Clinical Psychology Review 101 (2023) 102255

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Clinical Psychology Review

journal homepage: www.elsevier.com/locate/clinpsychrev

Review

“Don’t [ruminate], be happy”: A cognitive perspective linking depression

and anhedonia
Ashleigh V. Rutherford *, Samuel D. McDougle, Jutta Joormann
Department of Psychology, Yale University, New Haven, CT, USA

A R T I C L E I N F O A B S T R A C T

Keywords: Anhedonia, a lack of pleasure in things an individual once enjoyed, and rumination, the process of perseverative
Rumination and repetitive attention to specific thoughts, are hallmark features of depression. Though these both contribute to
Emotion regulation the same debilitating disorder, they have often been studied independently and through different theoretical
Working memory
lenses (e.g., biological vs. cognitive). Cognitive theories and research on rumination have largely focused on
Reinforcement learning
understanding negative affect in depression with much less focus on the etiology and maintenance of anhedonia.
Depression
In this paper, we argue that by examining the relation between cognitive constructs and deficits in positive affect,
we may better understand anhedonia in depression thereby improving prevention and intervention efforts. We
review the extant literature on cognitive deficits in depression and discuss how these dysfunctions may not only
lead to sustained negative affect but, importantly, interfere with an ability to attend to social and environmental
cues that could restore positive affect. Specifically, we discuss how rumination is associated to deficits in working
memory and propose that these deficits in working memory may contribute to anhedonia in depression. We
further argue that analytical approaches such as computational modeling are needed to study these questions
and, finally, discuss implications for treatment.

Major Depressive Disorder (MDD) is characterized by many debili­ theoretical perspectives: cognitive and biological mechanisms underly­
tating symptoms, particularly across two key domains: increases in ing the disorder. Biological processes have been at the forefront of the
negative affect and decreases in positive affect. MDD is also chronic and field’s study regarding mechanisms underlying anhedonia and deficits
recurrent. Though treatments are widely available and are helpful to in positive affect (e.g., Der-Avakian & Markou, 2012; Gorwood, 2022;
some patients, up to 40–50% of patients do not respond sufficiently to Wise, 2008). At the same time, cognitive theories have a long history in
either antidepressant medications (Furukawa et al., 2016; Trivedi, depression research (e.g., Abramson, Metalsky, & Alloy, 1989; Beck,
Greer, Grannemann, Chambliss, & Jordan, 2006) or standard-of-care 1967; Brown & Harris, 1978) but have largely focused on better un­
psychotherapies such as cognitive behavioral therapy (Cuijpers et al., derstanding sustained negative affect in this disorder. Even though we
2014; DeRubeis et al., 2005) (see Cuijpers, Karyotaki, de Wit, & Ebert, know anhedonia is a debilitating symptom of depression and is char­
2020 for a review). Among MDD patients who achieve remission, an acterized by deficits in positive affect, most cognitive theories have
estimated 40% relapse within two years (Boland, Keller, Gotlib, & focused less on understanding its etiology and maintenance. Exceptions
Hammen, 2009). Moreover, for individuals who do respond to first-line such as reward devaluation theory (e.g., Winer & Salem, 2016) as well as
treatments, anhedonia – the cardinal symptom of depression charac­ recent work on dampening (Bean, Summers, & Ciesla, 2022; Vanderlind,
terized by a lack of pleasure and/or motivation– often persists (Dunlop Everaert, & Joormann, 2021; Vanderlind, Millgram, Baskin-Sommers,
& Nemeroff, 2007; McCabe, Cowen, & Harmer, 2009a, 2009b; Nutt Clark, & Joormann, 2020) show the promise of integrating cognitive
et al., 2007; Price, Cole, & Goodwin, 2009; Shelton & Tomarken, 2001). theories and work on positive affect but more needs to be done to link
As such, researchers have been charged with the difficult and urgent task cognitive processes that characterize depression and anhedonia. Such an
of identifying mechanisms that contribute to this disorder, particularly improved understanding of processes underlying anhedonia promises to
with regard to anhedonia. help prevention and intervention efforts. As such, it is possible that by
The extant body of literature on depression has focused on two main examining the relation between cognitive constructs and deficits in

* Corresponding author at: Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT 06511, USA.
E-mail address: Ashleigh.rutherford@yale.edu (A.V. Rutherford).

https://doi.org/10.1016/j.cpr.2023.102255
Received 16 January 2022; Received in revised form 19 December 2022; Accepted 16 February 2023
Available online 20 February 2023
0272-7358/© 2023 Elsevier Ltd. All rights reserved.
A.V. Rutherford et al. Clinical Psychology Review 101 (2023) 102255

positive affect, we may better understand anhedonia in depression pleasure.

