1 text count = 4128 2 text pages = 15 3 tables = 1 4 figures = 1 5 references = 65 6 7Running Head: Inhibitory Learning Prediction Error Feedback Loop 8 9 Inhibitory Learning as Prediction Error Feedback Loop: 10 A Neurocognitive Framework & Model 11 12 Matthew S. Price1 13Affiliation: 141University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical 15Sciences 16Corresponding author: 17Matthew S. Price, 6767 Bertner Ave, Houston, TX 77030. [email protected] 18Prior presentations: None. 19Conflicts of interest: None. 20Declarations of interest: None. 21Funding sources: No public, commercial, or not-for-profit-sector grant funding was received. 22Keywords: prediction error, inhibitory learning, emotion regulation, intolerance of uncertainty 23 24 25 26 27 28 29 30 31 32 33
34
35
36
37
38 Inhibitory Learning Prediction Error Feedback Loop
1Abstract
2Inhibitory learning promotes emotion regulation via systematic exposure to fear-inducing stimuli. Given
3that inconsistencies between expectations, states, and outcomes may be experienced as elements of
4inhibitory learning, to what extent are prediction errors – mismatches between expectations and
5outcomes – a core neural element of inhibitory learning? This paper takes a complex systems approach
6to prediction errors and postulates that a prediction error feedback loop – a series of self-perpetuating
7disparities between expected and perceived outcomes – could be a correlate of or responsible for
8improved emotion regulation from inhibitory learning. The inhibitory learning prediction error feedback
9loop may additionally elucidate how human and animal studies demonstrate improved emotion
10regulation in the form of reduced fear responses without exposure to specific fear-inducing stimuli.
11Introduction
12Inhibitory learning is a behavioral learning approach employed by many modern psychotherapies that
13promote emotion regulation via systematic interaction with fear-inducing stimuli (Abramowitz & Arch
142014; Blakey & Abramowitz 2016; Gross 2015; Knowles & Olatunji 2019). Given that inhibitory learning
15involves discrepancies between: a) expectations; b) subjective states experienced due to exposure to
16fear-inducing stimuli; and c) the eventual outcome (emotion regulation), could a neuroeconomic
17process that relies on prediction errors – neural signaling that occurs as a result of mismatches
18between expectations and perceived outcomes – account for improvements in emotion regulation from
19the behavioral strategy?
20This paper aims to conceptualize inhibitory learning as a prediction error feedback loop, i.e., a neural
21algorithm of self-promoting mismatches between perceived and expected outcomes, that correlates
22with or promotes emotion regulation. Features of the inhibitory learning prediction error feedback loop
23may elucidate how emotion regulation in the form of reduced fear responses can be demonstrated
24without exposure to fear-inducing stimuli in animal and human studies. 1 Inhibitory Learning Prediction Error Feedback Loop
1Temporal Distance Learning & Prediction Errors
2Temporal difference learning is a type of unsupervised learning derived from the highly-influential
3Rescorla-Wagner model that suggests that stimulus–outcome associations may be formed during
4instrumental (or operant) learning — learning about rewards and punishments to guide appetitive and
5avoidant behavior — via comparisons of expectations or predictions to present experiences (Maia &
6Frank 2011; Miller, Barnet, & Grahame 1995; Robinson, Overstreet, Charney, Vytal, & Grillon 2013).
7During temporal difference learning, mental associations are mediated by differences or mismatch
8between predicted and perceived outcomes called prediction errors, which are hypothesized to encode
9the intensity of the arousal (surprise), as well as the valence (positivity/negativity), of a discrepancy
10between an expected reward or punishment and an outcome (Fouragnan, Retzler, & Philiastides 2018).
11The following types of prediction errors have been postulated to exist:
12 1. Reward prediction errors (RPE), which encode mismatch between expected and perceived
13 rewards. Behaviorally, RPE appear to update learned action valuations — differences between
14 a stimulus value (the ‘innate’ cost-benefit equation for the stimulus targeted for action) and
15 action costs (the cost of performing the behavior) (Suri, Sheppes, & Gross 2015). RPE may thus
16 provide information relevant to action selection — which action should be selected — not action
17 specification — how a selected action should be performed (Frömer, Nassar, Stürmer, Sommer,
18 & Yeung 2018). Two types of RPE are posited to exist:
19 a. positive RPE, which occur when outcomes are better than expected;
20 b. negative RPE, which occur due to failure to attain an expected reward;
21 2. Aversive prediction errors (APE), which encode mismatch between expected and perceived
22 punishments. Two types of APE are posited to exist:
23 a. positive APE, which occur when outcomes are worse than expected;
24 b. negative APE, which occur due to absence of an expected punishment (Roy et al. 2014).
