Available online at www.sciencedirect.com
ScienceDirect
When is a loss a loss? Excitatory and inhibitory
processes in loss-related decision-making
Ben Seymour1,2,3,4, Masaki Maruyama2,3 and
Benedetto De Martino4
One of the puzzles in neuroeconomics is the inconsistent
pattern of brain response seen in the striatum during evaluation
of losses. In some studies striatal responses appear to
represent loss as a negative reward (BOLD deactivation), while
in others as positive punishment (BOLD activation). We argue
that these discrepancies can be explained by the existence of
two fundamentally different types of loss: excitatory losses
signaling the presence of substantive punishment, and
inhibitory losses signaling cessation or omission of reward. We
then map different theories of motivational opponency to loss
related decision-making, and highlight five distinct underlying
computational processes. We suggest that this excitatory–
inhibitory model of loss provides a neurobiological framework
for understanding reference dependence in behavioral
economics.
Addresses
1
Computational and Biological Learning Laboratory, Department of
Engineering, University of Cambridge CB2 1PZ, United Kingdom
2
Center for Information and Neural Networks, National Institute for
Information and Communications Technology, 1-4 Yamadaoka, Suita
City, Osaka 565-0871, Japan
3
Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Osaka 565-0871, Japan
4
Department of Psychology, University of Cambridge, Downing Street,
Cambridge CB2 3EB, United Kingdom
Corresponding author: Seymour, Ben (bjs49@cam.ac.uk)
Current Opinion in Behavioral Sciences 2015, 5:122–127
This review comes from a themed issue on Neuroeconomics
Edited by John P O’Doherty and Colin C Camerer
For a complete overview see the Issue and the Editorial
Available online 3rd October 2015
http://dx.doi.org/10.1016/j.cobeha.2015.09.003
2352-1546/# 2015 Elsevier Ltd. All rights reserved.
Introduction
Over the past decade a set of divergent observations have
emerged in human neuroimaging studies of monetary
loss. In studies of the receipt (or prospect) of financial
loss, neuroimaging responses sometimes exhibit deactivation in BOLD signal of striatal brain areas associated
with motivation and decision-making (caudate, putamen,
and nucleus accumbens) [1,2,3,4], or little change at all
[5]. This has often been seen as consistent with a primary
Current Opinion in Behavioral Sciences 2015, 5:122–127
role of these regions in reward-related processing, and
these negative responses are usually seen in the same
regions showing activation to monetary gains. With
emerging evidence that striatal BOLD responses to reward were well described by prediction error activity in
the context of passive prediction tasks (Pavlovian learning), it was generally assumed that this activity represented a single reward-specific and putatively dopaminerelated signal [6,7].
However, this theory suffered when other studies involving loss, and especially involving primary punishments
such as pain, revealed positive activation in the striatum,
in very similar regions to those that showed deactivations
to financial loss [8,9]. Furthermore, the pattern of activity
resembled a prediction error, just like a reward prediction
with the opposite sign. This suggested that either the
striatum was encoding a more complex signal than originally thought — perhaps some sort of selective salience
signal [10], or that there was a second system for encoding
aversive outcomes that comes online with physical, but
less so financial, punishment. Why financial loss might
less reliably activate this system was unclear, but one
could posit it might be related to the fact that physical
punishments are primary outcomes ‘consumed’ immediately, whereas money is a secondary outcome whose
real outcome is fulfilled at a later date. A more reliable
way to ‘activate’ the striatum to loss was introduced with
a clever design from Delgado and colleagues: they had
subjects begin the experiment with a task in which
subjects could earn a decent sized money pot. Then
in a second, seemingly unrelated experiment, they
underwent a loss-conditioning study, which revealed
positive activation to monetary loss [11]. This result,
together with a more recent one [12], suggest that losing
money that had been earned on a previous task in some
way rendered it sufficient to reliably activate positive
aversive coding.
This raises the question as to what makes a loss look
sometimes primarily like a negative reward, and at other
times like a positive punishment. This is important,
because if there are substantially different ways of representing losses in the brain, then the associated loss
behavior may have very different characteristics.
To make matters more complex, subsequent studies involving the capacity to make active choices over monetary
loss or pain, that is, reducing or avoiding punishment, did
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When is a loss a loss? Seymour, Maruyama and De Martino 123
Animal learning theory provides a structured approach to
understanding the relationship between gains and losses,
and there is good evidence of the existence of two
separate motivational pathways for outcome prediction:
one governing rewards, and the other governing punishments [17]. In particular, accounts of interaction between
the two systems yield two distinct types of punishment:
excitatory, and inhibitory, depending on the context that
defines the nature of punishment. Accordingly, inhibitory
values emerge from two different instances for appetitiveaversive opponency: omission (Konorskian [18]) opponency, and offset (Solomon–Corbit [19]) opponency
(Figure 1).
