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Deliberating trade-offs with the future

Abstract

Many fundamental choices in life are intertemporal: they involve trade-offs between sooner and later outcomes. In recent years there has been a surge of interest into how people make intertemporal decisions, given that such decisions are ubiquitous in everyday life and central in domains from substance use to climate change action. While it is clear that people make decisions according to rules, intuitions and habits, they also commonly deliberate over their options, thinking through potential outcomes and reflecting on their own preferences. In this Perspective, we bring to bear recent research into the higher-order capacities that underpin deliberation—particularly those that enable people to think about the future (prospection) and their own thinking (metacognition)—to shed light on intertemporal decision-making. We show how a greater appreciation for these mechanisms of deliberation promises to advance our understanding of intertemporal decision-making and unify a wide range of otherwise disparate choice phenomena.
Deliberating trade-offs with the future
Adam Bulley1,2* and Daniel L. Schacter1
1 Department of Psychology, Harvard University, Cambridge, MA 02138, USA
2 The University of Sydney, School of Psychology and Brain and Mind Centre, NSW 2050,
Australia
* Corresponding author
Author accepted version of “Perspective” article in Nature Human Behaviour
Doi: http://dx.doi.org/10.1038/s41562-020-0834-9
Abstract
Many fundamental choices in life are intertemporal: they involve trade-offs between sooner
and later outcomes. In recent years there has been a surge of interest into how people make
intertemporal decisions, given that such decisions are ubiquitous in everyday life and central
in domains from substance use to climate change action. While it is clear that people make
decisions according to rules, intuitions, and habits, they also commonly deliberate over their
options; thinking through potential outcomes and reflecting on their own preferences. In this
Perspective, we bring to bear recent research into the higher-order capacities that underpin
deliberation – particularly those that enable people to think about the future (prospection) and
their own thinking (metacognition) to shed light on intertemporal decision-making. We
show how a greater appreciation for these mechanisms of deliberation promises to advance
our understanding of intertemporal decision-making and unify a wide range of otherwise
disparate choice phenomena.
1
Deliberating trade-offs with the future
Given that time runs in only one direction, many of the most fundamental choices in
life are ones that involve trade-offs between sooner and later outcomes. The causes and
consequences of intertemporal choice have received attention for centuries1–3, but now face
an increasing surge of interest. This is because intertemporal choices are ubiquitous in
everyday life, but also because they characterise a wide range of societally significant
behaviours from substance use to climate change action. Accordingly, basic and translational
scholarship on the phenomenon has accelerated in economics4, clinical psychology5,
cognitive neuroscience6, behavioural ecology7, genetics8, philosophy9, and in other branches
of the behavioural sciences.
One popular approach to understanding human decision-making has been to study the
rules, intuitions, and habits that influence it10–12. Often, however, people deliberate
considerably when they must make trade-offs over time; thinking through conceivable
payoffs and pitfalls, as well as reflecting on their own preferences. By a broad definition,
deliberation is the process by which a decision-maker considers their options (see Box 1,
glossary). This process entails multiple stages, including the representation of the possible
options and their subsequent outcomes, as well as the evaluation of these representations13–15.
It requires a decision-maker to construct and search through the cognitive space of option-
outcome paths, and to thereby settle on one route forward. Whether there are multiple
interacting systems or a single system that weighs up choice options 5,16–20, deliberation has a
central role in intertemporal decision-making that must be elucidated13,21–23.
In this Perspective, our primary contention is that the cognitive and neural
mechanisms that allow people to think about the future (prospection) and about their own
thinking (metacognition) are integral to deliberation, and that together these capacities
produce effects that are responsible for a range of decision-making idiosyncrasies. For
instance, the fact that people can anticipate the costs of waiting for a delayed reward means
that deliberation can result in seemingly paradoxical “farsighted impulsivity”, and that
therefore deliberation does not equate to patience as it is commonly defined24–26. We begin by
introducing the elements of deliberation, and explain how they interlink, before turning to a
range of illustrative choice phenomena. A great deal of research has implicated ingredients of
2
deliberation in the flexibility of human intertemporal choice such as general cognitive
effort27,28, reflection29,30, and working memory capacity31,32, and it is by building on that
background we develop an account of what deliberation entails and what role it plays in
intertemporal choice.
Prospection and metacognition are central to deliberation
Duckworth and colleagues33,34 theorize that self-control strategies require prospection
(the capacity to think about the future) and metacognition (the monitoring and control of
one’s cognitive processes), and have shown how the development of these abilities in
childhood underpins various self-control strategies for overcoming temptations. This idea has
roots in views of self-control as a series of interactions between different versions of the self
over time20,35–39. Here, we apply the insight to deliberation in intertemporal choice more
broadly. We explain how the abilities of prospection and metacognition are fundamental
when people deliberate over intertemporal trade-offs, and how the interaction of these two
abilities is not additive. The interaction results in qualitatively distinct effects from what
either process produces alone; with consequences for a range of phenomena in intertemporal
decision-making.
Prospection involves the representation and evaluation of possible futures.
Research into the psychology40–42, evolution43,44, and cognitive neuroscience45–47 of thinking
about the future has grown rapidly over the past decades, and it is has become increasingly
clear that the ability comprises a broad range of phenomena with many constituent elements.
Much of the work has focussed on episodic future thinking, which can be defined as the
capacity to imagine or simulate events that might occur in one’s personal future. Numerous
lines of evidence have indicated that episodic memory abilities contribute importantly to the
capacity for episodic future thinking43,46,47. For instance, neuroimaging research has revealed a
close correspondence in neural activity when people remember specific personal memories
and imagine possible future events, which led Schacter et al.48 to describe this shared neural
system as a core network involved in simulating both past and future experiences. Box 2
presents more detail and expands on the insights from neuroscience relevant to other sections
of this Perspective.
It has become clear from both neuroimaging studies, as well as behavioural and
cognitive research that the mechanisms of episodic simulation share close links to those of
emotion and valuation42,49–51. Episodic future thinking is therefore a plausible candidate
contributor to affective forecasting42, motivation in goal pursuit52, and the explicit evaluation
3
of choice outcomes indeed the prospection network is often incorporated into general
systems models of intertemporal decision-making6,53,54. Note, nonetheless, that patients with
hippocampal amnesia (who exhibit deficits in episodic memory and in some cases, episodic
future thinking) may make intertemporal choices similarly to healthy controls55, and this has
led to views emphasising the flexibility episodic future thinking affords rather than its
necessity for intertemporal choice per se e.g. 25,56,57. A parallel body of research into
reinforcement learning has begun to elucidate the processes of model-based control58. In
contrast to model-free control, which involves habitual responses based on the repetition of
stimulus-response pairings, model-based control enables flexible goal-directed planning and
is a plausible computational substrate of goal-directed cognition broadly, and episodic future
thinking in particular59,60.
Metacognition enables the evaluation and control of prospection. The capacity to
represent the relation between a representation and reality is known as metarepresentation61.
This ability is foundational for appreciating that other people may hold false beliefs (theory
of mind), and for thinking about alternative ways the past could have unfolded
(counterfactual reasoning)62,63. Metarepresentation also allows for the monitoring and control
of one’s own cognition – metacognitionfor example in taking stock of one’s own memory
strengths and weaknesses and compensating for them64,65. Recent perspectives from artificial
intelligence and computational neuroscience have emphasised the utility of meta-level
systems66–69. These systems are effective because they can regulate the execution of lower-
level processes, for example by determining the value of dedicating computational resources
towards solving particular problems over others37,70.
The critical role of metarepresentation in prospection has long been noted63,71. As
Redshaw and Suddendorf72–74 have argued, metarepresentation allows an individual to
evaluate their own prospective cognition (metaforesight), and this therefore produces: (i) the
insight that one could be wrong about one’s beliefs, predictions, or reasoning about the
future72, (ii) the awareness that the future has multiple possible paths that are not just
probabilistically different, but mutually exclusive73, and (iii) the ability to reflect on what the
strengths and weaknesses of one’s other cognitive abilities may be in the future74. In turn,
alternative representations of the future can be evaluated and appraised, for example in terms
of their likelihood, plausibility, or concordance with ones goals52,75,76 (Fig1.C). Jing et al.
report that an episodic specificity induction, a procedure that enhances the retrieval of
episodic details, boosts the number of alternative possible futures that people imagined
during problem-solving77. This increase had consequences for the perceived plausibility of the
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different outcomes, suggesting that the mechanisms of episodic simulation are tightly linked
with those responsible for the metacognitive evaluation of those simulations.
Understanding how prospection and metacognition interact in deliberation
sheds light on intertemporal choice phenomena
Scholars attempting to make sense of human decision-making have long grappled
with the malleability78, inconsistencies3, anomalies79, and apparent paradoxes51 of real human
choices, and noted how these quirks lead to frequent deviations from normative economic
rationality80, as well as away from decision-makers’ own best interests81. A fuller
understanding of the mechanisms underlying deliberation stands to bring many such
intertemporal choice phenomena together under a common explanatory framework and leads
to a number of explicit predictions and experimental avenues.