2 Inhibitory Learning Prediction Error Feedback Loop
1Corticothalamic Circuits & the Development & Treatment of
2Mental Illness
3Significant research has focused on the roles of dopamine (DA) and cortico-basal ganglia-
4thalamocortical loops (CBGTC circuits) in operant learning. Mesolimbic DA encoding plays a central
5role in CBGTC circuits and prediction error signaling (Maia & Frank 2011) in a process known as
6predictive processing (Kaaronen 2018). CBGTC circuits compose feedback loops used to process
7affective, cognitive, and motor information (Marchand 2012) extensively involved in inhibitory control
8(Wei & Wang 2016). In the salience network (SN), CBGTC circuits are postulated to formulate a
9mechanism for the development and treatment of many psychiatric conditions (Peters, Dunlop, &
10Downar 2016). These circuits play a role in disorders such as addiction, schizophrenia, obsessive-
11compulsive disorder (OCD), posttraumatic stress disorder (PTSD), chronic pain, depression,
12anhedonia, and suicide (Cisler et al. 2018; Gradin et al. 2011; Maia & Frank 2011; Ploner, Sorg, &
13Gross 2017; Schmaal et al. 2019; Shin & Liberzon 2009; Ubl et al. 2015), which share emotion
14dysregulation as an etiological and/or maintenance factor (Akram et al. 2020; Der-Avakian & Markou
152012; Dvir, Ford, Hill, & Frazier 2014; Eskelund, Karstoft, & Andersen 2018; Garfield, Lubman, & Yücel
162014; Riquino, Priddy, Howard, & Garland 2018; Winer et al. 2017). Thus, abnormalities in CBGTC
17circuits and prediction errors are not only associated with mental health problems characterized by a
18lack of cognitive control over maladaptive thoughts, impulsive behaviors, and inattention to relevant
19internal and external stimuli (Peters et al. 2016), but these circuits may also be relevant to the treatment
20of disorders which share emotion dysregulation more generally as an etiological and/or maintenance
21factor.
22Expected and Actual Reward Values & Emotions
23Subjective states such as emotions may rely in whole or in part on prediction error encoding. Prediction
24errors and emotions commonly signify intensity of arousal (surprise) and are positively or negatively 3 Inhibitory Learning Prediction Error Feedback Loop
1valenced; however, emotions also depend on other cognitive factors such as uncertainty and
2perceptions/interpretations of interoceptive responses to external and internal stimuli (Anderson,
3Carleton, Diefenbach, & Han 2019; Fouragnan et al. 2018; Seth & Critchley 2013). In this regard,
4subjective states such as emotions may rely in whole or in part on mismatches between the actual
5value of a reward or punishment — which is thought to consist of three components: 1) the magnitude
6or size; 2) probability; and 3) timing (immediate or delayed) of a predicted reward or punishment — and
7the expected value of a reward or punishment, defined as the product of the probability and magnitude
8of a reward or punishment (Abler, Walter, Erk, Kammerer, & Spitzer 2006; Fouragnan et al. 2018).
9For example, in rodents and monkeys, optogenetic DA inhibition – negative RPE – has been found to
10induce avoidance behaviors (Schultz 2017). In humans, the emotion ‘frustration’ is associated with
11negative RPE (Toates 1988), which may in turn be linked to the cognitive state of uncertainty. Though
12uncertainty may arise when we doubt whether a situation or outcome will or will not occur (Wever,
13Smeets, & Sternheim 2015), uncertainty may rather be said to exist “when the likelihood of future
14events [rewards and/or punishments] is indefinite or incalculable” (Knight 1921). In other words,
15uncertainty may be said to exist when a null value (ø) is present in the probability component of an
16expected value of a reward or punishment. However, “probability or risk is calculable or estimable”
17(Knight 1921), i.e., is a result of environmental feedback updating expected values of rewards and
18punishments with actual values.
19The ability to calculate the probability of attaining a reward is important for a number of reasons, one of
20which is that action cost (theoretically a function of expected value) enhances RPE signaling in
21midbrain DA neurons (Tanaka, O’Doherty, & Sakagami 2019). Thus, the duration of time that a null
22value (ø) is not updated in the probability component of an actual value of a reward can become
23associated with a likelihood of non-attainment of that reward, given that prolonged frustration (negative
24RPE) may result in learned helplessness, if no rewarding solution to a problem is found (Grzyb,
4 Inhibitory Learning Prediction Error Feedback Loop
1Boedecker, Asada, Pobil, & Smith 2011). Uncertainty and frustration may therefore serve as salient
2contextual motivators to avoid expenditure of scarce resources and thus behaviors that could lead to
3high-magnitude negative RPE signaling. Alternatively, prolonged frustration (negative RPE) may also
4induce a high-magnitude positive RPE signal in the form of elation if a rewarding solution is found
5(Grzyb et al. 2011). Positive RPE has in general been postulated to be responsible for positive
6emotions and approach behavior (Schultz 2017).