thereby improving prevention and intervention efforts. Consummatory pleasure relates more closely to “liking,” satiation, or
One specific cognitive process that has received much attention the pleasure experienced in the moment upon receipt of reward,
regarding its contribution to the maintenance of depression is rumina­ whereas anticipatory pleasure relates more closely to “wanting,” or to
tion, or the act of perseverative and repetitive attention being paid to the pleasure one expects to experience from a future rewarding experi­
specific—and often negative—thoughts. Critically, prior research has ence. Studies indicate that these two aspects of anhedonia may be
focused on rumination as being primarily linked to the sustaining of differentially impacted in depression, such that anticipatory pleasure is
negative affect (e.g., Alloy & Abramson, 1988; Beck, 2002; Thomsen, diminished in MDD, whereas consummatory pleasure may remain intact
2006) but given the importance of rumination in maintenance of (e.g., Dillon et al., 2008; Shankman, Klein, Tenke, & Bruder, 2007;
depression, it is possible that it not only affects negative affect but also Treadway & Zald, 2011), and further work indicates that motivational
interferes with processes important for the generation and experience of deficits, tied to anticipatory pleasure, may also provide a useful window
positive affect—such as the ability to learn from rewards in the envi­ to understanding anhedonia in depression (Treadway, Buckholtz,
ronment. This paper focuses on integrating research on cognitive pro­ Schwartzman, Lambert, & Zald, 2009).
cesses in depression with work on anhedonia to better understand how An early body of work (Berlin, Givry-Steiner, Lecrubier, & Puech,
rumination might affect depressed individuals’ ability to experience 1998; Amsterdam, Settle, Doty, Abelman, & Winokur, 1987) utilizing
positive affect. the sucrose sweet-taste test measured individuals’ hedonic responses to
We review the extant literature on cognitive deficits and biases in sweet tastes. This test is designed to measure consummatory reward
depression and discuss how these dysfunctions may not only lead to responding and has consistently demonstrated no differences between
sustained negative affect but, importantly, may also interfere with an MDD patients and controls in affective responses to the receipt of
ability to attend to social and environmental cues that could restore reward. Thus, individuals who are depressed tend to show no differences
positive affect. Specifically, we will discuss how rumination may be in so-called behavioral sensitivity to rewarding stimuli. This work pro­
related to deficits in working memory, and propose that these deficits in posed the influential notion that there may not be depression-related
working memory may contribute to anhedonia in depression. Lastly, we deficits in consummatory pleasure.
note how analytical approaches such as computational modeling can be One common task used to study reinforcement learning behaviors in
leveraged to study these questions and, finally, discuss implications for MDD is the Probabilistic Reward task, or PRT (Pizzagalli, Jahn, &
treatment. O’Shea, 2005a, 2005b), and it has been used to help distinguish sub­
jects’ sensitivity to rewards (consummatory) and their ability to learn
1. Facets of anhedonic experience from rewards. The PRT is a computer-based task in which participants
are presented with one of two perceptually similar cues and are asked to
Anhedonia is a heterogenous construct, and is linked to many psy­ use corresponding key presses to indicate which one cue had just been
chiatric disorders, including substance use disorders (Garfield, Lubman, presented. Participants are told that they will be rewarded “sometimes”
& Yücel, 2014; Hatzigiakoumis, Martinotti, Di Giannantonio, & Janiri, when they respond correctly, and never when they respond incorrectly.
2011), schizophrenia (Watson & Naragon-Gainey, 2010; Wolf, 2006) Unbeknownst to the participant, one cue (the “rich” stimulus) is more
and eating disorders (Tchanturia et al., 2012). For the purpose of this often rewarded than the other (the “lean” stimulus) throughout the task.
review, we consider the literature on anhedonia as it has been studied in The variable of interest in the PRT, the response bias, reflects the extent
major depressive disorder (see Pizzagalli, 2014; Treadway & Zald, 2011 to which the learner modulates their behavior in response to rein­
for additional reviews) and distinguish facets of anhedonia that high­ forcement history (Pizzagalli et al., 2005a, 2005b). Importantly, in­
light the need for interrogation from a cognitive perspective. dividuals with depression, as compared with controls, display a marked
Anhedonia is broadly defined as a symptom in which an individual inability to develop a response bias for the rich stimulus during the PRT
perceives a lack of interest or pleasure in activities and experiences that (Pizzagalli et al., 2008). Notably, in a study of 23 unmedicated
were once pleasurable to them (American Psychiatric Association, depressed subjects and matched controls, Pizzagalli et al. (2008) found
2022). Anhedonia has been tied to reward processing, which encom­ that those with depression showed significantly reduced reward
passes processes related to the engagement in goal-directed behavior responsiveness in the PRT (as indexed by an attenuated or absent
towards rewards (appetitive motivation), responses to rewarding stimuli response bias). Importantly, trial-by-trial probability analyses revealed
(reward sensitivity), and the ability to learn from rewards to adapt that depressed persons were responsive to the delivery of single rewards;
future behaviors (reinforcement learning) (Thomsen, Whybrow, & that is, they were simply unable to integrate the values of rewards over
Kringelbach, 2015). In particular, reinforcement learning has been time to generate a persistent response bias towards the more rewarded
linked to a large body of literature on anhedonia in MDD (e.g., Pechtel, (rich) cue in the task. These results (i.e., Pizzagalli et al., 2008) further
Dutra, Goetz, & Pizzagalli, 2013; Pizzagalli et al., 2008; Vrieze et al., reflect the complex nature of anhedonia symptomatology.
2013). This growing body of work has helped elucidate important nu­ In another behavioral study designed to parse consummatory and
ances in reward-related deficits observed in depression. anticipatory processes in depression, 38 depressed participants and
Past frameworks had characterized anhedonia in depression exclu­ matched controls rated their liking of humorous and non-humorous
sively as an inability to experience pleasure (e.g., Meehl, 2001; Ribot, cartoons (Sherdell, Waugh, & Gotlib, 2012). Participants then made a
1896); however, more recent frameworks have begun to disentangle the series of choices between viewing a cartoon from either group, and each
many aspects of reward processing in depression, revealing how reward choice required a specified amount of effort the participant would have
processing may be disrupted in multiple ways. For example, as Rizvi, to exert to view the chosen cartoon. Participants with MDD and control
Pizzagalli, Sproule, and Kennedy (2016) note in one review on the topic, participants did not differ in their consummatory pleasure (i.e., reported
“this equivocal conceptualization of anhedonia makes measurement “liking”) of the cartoons. However, whereas levels of reward “liking”
imprecise…and refining the concept is imperative if we hope to un­ predicted the amount of effort participants were willing to exert to view
derstand the neurobiological underpinnings of anhedonia” (pg. 3). the cartoon, high reward “liking” (consummatory pleasure) did not
These authors and others (e.g., Treadway & Zald, 2011) further assert predict whether depressed participants would exert effort to view the
that distinctions within the symptom domain can be made on many cartoon. Levels of anticipatory pleasure, on the other hand, did predict
levels, e.g., including when reward-related deficits occur in relation to the amount of effort depressed participants would exert to view the
receipt of reward. As such, another important distinction that may be cartoon, such that lower levels of anticipatory pleasure led to less effort
helpful in identifying the mechanisms underlying anhedonia in exerted by participants with MDD – an effect that was not seen in control
depression is the difference between consummatory and anticipatory participants. This study also suggests that individuals with depression

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A.V. Rutherford et al. Clinical Psychology Review 101 (2023) 102255