Decision-makers deliberate about – and compensate for – anticipated changes of
mind. People often express preferences for the future that are different from their preferences
in the present4,82,83. A smoker intends to quit, but only starting next week; a dieter intends to
stop eating carbs, but only in the New Year. The concavity of the delay discounting curves
that have been used to represent the loss in subjective value of a reward with increasing time
to its receipt captures this dynamic inconsistency3,51 (Figs. 1.A. & 1.D.) Hyperbolic
discounting functions are one modelling approach to intertemporal preferences. These models
describe higher discounting rates at shorter delays to an outcome, and lower discounting rates
at greater delays to an outcome; while a single parameter value k captures discounting
steepness35,84 (note that there are alternatives, such as quasi-hyperbolic models, heuristic
models, and attribute-based models10,85–88). In intertemporal choice studies, participants
commonly violate normative economic rationality (which typically models delay discounting
as a time-consistent exponential decay of value) by declaring for instance that they would
prefer $40 today over $50 in a month, but that they would also prefer $50 in 12 months over
$40 in 11 months. Here, how far away the decision-maker is from the options influences their
preference, holding constant the delay between the outcomes and the magnitude of the
rewards. Preference reversals occur when an originally stated preference for a larger, later
reward relative to a smaller, sooner one switches as the decision-maker moves closer in time
to the two options. In Fig 1.B., this is when the two hyperbolic discounting curves cross.
Hence the dieter, come New Year’s Day, shifts back to a preference for eating doughnuts (the
diet can always start tomorrow).
5
Prospection allows decision-makers to anticipate their own preference reversals
(Fig.1.C). Perhaps unlike any other animals (c.f. the Bischof-Köhler hypothesis43,89,90), humans
readily, though sometimes with difficulty, recognize that they may be angrier, hungrier, or
craving more intensely in the future than they are now42,91, and therefore that their
intertemporal preferences may change. This recognition entails the interaction of
metacognition and prospection because it requires a decision-maker to evaluate the
characteristics of a future simulation itself, or of their other cognitive processes in a future
simulation74,92 (for instance, “how likely am I to want the doughnuts after a long day at
work”). The recursive interplay of metacognition and prospection is thereby also expressed in
higher-order desires: people frequently want to want other things93, such as wanting to want
salad instead of doughnuts. Collectively these cognitive processes may be directly
responsible for the puzzling phenomenon of precommitment3,4,33,38,87,94, the establishment of
restraints over future options. For example, the dieter may decide to throw away all the
doughnuts in December, in order to pre-empt snacking once a currently undesired preference-
reversal occurs.
It has been questioned whether initiating a precommitment strategy necessarily
requires the simulation of a future preference reversal95,96, and this is a key target for future
research. To better elucidate the psychological and neural mechanisms, it may be useful to
integrate external precommitment into a recently proposed metacognitive model of cognitive
offloading, the use of physical action to alter the information processing demands of a task97–
100. Such a view would treat compensating for anticipated preference reversals via external
precommitment as analogous to, for example, compensating for anticipated memory failures
by setting reminders97,101,102. In both cases, a decision-maker has a prospective intention that
they wish to pursue (e.g. not to eat the doughnuts; not to forget to water the plants). And in
both cases, the interaction of metacognition and prospection is required to assess the relative
costs and benefits of external versus internal strategies (see also 103). Future research into the
correspondence between cognitive offloading and precommitment may therefore elucidate
the higher-order processes that facilitate the pursuit of intentions. For instance, perhaps
compensating for anticipated changes of mind in both cases initially requires effortful and
self-reflective deliberation but can then become automatized.
6
Fig.1. Deliberating over anticipated changes of mind a, A popular hyperbolic model
describing delay discounting; higher k values represent steeper discounting of value with
time, while lower k values represent shallower discounting. b, A preference for a larger, later,
reward relative to a smaller, sooner one can change when both move closer in time. The
interaction of metacognition and prospection enables people to anticipate these preference
reversals. c, This interaction also underlies the insight that the future contains mutually
exclusive possibilities, such as adhering to a diet or not. People evaluate these alternative
futures on dimensions such as plausibility or likelihood, which helps explain precommitment
(throwing away one’s doughnuts). Doughnut image credit: Sam Howzit; Salad image credit:
Marco Verch. d, In the hyperbolic discounting model k is a free scaling parameter that
accentuates or dampens the effect of delay on value.
7
Metacognition means prospection is recursive, which produces effects of
anticipated anticipation on choice. Economists have long noted that decision-makers derive
utility (or dis-utility) not only from outcomes during intertemporal choice, but also from the
delay to those outcomes80. The emotional experiences of dread and savouring are
representative cases, first expounded in detail by Loewenstein79,104; they reflect the negative
and positive value of anticipation during delay, respectively. In cognitive and clinical
psychology, there has meanwhile been much research into the interplay of emotion and
episodic simulation105–107, and this has underscored that episodic future thinking can readily
evoke emotion in a similar manner as-if an emotional event were really occurring108.
Anticipatory emotions like dread and savouring are therefore likely to rely upon episodic
processes104,105,109,110; though this conjecture has remained largely untested. Box 2 details an
initial promising neuroimaging and modelling approach that supports the conjecture.
Not only do people derive utility from simulating emotional future events, they also
adjust their decisions according to the value of the experience they expect to have during the
delay period. This leads decision-makers to postpone a vacation or save a bottle of wine so
they can enjoy the anticipation in violation of expectations from hyperbolic discounting
models104,111. Similarly, when given the choice between suffering different amounts of pain at
different times in the future, people will sometimes opt to “get it over with”112, which reflects
the mental accounting of anticipated dread, rather than just dread itself113,114. This
phenomenon may account for the fact that the value (impact) of negative future outcomes
tends to be discounted less steeply than positive outcomes (the sign effect), given that the
anticipated negativity of dread may contribute more to the impact of delayed negative
outcomes than anticipated savouring does to positive ones111,115.
Anticipating dread and changing decisions accordingly requires a decision-maker to
anticipate what they will anticipate if they make a certain choice (Fig 2.A). Anticipated dread
is thus also a recursive operation, in that the underlying process of anticipation calls upon
itself116. This raises the largely unexplored question about the role of episodic simulation in
deeper levels of recursive thinking during intertemporal choice, such as in anticipated
regret117,118. Anticipated regret has three levels of recursive embedding because regret itself is
a two-level counterfactual (i.e. it requires appreciating that “a different past choice would
have led to a different future”73,119), and it is associated with activity in core network regions
(perhaps implying episodic simulation120). The perspective outlined here predicts that the sign
effect would be absent in people incapable of episodic simulation of the delay period, such as
8
certain individuals with hippocampal amnesia (even if they discount delayed rewards
normally55,121,122).
Cuing people to simulate future events can reduce delay discounting. A number of
studies have directly cued participants to simulate future events while they make
intertemporal decisions, an episodic cuing procedure that produces robust reductions in delay
discounting (e.g. Fig.2.C). This effect has been directly and conceptually replicated a number
of times (see Bulley et al.25, and Schacter et al.122 for reviews, and Rung & Madden123 for a
recent meta-analysis). Studies on episodic cuing of discounting emerged after Boyer124
suggested that a major evolutionary function of imagining the future is the curtailing of delay
discounting. In this view, episodic simulation is proposed to act as a motivational brake on
short-term preferences. A computational model of the role of search processes in
intertemporal choice accounts for the episodic cuing effect by suggesting that it makes future
choice outcomes easier to locate and evaluate during deliberation21 though the precise
mechanisms of the effect remain opaque (see Box 2 for a discussion of the neural
mechanisms). There is also some variability in the size of the cuing effect between studies123,
and ongoing debates about what simulated content is responsible for the effect (including
regarding whether memory retrieval can also reduce discounting125–127). For instance, various
studies have shown the episodic cuing effect by having participants imagine relatively banal,
everyday events, while others have taken pains to have participants simulate goal-relevant
events where the effects may be stronger because of the close relationship between episodic
future thinking and goal pursuit52,128.
There are also conflicting results about the role of valence in the episodic cuing effect.
Two studies initially showed elevated delay discounting when participants were cued to
imagine negative future events relative to control imagination129,130, but in two subsequent
studies both negative and positive episodic future cuing resulted in reduced delay discounting
relative to control imagination125,131. Metacognitive evaluations may prove informative in
determining when different event simulations lead to different patterns of intertemporal
decision-making. One candidate pertains to the controllability of the imagined events. For
instance, it is possible that imagining a negative event as within one’s control might spur
preparatory motivation, while imagining a negative event that is out of one’s control might
encourage steeper delay discounting7,132. Given the preceding discussion of the role of
savouring and dread during discounting, one explanation for the positive episodic cuing effect
is that episodic simulation causes people to reflect on and anticipate the pleasure derived
9
from waiting for rewards (regardless of whether imagined events are negative or positive).
One interesting prediction is therefore that episodic cuing during discounting of negative
outcomes would, at least for a substantial portion of participants, accentuate “get it over with”
choices by bolstering the weight of anticipated dread.
Deliberation does not equate to patience. Contrary to its frequently assumed role,
greater deliberation does not necessarily lead to the pursuit of larger, later rewards 25,133,134. For
instance, people sometimes regret missing out on pleasurable experiences in pursuit of a later
goal135. Foreseeing this regret, a consumer may intentionally splurge on indulgences like an
expensive dinner26,136 and this would be incorrectly called shortsighted by those failing to
understand the causal metacognitive and prospective deliberation involved24. In a similar
vein, people may intentionally choose a smaller, sooner reward if they do not trust that they
will obtain a larger, later one137; a fact that helps explain the steeper discounting observed
amongst people living in poverty7,132,138, and perhaps also the robust individual and cross-
national associations between lower life expectancy and steeper delay discounting139–141. Even
young children will modify their intertemporal choices based on their expectations of
environmental reliability. In one study, when an experimenter broke a promise before
conducting a version of the marshmallow test142, the average waiting time among 3- to 5-
year-olds fell from 12 to 3 minutes143.
