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Anterior intraparietal sulcus and investing forex strategy libertex

Anterior intraparietal sulcus and investing

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First, a GLM was created with regressors for face and house blocks. Finally, the intersections between each Neurosynth activation map and the corresponding individual contrast map from the localizer task were computed. Six millimeter spheres were created, centered on the peak voxel coordinates within the remaining left and right clusters of the two intersection contrasts i.

Eye movements of 24 participants were recorded with a long-range optics infrared eye tracker EyeTrac 6, Applied Science Laboratories. Eye data of six participants were not recorded due to technical difficulties.

Data were collected at a sampling rate of Hz. Fixation epochs were computed to determine whether participants refrained from eye movements during trials. A fixation epoch started when six consecutive samples fell within an SD of 0. It ended when three consecutive samples fell outside of a 1. Trials that were completely within one fixation epoch were marked as fixation trials. The percentage of fixation trials per participant and condition was computed. Effects are displayed in Figure 3 A.

This indicates that participants were engaged in the task and able to discriminate between target-present and target-absent trials, also on the high-effort blocks. B , Subjective difficulty ratings. C , Subjective effort investment ratings. Because the split halved the number of trials in each cell, some participants had a miss rate or false alarm rate of 0 in some conditions.

This was corrected using log-linear transformations following recommendations of Hautus To decompose this interaction, post hoc paired-samples t tests were conducted comparing the early to the late stage in each condition. Note that because many subjects had nearly perfect scores in the low-effort conditions, a few slips in the second stage of the task may already have led to a significant difference with the early stage, offering a possible explanation for the observed effect.

The subjective ratings are displayed in Figure 3 , B and C. A main effect of Effort on difficulty rating was found, with higher ratings in the high-effort condition mean, A main effect of Effort on effort investment rating was also found, with higher ratings in the high-effort condition mean, Table 1 displays task-specific activation patterns in the low-effort and high-effort conditions.

As anticipated, areas related to the processing of faces, such as right FFA and superior temporal sulcus showed increased activation in the low-effort condition of the face task vs the low-effort house condition. After small volume correction, increased activity was also found in left FFA for this contrast. On the low-effort house task vs the low-effort face task , areas responsive to house images, such as bilateral PPA, showed increased activation.

In the high-effort face task vs the high-effort house condition , activation in bilateral FFA was found after small volume correction. In the high-effort house condition vs the high-effort face condition , bilateral PPA was activated. This shows that FFA and PPA were involved in the low-effort and high-effort condition, even despite the minor perceptual evidence in the high-effort condition.

To investigate brain areas that are involved in effort investment in a task-general way, we first mapped areas responsive to high-effort demands in both tasks. Next, a conjunction analysis was performed on the two contrasts. A , B , Brain activation in the high-effort versus low-effort demand condition on the face task A and the house task B. C , Conjunction analysis showing mutual activation for the high-effort versus low-effort demand condition on both the face and house tasks. Finally, no significant activation was found for the interaction between Task and Effort, indicating that, although activation of task-sensitive areas appeared to be stronger in the low-effort vs high-effort conditions, these differences did not survive statistical thresholding.

Assuming stronger bottom-up activation of task-specific areas on perceptually strong low-effort trials vs ambiguous high-effort trials , an interaction may have been expected. An opposite interaction would also have been plausible, if high-effort trials vs low-effort trials had elicited stronger top-down activation of task-specific areas. Note that because the two mechanisms operate in opposite directions, they may have countered each other. To explore whether activity profiles were affected by response accuracy, we created a new GLM with additional parametric modulators for versus incorrect trials.

These modulators show which voxels responded differently on incorrect vs correct trials. No modulation of brain activity by accuracy was found, suggesting that brain responses did not differ between correct and incorrect trials. For the gPPI analysis, the dACC cluster that showed activation in the high-effort versus low-effort conjunction analysis was used as a seed.

