Significance: The longstanding psychological principle that, all else being equal, people avoid cognitive effort,
has recently received direct empirical support [6]. An influential framing is that of an economic choice pitting
effort costs against accuracy gains and the resulting benefits [25]. Hence, academic performance may depend
on how students construe the benefits of their efforts [26,27]. Concomitantly, work in microeconomics suggests
that the costs of cognitive effort are sufficiently high to have detrimental impacts on economic decision-making.
“Decision costs” are blamed for inefficiencies arising in complex auctions and transactions. Importantly,
impacts can be ameliorated when incentives for effort are increased [4]. It’s not that individuals cannot make
good decisions, it’s that they must be properly motivated to do so when perceived effort costs are high.
The cost of cognitive effort may be particularly influential in demotivating cognitive engagement in a broad
class of syndromes characterized by anergia, psychomotor slowing, or apathy [28]. Individuals with Major
Depression, for example, can be distinguished by lower performance on effortful tasks despite intact
performance on less effortful tasks [5,29-33]. The prevailing interpretation is that depressed individuals perform
poorly because of reduced cognitive capacity. Another interpretation is that depressed individuals find effort
more subjectively costly and thus have lower motivation to perform on high effort tasks. Evidence that high
effort costs decrease motivation in depression, thus impacting cognitive function, would support treatments
targeted at augmenting motivation. It would also help explain why pharmacological interventions affecting
motivation appear to enhance the efficacy of cognitively demanding talk therapies [34]. Furthermore, if the high
cost of effort adversely impacts performance on intelligence batteries and intelligence is a predictor of
treatment outcomes [35] better prognostic tools may be developed if we could resolve the synergistic
influences of effort costs from those of motivation-independent cognitive capacity.
Despite a large literature in favor of the intuitively compelling link between depression and cognitive effort,
some studies have failed to provide support for this link [32,36]. One reason for mixed results may be the
indirect way in which tasks are designated “effortful”. Following a historical distinction [37], this literature
assumes that tasks are effortful if they are non-automatic and require controlled processing. Whether a given
task is actually subjectively effortful is not measured directly. Moreover, if performance differences stem from
motivational discrepancies, predictive models should incorporate quantitative measures of the subjective
cost of effort, for which experimental paradigms currently do not exist. Subjective report Likert scales of
conscious effort are used widely in the ergonomics literature [20,38-41]. Yet, none of these instruments yield
precise, quantitative effort cost measures. Moreover, they are all vulnerable to the inherent un-reliability of selfreport and Likert ratings (one person’s rating of “10” may bear very little psychological resemblance to
another’s) making inter-individual comparisons noisy. The lack of a cost measure has impeded research into
the factors that influence subjective effort, and that make some individuals perceive their effort as particularly
costly. It has also limited our understanding of the extent to which the subjective cost of cognitive effort impacts
task performance and decision quality both in healthy and clinically impaired populations.
Behavioral economists have generated a wealth of insights into the factors that diminish the subjective value of
outcomes. Neuroeconomists, in turn, have used behavioral economic formalisms of subjective value to
uncover the neural mechanisms by which decision-makers incorporate cost factors into estimates of the value
of their choices. One particularly productive approach has been to measure subjective costs using
discounting paradigms and then examine how those costs modulate activity in regions implicated in the
representation of choice value, thereby building a mechanistic account of value-based decision making. This
approach has been used to establish that the evaluation of physical effort costs and decisions to expend
physical effort depends on dopaminergic activity in a putative effort valuation and motivation network centered
on the anterior cingulate cortex (ACC) and the ventral striatum (VS) [8,9]. There is reason to think that these
same regions may be involved in cognitive effort evaluation and effort-based decision making [17,19], but
discounting has not yet been exploited to measure cognitive effort costs.