In addition to uncertainty about the likelihood of a delayed payoff, uncertainty about
the length of delay to receipt may similarly drive choices for sooner, smaller rewards given
certain prior beliefs144–146. For instance, in some cases the longer one has waited for an
outcome, the longer one might expect to wait (such as when waiting in a queue). Recent work
shows that people prioritize immediate relative to delayed rewards in line with such
predictions about the delay to a payoff146. Aside from representing such uncertainty as
intrinsic to various future events, representing future scenarios as uncertain may be
performed metacognitively, for instance when it involves assessing whether one’s simulation
of the future is plausible, accurate, or will actually occur – or when explicitly comparing and
appraising mutually exclusive possible future alternatives75 such as imagining both waiting
only a short time and waiting a long time for an outcome.
In the clinical domain (see also Box 3), people diagnosed with anorexia nervosa in an
acutely ill underweight state exhibit reduced (shallower) delay discounting relative to
controls, contrary to the vast majority of other patient groups who exhibit steeper
discounting5,147. Weight recovery in remitted anorexia leads to an increased prioritization of
10
sooner, smaller rewards148,149. This increase may be precisely because treatment re-establishes
cognitive resources that enable greater deliberation and executive control, and thus the
overcoming of pathological patience149,150. This case, as with the other examples presented in
this section, make it clear that deliberation and patience cannot be equated. Patterns of
“farsighted impulsivity” may explain recent null or opposite-to-predicted results when
researchers have explored links between delay discounting and model-based planning151
(though see 27), visualisation abilities152, or individual differences in episodic future
thinking153. If greater deliberation can lead to either greater patience or greater impulsivity,
the two constructs will not always correlate. Instead, a nuanced approach focussing on the
constituent processes of both deliberation and intertemporal choice may be revealing. In one
recent study, even though delay discounting and model-based control did not correlate, the
amount of time spent deliberating over intertemporal choices correlated with measures of
model-based multi-step planning154.
Framing and magnitude effects may result from meta-control of deliberation.
Intertemporal decisions depend on many contextual, situational, and framing variables78. We
highlight two here to illustrate how understanding the mechanisms at play during deliberation
can be informative: explicit zero framing, and the magnitude effect. Laboratory intertemporal
choice questions typically contain no reference to the foregone alternatives implicit to each
choice option. For example, the question: “would you prefer $40 today, OR $55 in 62 days?”
contains implicit zero values that can be made explicit as follows: “would you prefer $40
today and $0 in 62 days, OR $55 in 62 days and $0 today”. This explicit zero framing has
been shown to reliably reduce delay discounting155,156. One reason is that participants appear
to be drawn selectively to consider the opportunity cost of choosing the smaller, sooner
reward (the foregone opportunity to gain more money later)157.
Mentally accounting for delayed opportunity costs rests on the insight that there are
branching, mutually exclusive possible versions of the future, and that particular choices
close off particular branches73 (Fig 1.C). A recent study directly tested the possibility that
explicit zero framing would enhance the simulation of choice alternatives. Jenkins & Hsu158
report that explicit zero framing: (i) increased self-reported and other-rated imagination of
intertemporal choice outcomes, (ii) boosted imagination of larger, later rewards more than
smaller, sooner ones, and (iii) enhanced activation in regions of the core network involved in
episodic future thinking (relative to regions involved in executing willpower). The increased
11
imagination of choice alternatives, and in particular imagining larger, later reward outcomes,
predicted the framing-induced shift in willingness to wait.
People reliably discount future rewards less steeply when the options are of larger
magnitude. Ballard et al.159 propose that this magnitude effect occurs because people invest
greater cognitive control (and hence probably more deliberation) into choices deemed more
important. In support of this idea, having participants justify their choices, which requires
considering the reasons behind decisions, attenuated the magnitude effect. The justification-
manipulation selectively reduced delay discounting for smaller magnitude options, while
larger magnitude options which are presumed to already elicit high control were not
affected (Fig. 2.C; see Box 2 for additional supportive neuroscience findings).
If larger magnitude choices engender greater control processes, then the magnitude
effect may be a manifestation of the interaction between metacognition and prospection.
Meta-control systems are responsible for allocating levels of effort 70, and the same may be
true of deliberation about the future68. In a recent model, Gershman & Bhui160 show that the
magnitude effect could emerge from meta-control of prospective simulation. Simulating the
future is a noisy process161, but this noisiness can be attenuated by allocating greater (costly)
cognitive control. When the stakes of a choice are higher, the meta-control system should be
more willing to accept this cost and boost the precision of episodic simulations thereby
reducing delay discounting for higher magnitude relative to lower magnitude outcomes160.
Note, however, that at least one individual with hippocampal amnesia (with deficits in
episodic future thinking) showed the magnitude effect, raising questions about the relative
contributions of episodic and semantic prospection in the aforementioned deliberative
process55. The potential role of metacognitive control over deliberation in the magnitude
effect suggests a striking possibility: by primarily studying relatively small, mostly monetary
choices, we may have greatly underestimated the role of deliberation (and overestimated the
role of habits, biases, and intuitions) when people make intertemporal choices, which
frequently in the real world concern matters of much graver importance: what work to
pursue, whom to partner with, how to provide for one’s descendants.
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Fig. 2. Deliberation in intertemporal choice phenomena. a, Schematic of postulated role
for metacognition and prospection in delay discounting of negative outcomes. With
increasing time, the subjective impact of a negative outcome decreases: people tend to prefer
postponing negative events. However, with the capacity to anticipate the dread leading to a
negative outcome, people sometimes opt to get negative experiences “over with”. This would
correspond to a reduced discounting of the impact of a negative outcome. b, Cuing
participants to imagine either positive or negative future events reduces delay discounting
relative to neutral control mental imagery (n=297), data from Bulley et al 125. c, Asking
participants to justify their choices increases cognitive control, and this reduces the
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magnitude effect by selectively increasing patience for smaller rewards; large rewards are
presumed to already elicit greater control (n=1,382). Reproduced with permission from ref.
159, Sage Publications.
Conclusion
While it has been well documented that decision-making commonly results from the
operation of rules, intuitions, and habits, in this Perspective article we have emphasized the
other side of the coin: people also deliberate considerably about their intertemporal decisions.
Recent research into the psychological and neural mechanisms of deliberation stands to
provide a deeper understanding of how humans make intertemporal choices, with
implications for a range of recognised decision-making phenomena. We have focused on the
role of metacognition and prospection during deliberation, to show how together these
processes allow humans a sophisticated degree of mental accounting for the later
consequences of their decisions. We have only attempted to illustrate the promise of this
direction, by pointing to a range of work from cognitive neuroscience, behavioural
economics, clinical neuropsychology, and other areas of the behavioural sciences that have
contributed important insights. We have proposed a number of specific predictions about
intertemporal choice generated by the perspective that the interaction of metacognition and
prospection underpins deliberation. However, the burgeoning research in this area stands to
make a great deal of further progress by integrating new inter-disciplinary findings in pursuit
of a cohesive understanding of how humans think through trade-offs with the future.
Box 1 | Glossary
Cognitive offloading: the use of physical action to alter the information processing demands
of a task so as to reduce cognitive demand.
Core network: A network of brain regions that show increased activity both when people
remember past experiences and imagine future experiences.
Delay discounting: The decline in the subjective value of an outcome with delay to its
receipt.
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Deliberation: The process by which a decision-maker considers their options, involving both
representing the options and outcomes, as well as evaluating them. We argue that the higher-
order capacities for prospection and metacognition are integral to deliberation.
Dynamic inconsistency: A decision-maker holds dynamically inconsistent preferences when
their preferences change or reverse as choice options come closer in time.
Episodic future thinking: The capacity to imagine or simulate experiences that might occur
in one’s personal future.
Higher-order desire: A desire about a desire, such as when one wants a doughnut, but wants
to want a salad instead.
Intertemporal choices: Choices with consequences that play out over time, often involving
trade-offs between sooner and later outcomes.
Magnitude effect: People tend to discount future rewards less steeply when the values of all
choice options are greater, all else being equal.
Metacognition: Cognition about cognition; the capacity to monitor, evaluate, and control
one’s own cognitive processes.
Model-based control: In reinforcement learning, model-based control refers to behaviour
driven by an agent’s internal causal model of the environment. It is contrasted with model-
free control, a comparatively less accurate but less effortful strategy in which actions are
based on previous stimulus-response reinforcement.
Precommitment: The establishment of restraints over one’s own future choice options,
usually to lock in a preference for a larger, later reward.
Preference reversal: When an originally stated preference switches as the decision-maker
moves closer in time to the choice options.
Prospection: The capacity to mentally represent the future. This is an umbrella term that
refers to many forms of future-oriented cognition: episodic future thinking is one form.
Sign effect: People tend to discount the impact of delayed positive events more steeply than
they discount the impact of delayed negative events.
Box 2 | Insights from neuroscience
Identifying a core network: The core network of brain activity that supports remembering
the past and imagining the future, which closely overlaps with the default-mode network162,
includes regions of the medial temporal lobe, medial prefrontal cortex, posterior cingulate
and retrosplenial cortices, as well as regions of the lateral temporal and parietal cortices (see
Benoit & Schacter for a recent fMRI meta analysis163).
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Mechanisms of precommitment: Recent work on the neural mechanisms of precommitment
has implicated the frontopolar cortex164,165, which has also been associated with the core
network introduced above (more so in simulating the future than remembering the past)166, as
well as with prospective valuation, counterfactual thinking, and metacognitive control167–170.
These findings lend suggestive support to the conjecture that precommitment draws upon an
interaction of metacognition and prospection instantiated in higher-order executive brain
systems 170.