When comparing high-effort to low-effort demand in the face task, increased connectivity was found between dACC and multiple regions, including bilateral FFA Table 3 , Fig. An interaction contrast comparing effort effects between the face and house task i. Whole-brain connectivity with dACC. A , B , The connectivity values are contrasted between high-effort and low-effort demands in the face task A and the house task B.

Crucially, the interaction contrast comparing effort effects between the face and house task i. A , B , Effort-induced connectivity on the face task A and the house task B. C , Difference between A and B : difference in effort-increased connectivity between the face task and house task. Red, Increased connectivity on face task; blue, increased activity on house task.

Width of the lines corresponds to the strength of the effects. To explore whether ROI-to-ROI connectivity profiles differed between correct and incorrect trials, we created a new gPPI model with additional parametric modulators for correct versus incorrect responses. These modulators show in which ROIs the task-specific connectivity with dACC was different on incorrect vs correct trials. Finally, an exploratory analysis was also conducted to test for effects of cognitive fatigue i.

These modulators show ROIs in which the task-specific connectivity with dACC was different during the late vs the early stage of the task. On average, participants fixated on Effort investment is thought to be implemented in a hierarchical manner with a crucial role for dACC at the top of this hierarchy Holroyd and Yeung, ; Shenhav et al.

In this study, we investigated how this effort investment is implemented in the brain. We used a face detection task and a house detection task with different effort levels, and showed that increased effort investment is reflected in a general increased activation of dACC and related areas, independent of the task at hand.

Importantly, we also showed an effort-induced strengthening of connectivity between dACC and specialized lower-level perceptual areas, depending on the performed task. The increased functional connectivity between dACC and lower-level areas emerged in the high-effort condition, where effort investment was rated higher. This connectivity was also task specific: stronger dACC—FFA connectivity was found when high-effort versus low-effort faces had to be detected, while stronger dACC—PPA connectivity was found when high-effort versus low-effort houses had to be detected.

This fits with the proposed hierarchical position of dACC, allocating resources to task-relevant areas. One interpretation of the connectivity findings is that dACC amplifies the signal in task-relevant areas to increase performance in difficult conditions i. In the present study, the signal-to-noise ratio of images in the high-effort conditions was low, meaning that they lacked strength to elicit robust bottom-up activation of FFA or PPA.

Such activation is needed to make accurate decisions on the content of the presented image Heekeren et al. The increased connectivity between dACC and perceptual areas may serve as a compensatory mechanism for the lack of clear perceptual evidence present in the stimulus. Since participants knew beforehand what type of stimulus they had to detect, increased input from dACC may function to optimize neural processing of the stimulus by specialized areas FFA or PPA.

The exact neural mechanism underlying this optimization may be explained by response sensitization of task-relevant areas. There are several ways this might be implemented. For example, it has been proposed that stimulus-induced dynamics in cortical areas can be augmented by increasing their background activity Chawla et al. This may result in an increased synchronization of the neurons representing a stimulus Fries et al. In addition, top-down influences may also decrease noise correlations between neurons in a region.

Decreased noise correlations lead to an increase of the signal-to-noise ratio and hence the amount of information encoded by the neuronal ensemble Gilbert and Li, ; Ramalingam et al. Yet another explanation may be a top-down-induced increase in neural gain in task-relevant regions Aston-Jones and Cohen, Increasing gain suppresses weak activation typically, noise and increases strong activation typically, signal , thus functionally increasing the signal-to-noise ratio.

Processes like these can give a cortical area an advantage in subsequent stimulus processing. On a neural level, cognitive effort production may thus be seen as an attempt to overcome a compromised signal in a population of neurons. This idea can also be applied to neuronal fatigue, where adaptation may lead to a reduced signal-to-noise ratio in a brain area, as a result of repeatedly performing a cognitive action. If this is correct, then one would expect stronger connectivity between effort-producing areas dACC and task-specific regions toward the end of the task.