Recently, I adapted a discounting paradigm [42] to measure the cost of cognitive effort. The metrics available
from this paradigm provide powerful tools for studying the factors that influence subjective effort. They also
enable, for the first time ever, parametric investigation of the relationship between cognitive effort costs and the
neural mechanisms of effort-based decision making. I will use the measures resulting from my novel paradigm
to explore, in healthy individuals, the neural systems tracking subjective effort. I will also explore how costs are
factored into neural representations of choice value during effort-based decision-making. Rather than simple
task-related changes in BOLD signal, I will probe parametric relationships with effort costs, to ask targeted
questions about the role of individual components of a putative effort-based decision network. This work will
thereby provide a behavioral and neuroeconomic foundation for future studies exploring dysregulation of
networks involved in cognitive effort-based decision and their contributions to cognitive deficits in depression.
Background and preliminary data: The discounting framework has proven highly fruitful in behavioral
economics, for measuring subjective costs by how cost factors reduce the subjective value of choices. For
example, risk can make an option less appealing than would be expected by normative economic theory [43].
In addition to risk, discounting has also been used to investigate the subjective cost of delay [42,44-48,50], and
physical effort [3,51-53]. A common discounting procedure involves a series of paired offers that systematically
vary in the amount of a reward paired with fixed costs. Choices are always between a large reward at a large
cost and a small reward at a small cost. After each decision, the amount of the smaller reward is either
increased if the larger reward is chosen, or decreased if the smaller reward is chosen [42]. The effect over a
series of choices is like haggling over price. Each time a choice is made, the amount of the adjustment itself is
half as much as it was on the prior adjustment until the offer converges to a point where the decision-maker is
indifferent between offers. The difference in offer amounts at that point is precisely the subjective value of the
difference in cost. In an example from delay discounting, if a participant equally preferred receiving $2 now and
waiting 3 days for $6.25, then the subjective cost of the 3-day delay is estimated as $4.25 (i.e., it takes an
additional $4.25 to make the participant willing to wait 3 days for the reward). The same procedure is used to
ascertain indifference points for a range of delays.
Generating a range of indifference points required a task with
adjustable demands. Also, since the opportunity cost of time-on-task
could confound the results (participants may discount a task simply
because it takes longer, regardless of perceived effort), it was
important to select a task with fixed duration. A final criterion for task
selection was that the conscious phenomenal experience of effort
increased with objective demands. A growing body of work supports
that tasks are consciously effortful when cognitive control is required
[6,17,19]. Cognitive control refers to processes responsible for
biasing behavior in pursuit of goals [14,54-57]. Core functions
include updating and maintaining goal-relevant task sets thought to
bias the mapping of stimuli to goal-appropriate responses.
Subjective Value
The cognitive effort discounting paradigm: Recently, I adapted the discounting framework to measure the
subjective cost of cognitive effort. In my paradigm, offers are made between more money for more cognitive
effort and less money for less effort (distinguished, here, by working memory load). Offers are iterated with
each choice until participants are indifferent between options. At that point, the difference in amounts between
the larger and smaller offer is taken as the value of the additional effort.
Figure 1. An individual’s cognitive effort discounting.
Subjective value decreases as task level increases.
Lower values indicate the participant will take a lower
offer to avoid the effort of that level, relative to the
easiest task, n=1. Green lines connecting points
bound the Area Under the Curve (AUC)
One task known to heavily tax cognitive control is the n-back task
[16,58,59]. In the n-back, participants are tasked with identifying
targets from a fixed-length string of stimuli, based on whether the current stimulus was repeated n items back
in the sequence. Working memory contents of should thus span the last n items presented, and task sets thus
require updating with each new item. Task demands can be explicitly increased by increasing set size n. To
measure how costly participants perceived their cognitive effort on the n-back, participants decided between
offers, after practicing the tasks, to re-do specific levels of the task for pay. Choices were always between the
easiest (1-back) and harder levels. To promote careful decisions about effort on, say, the 2-back versus the 1back, participants were instructed that they would repeat some level of the task again up to ten more times,
and that every choice they make could determine both what task they complete again, and how much they
would be paid for doing so, as long as they maintained their effort. To pilot, 25 participants (ages 19–40, M =
24.5, SD = 4.8) were recruited from Washington University.