Evaluating imagined futures: Research has consistently linked regions of the core network,
in particular midline prefrontal regions such as the ventromedial prefrontal cortex (vmPFC),
to the integration of value into simulations50,171. The vmPFC also appears to play a role in the
value of simulated waiting periods: in one recent study, Iigaya and colleagues172 show that
core network regions, especially the vmPFC and hippocampus, are central to the pleasure of
anticipation via functional coupling with regions of the dopaminergic midbrain. The authors
suggest that signals from the dopaminergic system are projected to the hippocampus, and that
this amplifies the vivid imagination (and affective quality) of anticipation172. Other
neuroimaging work connects delay discounting to the role of vmPFC in mental simulation.
This includes findings that brain activity in vmPFC during future thinking directly predicts
delay discounting177, that increased vmPFC activity while participants imagine consuming
rewards correlates with shallower discounting178, and that a neural predictor of the valence of
episodic future thoughts, focussed on the vmPFC, predicted the subjective value of delayed
rewards in separate intertemporal choice tasks179. Among the first episodic cuing effect
studies, the core network was directly implicated with fMRI. Benoit et al.173 showed that
coupling of the rostromedial prefrontal cortex and hippocampus was associated with the cue-
driven reduction of delay discounting (for similar results implicating coupling of frontal
regions and medial temporal lobe regions see also174–176).
Interaction of control and prospection in the magnitude effect: Ballard et al.159 report
greater activity in prefrontal executive control network regions when people made difficult
higher magnitude intertemporal choices. A subsequent repetitive TMS study showed that
disrupting activity in the dlPFC reduced the magnitude effect, thereby providing causal
evidence for the dependency of the effect on prefrontal executive control regions. In a recent
neuroimaging meta-analysis163, the dlPFC was also shown to be part of the core network and
more active during episodic future simulation than episodic memory. The dlPFC is thus a
node both of the brain network involved in simulation, and the fronto-parietal control
16
network – with all of the above findings implicating it in controlled deliberation over choice
alternatives.
Modelling evidence accumulation in deliberation: People take longer to choose between
options that are more similar in subjective value, presumably because they deliberate more
about such choices15,180. The reason that deliberation takes time could be because it involves a
sequential sampling from memory (including via retrieval processes that support prospection)
in order to provide evidence about choice options180. Recent approaches to modelling value-
based decision-making as sequential sampling have revealed new insights about the neural
mechanisms of deliberation15, and may prove a fruitful testing ground for hypotheses about
intertemporal choice86,181–183, including those presented in this paper. For instance, do
metacognitive processes play a role in controlling sequential sampling from memory (and
thus prospection) such as by determining where the termination criterion should be set (i.e.
via an assessment of whether it is worth one’s time and effort to imagine any more alternative
possible futures)?
Box 3 | Promising avenues for clinical intervention
Steeper delay discounting has been observed in a variety of behavioural health issues
and psychopathologies, leading to calls for it to be considered a trans-disease process5. Delay
discounting is also therefore a primary target for clinical intervention6. The potential
clinically significant role of individual differences in prospection and metacognition in
deliberation during intertemporal choice has received less attention though see 184–186, but there are
a number of promising avenues for future research.
If deliberation is reduced, one possibility is to find alternative, compensatory
strategies that do not put demands on it. For example, establishing self or other-imposed
commitments, rules, habits, or principles that may require only an initial deliberative
commitment and then a less deliberative execution could be effective, but may also impose
other cognitive demands. For a comprehensive recent taxonomy of such strategies, and their
relative merits see Duckworth et al.187. This approach may be particularly useful in clinical
settings where deliberation or executive control has deteriorated. For instance, rigidity and
reduced cognitive flexibility (including impairments in episodic future thinking) manifest in a
range of dementia subtypes alongside maladaptive shifts in delay discounting (e.g.
behavioural variant frontotemporal dementia188,189), and so compensatory strategies here could
be particularly useful186. A parallel can be again evoked with cognitive offloading, which is
17
commonly adopted in the context of dementia or brain injury when memory begins to fail
(reminders, lists, Google calendars, etc.)190–192. Studying patient populations who have
selective impairments to certain kinds of prospection may also prove informative for basic
science, in delineating the respective role of episodic versus semantic processing in the
various effects of prospection discussed in this paper47,56.
It may be possible to selectively boost deliberation, instead of compensating for its
loss. Promising results discussed in this paper include the attenuation of the magnitude effect,
the episodic cuing effect (particularly in clinical contexts like obesity or substance use
disorder treatment193–195), and a range of other behavioural interventions such as
implementation intentions196). Recent results also suggest that metacognition can be directly
improved with training197. Together these studies suggest that intervening at the level of
specific deliberative processes may prove fruitful.
References
1. Plato. Protagoras. in B. Jowett and M. Ostwald Translation (ed. Vlastos, G.) (Bobbs-
Merrill, 1956).
2. Smith, A. Adam Smith: The theory of moral sentiments. Cambridge Texts in the
History of Philosophy (Cambridge University Press, 1759). doi:DOI:
10.1017/CBO9780511800153
3. Strotz, R. H. Myopia and inconsistency in dynamic utility maximization. Rev. Econ.
Stud. 23, 165–180 (1955).
4. Ericson, K. M. & Laibson, D. Intertemporal choice. in Handbook of Behavioral
Economics - Foundations and Applications 2, Volume 2 (eds. Bernheim, B. D.,
DellaVigna, S. & Laibson, D.) 1–67 (North-Holland, 2019). doi:10.1007/978-3-642-
36172-2_7034
5. Bickel, W. K. et al. Excessive discounting of delayed reinforcers as a trans-disease
process: Update on the state of the science. Curr. Opin. Psychol. 30, 59–64 (2019).
6. Lempert, K. M., Steinglass, J. E., Pinto, A., Kable, J. W. & Simpson, H. B. Can delay
discounting deliver on the promise of RDoC? Psychol. Med. 49, 190–199 (2019).
7. Pepper, G. V & Nettle, D. The behavioural constellation of deprivation: causes and
consequences. Behav. Brain Sci. 1–72 (2017). doi:10.1017/S0140525X1600234X
8. Sanchez-Roige, S. et al. Genome-wide association study of delay discounting in
23,217 adult research participants of European ancestry. Nat. Neurosci. 21, 16–20
18
(2018).
9. Viganò, E. Adam Smith’s theory of prudence updated with neuroscientific and
behavioral evidence. Neuroethics 10, 215–233 (2017).
10. Marzilli Ericson, K. M., White, J. M., Laibson, D. & Cohen, J. D. Money earlier or
later? Simple heuristics explain intertemporal choices better than delay discounting
does. Psychol. Sci. 26, 826–833 (2015).
11. Gigerenzer, G. & Todd, P. M. Simple heuristics that make us smart. (Oxford University
Press, USA, 1999).
12. Gilovich, T. ., Griffin, D. . & Kahneman, D. Heuristics and biases: The psychology of
intuitive judgment. (Cambridge University Press, 2002). doi:citeulike-article-
id:4057156
13. Redish, A. D. Vicarious trial and error. Nat. Rev. Neurosci. 17, 147–159 (2016).
14. Abram, S. V., Hanke, M., Redish, A. D. & MacDonald, A. W. Neural signatures
underlying deliberation in human foraging decisions. Cogn. Affect. Behav. Neurosci.
(2019). doi:10.3758/s13415-019-00733-z
15. Bakkour, A. et al. The hippocampus supports deliberation during value-based
decisions. Elife 8, 1–28 (2019).
16. Kable, J. W. & Glimcher, P. W. The neural correlates of subjective value during
intertemporal choice. Nat. Neurosci. 10, 1625–1633 (2007).
17. Peters, J. & Büchel, C. Overlapping and distinct neural systems code for subjective
value during intertemporal and risky decision making. J. Neurosci. 29, 15727–15734
(2009).
18. Kable, J. W. & Glimcher, P. W. An ‘as soon as possible’ effect in human intertemporal
decision making: Behavioral evidence and neural mechanisms. J. Neurophysiol. 103,
2513–2531 (2010).
19. McClure, S. M., Laibson, D. I., Loewenstein, G. F. & Cohen, J. D. Separate neural
systems value immediate and delayed monetary rewards. Science (80-. ). 306, 503–507
(2004).
20. Thaler, R. H. & Shefrin, H. An economic theory of self-contol. Journal of Political
Economy 89, 392–406 (1981).
21. Kurth-Nelson, Z., Bickel, W. & Redish, A. D. A theoretical account of cognitive effects
in delay discounting. Eur. J. Neurosci. 35, 1052–1064 (2012).
22. Regier, P. S. & Redish, A. D. Contingency management and deliberative decision-
making processes. Front. Psychiatry 6, 1–13 (2015).
19
23. Loewenstein, G., O’Donoghue, T. & Bhatia, S. Modeling the Interplay Between Affect
and Deliberation. Decision 2, 55–81 (2015).
24. Loewenstein, G. Self-control and its discontents: a commentary on Duckworth,
Milkman, and Laibson. Psychol. Sci. Public Interes. 19, 95–101 (2018).
25. Bulley, A., Henry, J. & Suddendorf, T. Prospection and the present moment: The role
of episodic foresight in intertemporal choices between immediate and delayed rewards.
Rev. Gen. Psychol. 20, 29–47 (2016).
26. Keinan, A. & Kivetz, R. Remedying hyperopia: the effects of self-control regret on
consumer behavior. J. Mark. Res. 45, 676–689 (2008).
27. Shenhav, A., Rand, D. G. & Greene, J. D. The relationship between intertemporal
choice and following the path of least resistance across choices, preferences, and
beliefs. Judgement Decis. Mak. 12, 1–18 (2017).
28. Kool, W., McGuire, J. T., Wang, G. J. & Botvinick, M. M. Neural and behavioral
evidence for an intrinsic cost of self-control. PLoS One 8, 1–6 (2013).
29. Frederick, S. Cognitive reflection and decision making. J. Econ. Perspect. 19, 25–42
(2005).