may not have deficits in the ability to experience pleasure; rather, they symptomatology.
may anticipate experiencing less pleasure with a given reward and thus Response bias, as measured by the PRT, has also been tied to the
fail to modulate their behavior as a function of past experience in order neurobiological framework of reward processing described above. For
to maximize future rewards. example, Pizzagalli et al. (2008) showed that administration of a low-
A third line of behavioral research, by Treadway, Bossaller, Shelton, dose dopamine agonist (hypothesized to decrease dopamine signaling
and Zald (2012); Treadway et al. (2009). In this task, effort-based de­ through presynaptic autoreceptor activation) impaired the acquisition
cision-making is studied as participants are presented with a series of of a response bias. These results—along with a large and influential body
trials in which they choose to expend more or less effort in order to gain of research (e.g., Schultz et al., 1997; Schultz, 2007; Niv, 2009; Tobler,
varying levels of monetary rewards. In one study, using an unselected 2010; Glimcher, 2011)—suggest that dopamine signaling is necessary to
sample of undergraduates, the researchers found that higher levels of reinforce actions that lead to reward. A recent study utilizing the PRT
self-reported anhedonia were associated with decreased willingness to and positron emission topography (PET) also found that individual
expend effort for rewards (Treadway et al., 2009). In another study, differences in reward learning on the PRT were related to dopamine
patients with a current MDD diagnosis were shown to exhibit decreased transporter binding potential in the ventral striatum (Kaiser et al.,
willingness to expend effort for rewards as compared to healthy controls 2018).
as well as a decreased ability to use past information about reward Although we know much about reinforcement learning in the
magnitude history and probability of reward receipt to modify and up­ framework of RPE and dopamine signaling—and extant literature have
date their future choice behavior (Treadway et al., 2009). Taken mostly focused on these such neurobiological correlates (see Kielisch,
together, these studies provide stark behavioral evidence for depression- Valton, & Roiser, 2022 for an extensive review on this topic)—there are
related deficits that are specific to the motivational (i.e., anticipatory) important individual differences in the ability to process environmental
domain. cues related to reward. Prior research has attributed some of these dif­
Neuroimaging research further supports the notion that different ferences to aberrant RPE signaling (see Treadway & Zald, 2011 for re­
facets of anhedonia are differentially linked with symptoms seen in view). However, there is reason to believe that additional cognitive
depression. For example, Kocsel et al. (2017), used fMRI to examine processes (beyond canonical corticostriatal reinforcement learning) may
activation in reward-related areas of the midbrain while individuals play an important role – that is, individual differences in how humans
with a spectrum of scores on the ruminative response scale (RRS; assign values to cues in the environment may not be captured by vari­
Treynor, Gonzalez, & Nolen-Hoeksema, 2003) completed a monetary ance in this single neural mechanism. Critically, we know also that
incentive delay task (MID; Knutson, Westdorp, Kaiser, & Hommer, learning from rewards is not a singular process – it also appears to rely
2000). In the MID task, visual stimuli (e.g., shapes and colors) are used heavily on the flexible updating of working memory (Rmus, McDougle,
as incentive cues to convey the probability and magnitude of monetary & Collins, 2021; Taylor et al., 2004), a cognitive construct shown to be
rewards. The authors found that participants who scored high on trait strongly implicated in depressive symptomatology (Christopher &
rumination showed reduced midbrain activation during reward antici­ MacDonald, 2005; Yoon, LeMoult, & Joormann, 2014). As such, by
pation; however, they found no relation between rumination and focusing narrowly on the canonical reinforcement learning system, we
midbrain activation in response to reward consumption. Though this may be missing a key part of the story concerning MDD and reward
study was conducted in a group of non-depressed individuals, the au­ processing. By taking into account individual differences in cognitive
thors conclude that rumination may relate to the disrupted processing of processes that contribute to the maintenance, updating, and integration
anticipatory/motivational reward responses, rather than consummatory of action and stimulus values over time, a more complete picture may
reward responses. emerge.
Taken together, the aforementioned findings support the idea that One key cognitive process to examine in completing this picture is
cognitive processes associated with depression, such as ruminative working memory functioning. Working memory refers to the cognitive
thinking, are related to difficulties anticipating rather than consuming system for temporarily storing, actively maintaining, and manipulating
rewards. These findings therefore raise the question: if individuals with information across a short delay (Cowan, 2008). Working memory is
depression show some intact ability to experience the benefits of rein­ also necessary to carry out temporally relevant goal-directed tasks
forcement, what, then, is hindering their ability to integrate the values (Miller, 2013). In the past several years, work by Collins and Frank
of rewards over time (e.g., Pizzagalli et al., 2008), anticipate pleasure (e. (2012); Collins, Albrecht, Waltz, Gold, and Frank (2017); Collins and
g., Sherdell et al., 2012), and motivate themselves to engage in goal- Frank (2018); Collins, Ciullo, Frank and Badre (2017) has shown that
oriented behavior? the learning during simple stimulus-response reinforcement learning
paradigms is closely related to working memory. In these studies, sub­
2. Reward processing in depression jects participated in a basic instrumental learning task where the num­
ber of stimuli-response pairings (i.e., the “set size”) that had to be
Previous work has identified neurobiological correlates of anhe­ learned at a given time was varied throughout the task. Varying cogni­
donia, focusing on constructs like appetitive motivation (Germans & tive load in this manner allows one to separately track the reinforcement
Kring, 2000), reward sensitivity (Thomsen et al., 2015), and reinforce­ learning and working memory systems, via behavioral (Collins & Frank,
ment learning (Huys, Pizzagalli, Bogdan, & Dayan, 2013). A consensus 2012, 2018) and neural signatures (Collins et al., 2017, Collins & Frank,
has emerged, stating that key brain structures, such as the basal ganglia, 2018). Two pieces of recent evidence from Anne Collins and colleagues
amygdala, medial prefrontal cortex, and orbital prefrontal cortex are point to a complex interaction, where working memory feeds pre­
critically implicated in the ability to carry out reward processing, and dictions to the reinforcement learning system (i.e., cooperation) but the
that this is ultimately driven by midbrain dopaminergic neurons (Haber two systems compete during decision-making (Collins & Frank, 2018).
& Knutson, 2010). More specifically, decades of research provide evi­ Interestingly, if working memory broadcasts predictions to the rein­
dence for the integral role of the neurotransmitter dopamine, originating forcement learning system (e.g., “if you perform action X you will likely
from the ventral tegmental area and projecting to the ventral striatum, be rewarded”), computational modeling suggests that the RL system
in generating reward prediction errors (RPEs) – the difference between should then be less “surprised” by the working memory-predicted
expected and received rewards. The RPE is the primary teaching signal outcome (e.g., a reward). Indeed, this interaction has been revealed in
of the reinforcement learning system, promoting learning from rein­ fMRI – during learning conditions that rely heavily on working memory,
forcement over time (Schultz, Dayan, & Montague, 1997). reward prediction error signals in the striatum, key node in the RL
Through this work, we have learned much about the neurobiological network, are attenuated (Collins et al., 2017). Taken together, these
structures and deficits in RPE-signaling that are implicated in anhedonic findings suggest that taxing working memory should have a complex

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A.V. Rutherford et al. Clinical Psychology Review 101 (2023) 102255