Preliminary results: Our initial results with this novel paradigm were very promising. All but one of the 25
participants showed robust cognitive effort discounting: lower subjective value for offers to do more demanding
tasks (reflecting the higher cost of greater effort; Figures 1-2). Importantly, there was significant variation in the
extent to which individuals find their own effort costly, even for a relatively homogeneous, predominately
undergraduate student sample (Figure 2). Variation can be measured by multiple parameters. Each level of the
task is associated with its own subjective value, for example. Also, subjective values can be fit with a linear
discounting function yielding a slope parameter describing how costly an individual finds their effort across
levels of the task. Furthermore, Area Under the Curve (AUC), comprised of line segments connecting
subjective values AUC, is a useful, atheoretical measure of effort discounting, which is desirable given the lack
of prior theoretical models of cognitive effort-based discounting and a priori scaling of the abscissa. AUC may
thus prove a more reliable estimate of an individual’s effort costs than simple linear slope parameters [60].
Subjective Value
Area Under the Curve
Neural systems tracking cognitive effort: For the brain to make
effort-based decisions about task engagement, it must monitor effort
expenditure. Two likely candidates for tracking cognitive effort
include the dorsolateral prefrontal cortex (DLPFC) and the anterior
cingulate cortex (ACC). The DLPFC plays a central role in working
memory [61] and task set maintenance (rules for flexible mapping of
stimuli to responses) for cognitive control [55], and DLPFC activity
increases with objective load in cognitive tasks [16,17,59,62,63].
However, effort is thought to depend not just on objective demands
Participant
but also on individuals’ cognitive capacities. Differences in
processing efficiency mean that some individuals utilize more of
their capacity to achieve the same performance. Hence, effort will
be greatest when task demands are high and when capacities are
low. In support of this, both greater task demands and reduced fluid
intelligence drive up BOLD signal in DLPFC during working memory
tasks [22]. Hence, DLPFC recruitment has been interpreted as
increasing capacity utilization in cognitive tasks [16,22]. Numerous
lines of research also point to the ACC as an effort monitor. First,
neuroimaging studies show increased activity in the ACC
Task level ‘n’
corresponding to greater physical effort [52,64] and also greater
demands for cognitive control in a cognitive effort task [19,65]. Figure 2. (A) AUC measures for all 25 participants,
Second, animal lesion studies have implicated midbrain dopamine ranked by magnitude shows wide inter-individual
variability in discounting. (B) Discounting results
and the ACC in effort-based decision making where a lesion of averaged for all 25 participants across task level, n.
either system will lead to a bias against high effort choices [2,11,6668]. The tension between a putative role in overcoming bias against effort in the animal studies and in
monitoring effort costs in the neuroimaging studies has motivated proposals that the role of the ACC is to
select actions, taking into account high-level representations of action costs versus outcome benefits [69,70].
Third, the neuropsychiatric literature includes case studies of patients with ACC lesions who lack the conscious
experience of cognitive effort, but have otherwise intact knowledge of task procedure and ability to complete
the task [71]. All three lines point to the ACC as an effort monitor in the service of action selection.