Omission opponency describes the frustrative loss that
occurs when an expected reward does not occur. Here,
excitatory losses are due to the positive presence of a
punishment, and inhibitory losses due to absence of an
expected gain. A slightly different type of frustrative loss
occurs when a tonically presented reward terminates. In
this case, Solomon and Corbitt proposed the accrual of a
slow adaptive process from which acute changes were
compared. Both processes illustrate the clear distinction
between excitatory and inhibitory losses, with the inhibitory type being generated either comparison of neutral
outcome with an expectation of or tonic baseline level of
reward. This is exactly mirrored in the opposite valence:
inhibitory reward being evoked with the relief at the
termination or omission of punishment [20].
The existence of different types of loss offers an explanation into the pattern of brain responses seen above. In
most experiments, for ethical and practical reasons, loss is
operationalized by a reduction in the participant monetary compensation (future expected reward). This procedure would augment the inhibitory loss representation
and therefore tend toward a deactivation in striatal areas.
However, for primary punishments and financial outcomes that were already considered ‘owned’, we would
expect a dominant excitatory loss representation, and
activation of an aversive system observable in the striatum. In many situations, it may be that excitatory and
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Excitatory and Inhibitory Losses
excitatory
Lose $
(excitatory loss)
Win $
(excitatory reward)
inhibitory
Excitatory and inhibitory loss
Figure 1
Loss signal
Loss
omitted
Win signal
Win
omitted
(Konorskian relief)
(Konorskian frustration)
Losses
inhibitory
not fit either pattern simply. Here, for both money and
pain, striatal activity shows positive activation for avoidance actions and avoided outcomes [13,14]. Rather than
representing the magnitude of the expected punishment
(in probabilistic avoidance), it seemed to represent the
relative positive value of avoidance [15]. That is, activity
again looks like a reward signal — this time for actions,
with no consistent evidence of an aversive striatal system in
operation, even for painful outcomes. A positive aversive
signal is sometimes seen elsewhere, such as the anterior
insula cortex [16], but its contribution to decision making
was less clear.
Wins
Loss
omitted
(Solomon Corbit relief)
Win
omitted
(Solomon Corbit frustration)
Current Opinion in Behavioral Sciences
Excitatory and inhibitory processes underlying reward and
punishment. Excitatory values occur with the receipt, or prediction of
receipt, of a primary reward or punishment. Inhibitory values occur
with either the omission of an expected outcome (e.g. requiring, of
course, an expectation to be generated by some process, such as
Pavlovian Conditioning), or with the termination of a tonic or
repetitively received outcome.
inhibitory processes co-occur [21], and hence partially or
fully cancel out the subsequent fMRI BOLD response.
From loss prediction to decision-making
How then, is loss-related computation related to decisionmaking? Clearly, what motivates choice in the context of
any type of loss is a desire to reduce it, and this can be
used to define loss or punishment. The Konorskian and
Solomon–Corbitt framework deals with passive (Pavlovian) predictions, but are conventionally thought to govern
two distinct types of aversive decision: avoidance, and
escape [22,23]. The control of escape and avoidance may
be different, because the nature by which information
about outcomes is garnered is different, but in both cases
it is the absence of punishment that motivates behavior.
The paradox created by the ability of ‘nothing’ to act as an
incentive and reinforce actions has stimulated considerable research and debate [24]. Two putative solutions to
the avoidance problem are provided by inhibitory
rewards: in the one case (two-factor theory), behavior is
driven by the escape from fear (i.e. offset relief) elicited
by any signal that predicts punishment [25]. In a second
case (safety signal hypothesis), behavior is driven by the
Current Opinion in Behavioral Sciences 2015, 5:122–127
124 Neuroeconomics
(conditioned) reinforcement provided by the inhibitory
outcome state that signals the absence of punishment
(omission relief) [26,27,28]. There is good evidence for
both these processes [29], but they lead to a problem: can
they sustain behavior after repeated avoidance success,
because the inhibitory learning process should extinguish? One possibility is that a simple habit-based system
might take over, which stamps in the repetitive behavior
as if it were a reward [30].
However, these theories struggle to account for avoidance
in the absence of any signals (‘free-operant’ avoidance),
such as when a minimum baseline rate of action execution
is required to ensure no punishments are presented [31]
(e.g. pressing a lever every 20 s to ensure no electric shock
is delivered). This has led to theories of cognitive avoidance that appeal to the remembered representation of
avoided outcomes in the brain [32]. Similar to ‘modelbased’ control for reward-based decision-making [33],
this type of behavior relies on some sort of internal model
of the environmental structure and contingencies.