effect on reinforcement learning; perhaps making it learn more slowly, important risk factor for and symptom of depression (Durbin & Shafir,
but also allowing it to contribute more to decision-making. This work is 2008), and depression is frequently considered a disorder of emotion
an important first step to uncovering the interactions between working dysregulation (Joormann & Stanton, 2016). Thus, emotion regulation
memory and reinforcement learning – rather than viewing reward has provided an important bridge to understanding the role cognitive
learning as a monolithic process, these authors (Collins, 2019; Collins, deficits and biases play in the sustained negative affect that is charac­
Albrecht, et al., 2017; Collins, 2018; Collins & Frank, 2012) suggest that teristic of depression. Rumination, for example, a process in which re­
working memory and reinforcement learning are dynamically inte­ petitive and perseverative attention is paid to specific thoughts, may
grated and interact with one another (Collins & Frank, 2018), simulta­ reflect a failure of cognitive control in that attention gets “stuck” on
neously shaping choice behavior. salient, but not necessarily goal-relevant, aspects of a situation (Nolen-
Given that individuals with MDD show broad impairments across Hoeksema, 2000). Rumination is also associated with the onset (Nolen-
working memory and other areas of executive functioning (see Snyder, Hoeksema, 2000), duration (Nolen-Hoeksema & Morrow, 1993), and
2013 for a review), the aforementioned findings suggest that working severity (Just & Alloy, 1997) of depression. Cognitive reappraisal, on the
memory processes should be examined further with respect to MDD and other hand, a process in which an individual actively changes their
reward processing. Particular promise for this research may lie specif­ thoughts about an emotional event/stimulus (Gross, 1998), has been
ically in executive function constructs of working memory and cognitive identified as an important emotion regulation strategy that is closely
control, which are implicated in the updating and monitoring of choice related to people’s ability to exert cognitive control (McRae, Ochsner,
values (Domenech & Koechlin, 2015; McDougle & Collins, 2021; Mauss, Gabrieli, & Gross, 2008) and remove negative, repetitive content
McDougle, Ballard, Baribault, Bishop, & Collins, 2022). For example, we from working memory. Cognitive reappraisal is thus associated with
know that extrastriatal regions, such as the anterior cingulate cortex better treatment outcomes in depression (e.g., Garnefski & Kraaij, 2006;
(ACC) and the dorsolateral prefrontal cortex (DLPFC), are uniquely tied Kraaij, Pruymboom, & Garnefski, 2002).
to updating the value of choices and/or stimuli. We also know that Effective emotion regulation requires updating the content of
cognitive control, or the ability to exert mental effort needed to sustain working memory and exerting control over mood-congruent, goal-
task-relevant behavior, is thought to be necessary for carrying undis­ irrelevant thoughts, and replacing them with goal-relevant information.
rupted RPE signals to necessary brain regions (Holroyd & Umemoto, For example, imagine the following situation:
2016). How can we combine this knowledge to better understand the
John arrives to work one morning after a long commute, only to realize he
clinical presentation of depression and dissociate deficits in reinforce­
forgot his lunch and his wallet at home. John becomes extremely upset,
ment learning versus executive function?
and begins thinking “Why can’t I do anything right? I forget everything
We propose that cognitive deficits may contribute to disrupted
these days! What is wrong with me? I am such a failure!” John spends
learning from rewards in MDD and as outlined below from a cognitive
most of the morning perseverating on this mistake and how he “can’t do
perspective, we suggest that rumination may play a key role in under­
anything right” that he misses his first meeting. He has become so upset
standing these impairments.
and angry at himself that by noon he has not accomplished any tasks and
can’t think of anything but his mistakes, so he leaves work early to go
3. Cognition and emotion regulation in depression
home.

Individuals with MDD—and even those with subthreshold depressive In this situation, we see that an event (John forgetting his lunch)
symptoms—report a number of cognitive difficulties. In addition to is­ activates an emotional response (anger and sadness), leading to an
sues with concentration, deficits and biases in memory and attention overhaul of mood-congruent thoughts in working memory (i.e., rumi­
have been reported during depressive episodes (Trivedi & Greer, 2014). nation). Though rumination is masked here as “problem-solving,” such
Whereas biases refer to preferences for one emotion over another (or as trying to understand “what’s wrong” with oneself, this example
over neutral), deficits lead to more errors and/or reduced efficiency in demonstrates the goal-irrelevance of mood-congruent rumination. In
responding. Research has shown both global and emotion− /content- fact, this cognitive process—often labeled ruminative brooding (Rude,
specific impairments in executive functions (see Rutherford & Joor­ Little Maestas, & Neff, 2007)—serves only to exacerbate John’s anger
mann, 2022 for a review covering both). We know that depressed in­ and sadness. Alternatively, if John were to instead exert cognitive con­
dividuals show overall deficits in executive functioning (e.g., Snyder, trol over these thoughts and divert his attention from them (thus
2013), for example, but it is likely that depression-related memory and removing the ruminative content from working memory), he might have
attentional biases contribute to this (see Everaert, Koster, & Derakshan, been able to implement goal-relevant solutions, such as asking a
2012 for a review on this topic). Research on these deficits and biases coworker to borrow money for lunch, and thus not miss his meeting.
suggest that they may also precede the onset of depression, indicating As illustrated above, understanding the relation between rumination
that they play a role in one’s vulnerability to the disorder (Goodyer, and working memory—particularly, focusing on the executive functions
Herbert, Tamplin, & Altham, 2000). Importantly, these deficits may be that subserve working memory, such as the ability to attend to salient
linked to the emotional problems that define depression: sustained and relevant cues, exert cognitive control, monitor and update the
negative affect and decreased positive affect (Gotlib & Joormann, 2010). contents of working memory, and inhibit ruminative thoughts—is
In particular, cognitive biases may help explain individual differences in crucial for understanding how individuals regulate emotion. As such,
the ongoing maintenance of negative affect and difficulties with expe­ homing in on literature that examines relations between rumination and
riencing positive affect, which are hallmark features of depression. working memory may provide a useful cognitive perspective into not
Mood-congruent cognitive biases maintain attention on negative stimuli only increases in negative affect, but also the lack of positive affect in
in the environment, increase accessibility of negative material in depression. Specifically, because evidence suggests that working mem­
memory, and result in negative interpretation of ambiguous material, all ory and reinforcement learning are dynamically integrated (e.g., Collins
of which prolong negative affect and hinder the regulation of negative & Frank, 2012), rumination may indirectly affect the ability to learn
mood states. from rewards and experience positive affect by interfering with working
Cognitive processes are closely related to individual differences in memory.
emotion regulation, or the ability to manage and modulate one’s emo­
tions in response to affective experiences (Gross & Thompson, 2007), 4. Working memory and executive function in depression
and working memory in particular has been linked to emotion regula­
tion ability in individuals with depression (see Joormann & Quinn, 2014 Nearly all models of working memory highlight its role in complex
for a review). Emotion regulation deficits have been identified as an cognitive tasks such as planning, learning, and reasoning. Importantly,