7The probability component of the expected value of a punishment appears to play a role in APE, as
8well. Fear conditioning and anxiety are associated with positive APE (McNally, Johansen, & Blair 2011).
9However, fear may exist when there is a high probability of a specific punishment (a charging
10rhinoceros, for example), while anxiety may diffusely depend on the absence of a known probability –
11cognitive uncertainty – in the avoidance of an undesired outcome (Grupe & Nitschke 2013). Negative
12APE is associated with fear extinction and anxiety relief (Wright, DiLeo, & McDannald 2015).
13Emotion Regulation & Negative Reinforcement as a Reward
14Gross (2001) describes emotion regulation as the totality of the conscious and nonconscious methods
15that can be used to upregulate, maintain, or downregulate the subjective, behavioral, and physiological
16components of an emotional response. A component of emotion regulation relevant to inhibitory
17learning may therefore be a behavioral concept coined by Skinner (1938, 1963) – negative
18reinforcement – which describes the learned reduction in, or absence of, a negative response that
19tends to predict the repetition of the behavior that reduces or absents a subsequent negative response.
20In human populations, negative reinforcement appears to take two distinct forms, maladaptive and
21adaptive negative reinforcement:
22 1. Maladaptive negative reinforcement, probably most closely related to the commonly used
23 psychological term experiential avoidance, may be viewed as the negative situational evaluation
24 of internal negative phenomena (thoughts, feelings, sensations, conflicts) which economically 5 Inhibitory Learning Prediction Error Feedback Loop
1 values short-term cessation of negative responses, behaviorally leading to negative
2 reinforcement by means of avoiding an aversive stimulus, and promotes mental health issues
3 such as emotion dysregulation and psychopathology (Hayes, Wilson, Gifford, Follette, &
4 Strosahl 1996; Servatius 2016). Experiential avoidance is predicted by intolerance of
5 uncertainty, an individual’s tendency to experience conflict regarding their ability to endure the
6 possible occurrence of an unpleasant event for which insufficient information exists to make an
7 informed decision (Anderson et al. 2019; Carleton 2016; Lauriola et al. 2018; Lee, Orsillo,
8 Roemer, & Allen 2010; Wever et al. 2015). Intolerance of uncertainty is a transdiagnostic factor
9 in many disorders in which emotion dysregulation is a factor, such as generalized anxiety
10 disorder, panic disorder, social anxiety disorder, PTSD, OCD, eating disorders, and depression
11 (Boswell, Thompson-Hollands, Farchione, & Barlow 2013; Carleton 2016). High intolerance of
12 uncertainty tends to predict poor performance in fear extinction protocols (Morriss, Christakou, &
13 van Reekum 2015). A factor in this poor performance may be the correlation between high
14 intolerance of uncertainty and the tendency to select immediately available, but smaller
15 magnitude and less probable rewards (Luhmann, Ishida, & Hajcak 2011) (see Table 1).
16 2. Adaptive negative reinforcement, probably most closely related to the term inhibitory learning, is
17 the cognitive-behavioral strategy that values and promotes long-term extinction of negative
18 internal phenomena (thoughts, feelings, sensations, conflicts) via systematic, appetitive
19 interaction with aversive stimuli. Adaptive negative reinforcement is predicted and mediated by
20 distress tolerance, the dispositional tendency, either perceived or actual, to believe that
21 exposure to aversive or threatening stimuli may be endured in the service of goal-oriented
22 behaviors that are hypothesized to facilitate the development of extinction associations as a
23 function of automatic or bottom-up and effortful or top-down neurobehavioral phenomena
24 (Simons & Gaher 2005; Zvolensky, Leyro, Bernstein, & Vujanovic 2011). Distress tolerance is a
25 goal of many modern psychotherapies, which often employ inhibitory learning to facilitate 6 Inhibitory Learning Prediction Error Feedback Loop
1 reductions in negative responses (Abramowitz & Arch 2014; Blakey & Abramowitz 2016;