Together, this illustrates the fundamental difference
between reward-based and avoidance-based reinforcement, with avoidance being driven by a relief processes
generated through inhibitory interactions, and not
through a primary excitatory process. However, this
should only be manifest in avoidance tasks in which
stimulus driven learning is possible. In explicit tasks
(verbal or written), in which learning is not required or
possible, we would expect simply a model-based system
evaluating the best actions. In addition, during either type
of task, we would expect a reward-like habit system to
take control as soon as a clear pattern of avoidance
becomes successful. Importantly, therefore, in the case
of learned or explicit loss avoidance, one would predict an
overall similar reward-dominant pattern of brain activity
for both reward acquisition and loss avoidance — as is
typically seen.
However, the difference between the reward seeking and
avoidance should be observable early in learning, when
we would expect relief driven reinforcement to be represented as a negative punishment — a striatal deactivation
preceded by aversive activation induced by the pre-action
state, as occurs for negative Pavlovian prediction errors for
excitatory losses. However, to our knowledge previous
studies have not explicitly looked specifically at the
earliest versus later stages of learning. One reason may
be the methodological problems of doing extended training in fMRI studies, with confounding effects of time and
attention.
In the case of inhibitory losses, a plausible prediction is
that an aversively motivated avoidance system is not
required at all: since avoidance would require inhibition
of inhibition (e.g. an inhibitory safety signal reflecting the
Current Opinion in Behavioral Sciences 2015, 5:122–127
absence of loss that itself signaled the absence of reward).
Therefore, inhibitory loss avoidance problems could be
solved with just a reward system, by contrast to the
complex Pavlovian-instrumental interactions required
for excitatory loss avoidance.
A further difference comes from the responses directly
associated with the (excitatory or inhibitory) loss prediction itself. In particular, excitatory losses would be
expected to evoke much stronger innate responses (‘species-specific defence responses’ (SSDRs)) which directly
interfere with behavior [34]. In animal studies of primary
punishments, this direct Pavlovian response can exert
powerful control of behavior — sometimes called ‘Pavlovian warping’ [35,36]. Again, however, we would only
expect this excitatory Pavlovian response (a positive
striatal signal) early in avoidance behavior, because it
would extinguish as successful avoidance becomes the
norm [37].
In summary, based on good evidence primarily from
animal studies, it seems probably that loss-related decision-making is under the control of at least five separate
mechanistic processes (Figure 2). As a result, the pattern
of behavior and accompanying neural activity depends
critically on a number of factors, in particular the level of
controllability over loss [38], the amount of experience,
Figure 2
Architecture of excitatory loss avoidance
Intemal model
Pre-action
state
Loss
Choice
mechanism
Innate response
se
(SSDR)
Conditioned
inhibition generates
safety signal
interference
SR habitization
No Loss
Offset relief following escape from preaction state induced fear
Current Opinion in Behavioral Sciences
The architecture of loss avoidance. A set of coordinated processes
mediate the acquisition and maintenance of avoidance learning, and
similarly, any decision over options involving choosing the lesser of
two punishments. The processes include first, an innate Pavlovian
response to anticipated loss (including species specific defence
reactions (‘SSDR’s), second, escape from fear associated with the
loss predictive pre-action state, third, conditioned reinforcement of a
safety signal, generated through conditioned reinforcement of the relief
state, fourth, goal-directed (model-based) internal model and cognitive
decision system, and fifth, habitization of the avoided action, after
prolonged experience.
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When is a loss a loss? Seymour, Maruyama and De Martino 125
the presence of cues signally the requirement to avoid,
the schedule of loss delivery, the tonic level of reward or
loss, conditioned and explicitly expected outcomes, the
presence of signals indicating the success of behavior, and
the nature of the loss itself (inhibitory or excitatory).
However, quite which systems are active in any task
might not readily apparent from basic analysis of neuroimaging data.
Relation to economic theories of loss
behavior
In the standard formulation of Expected Utility Theory
[39] the agents maximize their utility over a concave
utility-of-wealth. In this framework losses are just lower
levels of (positive) wealth. As the local curvature (concavity) of the utility function increases, agents behave in a
more risk-averse manner. Importantly Prospect Theory
[40] introduced a different functional form of utility in
which losses are conceptually different from gains, since
they are evaluated relative to a reference point. Notably
ventral striatum computations have been shown to be
sensitive to the manipulation in the reference-point
through market transactions [41]. Around the reference
point, the Prospect Theory utility function is S-shaped
and asymmetrical. The S-shape reflects concavity for
potential gains (risk adverse behavior for prospective
gains) and convexity for potential losses (risk seeking
behavior for prospective losses). The asymmetry relative
to the reference point reflects the fact that the function is
steeper in the loss domain. The magnitude of this asymmetry is characterized by a parameter called lambda (l)
that neatly account for loss averse behavior (the widespread
evidence people strongly prefer avoiding losses to acquiring gains). Therefore in Prospect Theory how the reference-point is set is crucial to distinguish a gain from a loss.