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however, working memory is capacity-limited. As such, information that this negative valence-specific impairment in inhibition has been shown
is stored and manipulated in working memory should be goal-relevant. to persist even among remitted-depressed individuals (Joormann &
Under the purview of working memory, executive functions are defined Gotlib, 2007; Joormann & Siemer, 2004) and never-depressed daughters
as “general-purpose control mechanisms that modulate the operation of of depressed mothers (Joormann, Talbot, & Gotlib, 2007). More recent
various cognitive subprocesses and thereby regulate the dynamics of work has elucidated what appears to be a unique role of rumination in
human cognition” (Miyake et al., 2000). Many different aspects of ex­ this effect.
ecutive functions have been proposed to relate to depression, including For example, one study by Yoon et al. (2014) compared working
maintenance of information, switching between internal sets, inhibiting memory capabilities across individuals with MDD and social anxiety
prepotent behaviors, selecting among different options, and monitoring disorder (SAD), two disorders that are often found to be comorbid with
representations within working memory such that the appropriate one another (e.g., Adams, Balbuena, Meng, & Asmundson, 2016). Par­
updating of working memory can occur in light of new information ticipants in this task memorized two lists of words on each trial and were
(Miyake et al., 2000). told to ignore one of the lists of words. Later, participants were asked to
A wealth of research has corroborated the theory that the above indicate whether or not a single word belonged to the relevant of the two
facets of working memory are highly implicated in depression and tied lists. The authors found that individuals with MDD had greater difficulty
to rumination. Indeed, early research by Nolen-Hoeksema (1991) illus­ discarding and inhibiting no-longer-relevant information from working
trated that an increased tendency to ruminate on negative information, memory (i.e., greater working memory intrusion), whereas individuals
combined with difficulties distracting oneself from such negative ma­ with SAD showed no evidence of such interference. These authors
terial, play a central role in the maintenance of depressed mood. It is also theorize that the working memory capacities may be largely influenced
known that a stable tendency to respond to negative life events and by a motivation to “do well,” as would be expected for those with SAD,
negative mood states with ruminative thinking (a ruminative style) is a but perhaps not for those with motivational deficits as seen in depres­
marker of vulnerability for developing depression. sion. Interestingly, working memory performance in this study corre­
Donaldson, Lam, and Mathews (2007) studied the effect of rumina­ lated with ruminative tendencies only for individuals with MDD,
tion on attention and found that MDD participants who score high on suggesting an important interaction between depressive symptom­
rumination tend to perseverate more on negatively valenced stimuli. atology and rumination, which predicts working memory performance.
The authors theorize that an inability to divert attention from negative Similarly, De Lissnyder, Koster, Derakshan, and De Raedt (2010)
stimuli may be an initial route through which rumination affects used the Affective Shift Task to examine the relation between depressive
cognition and, therefore, by which negative thinking persists. Pe et al. symptoms and executive functioning and probed the role of rumination.
(2013) also studied the impact of rumination and other forms of In this task, participants are asked to perform an odd-one-out search
perseverative thinking (i.e., worry) on attentional biases towards based on a stimulus characteristic that was cued at the beginning of the
negative stimuli. Similarly, they also demonstrated that rumination is trial (e.g., specific emotions, gender). The authors found that among all
associated with a bias for negatively valenced stimuli and is negatively participants, depressive symptoms in general were not related to inhi­
associated with attention to positively valenced stimuli. This maladap­ bition abilities and were only moderately related to set-shifting abilities.
tive bias towards negative stimuli, coupled with a failure to acknowl­ However, rumination (ruminative brooding in particular) was related to
edge positive stimuli, may contribute to persistent rumination and the valence-specific impairments in inhibition and set shifting. That is,
inability to disengage from ruminative cycles. These authors are not the participants higher on rumination had more difficulty discarding in­
first to recognize negative emotion-related attentional biases in formation from previously negative cues, limiting their ability to both
depression; a large body of literature corroborates that depressed in­ shift mental sets to respond to a new target and inhibit prepotent re­
dividuals may both preferentially attend to negative stimuli and be less sponses to select information in line with new goals. This work high­
likely to attend to positive stimuli (Armstrong & Olatunji, 2012; Bou­ lights a specific role of rumination in the ability to shift attention away
huys, Geerts, & Gordijn, 1999; Bradley & Mathews, 1983; Bradley, from negative content and efficiently utilize executive function for goal-
Mogg, & Williams, 1995; Gotlib et al., 2004; Gur et al., 1992; Joormann relevant purposes.
& Gotlib, 2010; Joormann & Gotlib, 2007; Winer & Salem, 2016). Additional studies have shown that depression and, more specif­
Importantly, however, as Mathews and MacLeod (2005) assert in a ically, the tendency to ruminate, is associated with an impaired ability to
review of this literature, and as others (e.g., LeMoult & Gotlib, 2019) remove negative information from working memory and update its
have supported, the issue may not simply be that too much negative contents (e.g., Joormann & Gotlib, 2008). Meta-analytic work by Yang,
information is entering working memory and conscious awareness. Cao, Shields, Teng, and Liu (2017) assessing rumination and core ex­
Rather, a more important predictor of depression onset, severity, and ecutive functions found robust negative associations between rumina­
chronicity may be that this information is not making its way out. tion and both shifting and inhibition abilities. This again suggests that
Widely accepted models of working memory and executive functions through rumination, depressed persons do not only have too much
(e.g., Friedman et al., 2008; Miyake et al., 2000) focus on three specific negative irrelevant information making its way into working memory,
executive functions that are necessary for optimal functioning: (1) but have difficulty switching between mental sets (i.e., away from the
“updating” (adding or removing) of relevant (or irrelevant) information negative information) to address current goals.
in working memory, (2) “shifting” between tasks or mental states/sets, Additional research has shown that working memory capacity itself
and (3) “inhibiting” or suppressing automatic responses to stimuli. may be reduced because of depressive cognitions (e.g., ruminative
Because working memory is capacity limited, it is important that in­ thoughts) (Hubbard et al., 2016). This work demonstrated that when a
dividuals continue to monitor and update the limited information that working memory span task is modified to include depressive cues,
can be held in working memory at any given time. Indeed, these func­ dysphoric individuals show greater deficits in working memory perfor­
tions have been targets for research in understanding cognition and mance than they do on a non-modified task. Importantly, this effect is
depression, as they are integral processes for the monitoring and enduring, such that when dysphoric individuals receive depressive cuing
removal of negative information and the integration of positive material in a task prior to the un-cued task, they perform worse on the un-cued
into working memory—two processes that, when disrupted, are illus­ task compared to dysphoric participants who received the two tasks in
trative of depressive symptomatology. the opposite order. Thus, when depressive thoughts become a part of
A body of work has shown an impairment in the ability to inhibit depressed individuals’ conscious awareness, working memory resources
negative information among a variety of relevant populations, including are occupied. Lyubomirsky, Kasri, and Zehm (2003) reported similar
depressed patients (Goeleven, De Raedt, Baert, & Koster, 2006) and findings in a study where they asked dysphoric and non-dysphoric in­
dysphoric undergraduates (Joormann & Siemer, 2004). Furthermore, dividuals to concentrate on either neutral, self-focused, general

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A.V. Rutherford et al. Clinical Psychology Review 101 (2023) 102255