However, the current results lend no support for such a mechanism, as exploratory brain connectivity analyses showed no effects of time-on-task. Response sensitization of task-relevant areas is also in accordance with the proposed role of dACC in specification of the effort allocation signal. From this perspective, dACC would be involved in the decision about which control signal to apply i. A second interpretation of the connectivity findings is also possible effort requirement account.

It has been suggested that dACC monitors the current circumstances and tracks how well the cognitive system is meeting task demands Botvinick et al. In the current experiment, this would imply that dACC may be informed by task-related areas about the greater effort required to solve the task. For example, the smaller perceptual difference between target-present and target-absent trials in the high-effort condition may have triggered response conflict. This conflict may have served as the indicator to allocate additional cognitive effort, reflected in conflict-induced activation of dACC in the high-effort condition Botvinick et al.

This explanation would be compatible with an increased connectivity in the opposite direction: from task-relevant areas to dACC. In the effort requirement account, dACC tracks the adequacy of the implemented effort. When performance deteriorates, for example because of inadequate filtering of distraction or conflict, an increased need for effort will be signaled. This mechanism can be extended to conditions where dACC monitors and specifies behavior to maximize value Shenhav et al.

In that case, dACC integrates effort costs and reward values and is involved in the decision whether or not it is worthwhile to invest a given level of effort Klein-Flugge et al. This role of dACC in effort-based decision-making is supported by the finding that inactivation of dACC in rats reduced the willingness to invest effort, yet did not affect performance on cognitively demanding tasks Hosking et al. In the present study, however, no reward was offered and no cost—benefit decision had to made.

Still, increased dACC activity was found, indicating that dACC also operates whenever more effort is required, regardless of cost—benefit decision-making but see Vassena et al. The effort production and effort requirement accounts are not mutually exclusive and are in fact proposed to be integrated by dACC Shenhav et al.

They are also both compatible with the present results, given that connectivity analyses cannot attest to directionality. We also found that activity in dACC scaled with the level of effort investment, independent of the task. In the high-effort conditions, larger dACC activity was found than in the low-effort conditions.

Conjunction analysis showed that this was true for both the face-detection and house-detection task, with jointly activated dACC, bilateral AI, and right IPS. These areas thus constitute a task-independent network of brain regions involved in effortful behavior. Within this network, the importance of dACC for effortful behavior converges with model simulations showing that dACC-lesioned rats are less likely to engage in effortful behavior Holroyd and Mcclure, , and by the fact that dACC lesions are associated with a lack of motivation or anergia Cohen et al.

It is also consistent with studies relating dACC activity to self-reported effort investment Mulert et al. However, the exact way in which dACC is involved in effort-based decision-making, effort production, or effort requirement is a topic that warrants future consideration. Another function often ascribed to dACC is the monitoring of errors Gehring et al. In the current study, however, no error-related effects on brain activity or connectivity were found.

Indeed, the current paradigm offered not much opportunity for error monitoring for two reasons. First, participants did not receive trial feedback. Second, it is hard to become metacognitively aware of an error in the high-effort condition because there is never clear evidence in favor of one of the two response options.

In addition, participants had to withhold their responses for 1 s. This is different from speeded response tasks where participants occasionally slip and make an error while the correct response is readily available. In such cases, participants often do become aware of the error they made, even without feedback.

These regions are often conjointly activated, together with dlPFC, on a wide range of cognitive tasks that demand attention, working memory, or cognitive control Corbetta and Shulman, ; Dosenbach et al. Anterior insula has traditionally been related to the detection of salient events Downar et al. In the present study, saliency of the presented stimuli was constant. The function of AI therefore seems more consistent with accounts postulating that AI subserves maintenance of a task-set Dosenbach et al.

Such processes may have been more profound in the high-effort conditions. For example, a tonic activation of AI in high-effort conditions may alert the system that the demand to detect a target is higher Han et al. This way, dACC may also be informed to intensify its control signal to task-relevant areas. We conclude that dACC, AI, and IPS constitute a general effort-responsive network and that the neural implementation of cognitive effort may involve dACC-initiated response sensitization of task-dependent areas.

