision. If the experiment given here were repeated, but the participants asked to report their decision before carrying it out would we see a change in DMN network activity especially in the application phase when the initial stage of the movement may be subconscious anyway?