A
B
Effort monitors like the ACC and DLPFC should predict subsequent discounting by task level. However,
DLPFC activity may only reliably predict discounting up to the point where capacity is fully utilized. Indeed,
non-monotonic asymptotes and even decreases in DLPFC activity at very demanding levels of the n-back have
been interpreted as indicating excessive demands for cognitive capacity [59,62]. Beyond that point, cognitive
effort may become more costly, as measured by discounting, but this increase may no longer be tracked by
DLPFC activity. By contrast, ACC activity continues to rise for higher levels of the n-back [59,62] (but, to date,
this has never been tested beyond n = 3). Hence both ACC and DLPFC activity should correlate with
discounting for lower levels of the n-back task, but only ACC should correlate at higher levels of n. This
dissociation of DLPFC and ACC at higher levels of n has been heretofore difficult to interpret. Though declining
DLPFC activity might suggest disengagement from excessively demanding tasks, sustained increases in ACC
activity has been interpreted as evidence against the hypothesis that the brain is disengaging [62]. The willful
disengagement hypothesis requires evidence of decreased willingness to engage in the task, which is
precisely what the novel discounting measure provides. Discounting could thus provide critical evidence that
decreased DLPFC activity reflects willful disengagement, while sustained ACC increases reflects action
selection and shifting strategies under excessive demands. Note that discounting provides valuable tools for
circumventing the traditional difficulty of interpreting inter-subject BOLD comparisons: discerning whether
differences reflect differential capacity or changes in “willful” effort [72,73]. The discounting procedure
measures willingness to engage in a task or, conversely, willful effort required.
Neural mechanisms of effort-based decisions: When deciding whether to pursue a goal, the brain must
represent the subjective value of the outcome, factoring in the cost of effort to pursue the goal. The central
endeavor of neuroeconomics is demonstrating neural representations of subjective value and choice
dimensions impacting value [18,74-76], including rewards, but also costs such as delay [23,77,78], probability
[24], and physical effort [52,64]. The central innovation of this proposal is the use of a discounting paradigm to
provide first-ever measures of cognitive effort costs, which can be used to test whether brain regions represent
subjective value, factoring in such costs. Regions likely to be important to effort-based decision making include
the ACC, for monitoring effort, and the ventral striatum (VS), which has been shown to respond to physical
[64,79] and cognitive [19] effort such that activity decreases with increasing effort and/or decreasing reward.
An influential account of physical effort holds that net cost-benefit evaluations, informed by ACC activity, are
represented in the VS, thereby regulating the vigor of responding [10,80,81] and, perhaps, effortful
engagement with cognitive tasks. Potentially important regions beyond the VS include the orbitofrontal cortex
(OFC) and ventromedial prefrontal cortex (VMPFC), which have both been implicated in representing
subjective value during decision-making. Both the OFC and VMPFC are central to the valuation system of the
human brain [74]. Single unit recordings in primate OFC [82,83] and human neuroimaging studies [23,24,84]
have shown the OFC to be sensitive to multiple determinants of choice including, for example, reward quality
dimensions and also delay to reward. However, it is not clear that the OFC/VMPFC will be sensitive to
cognitive effort costs. The OFC, for its part, has been hypothesized to represent value of goods, independent
of the action costs to obtain those goods [18]. Moreover, an fMRI study found that the VS, but neither the OFC
nor VMPFC were sensitive to the physical effort required to obtain rewards [64]. However, to date, no studies
have focused on neural activity during active decision-making about cognitive effort. Hence, it remains open as
to whether OFC/VMPFC activity will also be sensitive to costs during effort-based decision making.
Approach: The methodological framework of this proposal is to utilize the novel discounting paradigm to test
specific hypotheses about factors driving costs, neural systems tracking effort, and those involved in cognitive
effort-based decision-making. I will thus recruit subjects to participate in three sessions: one behavioral session
to validate the novel paradigm, one scanning session to investigate effort tracking during the n-back, and a
second scanning session while participants make decisions in the discounting paradigm. This multi-session
study will also enable an examination of test/re-test reliability, critical for rigorously validating these discounting
measures, and determining whether cognitive effort costs serve as a stable and trait-like index.