30. Imas, A., Kuhn, M. A. & Mironova, V. Waiting to choose. SSRN Electron. J. 1–42
(2018).
31. Aranovich, G. J., McClure, S. M., Fryer, S. & Mathalon, D. H. The effect of cognitive
challenge on delay discounting. Neuroimage 124, 733–739 (2016).
32. Bickel, W. K., Yi, R., Landes, R. D., Hill, P. F. & Baxter, C. Remember the future:
working memory training decreases delay discounting among stimulant addicts. Biol.
Psychiatry 69, 260–265 (2011).
33. Duckworth, A. L., Gendler, T. S. & Gross, J. J. Self-control in school-age children.
Educ. Psychol. 49, 199–217 (2014).
34. Robinson, C. D., Pons, G. A., Duckworth, A. L. & Rogers, T. Some middle school
students want behavior commitment devices (but take-up does not affect their
behavior). Front. Psychol. 9, 1–10 (2018).
35. Rachlin, H. The science of self-control. (Harvard University Press, 2000).
36. Stanovich, K. E. On the coexistence of cognitivism and intertemporal bargaining.
Behav. Brain Sci. 28, 661–662 (2005).
37. Foxall, G. R. Metacognitive control of categorial neurobehavioral decision systems.
Front. Psychol. 7, 1–18 (2016).
38. Schelling, T. C. Choice and consequence. (Harvard University Press, 1984).
20
39. Ainslie, G. Picoeconomics: The strategic interaction of successive motivational states
within the person. (Cambridge University Press, 1992).
40. Atance, C. M. & O’Neill, D. K. Episodic future thinking. Trends Cogn. Sci. 5, 533–
539 (2001).
41. Seligman, M. E. P., Railton, P., Baumeister, R. F. & Sripada, C. Navigating Into the
future or driven by the past. Perspect. Psychol. Sci. 8, 119–141 (2013).
42. Gilbert, D. T. & Wilson, T. D. Prospection : experiencing the future. Science (80-. ).
317, 1351–1354 (2007).
43. Suddendorf, T. & Corballis, M. C. The evolution of foresight: What is mental time
travel, and is it unique to humans? Behav. Brain Sci. 30, 299–351 (2007).
44. Suddendorf, T., Bulley, A. & Miloyan, B. Prospection and natural selection. Curr.
Opin. Behav. Sci. 24, 26–31 (2018).
45. Szpunar, K. K. Episodic future thought: An emerging concept. Perspect. Psychol. Sci.
5, 142–162 (2010).
46. Schacter, D. L. & Addis, D. R. The cognitive neuroscience of constructive memory:
remembering the past and imagining the future. Philos. Trans. B 362, 773–786 (2007).
47. Irish, M., Addis, D. R., Hodges, J. R. & Piguet, O. Considering the role of semantic
memory in episodic future thinking: evidence from semantic dementia. Brain 135,
2178–2191 (2012).
48. Schacter, D. L., Addis, D. R. & Buckner, R. L. Remembering the past to imagine the
future: the prospective brain. Nat. Rev. Neurosci. 8, 657–661 (2007).
49. Miloyan, B. & Suddendorf, T. Feelings of the future. Trends Cogn. Sci. (2015).
doi:10.1016/j.tics.2015.01.008
50. Benoit, R. G., Paulus, P. C. & Schacter, D. L. Forming attitudes via neural activity
supporting affective episodic simulations. Nat. Commun. 10, 2215 (2019).
51. Ainslie, G. Précis of Breakdown of Will. Behav. Brain Sci. 28, 660–661 (2005).
52. Ernst, A., Philippe, F. L. & D’argembeau, A. Wanting or having to: The role of goal
self-concordance in episodic future thinking. Conscious. Cogn. 66, 26–39 (2018).
53. Peters, J. & Büchel, C. The neural mechanisms of inter-temporal decision-making:
Understanding variability. Trends Cogn. Sci. 15, 227–239 (2011).
54. Sellitto, M., Ciaramelli, E. & Di Pellegrino, G. The neurobiology of intertemporal
choice: Insight from imaging and lesion studies. Rev. Neurosci. 22, 565–574 (2011).
55. Kwan, D. et al. Future decision-making without episodic mental time travel.
Hippocampus 22, 1215–1219 (2012).
21
56. Palombo, D. J., Keane, M. M. & Verfaellie, M. Using future thinking to reduce
temporal discounting: Under what circumstances are the medial temporal lobes
critical? Neuropsychologia 89, 437–444 (2016).
57. Palombo, D. J., Keane, M. M. & Verfaellie, M. The medial temporal lobes are critical
for reward-based decision making under conditions that promote episodic future
thinking. Hippocampus (2014). doi:10.1002/hipo.22376
58. Dolan, R. J. & Dayan, P. Goals and habits in the brain. Neuron 80, 312–325 (2013).
59. Gershman, S. J. Predicting the past, remembering the future. Curr. Opin. Behav. Sci.
17, 7–13 (2017).
60. Pezzulo, G. & Rigoli, F. The value of foresight: How prospection affects decision
making. Front. Neurosci. 5, (2011).
61. Pylyshyn, Z. W. When is attribution of beliefs justified? Behav. Brain Sci. 1, 592–593
(1978).
62. Perner, J. Understanding the representational mind. (The MIT Press, 1991).
63. Suddendorf, T. The rise of the metamind. in The Descent of Mind (ed. Corballis MC,
L. C.) 218–260 (Oxford University Press, 1999).
64. Dunlosky, J. & Metcalfe, J. Metacognition. (Sage Publications, 2008).
65. Flavell, J. H. & Wellman, H. M. Metamemory. (1975).
66. Wang, J. X. et al. Prefrontal cortex as a meta-reinforcement learning system. Nat.
Neurosci. 21, 860–868 (2018).
67. Gershman, S. J., Horvitz, E. J. & Tenenbaum, J. B. Computational rationality: A
converging paradigm for intelligence in brains, minds, and machines. Science (80-. ).
349, 273–278 (2015).
68. Boureau, Y. L., Sokol-Hessner, P. & Daw, N. D. Deciding how to decide: self-control
and meta-decision making. Trends Cogn. Sci. 19, 700–710 (2015).
69. Botvinick, M. et al. Reinforcement learning, fast and slow. Trends Cogn. Sci. 23, 408–
422 (2019).
70. Kool, W. & Botvinick, M. Mental labour. Nat. Hum. Behav. (2018).
doi:10.1038/s41562-018-0401-9
71. Suddendorf, T. & Corballis, M. C. Mental time travel and the evolution of the human
mind. Genet. Soc. Gen. Psychol. Monogr. 123, 133–167 (1997).
72. Redshaw, J. Does metarepresentation make human mental time travel unique? Wiley
Interdiscip. Rev. Cogn. Sci. 5, 519–531 (2014).
73. Redshaw, J. & Suddendorf, T. Temporal junctures in the mind. Trends in Cognitive
22
Sciences (2020).
74. Bulley, A., Redshaw, J. & Suddendorf, T. The future-directed functions of the
imagination: From prediction to metaforesight. in The Cambridge Handbook of the
Imagination (ed. Abraham, A.) 1–50 (Cambridge University Press, 2019).
75. Ernst, A. & D’Argembeau, A. Make it real: Belief in occurrence within episodic future
thought. Mem. Cognit. 45, 1045–1061 (2017).
76. Ernst, A., Scoboria, A. & D’Argembeau, A. On the role of autobiographical knowledge
in shaping belief in the future occurrence of imagined events. Q. J. Exp. Psychol.
174702181985562 (2019). doi:10.1177/1747021819855621
77. Jing, H. G., Madore, K. P. & Schacter, D. L. Preparing for what might happen: An
episodic specificity induction impacts the generation of alternative future events.
Cognition 169, 118–128 (2017).
78. Lempert, K. M. & Phelps, E. a. The malleability of intertemporal choice. Trends Cogn.
Sci. 20, 64–74 (2016).
79. Loewenstein, G. & Thaler, R. H. Anomalies: intertemporal choice. J. Econ. Perspect.
3, 181–193 (1989).
80. Loewenstein, G. & Elster, J. Choice over time. (Russell Sage Foundation, 1992).
81. Mele, A. R. Irrationality: An essay on akrasia, self-deception, and self-control.
(Oxford University Press, 1987).
82. Read, D., Loewenstein, G. & Kalyanaraman, S. Mixing virtue and vice: Combining the
immediacy effect and the diversification heuristic. J. Behav. Decis. Mak. 12, 257–273
(1999).
83. Berns, G. S., Laibson, D. & Loewenstein, G. Intertemporal choice - toward an
integrative framework. Trends Cogn. Sci. 11, 482–488 (2007).
84. Mazur, J. E. An adjusting procedure for studying delayed reinforcement. Commons,
ML.; Maz. JE.; Nevin, JA 55–73 (1987).
85. Stevens, J. R. Intertemporal Similarity: Discounting as a Last Resort. J. Behav. Decis.
Mak. 29, 12–24 (2016).
86. Amasino, D. R., Sullivan, N. J., Kranton, R. E. & Huettel, S. A. Amount and time exert
independent influences on intertemporal choice. Nat. Hum. Behav. 3, 383–392 (2019).
87. Laibson, D. Golden eggs and hyperbolic discounting. Q. J. Econ. 112, 443–447 (1997).
88. Rubinstein, A. ‘Economics and Psychology’? The Case of Hyperbolic Discounting.
Int. Econ. Rev. (Philadelphia). 44, 1207–1216 (2003).