However, how the reference-point should be derived is
unclear. Tversky and Kahneman originally proposed that
the reference point is set by the status quo, a subject’s
wealth level at the time of each decision. Other alternative proposed are the mean of the chosen lottery [42] or a
lagged status quo [43], which predicts the willingness to
take unfavorable risks to regain the previous status quo. A
more recent proposal from Koszegi and Rabin [44,45]
suggest that subject’s expectations (and not the status
quo) shapes the reference point and determines what an
agent would perceived as loss. In this model the decisionmaker maximizes a linear combination of a consumption
utility m(w) that is the riskless intrinsic consumption
utility associated with a given level of wealth w and
m(r) the utility associated with a given reference level
of wealth:
uðwjrÞ ¼ mðwÞ þ mðmðwÞ mðrÞÞ
The reference dependent component of this account of
utility ðmðmðwÞ mðrÞÞÞ strongly resonates with the concept of inhibitory loss described we highlighted here. For
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example, a CEO that expect a profit for her company of
1.3 millions dollars (for the current year) will perceive a
profit of one million as a loss even if the company profit for
previous years were below one million. Similarly if the
same CEO expects that the company will produce a net
loss of one million at the end of the year, will perceive a
loss of half-million as a gain. Critically in this framework
the final utility is not only function of the reference
dependent component but it is also function of consumption utility (m(w)) that is the intrinsic utility for a given
level of wealth w. This other component of utility could
be mapped on what we have described here as an excitatory loss and would elicit an active averse representation
since it will impact directly on the wealth of the agent.
Therefore an intriguing possibility is that discrepancies
observed in previous studies arise from a manipulation
have impacted more or less on one of these two components that give rise to the final utility; that (as we propose
here) have a segregated computational representation in
the brain. One can imagine, for example, that the method
used in most experiments to generate losses by reducing
the participant monetary compensation by small amounts
might impact significantly on the reference dependent
component of utility (the participant expectation prior the
beginning of the experiment) but might have a negligible
impact on the overall level of the participant’s wealth
dependent utility Further studies should directly and
orthogonally manipulate these two components of utility
to test empirically this hypothesis.
Finally, an interesting view has recently emerged in
behavioral economics suggesting that reference-dependent behavior arises from modulation of attentional
resources. In this framework, loss averse behavior arises
naturally if one assumes that a loss (a reduction in consumption) has a stronger impact on an attention-biased
utility than a corresponding gain [46] without assuming an
asymmetry in the value function. Consistent with this
hypothesis it has been recently shown that losses have a
distinct effect on attention but do not lead to an asymmetry in subjective value [47]. However, the exact role
played by attention in shaping response to losses is still
unclear and further empirical investigation is required.
However, according to the framework proposed here it is
tempting to suggest that excitatory losses should be more
salient than inhibitory losses and therefore differentially
engage the decision-maker attentional resources.
Conclusions and predictions
Loss related behavior is both neurobiologically complex
and fundamentally different from reward-related behavior — a fact that is sometimes overlooked. Importantly
the experimental conditions under which loss behavior is
studied critically determines the recruitment of different
brain systems. Here, we propose that the relative contribution of two types of loss: excitatory and inhibitory
losses, and that their relative representation determines
Current Opinion in Behavioral Sciences 2015, 5:122–127
126 Neuroeconomics
the nature of the brain response in the striatum in passive
receipt of loss, and together with the recruitment of
inhibitory rewards, determines to some extent the nature
of loss related decision-making (avoidance).
It is important to note that other structures aside from the
striatum are also strongly implicated in the representation
and control over losses, in particular the amygdala and
insula cortex. The amygdala also represents distinct integrated reward and loss signals [48,49], but is typically a
little harder to charactise in imaging studies because of its
smaller size and susceptibility to artifact. The insula
cortex appears to have a more complex role in both
value-sensitive functions and ‘interoceptive’ sensory processing [50], making interpretation even more difficult.
Our model makes several testable predictions. First, in
the case of passive loss prediction of simultaneous reward
money and loss, the striatum is known to code a net
reward signal. But for simultaneous pain and reward,
evoking both positive reward and positive aversive systems, we would expect an amplified signal, even though
the net value would be close to zero.
Second, in purely aversive contexts in which subjects
genuinely felt they could only lose their own money
during an experiment, gains of money should be represented by negative responses of a dominant aversive
system in striatum.
Third, during excitatory but not inhibitory signaled loss
avoidance (but not free operant avoidance), we would
expect a positive striatal loss signal during very early
learning, that extinguishes along with cue-driven Pavlovian responses, and switches to becomes a positive signal as
successful avoidance is learned.
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Conflict of interest statement
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