Read article at publisher's site DOI : Sci Rep , 11 1 , 11 May Neurology , 96 21 :ee, 15 Apr Cogn Affect Behav Neurosci , 21 5 , 06 May Cited by: 0 articles PMID: Front Neurol , , 01 Jan J Neurosci , 41 7 , 11 Dec To arrive at the top five similar articles we use a word-weighted algorithm to compare words from the Title and Abstract of each citation.

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Search life-sciences literature Over 39 million articles, preprints and more Search Advanced search. This website requires cookies, and the limited processing of your personal data in order to function. By using the site you are agreeing to this as outlined in our privacy notice and cookie policy. Aben B 1 ,. Search articles by 'Cristian Buc Calderon'. Buc Calderon C 2 ,. Van den Bussche E 3 ,.

Verguts T 2. Affiliations 1 author 1. Share this article Share with email Share with twitter Share with linkedin Share with facebook. Abstract Investment of cognitive effort is required in everyday life and has received ample attention in recent neurocognitive frameworks.

Free full text. J Neurosci. PMID: Author information Article notes Copyright and License information Disclaimer. Corresponding author. Correspondence should be addressed to Bart Aben at eb. Contributed by Author contributions: B. This article has been cited by other articles in PMC. Go to:. Participants Thirty healthy Ghent University students gave written informed consent to participate 19 females, 11 males; mean age, Stimuli Grayscale images of 18 faces and 18 houses were used Fig.

Open in a separate window. Figure 1. Experimental design and statistical analysis Target detection tasks. Subjective rating task. Subjective rating task analysis. Staircase procedure. Figure 2. Eye movement data analysis Eye movements of 24 participants were recorded with a long-range optics infrared eye tracker EyeTrac 6, Applied Science Laboratories. Figure 3. Subjective rating task The subjective ratings are displayed in Figure 3 , B and C. Table 1 Summary of the task-specific activation clusters.

L, Left; R, right. Effort-specific effects To investigate brain areas that are involved in effort investment in a task-general way, we first mapped areas responsive to high-effort demands in both tasks. Table 2 Summary of the activation clusters on the high-effort versus low-effort contrasts.

Figure 4. Functional connectivity For the gPPI analysis, the dACC cluster that showed activation in the high-effort versus low-effort conjunction analysis was used as a seed. Table 3 Summary of the activation clusters from the gPPI analysis. Figure 5. Figure 6. The authors declare no competing financial interests.

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Brain Struct Funct — Prior to release, participants were required to pass an evaluation of basic perceptual, short term, and working memory function, awareness of current time and location. Participants were contacted 24 h after the study to check for any experimental side effects none were reported. Participants made self-paced choices between certain e. Uncertain options were either risky e.

Briefly, gambles were presented side-by-side on a gray background, with position of the risky gamble randomized. Certain gambles were thus entirely blue. Ambiguous gambles, by contrast, had their shading hidden beneath a white field featuring a black question mark. Dollar magnitudes available to win were superimposed over each shaded area in white, or, for ambiguous gambles, set outside the hidden circle marker in black.

For each trial, a fixation dot ms was followed by the options. Self-paced choices were indicated using the right hand to press the left or right arrow keys, although if longer than ms or less than ms, a brief message encouraged faster or slower responses, respectively. A box indicated the chosen gamble for ms. Participants did not observe the outcomes of any gambles or receive any gamble bonus winnings until after the completion of all four task runs. This set of gambles was repeated for each task run with gamble order randomized, allowing repeated measures comparisons controlling for subject, gamble, and subject-by-gamble effects.

The majority of these gambles out of were constructed such that the uncertain option had a higher expected value than the certain option, thereby providing an incentive to choose the uncertain option 17 had equal expected value, and 20 had greater certain expected value. The ratio of the uncertain vs. To provide participants an incentive to choose according to their preferences, we explained that for each run of the task gamble trials we would randomly select one trial, and that at the end of the experiment, we would resolve the gambles from those trials according to their choice, and pay them the winnings from each trial.