emotion-focused, or symptom-focused (i.e., ruminative) thoughts before would like to also assert that computational methods may be one of
completing a series of cognitive tasks. They found that individuals who many particularly helpful tools for examining this question. Computa­
were dysphoric and asked to ruminate on their negative affect showed tional models have been used in some studies, and not others, which we
significantly poorer performance on the tasks, and also reported more argue is one limitation to this body of work. Computational research
task-irrelevant thoughts compared to non-dysphoric individuals and involves using precise mathematical formulas to make sense of the
dysphoric individuals not asked to ruminate. Taken together, this work behavioral data we gather. By doing so, we can probe and parse the
suggests that depressive rumination—and even the mere cuing of discrete mechanisms that underlie complex behaviors. Unfortunately,
depressive thoughts in individuals with depressive symptomatolo­ by only studying the general behavioral outcomes of reinforcement
gy—has a significant deleterious effect on working memory. learning tasks (e.g., overall accuracy), it is difficult to separate rein­
forcement learning effects from working memory effects (Collins &
5. “Be Happy” Frank, 2012; Eckstein, Wilbrecht, & Collins, 2021), a point we return to
below.
The aforementioned body of work demonstrates how ruminative To our knowledge, only two studies have directly examined rumi­
thoughts are tied both to depression-related outcomes and impaired nation and reinforcement learning. In one study, Whitmer, Frank, and
working memory and executive functioning. This work has critical im­ Gotlib (2012) experimentally induced rumination utilizing an induction
plications in considering how we treat the symptoms of depression. To protocol famously created by Nolen-Hoeksema and colleagues (Lyubo­
date, however, much of the work on the treatment of depression has mirsky & Nolen-Hoeksema, 1995; see Lyubomirsky, Layous, Chancellor,
focused on rumination’s role in maintaining negative affect, despite & Nelson, 2015 for a review of this literature). Dysphoric and non-
individuals with MDD consistently reporting the restoration of positive dysphoric individuals participated in this experiment and were
affect as their primary treatment goal (Demyttenaere et al., 2015). The assigned to either rumination or distraction conditions. In the rumina­
literature reviewed above suggests that through rumination, depressed tion condition, participants were instructed to concentrate on a series of
persons do not only have too much negative irrelevant information prompts that were self-focused and abstract, such as, “Think about why
intruding into working memory but also have difficulty switching be­ you react the way you do.” In the distraction condition, participants
tween mental sets (i.e., away from negative information) to complete were instructed to concentrate on prompts that were not self-focused,
their goals. were concrete, and were neutral in valence, such as “Think about a
As such—and as illustrated in Fig. 1—it is also possible that the boat slowly crossing the Atlantic.” The distraction inductions therefore
process of rumination, which taxes core executive functions, interferes served as a control condition. Participants then completed the Proba­
with one’s ability to learn from rewards in the environment, and thus bilistic Selection Task (PST; Frank, Seeberger, & O’reilly, 2004) and,
might also constrain positive affect (i.e., anhedonia). In other words, although computational modeling was not performed, behavioral re­
researchers have leveraged cognitive processes to understand what sponses to reward and punishment were analyzed. The researchers
makes “don’t ruminate” a difficult task, but has yet to leverage cognitive found that dysphoric individuals in the rumination condition more often
processes to gain a better understanding of what makes “be happy” chose stimuli which rarely yielded rewards. Thus, participants demon­
equally challenging. strated an impaired ability to learn which actions were not worthwhile.
Past ideas about anhedonia and reward-related deficits in depression Translating this to the clinical presentation of rumination and its dis­
have viewed reinforcement learning as a singular process stemming ruptions to learning, we can think back to our earlier example of John,
from the generation of reward prediction errors which guide our future the individual who forgot his lunch on his way to work. For an already
behavior (see Glimcher, 2011 for a review). However, attention must be dysphoric individual like John, these results help to explain his
paid to the process of rumination and how cognitive processes may play continued engagement in ruminative thinking about his forgetfulness
a pivotal role in the learning deficits that contribute to anhedonia. As and failures, such as “What is wrong with me?” and “Why can’t I do
such, future work should examine how perseverative cognition affects anything right?” despite the fact that such mental queries reliably fail to
working memory and interferes with reinforcement learning to guide provide him reassurance or to alter his mood.
human behavior and affect in depressed individuals. In another study, the relation between rumination and reinforcement
learning was interrogated with particular attention paid to the role of
6. How to study the question at hand selective attention. In this study, rumination was manipulated within
subjects using a modified rumination induction, while participants
We have made the case for a need to interrogate the relation between completed a multidimensional learning task designed to rely on atten­
rumination, working memory, and reinforcement learning to better tion. Results of this study demonstrated that state rumination did impair
understand the maintenance of anhedonia as it occurs in depression. We performance learning, but that this impairment was not related to

Fig. 1. Rumination taxes facets of working memory (i.e., shifting inhibition and updating skills). Deficits in working memory interfere with the ability to properly
carry out reinforcement learning. Decreased reinforcement learning ability is associated with symptoms of anhedonia. As such, rumination indirectly affects the
ability to learn from reinforcements via its effects on working memory.

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A.V. Rutherford et al. Clinical Psychology Review 101 (2023) 102255