First, I will seek to validate my novel discounting paradigm by correlating discounting metrics with traditional
performance-based, self-report (NASA Task Load Index: TLX), and pupillometric (Task Evoked Pupillary
Response: TEPR) measures of cognitive effort. The TLX [20] is a commonly used self-report scale that solicits
Likert ratings of perceived difficulty, effort, frustration, etc. The phasic TEPR is thought to reflect cognitive
effort-related sympathetic arousal [21]. Both TEPRs and TLX scores are well-established measures that
increase with objective demands [20,21,85]. TEPRs, however, also depend on cognitive capacity and, like
DLPFC activity, are best conceived of as a marker of cognitive effort that indexes capacity utilization [63]. Aim
1 of this proposal thus seeks to validate the novel cost measures by demonstrating correlation between
discounting and traditional measures, while also showing where they diverge. The behavioral economic
approach holds that the cost of effort will be the most direct predictor of the decision to engage in a task.
Hence, traditional measures of effort like TEPRs should correlate with effort discounting up to a point, but may
be useful only as indirect measures of the relevant cost variable. In Aim 2, discounting metrics will be used to
test specific hypotheses about regions tracking effort, taking advantage of their direct measurement of cost to
interpret modulation of the BOLD signal as either directly tracking effort (ACC), or as tracking capacity
utilization (DLPFC). Finally, in Aim 3, I will use the cost measures to test hypotheses about regions involved in
cognitive effort-based decision-making (e.g. VS representing subjective value) while participants make
decisions in the novel paradigm. I will also use the novel measures to conduct whole-brain exploratory
analyses in Aims 2 and 3 to search for other regions tracking effort or representing subjective value.
Participants: I will first recruit enough subjects to show reliable differences in traditional measures of effort for
correlation with my novel discounting measures. Based on pilot data (not presented) of the effect size (0.63) of
pupillary dilation by task level n, I will have ~95% power to detect reliable differences in dilation between n = 1
and 2, and n = 2 and 3 using 50 participants. I will next select a subset of these participants, showing a range
of individual differences in sensitivity to effort costs for the two scanning sessions. Based on effect size (0.31)
in a prior study [16] of parametric modulation of DLPFC activity by task level n, I will have ~80% power to
detect a reliable linear relationship between DLPFC recruitment and n (for n = 1-3) with a sample of 30
participants. These same 30 participants will be brought back for the second scanning session. Participants will
be recruited from the Washington University community. All will be right-handed young adults (ages 18-40),
neurologically normal, with no history of mental illness or drug abuse, not currently taking any psychotropic
medication, and with normal to corrected vision.
Aim 1: Validate my novel effort discounting paradigm by examining relationships to traditional
measures of cognitive effort. To establish construct validity, I will show that three factors predict effort
discounting: performance (accuracy, or signal detection measures such as d’; response time), self-reported
effort (TLX effort and demand subscales), and TEPRs, which I will measure at levels (1-6) of the n-back.
TEPRs (measured as peak deflection in the 1.5 sec following stimulus presentation, relative to the baseline 1.0
sec preceding stimulus onset [21]) will be compared to performance to demonstrate their utility as an index of
capacity utilization. My Sponsor’s lab has a dedicated pupillometry suite, which I have already used for piloting
and data analysis, and (relevant to Aim 2) the NeuroImaging Laboratory (NIL) has an fMRI scanner equipped
with a pupillometer, which our lab has already used in joint fMRI-pupillometry studies. I will correlate these
traditional effort measures with discounting both within (across task levels) and between individuals. I will also
examine the extent to which each measure explains unique between and within individual variance in
discounting to reveal their independent contributions to the cost of cognitive effort. As before, after three
practice blocks of each level of the n-back, participants will make discounting decisions about which levels they
would be willing to complete again on the instruction that they will be paid as long as they maintain their effort.
Analyses and Predictions: I will first test for pairwise inter-correlations between discounting and self-report,
performance, and pupillometric measures. I predict that as objective demands (task level n) rise, both selfreported effort and discounting will increase, supporting a link between effort discounting and conscious,
phenomenal effort. I also predict that performance will decrease (longer response times, lower accuracy),
supporting that the cost of effort rises when tasks deplete cognitive capacity. I further predict a positive
correlation with TEPR magnitudes, supporting the relationship with capacity utilization. Such a finding would be
consistent with other work showing that subjective effort is greater when performance is diminished [86].