Participants completed four runs of the task initial practice run and three runs following rTMS sessions and were thus paid for a total of four such bonus trials. Anatomical imaging was conducted on a 3. This paradigm has been previously shown to inhibit primary motor cortex, reduce signal strength in visual processing, and perturb social and economic decision making Knoch et al.

Research participants were unaware of both the study hypotheses and the presence of a placebo target condition vertex stimulation. The experiment thus reflected a single-blind placebo-controlled design. Motor threshold was determined for each participant using electromyographic recording of the dorsal interosseus muscle of the right hand.

Following standard procedures Rossi et al. Coordinates were identified for each participant using their structural MRI scan and a neuro-navigated rTMS procedure implemented using the Brainsight suite of tools and software Rogue Research, Montreal, Canada. Each participant's anatomical MRI image was mapped to MNI standard space based on manual registration landmarks anterior commissure, posterior commissure, brain size, and edges , allowing rTMS targets defined in MNI coordinates to be translated to each individual's native brain anatomy.

Next, we co-registered our participants' cranial features with their anatomical MRI scans, using the left and right intertragal notch, nasion, and tip of nose. Participants were re-registered prior to each rTMS administration to insure accurate administration. The dependent variables of primary interest were choice selection of the certain or uncertain option and decision time ms. We used multilevel logistic regression with a logit link function and binary distribution to analyze choices, and multilevel generalized linear regression with a lognormal distribution and an identity link function to analyze decision times.

We also estimated the theoretical impact of rTMS stimulation on the average expected return from participants' choices i. This approach was selected because its repeated-measures nature paralleled our analysis of choices and RT's, and because our small number of compensated trials 1 per run precluded any meaningful analysis of rTMS consequences on participant's real take-home bonus pay.

Variables included in our models but not of primary interest were a categorical variable reflecting the rTMS condition order controlling for any order effects and variables included to control for any time period effects. For the choice model, we included a categorical fixed-effects time variable reflecting the task run number, while in the decision time model, we included both fixed, and random-effects for a continuous variable reflecting the total number of trials already completed i.

These control variables helped account for time effects including a clear practice effects for response times as well as a slight increase in risky choices by the end of the study. Study personnel responsible for data analysis and modeling were blinded to the rTMS treatment conditions during the primary stages of data analysis, as rTMS conditions were coded as an arbitrary single-digit number.

This coding was maintained until after omnibus tests demonstrated significant interaction of coded rTMS treatment with uncertainty type. The code blinding was lifted only when it became necessary to test previously hypothesized contrasts between the experimental and control conditions.

We thus controlled for sources of dependency by simultaneously accounting for variance at the trial and participant levels, allowing for valid inferences regarding expected rTMS effects in the broader population. High intraclass correlations indicating a violation of the independence assumption for each of our independent variables confirmed that a mixed model approach was justified. This approach also allowed us to address trial-varying effects i.

We fit models using SAS 9. Models were estimated using residual pseudo-likelihood estimation with subject-specific Taylor series expansion Breslow and Clayton, The residual degrees of freedom were determined using the improved F approximation procedure described by Kenward and Roger To avoid unnecessary statistical comparisons between conditions, we restricted pairwise comparison in two ways.

First, we examined only rTMS treatment effects, always matching other model factors across comparisons i. An advantage of a multilevel models approach to repeated measures is that missing-at-random observations are permissible.

We examined the effects of rTMS on decision making by recording choice and response time on each gamble trial. We hypothesized that, compared to control rTMS, perturbation of processing within IPS would reduce risk-taking for risky choices, while disruption of IFJ processing would similarly affect ambiguous choices cf. Huettel et al. IFJ stimulation, by contrast, produced no reliable effect on choices all P s 0. Descriptive statistics reflect raw, model-unadjusted effects in participants completing all study conditions.