participants’ attentional breadth, which was the authors’ proposed frameworks should prove useful for gathering precise measurements of
mechanism for learning disruption (Hitchcock et al., 2022). The authors specific clinical deficits in depression as well.
note that it is important to for future research to capitalize on additional Above work, such as that of Rupprechter et al. (2018), demonstrates
computational models, which may capture other precise processes un­ how broad behavioral findings, such as patients with MDD (as compared
derlying these learning deficits. to controls) perform worse in learning overall can be attributed to spe­
Whitmer et al. (2012) and Hitchcock et al. (2022) have provided the cific mechanisms that underlie behavioral dysfunction (e.g., the ability
only evidence, to our knowledge, of investigations regarding rumination to encode memory of reward and update representations of values) via
and its effect on reinforcement learning. Thus, though we have theo­ computational modeling. Examples such as these highlight the need to
retical (see above sections) and empirical (e.g., Jones, Siegle, Muelly, further explore computational methods to gain a better understanding of
Haggerty, & Ghinassi, 2010; Kaiser et al., 2016; Watkins & Brown, 2002) what precisely goes awry when individuals with MDD and those who are
reason to believe that rumination’s interference with working memory actively ruminating exhibit learning difficulties, and how this might
plays a pivotal role in the ability to execute successful reinforcement relate to a general impaired ability to learn from rewards in one’s
learning, this has yet to be clearly interrogated scientifically. environment, as is fundamental to anhedonic clinical presentations.
Another reason that relations among rumination, working memory,
and reinforcement learning, have been so difficult to study, is that many 7. Future directions and limitations
studies on depression and cognition utilize different paradigms and
computational frameworks, making it difficult to draw general conclu­ We have outlined a hypothesis explaining how rumination, working
sions. For example, Blanco, Otto, Maddox, Beevers, and Love (2013) memory, and reinforcement learning may be linked to contribute to and
used a reinforcement learning task and computational modeling to maintain anhedonia in depressed individuals throughout this review.
dissociate model-based learning (i.e., learning that uses a rich model of However, it is important to note a few limitations and areas for future
the task environment) from model-free learning (i.e., a simpler habitual research. Firstly, though we note that the body of work interrogating
learning system). Depressive individuals’ choices were better explained cognitive mechanisms underlying anhedonia is limited, one existent
by the simpler reinforcement learning model, suggesting that depression theory is worth reviewing. One important line of research has used
may be associated with a model-based reinforcement learning deficit cognition to help explain why appetitive, reward-seeking behaviors are
(Huys et al., 2013). In another recent example, Rupprechter, Stankevi­ decreased in depression and deserves mentioning. One such theory, the
cius, Huys, Steele, and Seriès (2018) observed diminished memory of reward devaluation theory, proposes that depressed individuals actively
reward history in participants with MDD during a Pavlovian reward- avoid positive/rewarding stimuli due to a chronic deficit in approach
learning task. Finally, a recent study using a reversal learning task motivation (Winer & Salem, 2016). Proponents of this theory have
revealed slower adjustment to abrupt changes in task contingencies in argued that due to experiences throughout development, individuals
MDD (Mukherjee, Filipowicz, Vo, Satterthwaite, & Kable, 2020). Could with psychopathology have learned that positive information may
these three examples – an association of depressive symptoms with actually be more threatening than neutral information. This is because,
attenuated model-based reinforcement learning, reduced memory of unlike negative information, which cues avoidance and protective
recent reward history, and disrupted reversal learning – be explained by behavior, positive information promotes approach behavior, only to
a similar underlying deficit, perhaps involving executive function and disappoint—or even endanger—the depressed individual if and when it
working memory? Here, computational psychiatric approaches could proves negative or harmful. Reward devaluation theorists therefore
help synthesize disparate results from different tasks by formalizing propose that depressed individuals show biases away from positive in­
general cognitive systems that may be useful across those tasks. formation/stimuli as a result. This theory also suggests that individuals
One paradigm that shows particular promise for approaching this suffering from depression show a propensity to diminish or eliminate
question involves use of a task and computational model developed by their positive responses to rewards when they are unable to avoid pos­
Collins and colleagues, called the Reinforcement Learning-Working itive information altogether. The latter portion of this theory is well
Memory Task (RLWM; 2012). Importantly, this task embeds a working aligned with traditional theories of “emotional dampening,” which is an
memory manipulation within a simple reinforcement learning para­ emotion regulation strategy akin to rumination in which individuals
digm, allowing researchers to separately examine the effects of these down-regulate positive emotions rather than savoring them. Indeed,
two processes. In this task, participants learn to pick one of three actions many studies have shown that depression and depressive symptoms
in response to stimuli over the course of 12 separate experimental (specifically anhedonia) are strongly associated with the use of positive
“blocks.” Set size in each block varies from n = 2 to n = 5, allowing one emotion dampening (e.g., Feldman, Joormann, & Johnson, 2008; Raes,
to model the separate influences of capacity-limited working memory Smets, Nelis, & Schoofs, 2012; Werner-Seidler, Banks, Dunn, & Moulds,
from incremental reinforcement learning systems (see Eckstein et al., 2013) and reward devaluation theory may help to explain why.
2021 for a review on this topic). This powerful task has recently been Our proposed hypothesis differs from reward devaluation theory in a
used in the clinical setting: Compared to healthy controls, patients crucial regard, however, by highlighting differential deficits in antici­
diagnosed with schizophrenia not only perform worse (in terms of patory/approach motivation vs. consumption phases of reward pro­
learning) overall, but their deficits can be accounted for by changes in cessing. Though individuals with depression may experience an urge to
working memory parameters (Collins et al., 2017). Interestingly, the “turn down” positive emotions for fear that a negative event may loom
computational model parameters and behavioral analyses in that study around the corner, we highlight that these individuals do have the
indicated that reward-based reinforcement learning was actually unaf­ ability to experience pleasure, nonetheless. As such, over time, princi­
fected in that group. Thus, previous theories that linked schizophrenia ples of basic reinforcement learning would suggest these individuals
and reinforcement learning may have conflated deficits in working would modify their behavior to maximize rewards. Unfortunately,
memory and deficits in learning. Though it should be noted that studies showing a key deficit in the ability to anticipate and project the
schizophrenia is a categorically different psychiatric illness than major magnitude of a rewards in depression (e.g., Treadway et al., 2012)
depression, this illustrates one example of how computational modeling suggest that the value of rewards are, in fact, not being updated overall.
can—and has been—applied to behavioral data to parse working We hypothesize that deficits in working memory due to ruminative
memory and reinforcement learning dynamics and answer complex processes play a key role in disrupting this learning process. It may be
clinical questions. Unfortunately, there is no published work disen­ the case, however, that post-event value diminishment (as in reward
tangling working memory versus reinforcement learning deficits in devaluation) may also play a role, and this should certainly be interro­
depression, suggesting this is an area ripe for future research. Carefully gated in future research.
designed tasks such as RLWM and more holistic computational Secondly, in this paper, we refer to rumination broadly as the process

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A.V. Rutherford et al. Clinical Psychology Review 101 (2023) 102255