However, I also predict that the correlation between discounting and self-reported effort will hold, even after
controlling performance measures. If it does not, the interpretation would be that discounting reflects aversion
to error commission rather than to the cost of effort per se. Pupillometric time series data are rich, however,
and provide the possibility of further analyses of more complex hypotheses related to effort discounting. For
example, a recent study has shown, that the ratio of TEPR-to-tonic pupil dilation predicts task disengagement
in the context of declining perceived efficacy [87]. Task disengagement may be simultaneously characterized
by both TEPR and tonic pupil measures (expressed as a ratio), which together predict increased effort
discounting. Such a pattern would support the hypothesis that high effort costs drive task disengagement.
Aim 2: Test specific hypotheses about how the brain tracks cognitive effort. (Scan Session 1): Using an
fMRI scanner equipped with a pupillometer (already in use at the NIL), I will record simultaneous BOLD and
pupillometric data during two runs of each level (1-6) of the n-back. Examining neural responses at this many
levels has never been done before and will allow me to test hypothesized neural responses under manageable
and excessive demands for all subjects. I will use a blocked design to compare BOLD signal during task runs,
relative to fixation, to maximize power to resolve differential neural activity across task levels [88]. Following
the scanning session, and a rest break, participants will complete the discounting paradigm again outside of
the scanner so that activity evoked during task engagement can be compared to subsequent discounting at
each level, and also to examine test/re-test reliability in task discounting from the first behavioral session.
Analyses and Predictions: I will first conduct whole brain analyses to test for regions tracking cognitive effort,
selecting those for which a group-level monotonic increase with task level, n, is observed, for levels n = 1 to 3
[16,61]. I will next examine correlations, by region, with effort discounting. I predict that activity in both the ACC
and DLPFC will track effort and therefore discounting better than other regions, but do so differently. ACC will
scale reliably across all task levels (n = 2-6), indicating a role in effort tracking, while DLPFC will be reliably
related for lower task levels (n = 2-3), but not for individual-specific levels beyond which performance indicates
that task demands exceed cognitive capacity. Specifically, I will classify n-back task levels by a performance
threshold (d’), which will be used to determine whether that level is above or below the participants’ maximum
capacity utilization. I will then correlate brain activity in the selected regions with effort discounting for each
level, separately for each participant. Specifically, I will test whether effort discounting is significantly correlated
with brain activity in ACC and/or DLPFC, as a function of whether the task level is above or below the capacity
threshold. I predict that average ACC correlation coefficients will be significant across participants, both above
and below capacity, while DLPFC correlations will only be significant below maximum capacity utilization. This
would support the hypothesis that ACC tracks effort regardless of capacity utilization, while DLPFC does this
only when there is capacity to spare. Between individuals, I will examine the relationship between modulation
of neural activity by task level n and area under the discounting curve (AUC), as a measure of participantspecific effort costliness. I will conduct a hierarchical regression, entering average accuracy (d’) and response
time first to control for performance. Next, I will enter the regression coefficient of DLPFC activity on n as a
predictor, which, if it explains unique variance, will support that capacity utilization drives subjective effort. The
prediction is that the steepness of correlations between DLPFC activity and n indicate capacity utilization
across levels and that this should predict variance in individual discounting. I also predict that subsequent
addition of the regression coefficient of TEPRs on n will explain no additional variance (or vice versa if the
TEPR coefficient is entered first) indicating that TEPRs and DLPFC activity are convergent indices of capacity
utilization, thus explaining the same variance in effort costs. Finally, I will add the regression coefficient of ACC
activity on n as a predictor, which, if it explains unique variance, will support the hypothesis that subjective
cognitive effort costs, as monitored by the ACC, are driven by factors above and beyond error rate and
capacity utilization. In particular, effort discounting may vary between individuals simply as a matter of
individual differences in sensitivity to task demands. Such a finding would support capacity-independent
differences in effort cost sensitivity. Finally, I predict AUC measures in this scanning session will correlate
robustly with AUC measures from the first behavioral session, suggesting that the cost of effort is trait-like.