Descriptive statistics reported are distinct from inferential, model-adjusted statistics reported in the manuscript text. A null effect of rTMS on choice would show an effect near zero. B Disruption of IPS biased response times for risky trials, speeding selection of the certain option but slowing selection of the risky option.

By contrast, IFJ stimulation slowed decisions across both risky and ambiguous trials, regardless of choice. Since the risky option was incentivized for most of our gambles, more conservative decision making would be expected to reduce uncertainty, but also to reduce expected earnings. To quantify the impact of IPS stimulation on expected earnings i.

To gain further insight into the observed decrease in risk-taking following IPS stimulation, we examined response time RT , which is often better suited to revealing subtle influences of rTMS on the efficiency of information processing Luber and Lisanby, No other contrasts showed evidence of decision speeds faster than their corresponding vertex control Supplemental Results: Response Times. Effective decision makers are adept at weighing the potential benefits of an opportunity against the uncertainty surrounding their realization.

IPS stimulation reduced risk-taking on risky decision trials, while IFJ stimulation slowed decision responses across both risky and ambiguous decision trials. These results provide the first causal evidence differentiating parietal and frontal contributions to risky decision making, and highlight the IPS as a key region supporting the expression of risk-tolerant choices.

In this study, disrupting IPS activity reduced risk-taking—lowering risk at a cost to expected earnings—for decisions with high but known risks and uncertain outcomes. These results demonstrate a causal role for the IPS in risky decision making that is in line with correlative evidence from previous neuroimaging studies.

Our study, which estimated rTMS treatment effects on decisions by repeating gambles within-subjects, compliments prior methods by applying rTMS with strong experimental controls for individual differences. The results of our stimulation study buttress the existing evidence by providing the first causal evidence for the necessity of IPS function for pursuing risky decisions. Given previous results linking IPS activation to individual differences in risk preferences Huettel et al.

Our results confirm the importance of the IPS for risky decision making, but provide only indirect evidence for how computations within that region were disrupted. Evidence suggesting that the IPS directly represents probability or outcome uncertainty is lacking, however Tobler et al.

Rather, IPS appears to represent higher-order decision quantities reflecting the integration of probabilistic information with other reward and preference signals Huettel et al. Similarly, studies of perceptual decision making in non-human primates indicate that this area of parietal cortex is essential for the integration of evidence in favor of competing choices Shadlen and Newsome, ; Huk and Shadlen, This mechanism has been extended to value-based decision making, with electrophysiological Kiani and Shadlen, and human neuroimaging Basten et al.

Consistent with this putative mechanism, we observed that inhibition of IPS led to faster selection of the competing certain option but slower selection of the risky option. IPS stimulation may thus directly impact uncertain decision processing by disrupting the integration of probabilistic costs and benefits required to justify a risky choice. The IPS is also known as a key locus within a network supporting numerical cognition Cohen Kadosh et al.

A natural supposition, therefore, is that IPS stimulation disrupts processes necessary for quantifying costs and benefits during risky decision making, such as expected value computation or risk-discounted value comparison. Additionally, IPS activation has been shown to parametrically represent the closeness of numeric magnitudes Ansari et al. Given this pattern of results, we speculate that rTMS may have disrupted relative value comparison processes supported by the IPS Dorris and Glimcher, , as opposed to altering the direct computation of risky expected values.

Decreased confidence in the relative premium offered by the risky option could thereby reduce willingness to accept those options. Despite previous evidence linking IFJ activation with ambiguity preferences Huettel et al. Further studies integrating executive and motor control tasks are required to determine whether this slowing results from interference with higher-order decision-control or lower-order decision-implementation processes.

Recent findings suggest that although IFJ is engaged by the presence of ambiguity, its activity does not scale with increasing degrees of ambiguity Bach et al. Instead, IFJ engagement during ambiguous choice may reflect the increased cognitive control required to address problems featuring missing or hidden information Koechlin et al.