89. Bischof-Köhler, D. Zur phylogenese menschlicher motivation. (1985).
23
90. Bischof, N. Das Rätsel Ödipus. (Piper, 1985).
91. Suddendorf, T. & Busby, J. Making decisions with the future in mind: Developmental
and comparative identification of mental time travel. Learn. Motiv. 36, 110–125
(2005).
92. Redshaw, J., Bulley, A. & Suddendorf, T. Thinking about thinking about time. Behav.
Brain Sci. (2019).
93. Frankfurt, H. G. Freedom of the will and the concept of a person. in What Is a Person?
127–144 (Springer, 1988).
94. Elster, J. Ulysses unbound: Studies in rationality, precommitment, and constraints.
(Cambridge University Press, 2000).
95. Kurth-Nelson, Z. & Redish, A. D. Dont let me do that! - models of precommitment.
Front. Neurosci. 6, 1–9 (2012).
96. Kurth-Nelson, Z. & Redish, A. D. A reinforcement learning model of precommitment
in decision making. Front. Behav. Neurosci. 4, 1–13 (2010).
97. Risko, E. F. & Gilbert, S. J. Cognitive offloading. Trends Cogn. Sci. 20, 676–688
(2016).
98. Risko, E. F. & Dunn, T. L. Storing information in-the-world: Metacognition and
cognitive offloading in a short-term memory task. Conscious. Cogn. 36, 61–74 (2015).
99. Gilbert, S., Carpenter, J., Fleming, S., Tsai, P.-C. & Bird, A. Optimal use of reminders:
Metacognition, effort, and cognitive offloading. Psyarxiv 44, 1–39 (2018).
100. Redshaw, J., Vandersee, J., Bulley, A. & Gilbert, S. J. Development of children’s use of
external reminders for hard-to-remember intentions. Child Dev. 89, 2099–2108 (2018).
101. Paglieri, F. Ulysses’ will: self-control, external constraints, and games. in
Consciousness in interaction: the role of the natural and social context in shaping
consciousness (ed. Paglieri, F.) (John Benjamins Publishing, 2012).
102. Hu, X., Luo, L. & Fleming, S. M. A role for metamemory in cognitive offloading.
Cognition 193, 104012 (2019).
103. Laibson, B. D. Why don’t present-biased agents make commitments. Am. Econ. Rev.
Pap. Proc. 105, 267–272 (2015).
104. Loewenstein, G. F. Anticipation and the valuation of delayed consumption. Econ. J.
97, 666–684 (1987).
105. Bulley, A., Henry, J. D. & Suddendorf, T. Thinking about threats: Memory and
prospection in human threat management. Conscious. Cogn. 49, 53–69 (2017).
106. Jing, H. G., Madore, K. P. & Schacter, D. L. Worrying about the future: an episodic
24
specificity induction impacts problem solving, reappraisal, and well-being. J. Exp.
Psychol. Gen. 145, 402–418 (2016).
107. Miloyan, B., Pachana, N. a & Suddendorf, T. The future is here: a review of foresight
systems in anxiety and depression. Cogn. Emot. 28, 795–810 (2014).
108. Damasio, A. R. Looking for Spinoza: Joy, sorrow, and the feeling brain. (Random
House, 2004).
109. Miloyan, B., Bulley, A. & Suddendorf, T. Anxiety: here and beyond. Emot. Rev.
(2018). doi:10.1177/1754073917738570
110. Jimura, K., Chushak, M. S. & Braver, T. S. Impulsivity and self-control during
intertemporal decision making linked to the neural dynamics of reward value
representation. J. Neurosci. 33, 344–357 (2013).
111. Hardisty, D. J. Kisses vs. shocks: Contemplation asymmetries (partly) explain why
negative events are discounted less than positive events. SSRN Electron. J. 1–47
(2012).
112. Thaler, R. Some empirical evidence on dynamic inconsistency. Econ. Lett. 8, 201–207
(1981).
113. Story, G. et al. Dread and the disvalue of future pain. PLoS Comput Biol 9, e1003335
(2013).
114. Berns, G. S. et al. Neurobiological substrates of dread. Science (80-. ). 312, 754–758
(2006).
115. Molouki, S., Hardisty, D. J. & Caruso, E. M. The sign effect in past and future
discounting. Psychol. Sci. 30, 1674–1695 (2019).
116. Corballis, M. C. The recursive mind: The origins of human language, thought, and
civilization. (Princeton University Press, 2011).
117. Schacter, D. L., Benoit, R. G., De Brigard, F. & Szpunar, K. K. Episodic future
thinking and episodic counterfactual thinking: Intersections between memory and
decisions. Neurobiol. Learn. Mem. 117, 14–21 (2015).
118. De Brigard, F. & Parikh, N. Episodic counterfactual thinking. Curr. Dir. Psychol. Sci.
28, 59–66 (2019).
119. Hoerl, C. & McCormack, T. Making decisions about the future: regret and the
cognitive function of episodic memory. in Seeing the Future: Theoretical Perspectives
on Future-Oriented Mental Time Travel (eds. Michaelian, K., Klein, S. B. & Szpunar,
K. K.) 241–266 (Oxford University Press, 2016).
120. Coricelli, G. et al. Regret and its avoidance: A neuroimaging study of choice behavior.
25
Nat. Neurosci. 8, 1255–1262 (2005).
121. Palombo, D. J., Keane, M. M. & Verfaellie, M. How do lesion studies elucidate the
role of the hippocampus in intertemporal choice? Hippocampus 25, 407–408 (2015).
122. Schacter, D. L., Benoit, R. G. & Szpunar, K. K. Episodic future thinking: mechanisms
and functions. Curr. Opin. Behav. Sci. 17, 41–50 (2017).
123. Rung, J. M. & Madden, G. J. Experimental reductions of delay discounting and
impulsive choice: A systematic review and meta-analysis. J. Exp. Psychol. Gen. 147,
1349–13831 (2018).
124. Boyer, P. Evolutionary economics of mental time travel? Trends Cogn. Sci. 12, 219–
224 (2008).
125. Bulley, A. et al. Cuing both positive and negative episodic foresight reduces delay
discounting but does not affect risk-taking. Q. J. Exp. Psychol. 72, 1998–2017 (2019).
126. Lempert, K. M., Speer, M. E., Delgado, M. R. & Phelps, E. A. Positive
autobiographical memory retrieval reduces temporal discounting. Soc. Cogn. Affect.
Neurosci. 12, 1584–1593 (2017).
127. Ciaramelli, E., Sellitto, M., Tosarelli, G. & Pellegrino, G. Imagining Events Alternative
to the Present Can Attenuate Delay Discounting. Front. Behav. Neurosci. 13, 1–9
(2019).
128. O’Donnell, S., Oluyomi Daniel, T. & Epstein, L. H. Does goal relevant episodic future
thinking amplify the effect on delay discounting? Conscious. Cogn. 51, 10–16 (2017).
129. Liu, L., Feng, T., Chen, J. & Li, H. The value of emotion: How does episodic
prospection modulate delay discounting? PLoS One 8, (2013).
130. Zhang, S., Peng, J., Qin, L., Suo, T. & Feng, T. Prospective emotion enables episodic
prospection to shift time preference. Br. J. Psychol. 1–13 (2018).
doi:10.1111/bjop.12284
131. Calluso, C., Tosoni, A., Cannito, L. & Committeri, G. Concreteness and emotional
valence of episodic future thinking ( EFT ) independently affect the dynamics of
intertemporal decisions. PLoS One 1–22 (2019). doi:10.6084/m9.figshare.7072568
132. Frankenhuis, W. E., Panchanathan, K. & Nettle, D. Cognition in harsh and
unpredictable environments. Curr. Opin. Psychol. 7, 76–80 (2016).
133. Bulley, A., Pepper, G. & Suddendorf, T. Using foresight to prioritise the present.
Behav. Brain Sci. 40, (2017).
134. Paglieri, F. Social choice for one: On the rationality of intertemporal decisions. Behav.
Processes 127, 97–108 (2016).
26
135. Kivetz, R. & Keinan, A. Repenting hyperopia: An analysis of self-control regrets. J.
Consum. Res. 33, 273–282 (2006).
136. Kivetz, R. & Simonson, I. Self-control for the righteous: Toward a theory of
precommitment to indulgence. J. Consum. Res. 29, 199–217 (2002).
137. Michaelson, L., de la Vega, A., Chatham, C. & Munakata, Y. Delaying gratification
depends on social trust. Front. Psychol. 4, (2013).
138. Jachimowicz, J. M., Chafik, S., Munrat, S., Prabhu, J. C. & Weber, E. U. Community
trust reduces myopic decisions of low-income individuals. Proc. Natl. Acad. Sci. U. S.
A. 114, 5401–5406 (2017).
139. Bulley, A. & Pepper, G. V. Cross-country relationships between life expectancy,
intertemporal choice and age at first birth. Evol. Hum. Behav. 38, 652–658 (2017).
140. Lee, A. J., DeBruine, L. & Jones, B. C. Individual-specific mortality is associated with
how individuals evaluate future discounting decisions. (2018).
doi:10.17605/OSF.IO/FGVQM
141. Falk, A. et al. The nature and predictive power of preferences: Global evidence. IZA
Discuss. Pap. Ser. 9504, 1–76 (2015).
142. Mischel, W., Shoda, Y. & Rodriguez, M. I. Delay of gratification in children. Science
(80-. ). 244, 933–938 (1989).
143. Kidd, C., Palmeri, H. & Aslin, R. N. Rational snacking: Young children’s decision-
making on the marshmallow task is moderated by beliefs about environmental
reliability. Cognition 126, 109–114 (2013).
144. Dai, J., Pachur, T., Pleskac, T. J. & Hertwig, R. What the future holds and when: A
description–experience gap in intertemporal choice. Psychol. Sci. 30, 1218–1233
(2019).