of perseverative, negative thinking about one’s thoughts and feelings, example, rumination may not impair working memory (e.g., De Liss­
and their causes and consequences. Research has shown, however, that nyder et al., 2012; Joormann, Levens, & Gotlib, 2011; Yang et al., 2017)
rumination is a multi-faceted construct (Bernstein, Heeren, & McNally, but deficits in working memory may also lead to an increase in rumi­
2019), distinguished by at least two major types of rumination: brooding native thoughts (Cohen, Mor, & Henik, 2015; Hoorelbeke, Koster,
and reflection (Schoofs, Hermans, & Raes, 2010). Whereas brooding Vanderhasselt, Callewaert, & Demeyer, 2015). As such, we emphasize
tends to be characterized by negative thoughts about one’s internal that our model is theoretical in nature, based on extant and distinct
states and is shown to be a transdiagnostic feature of many psychiatric bodies of work, rather than the results of a systematic review. It will be
disorders (Watkins, 2009), reflective rumination describes a process in important for future empirical work to interrogate causality among the
which one analyzes past events for problem-solving. Indeed, the latter relationships outlined in this paper.
form of rumination can be effective (e.g., Kross, 2009; Kross, Gard,
Deldin, Clifton, & Ayduk, 2012a, 2012b); however, it is not generally the 8. Implications for treatment
common form of rumination reported and treated in depression, and
high rates of brooding are reported in mood disorders (Olatunji, Having established that treatments for depression show lackluster
Naragon-Gainey, & Wolitzky-Taylor, 2013). As such, though we referred rates of success overall and are particularly poor at restoring positive
to rumination as a general process here, we acknowledge that it, too, is a affect (Cuijpers et al., 2020), it is fundamental that we consider how a
heterogeneous construct and the type of rumination we consider fitting cognitive perspective of anhedonia may translate to improved treatment
most into our framework would be characterized more closely to outcomes. While most treatments for depression have focused on low
emotion-driven brooding rather than problem-solving reflection. mood states rather than deficits in the appetitive symptom domain
Further work should explore nuances in how the type of rumination one (Craske, Meuret, Ritz, Treanor, & Dour, 2016), a few treatments posit
engages with is associated with executive functioning. the importance of targeting anticipatory and motivational deficits
Thirdly, we present computational modeling in this paper as one related to anhedonia. For example, a common first-line therapy for in­
very helpful tool for interrogating complex relationships among clinical dividuals with MDD who exhibit distress and inhibited experiencing of
symptoms. Nevertheless, is important to note that interpretations of positive affect is Behavioral Activation therapy (BA; Ferster, 1973;
parameters derived from a model rely on the assumption that one’s Lewinsohn, 1974; Lewinsohn & Graf, 1973). In BA, patients are
model is valid. Though model validity (i.e., how “accurate” a compu­ instructed to use pleasant activity schedules to actively plan when,
tational model is at measuring a specific phenomenon) is a difficult issue throughout a week, they can partake in rewarding activities. Thus, BA
in this area of research, there are several strategies future researchers attempts to increase overt behaviors that will bring patients into contact
can implement to help increase the validity of their models. For with reinforcing environmental contingencies (Hopko, Armento, Cantu,
example, out-of-set cross-validation is one useful way to test the ability Chambers, & Lejuez, 2003).
of a model to generalize across subjects. Moreover, directly comparing Meta-analytic work has shown that BA is remarkably effective for
the capacity of model-derived metrics (i.e., free parameters fit to subject short-term treatment of anhedonia in depression (see Cuijpers, van
behavior) versus behavioral summary metrics (e.g., % correct in a Straten, & Warmerdam, 2007, for a review). Treatment work indicates
learning task) to predict depressive symptomology in a sample will be that BA is useful insofar as it creates a structure by which depressed
key to validating models (e.g., Mukherjee et al., 2020). Additionally, individuals must engage with reinforcing contingencies; the premise
testing generative performance of a given model (i.e., the ability to being, if one engages, they will experience the benefit of the reward.
reproduce the behavioral effect of interest in simulated datasets) will Nevertheless, BA is generally a short-term treatment, in part, because
also greatly improve model validity (e.g., Palminteri, Wyart, & Koechlin, psychotherapists cannot sustain pleasant activity scheduling as a long-
2017). Finally, the combined modeling of behavioral and neuroimaging term treatment solution. Furthermore, work suggests that cognitive
data can help ground computational theories of learning, and the effects interference may hinder individuals with depression from actually
of depression on learning, in the brain (e.g., Rutledge et al., 2017). “learning” from the reinforcements that BA brings individuals into
Lastly, we would like to note and emphasize two separate points. contact with, such that behavior can be sustainably modified in the long-
First, many learning theories of depression have focused on how rumi­ term (Martell, Dimidjian, & Herman-Dunn, 2013, pp. 129–148).
nation is “reinforced” and maintained, because it is viewed by those with As cognitive deficits and biases have been shown to play a key role in
depression as having a positive value (e.g., gaining deeper insight and depressive symptomatology, prior research has looked to trainings tar­
understanding of one’s problems) (see Ramnerö, Folke, & Kanter, 2016 geting these domains. Unfortunately, cognitive bias modifications (e.g.,
and Watkins & Nolen-Hoeksema, 2014 for learning theory accounts of attention bias modification, interpretation bias modification, approach/
depression). In Ramnerö et al. (2016) paper on learning theory in avoidance training) have shown mixed results for depressed individuals,
depression, the authors state that rumination is maintained through with meta-analytic work showing small improvements with low reli­
both negative (e.g., Martell, Addis, & Jacobson, 2001) and positive (e.g., ability (e.g., Fodor et al., 2020). Similarly, treatments focused on
Nolen-Hoeksema, 2013; Wray, Dougher, Hamilton, & Guinther, 2012) training cognitive deficits (e.g., cognitive control, working memory,
reinforcement. However, the authors also state that “while engaged in a motor speed, verbal fluency, etc.) through “drill-and-practice” methods
ruminative process, the individual may be less sensitive to the actual have also proven largely unsuccessful at treating depressive symptoms
contingencies that triggered the process. This will have the further effect long term (e.g., Legemaat et al., 2021). Despite the clear cognitive im­
of reducing the individual’s contact with other potentially rewarding pairments in depression, these trainings have not been efficacious at
and reinforcing events and impairing the individual’s ability to actively treating depression, in part because the field is still not clear what the
cope with the events that actually could be resolved with better contact exact mechanisms are that maintain these impairments in depression,
with the situation. Such consequences may be the common denominator making them extremely difficult to target.
in depressogenic learning processes (Kanter, Busch, Weeks, & Landes, Taylor, Lyubomirsky, and Stein (2017) recently developed and
2008; Martell et al., 2001; Nolen-Hoeksema, Wisco, & Lyubomirsky, piloted another therapy called “positive activity intervention,” which
2008)” (pp. 76–77). It is this latter point that we aim to address in this was also designed to specifically target the restoration of positive affect
paper. in those with depression and anxiety. This therapy entailed a 10-session
Second, we would like to note that the model we have presented in protocol of scheduled positive activity interventions to improve positive
this review is a theory outlining only one pathway through which in thinking, emotions and behaviors, such as practicing gratitude and acts
which rumination may lead to anhedonia (by taxing working memory of kindness. Though the treatment was shown to be effective within the
and interfering with reinforcement learning processes). However, it is authors’ pilot study, this small cohort of 29 participants are the only
very much possible that other connections and feedback loops exist. For reported data using this treatment, and it is not widely accessible.

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A.V. Rutherford et al. Clinical Psychology Review 101 (2023) 102255

Additionally, we raise similar concerns with this treatment as with sets (i.e., away from the negative information) and updating the content
behavioral activation, as this treatment does not address potential issues of working memory in order to complete necessary goals. As such, the
of cognitive interference. process of rumination taxes core executive functions and working
Another new behavioral therapy that has been geared towards both memory (by way of shifting, inhibition, and updating), therefore inter­
increasing positive affect while decreasing negative affect is the fering with one’s ability to learn from rewarding cues in the environ­
Augmented Depression Treatment (ADepT), developed by Dunn et al. ment, contributing to deficits in anticipatory pleasure as seen in
(2019). ADepT is characterized as a “solution-focused, cognitively depressed individuals. We propose that the burgeoning field of compu­
augmented, behavioural activation individual therapy approach” tational psychiatry can provide important insight into the relations
(Dunn, Widnall, Reed, Owens, et al., 2019, pp. 2) that consists of 15 among rumination, working memory, and reinforcement learning, and
“acute treatment sessions” and up to 5 “booster” sessions. Throughout allow for more theoretically precise clinical frameworks. We are opti­
these sessions, clients are encouraged to identify goals consistent with mistic that such research findings will continue to lead to improved
their values and behaviorally activate towards those goals, while treatments for individuals experiencing anhedonia, for whom standard-
simultaneously identifying and “acting opposite to” negative cognitions of-care treatments have shown immense difficulty treating.
such as rumination, avoidance thoughts, self-criticism, dampening, etc. Declaration of Competing Interest
As such, positive and negative affect are targeted in tandem to both This research did not receive any specific grant from funding
decrease depressive symptoms and increase wellbeing. Encouragingly, agencies in the public, commercial, or not-for-profit sectors. The authors
when comparing the effect sizes from a pilot trial of ADepT to effect sizes do not have any conflicts of interest to disclose.
in other datasets that have implemented CBT and BA, ADepT demon­
strated superior outcomes in treating anhedonia (Dunn et al., 2019; Data availability
Dunn, Widnall, Reed, Owens, et al., 2019). That said, RCTs are needed to
replicate this finding on a larger scale. No data was used for the research described in the article.
Lastly, a similarly promising avenue for treatment that has arisen at
the intersection of cognition and anhedonia is a newly developed References
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