Ambiguous decisions may be implemented in qualitatively different ways less reliant on numerical processing or IPS-mediated magnitude comparison Camerer and Weber, ; Bach and Dolan, , as we found no clear evidence supporting an effect of IPS stimulation on ambiguous choices. Variable strategic responses and a relatively low trial-level sample size for ambiguous gambles may have limited our power to identify effects.

Our investigation relied on the ability of 1 Hz rTMS to induce short-term neurophysiological changes in target brain regions, thereby disrupting typical cognitive processing. Though this effect permits causal investigations of neuroanatomical hypotheses, it also imposes limitations on the interpretation of our within-subject design study, as residual effects from earlier stimulation sessions have the potential to carry over to subsequent sessions. Consideration of the time course of these effects is of particular importance for our design, since we conducted three consecutive rTMS sessions per subject.

In our study, we sought to limit the carryover of behavioral influences from prior rTMS sessions by imposing a break period between sessions. Approximately 30 min separated task runs from previous rTMS sessions, a period which is 2—3 times longer than the expected time required for behavioral effects of rTMS to dissipate. However, while behavioral effects of our rTMS stimulation sessions were expected to dissipate within 15 min, prior studies have found subtle electrophysiological after-effects of 1 Hz rTMS up to about 40 min post-stimulation, even in the absence of behavioral effects as reviewed by Rossi et al.

Research into the neurophysiological basis of such extended rTMS effects is ongoing Hamidi et al. Within the design and analysis approach of the present study, we attempted to mitigate against these potential longer-term carryover effects via two additional counter-measures. First, we counterbalanced the order of rTMS sessions, such that any carryover effects should in theory be similarly affecting the different stimulation conditions. Second, we also included the sequence order of rTMS sessions as a control predictor in our statistical models Supplemental Results, Tables S1 and S6 , such that any effects of stimulation order should in principle be accounted for in the results.

Nevertheless, given the inherent susceptibility of a within-subjects design to carryover effects, it would be beneficial for independent researchers to corroborate the present findings using convergent methods, including between-subjects designs and alternative rTMS protocols such as 5 Hz or theta-burst stimulation Peinemann et al.

Our results are the first to demonstrate the necessity of unperturbed IPS function for risk tolerance during uncertain decision making, and provide insight into the functions and interactions of fronto-parietal decision circuits. Our focus on the parietal cortex during risky decision making also complements prior rTMS work showing increased risk-taking and impulsivity following disruption of prefrontal self-control processes Knoch et al. The present findings suggest that engagement of IPS during decision making may support the ability to trade certainty for a chance at greater expected gains.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors thank Holly Lisanby and Bruce Luber for advice regarding the study design.

Front Neurosci. Published online Dec Christopher G. Coutlee , Anastasia Kiyonaga , Franziska M. Korb , Scott A. Author information Article notes Copyright and License information Disclaimer. Burke, University of Zurich, Switzerland. This article was submitted to Decision Neuroscience, a section of the journal Frontiers in Neuroscience.

Received Aug 15; Accepted Dec 7. The use, distribution or reproduction in other forums is permitted, provided the original author s or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. This article has been cited by other articles in PMC. PDF K. Abstract Decision makers frequently encounter opportunities to pursue great gains—assuming they are willing to accept greater risks.

Keywords: risk, ambiguity, uncertainty, neuroeconomics, intraparietal sulcus, TMS. Introduction Decision making is often characterized by the need to make difficult tradeoffs between uncertain risks and rewards. Open in a separate window. Figure 1.

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Both aIPS and FEF were engaged during selective attention. FEF, but not aIPS, was sensitive to the direction of spatial attention. Conversely. Although excessive risk-seeking can be problematic (Yates, ), investors and economists have also recognized that obtaining greater rewards. We investigated whether left and/or right intraparietal sulcus (IPS) contributed to the AB bottleneck using transcranial magnetic stimulation (TMS).