145. Dai, J., Pachur, T., Pleskac, T. J. & Hertwig, R. Tomorrow never knows - why and how
uncertainty matters in intertemporal choice. in Taming Uncertainty (eds. Hertwig, R.,
Pleskac, T. J. & Pachur, T.) 175–189 (The MIT Press, 2019).
146. McGuire, J. T. & Kable, J. W. Rational temporal predictions can underlie apparent
failures to delay gratification. Psychol. Rev. 120, 395–410 (2013).
147. Amlung, M. et al. Delay discounting as a transdiagnostic process in psychiatric
disorders. JAMA Psychiatry 1–11 (2019). doi:10.1001/jamapsychiatry.2019.2102
148. King, J. A. et al. Intact value-based decision-making during intertemporal choice in
women with remitted anorexia nervosa? An fMRI study. J. Psychiatry Neurosci. 44,
180252 (2019).
27
149. Decker, J. H., Figner, B. & Steinglass, J. E. On weight and waiting: Delay discounting
in anorexia nervosa pretreatment and posttreatment. Biol. Psychiatry 78, 606–614
(2015).
150. Foerde, A. K., Daw, N. D., Rufin, T. & Walsh, B. T. Deficient goal-directed control in
a population characterized by extreme goal pursuit. 1–32 (2019).
151. Solway, A., Lohrenz, T. & Montague, P. R. Simulating future value in intertemporal
choice. Sci. Rep. 7, 43119 (2017).
152. Parthasarathi, T., McConnell, M. H., Luery, J. & Kable, J. W. The vivid present:
Visualization abilities are associated with steep discounting of future rewards. Front.
Psychol. 8, 289 (2017).
153. Wiehler, A., Bromberg, U. & Peters, J. The role of prospection in steep temporal
reward discounting in gambling addiction. Front. Psychiatry 6, (2015).
154. Hunter, L. E., Bornstein, A. M. & Hartley, C. A. A common deliberative process
underlies model-based planning and patient intertemporal choice. bioRxiv 499707
(2018). doi:10.1101/499707
155. Radu, P. T., Yi, R., Bickel, W. K., Gross, J. J. & McClure, S. M. A mechanism for
reducing delay discounting by altering temporal attention. J. Exp. Anal. Behav. 96,
363–385 (2011).
156. Rung, J. M., Peck, S., Hinnenkamp, J. E., Preston, E. & Madden, G. J. Changing delay
discounting and impulsive choice: implications for addictions, prevention, and human
health. Perspect. Behav. Sci. 42, 1–21 (2019).
157. Read, D., Olivola, C. Y. & Hardisty, D. J. The value of nothing: Asymmetric attention
to opportunity costs drives intertemporal decision making. Manage. Sci. 63, 4277–
4297 (2016).
158. Jenkins, A. C. & Hsu, M. Dissociable contributions of imagination and willpower to
the malleability of human patience. Psychol. Sci. 28, 894–906 (2017).
159. Ballard, I. C. et al. More Is meaningful: The magnitude effect in intertemporal choice
depends on self-control. Psychol. Sci. 28, 1443–1454 (2017).
160. Gershman, S. J. & Bhui, R. Rationally inattentive intertemporal choice. bioRxiv
680652 (2019). doi:10.1101/680652
161. Gabaix, X. & Laibson, D. Myopia and discounting. NBER Work. Pap. Ser. (2017).
162. Buckner, R. L. & DiNicola, L. M. The brain’s default network: updated anatomy,
physiology and evolving insights. Nat. Rev. Neurosci. 20, 593–608 (2019).
163. Benoit, R. G. & Schacter, D. L. Specifying the core network supporting episodic
28
simulation and episodic memory by activation likelihood estimation.
Neuropsychologia 75, 450–457 (2015).
164. Soutschek, A. et al. Binding oneself to the mast: stimulating frontopolar cortex
enhances precommitment. Soc. Cogn. Affect. Neurosci. 12, 635–642 (2017).
165. Crockett, M. J. et al. Restricting temptations: Neural mechanisms of precommitment.
Neuron 79, 391–401 (2013).
166. Addis, D. R., Wong, A. T. & Schacter, D. L. Remembering the past and imagining the
future: Common and distinct neural substrates during event construction and
elaboration. Neuropsychologia 45, 1363–1377 (2007).
167. De Martino, B., Fleming, S. M., Garrett, N. & Dolan, R. J. Confidence in value-based
choice. Nat. Neurosci. 16, 105–110 (2012).
168. Burgess, P. W., Dumontheil, I. & Gilbert, S. J. The gateway hypothesis of rostral
prefrontal cortex (area 10) function. Trends Cogn. Sci. 11, 290–298 (2007).
169. Boorman, E. D., Behrens, T. E. J., Woolrich, M. W. & Rushworth, M. F. S. How green
is the grass on the other side? Frontopolar cortex and the evidence in favor of
alternative courses of action. Neuron 62, 733–743 (2009).
170. Koechlin, E. & Hyafil, A. Anterior prefrontal function and the limits of human
decision-making. Science (80-. ). 318, 594–598 (2007).
171. D’Argembeau, A. On the Role of the Ventromedial Prefrontal Cortex in Self-
Processing: The Valuation Hypothesis. Front. Hum. Neurosci. 7, 372 (2013).
172. Iigaya, K. et al. Hippocampal-midbrain circuit enhances the pleasure of anticipation in
the prefrontal cortex. bioRxiv 588699 (2019). doi:10.1101/588699
173. Benoit, R. G., Gilbert, S. J. & Burgess, P. W. A neural mechanism mediating the
impact of episodic prospection on farsighted decisions. J. Neurosci. 31, 6771–6779
(2011).
174. Peters, J. & Büchel, C. Episodic future thinking reduces reward delay discounting
through an enhancement of prefrontal-mediotemporal interactions. Neuron 66, 138–
148 (2010).
175. Sasse, L. K., Peters, J. & Brassen, S. Cognitive control modulates effects of episodic
simulation on delay discounting in aging. Front. Aging Neurosci. 9, 1–11 (2017).
176. Hu, X., Kleinschmidt, H., Martin, J. A., Han, Y. & Thelen, M. A reduction in delay
discounting by using episodic future imagination and the association with episodic
memory capacity. 10, 1–20 (2017).
177. Cooper, N., Kable, J. W., Kim, B. K. & Zauberman, G. Brain activity in valuation
29
regions while thinking about the future predicts individual discount rates. J. Neurosci.
33, 13150–6 (2013).
178. Hakimi, S. & Hare, T. a. Enhanced neural responses to imagined primary rewards
predict reduced monetary temporal discounting. J. Neurosci. 35, 13103–13109 (2015).
179. Lee, S., Parthasarathi, T. & Kable, J. W. Neural predictor of concreteness also predicts
temporal proximity in intertemporal choice. Unpubl. Prepr. (2019).
180. Shadlen, M. N. N. & Shohamy, D. Decision Making and Sequential Sampling from
Memory. Neuron 90, 927–939 (2016).
181. Rodriguez, C. A., Turner, B. M. & McClure, S. M. Intertemporal choice as discounted
value accumulation. PLoS One 9, (2014).
182. Peters, J. & D’Esposito, M. The drift diffusion model as the choice rule in inter-
temporal and risky choice: a case study in medial orbitofrontal cortex lesion patients
and controls. bioRxiv 642587 (2019). doi:10.1101/642587
183. Zhao, W. J., Diederich, A., Trueblood, J. S. & Bhatia, S. Automatic biases in
intertemporal choice. Psychon. Bull. Rev. 26, 661–668 (2019).
184. Noël, X., Jaafari, N. & Bechara, A. Addictive behaviors: Why and how impaired
mental time matters? 235, 219–237 (2017).
185. Redish, A. D., Jensen, S. & Johnson, A. A unified framework for addiction:
Vulnerabilities in the decision process. Behav. Brain Sci. 31, 415–437 (2008).
186. Bulley, A. & Irish, M. The functions of prospection: Variations in health and disease.
Front. Psychol. 9, 1–8 (2018).
187. Duckworth, A. L., Milkman, K. L. & Laibson, D. Beyond willpower: strategies for
reducing failures of self-control. Psychol. Sci. Public Interest 19, 102–129 (2018).
188. Bertoux, M., de Souza, L. C., Zamith, P., Dubois, B. & Bourgeois-Gironde, S.
Discounting of future rewards in behavioural variant Frontotemporal Dementia &
Alzheimer’s Disease. Neuropsychology 29, 933–939 (2015).
189. Irish, M., Hodges, J. R. & Piguet, O. Episodic future thinking is impaired in the
behavioural variant of frontotemporal dementia. Cortex 49, 2377–2388 (2013).
190. Svoboda, E. & Richards, B. Compensating for anterograde amnesia: A new training
method that capitalizes on emerging smartphone technologies. J. Int. Neuropsychol.
Soc. 15, 629–638 (2009).
191. McDonald, A. et al. Google calendar: A memory aid to manage prospective memory
deficits following acquired brain injury. Neuropsychol. Rehabil. 21, 784–807 (2011).
192. Clark, A. & Chalmers, D. The extended mind. Analysis 58, 7–19 (1998).
30
193. O’Neill, J., Daniel, T. O. & Epstein, L. H. Episodic future thinking reduces eating in a
food court. Eat. Behav. (2015). doi:10.1016/j.eatbeh.2015.10.002
194. Stein, J. S. et al. Unstuck in time: episodic future thinking reduces delay discounting
and cigarette smoking. Psychopharmacology (Berl). 233, 3771–3778 (2016).
195. Bulley, A. & Gullo, M. J. The influence of episodic foresight on delay discounting and
demand for alcohol. Addict. Behav. 66, 1–6 (2017).