Aim 3: Test specific hypotheses about how effort costs are factored into effort-based decisions. (Scan
Session 2): To test whether regions are sensitive to subjective value, incorporating the cost of cognitive effort
during effort-based decision-making, I will analyze neural responses as participants are engaged in making
decisions about whether or not to engage in a cognitively effortful task, using methods adapted from fMRI
studies of delay discounting [23]. A common approach is to run separate sessions to establish participants’
discounting, and use the results to program the offers they will receive during imaging. Hence I will use
participants’ prior (Aim 2) discounting to program the offers they will receive while deciding about offers in this
scanning session. Trials will be set up based each individual’s discounting, to span the range of where the two
offers are close in subjective value and trials where they are far apart. Trials will also be set up to bias choice
of the easy task and hard task equally often. Prior to scanning, participants will be tasked with completing one
round of each level of the n-back (to refresh their memory regarding the subjective cognitive effort associated
with each task level). In the scanner, they will complete the discounting paradigm. I will make a series of offers
including 5 levels of the task (n = 2 to 6, versus n = 1) and 6 different offer amounts for a total of 30 unique
decisions. I will repeat each in 4 different blocks for a total of 120 choice trials. Finally, I will test whether the
fMRI data provides evidence for communication in the form of increased functional connectivity between
regions representing subjective value (identified in this aim) and regions tracking effort costs (identified in Aim
2) during effort-based decisions.
Analyses and Predictions: I will first conduct a whole-brain analysis to identify regions covarying with the
subjective value of selected offers across all subjects. I predict that subjective values will correlate with BOLD
response in the VS and the OFC/VMPFC as participants consider each offer. Next, I will test whether these
regions meet three neuroeconomic criteria for representing subjective value: regions should be sensitive to not
only reward amount and objective cost (n-back level), but also a subject-specific factor indicating how
subjective costs scale with these objective dimensions [23,24]. To test this, I will conduct a hierarchical
regression of the magnitude of BOLD response in candidate subjective value regions (VS, OFC/VMPFC)
across subjects, entering selected offered reward amount first, which should, by definition, explain variance in
all regions. Second I will add in objective load (n-back level), which should also explain variance in all regions.
Alternatively, should either the OFC or VMPFC be insensitive to effort because they represent economic value
in the space of goods and not actions to obtain them [18], no variance in BOLD signal will be explained in this
step. Finally, I will add AUC, as a measure of individual-specific effort costliness, which I also predict to explain
variance in VS and OFC/VMPFC activity, implicating them in the representation of subjective value.
I will next test the hypothesis that regions represent subjective value by incorporating cost information from
effort tracking regions. To test this, I will examine functional connectivity between subjective value regions and
effort tracking regions identified in Aim 2 while participants make effort-based decisions. To avoid confounding
connectivity with spurious task-evoked activity, I will test for “background connectivity” [89]. Background
connectivity measures residual correlations after subtracting out mean task-evoked activity. I predict that
consideration of effort demands will generate increased background connectivity between the ACC and
subjective value regions: the VS and OFC/VMPFC. This will support that effort costs, stored in the ACC, are
communicated to value regions for the purpose of generating subjective value estimates during effort-based
decisions. I further predict, given the putative role of the ACC in action selection, that mean ACC activity will be
greatest when participants consider offers that are close in subjective value [90]. Importantly, the pattern of
ACC behavior during effort-based decision-making will differ from ACC activity during the n-back when it is
expected to increase monotonically with task demands. The most parsimonious explanation of this pattern of
response: that the ACC tracks both n-back demands and also offer proximity during discounting, is that this
region serves as an effort monitor that is engaged not only during cognitive task performance, but also when
effort information is incorporated into decision-making about whether to volitionally engage in such tasks.