196. Gollwitzer, P. M. Weakness of the will: Is a quick fix possible? Motiv. Emot. 38, 305–
322 (2014).
197. Carpenter, J. et al. Domain-general enhancements of metacognitive ability through
adaptive training. J. Exp. Psychol. Gen. 148, 51–64 (2019).
Acknowledgements
We thank R. Bhui, N. Brashier, T. Cochard, C. Conwell, D. Bulley, B. Leahy, J. Mahr,
H. Pailian, D. Palombo, J. Redshaw, T. Suddendorf and M. Wilks for helpful discussions and
comments on a previous draft of this article. AB is supported by an Australian National
Health and Medical Research Council CJ Martin Biomedical Fellowship APP1162811. DLS
is supported by National Institute of Mental Health grant R01 MH060941 and National
Institute on Aging grant R01 AG008441.
Author Contributions
AB and DLS contributed to writing the manuscript.
Competing Interests
The authors declare no competing interests.
31
... An increased preference for smaller but sooner (especially immediate) rewards is considered as an index of impulsivity and low self-control [9,11,12]. Accordingly, increased delay discounting has been proposed as a trans-disease process [13,14] and has been documented in several clinical populations typically associated with impulsive behavior [15], such as drug addiction [16][17][18][19][20], attention deficit hyperactivity disorder [21], compulsive gambling [22], and obesity [23][24][25][26]. However, delay discounting is also relevant in contexts that go beyond the pure clinical practice. ...
... However, delay discounting is also relevant in contexts that go beyond the pure clinical practice. Increased delay discounting has indeed been proposed as a useful marker of poor behavioral patterns in situations where, in fact, favoring distant goals over immediate gratification is extremely crucial, such as climate change, welfare policies action, and financial decisions [15,[27][28][29][30][31]. ...
... In this sense, the present findings open a path to applications in both clinical and non-clinical contexts. Indeed, increased delay discounting is classically associated with maladaptive decision-making [3][4][5][6][7][8][9][10][11][12][13][14][15][16], whereas self-control is correlated with beneficial outcomes in terms of financial stability [70,71], academic achievement [72,73], social success [74,75], healthy living [76], and wellbeing [77,78]. In the same vein, research on delay of gratification revealed that high-delayers, as compared to low-delayers, show better inhibitory control (associated with more activated prefrontal cortex in tasks demanding increased inhibition) and that, during childhood, it can predict life outcomes [1]. ...
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... We believe that the primary psychological mechanism here is simulation, with stimulation being the outcome; and it is in this context that we introduce the idea of prospection in our updated model. Prospection is the ability to pre-experience the future by simulating it in our minds (Gilbert and Wilson, 2007) and involves the representation and evaluation of possible futures (Bulley and Schacter, 2020). Prospection can lead to fantasies (Oettingen and Mayer, 2002) and sense-making (Vazeou-Nieuwenhuis et al., 2017). ...
... Studies in neuroscience (Denny et al., 2014;Jack et al., 2013a, b) that have focused on the neural correlates of imagining the future self have revealed the close relationship of what is meaningful in the present with what is aspirational for the future. Prospection or episodic future thinking (Seligman et al., 2013) is a plausible candidate contributor to affective forecasting, motivation in goal pursuit and the explicit evaluation of choice outcomes (Bulley and Schacter, 2020). In other words, it is the act of prospection that allows a person to conjure up a vision of their ideal self. ...
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... Moreover, the link between WM and DRD is also supported by studies suggesting related developmental trajectories, with a progressive (but not necessarily linear) improvement from preschool age to adolescence (e.g., Best & Miller, 2010;Fernández Garcia, Merchán, Phillips-Silver, & Daza González, 2021;Steinberg et al., 2009; but see Kouklari, Tsermentseli, & Monks, 2018). At a psychological level, intertemporal decision making (ITD) might be suggested to require thinking through or mentally simulating the choice options (see also Bulley & Schacter, 2020), combining and updating information about both reward and delay. Arguably, such mental operations require the ability to maintain and manipulate information in memory or, in other words, WM. ...
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... Importantly, the stayers showed a stronger AU effect in the HPC, whereas in the explores this effect was stronger in the vlPFC ( Fig. 4c; Supplementary Fig. 6d). It is interesting that in the foraging trials, a stronger AU effect in the HPC was also observed in the stayers, suggesting that the stay strategy may be guided by HPC-dependent encoding and reproduction of anticipation of a future reward 42,43,47 . ...
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... Recent research in MCI and AD demonstrates that graded disruptions to both episodic memory and episodic future thinking are associated with steeper delay discounting in these patients [166,167]. In healthy younger adults, promising intervention studies show that cuing participants to imagine future outcomes while making intertemporal choices can reduce delay discounting, both for money as well as for other commodities, such as food, cigarettes, or alcohol [176,[189][190][191]. However, older adults [192] and individuals with subjective cognitive decline [193] who exhibit deficits in future imagining show less susceptibility to this episodic future cueing effect. ...
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Background: Extreme restrictive food choice in anorexia nervosa is thought to reflect excessive self-control and/or abnormal reward sensitivity. Studies using intertemporal choice paradigms have suggested an increased capacity to delay reward in anorexia nervosa, and this may explain an unusual ability to resist immediate temptation and override hunger in the long-term pursuit of thinness. It remains unclear, however, whether altered delay discounting in anorexia nervosa constitutes a state effect of acute illness or a trait marker observable after recovery. Methods: We repeated the analysis from our previous fMRI investigation of intertemporal choice in acutely underweight patients with anorexia nervosa in a sample of weight-recovered women with anorexia nervosa (n = 36) and age-matched healthy controls (n = 36) who participated in the same study protocol. Follow-up analyses explored functional connectivity separately in both the weight-recovered/healthy controls sample and the acute/healthy controls sample. Results: In contrast to our previous findings in acutely underweight patients with anorexia nervosa, we found no differences between weight-recovered patients with anorexia nervosa and healthy controls at either behavioural or neural levels. New analysis of data from the acute/healthy controls sample sample revealed increased coupling between dorsal anterior cingulate cortex and posterior brain regions as a function of decision difficulty, supporting the hypothesis of altered neural efficiency in the underweight state. Limitations: This was a cross-sectional study, and the results may be task-specific. Conclusion: Although our results underlined previous demonstrations of divergent temporal reward discounting in acutely underweight patients with anorexia nervosa, we found no evidence of alteration in patients with weight-recovered anorexia nervosa. Together, these findings suggest that impaired valuebased decision-making may not constitute a defining trait variable or “scar” of the disorder.
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Discoveries over the past two decades demonstrate that regions distributed throughout the association cortex, often called the default network, are suppressed during tasks that demand external attention and are active during remembering, envisioning the future and making social inferences. This Review describes progress in understanding the organization and function of networks embedded within these association regions. Detailed high-resolution analyses of single individuals suggest that the default network is not a single network, as historically described, but instead comprises multiple interwoven networks. The multiple networks share a common organizational motif (also evident in marmoset and macaque anatomical circuits) that might support a general class of processing function dependent on internally constructed rather than externally constrained representations, with each separate interwoven network specialized for a distinct processing domain. Direct neuronal recordings in humans and monkeys reveal evidence for competitive relationships between the internally and externally oriented networks. Findings from rodent studies suggest that the thalamus might be essential to controlling which networks are engaged through specialized thalamic reticular neurons, including antagonistic subpopulations. These association networks (and presumably thalamocortical circuits) are expanded in humans and might be particularly vulnerable to dysregulation implicated in mental illness.
Article
Importance Delay discounting is a behavioral economic index of impulsive preferences for smaller-immediate or larger-delayed rewards that is argued to be a transdiagnostic process across health conditions. Studies suggest some psychiatric disorders are associated with differences in discounting compared with controls, but null findings have also been reported. Objective To conduct a meta-analysis of the published literature on delay discounting in people with psychiatric disorders. Data Sources PubMed, MEDLINE, PsycInfo, Embase, and Web of Science databases were searched through December 10, 2018. The psychiatric keywords used were based on DSM-IV or DSM-5 diagnostic categories. Collected data were analyzed from December 10, 2018, through June 1, 2019. Study Selection Following a preregistered Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol, 2 independent raters reviewed titles, abstracts, and full-text articles. English-language articles comparing monetary delay discounting between participants with psychiatric disorders and controls were included. Data Extraction and Synthesis Hedges g effect sizes were computed and random-effects models were used for all analyses. Heterogeneity statistics, one-study-removed analyses, and publication bias indices were also examined. Main Outcomes and Measures Categorical comparisons of delay discounting between a psychiatric group and a control group. Results The sample included 57 effect sizes from 43 studies across 8 diagnostic categories. Significantly steeper discounting for individuals with a psychiatric disorder compared with controls was observed for major depressive disorder (Hedges g = 0.37; P = .002; k = 7), schizophrenia (Hedges g = 0.46; P = .004; k = 12), borderline personality disorder (Hedges g = 0.60; P < .001; k = 8), bipolar disorder (Hedges g = 0.68; P < .001; k = 4), bulimia nervosa (Hedges g = 0.41; P = .001; k = 4), and binge-eating disorder (Hedges g = 0.34; P = .001; k = 7). In contrast, anorexia nervosa exhibited statistically significantly shallower discounting (Hedges g = –0.30; P < .001; k = 10). Modest evidence of publication bias was indicated by a statistically significant Egger test for schizophrenia and at the aggregate level across studies. Conclusions and Relevance Results of this study appear to provide empirical support for delay discounting as a transdiagnostic process across most of the psychiatric disorders examined; the literature search also revealed limited studies in some disorders, notably posttraumatic stress disorder, which is a priority area for research.