2 Knowles & Olatunji 2019) as an emotion regulation strategy (Gross 2015).
3Intolerance of uncertainty and distress tolerance are documented to be negatively correlated (Laposa,
4Collimore, Hawley, & Rector 2015). As such, experiential avoidance and intolerance of uncertainty may
5reflect a preference for immediate gratification in the pursuit of the reward of negative reinforcement,
6while inhibitory learning and distress tolerance may reflect a preference for delayed gratification in the
7pursuit of the reward of negative reinforcement (Simons & Gaher 2005; Zvolensky et al. 2011). Thus,
8being uncertain or not knowing how to achieve persistent inhibition of negative responses may cause a
9default behavioral preference for immediate, maladaptive negative reinforcement until an individual is
10educated in or discovers delayed, adaptive negative reinforcement as an alternative method (i.e.,
11inhibitory learning). The table below indicates dispositional preferences in strategies to both
12maladaptive and adaptive negative reinforcement in light of prediction error actual value components.
13 Table 1: Prediction Error Actual Value Component Tolerances for Adaptive and Maladaptive 14 Negative Reinforcement Strategies
Type of Negative Disposition Magnitude Probability Timing Reinforcement Adaptive Distress Tolerance Higher Higher Delayed Maladaptive Intolerance of Uncertainty Lower Lower Immediate
15Prediction Errors as a Framework for Inhibitory Learning
16In a clinical context, inhibitory learning is known as exposure therapy, which is viewed as a method of
17promoting fear extinction that stabilizes novel fear extinction associations. This process is known as
18consolidation and involves strengthening: a) top-down factors leading to cognitive control over fear
19responses due to prefrontal cortex-mediated inhibition of the amygdala, as well as b) bottom-up factors
20that modulate sensory processing of fear-provoking stimuli, such as reduced amygdala responsiveness
21(Björkstrand et al. 2020; Hauner, Mineka, Voss, & Paller 2012). Exposure is commonly conceptualized
22as requiring that a client systematically interact with fear-inducing stimuli that are either: a) in-vivo
7 Inhibitory Learning Prediction Error Feedback Loop
1(objects, situations, or activities); or b) internal (thoughts, somatic sensations, imaginations). Exposure
2has broad empirical support in the management of PTSD, comorbid PTSD and depression, OCD,
3phobias, and anxiety disorders (American Psychological Association 2017; Koran et al. 2007; Rauch,
4Eftekhari, & Ruzek 2012; Sewart & Craske 2020; Wolitzky-Taylor, Horowitz, Powers, & Telch 2008).
5Exposure: Neurophenomenology 6A neurocognitive framework and model for inhibitory learning may begin with a hypothetical client who
7seeks out a clinician for anxiety management. This client is likely motivated by the view that a persistent
8reduction in avoidant reactions (adaptive negative reinforcement) is a reward (Andreatta, Mühlberger,
9Yarali, Gerber, & Pauli 2010). However, the clinician will provide the client with psychoeducation prior to
10commencement of exposure that describes exposure as a process involving a cognitive shift away from
11viewing negative reinforcement as a maladaptive, immediate reward to an adaptive, expected reward
12that requires repeated interaction with aversive stimuli.
13At this time, the client may be inclined to ask themselves: How can repeatedly going through what I
14want to avoid get me where I want to go? Exposure may therefore – upon description of the therapy –
15tend to induce negative RPE signaling as an initial step in the therapeutic process, as well as establish
16a cognitive conflict between an expectation of an immediate punishment (states they want to avoid) and
17an expected reward (adaptive negative reinforcement).
18Exposure sessions may then induce positive APE encoding via multiple uncertainty-inducing
19mechanisms. First, treatment may promote positive APE signaling given that the client has decided to
20pursue a novel strategy of delayed, as opposed to immediate, reward-seeking, and the therapy is
21continuously inducing immediate ‘punishment’ states contrary to the expected reward of adaptive
22negative reinforcement. Thus, the ‘expected punishment’ of exposure may counterintuitively promote
23positive APE in light of the expected reward of adaptive negative reinforcement.
8 Inhibitory Learning Prediction Error Feedback Loop
1Relevantly, high rates of avoidance and drop-out from the therapy (Najavits 2015) may be explained
2due to the magnitude of the magnitude of the actual value of the ‘punishment’ of exposure and the high
3magnitude expected value of the reward of adaptive negative reinforcement (see Table 1) producing
4significant cognitive-affective conflict. However, the expected value of the reward of adaptive negative
5reinforcement does not reference a timing (immediate or delayed) component. Thus, the longer it takes
6for exposure to have an effect, a null value (ø) will persist in the probability component of the actual
7value of the reward of adaptive negative reinforcement for the client, potentiating learned helplessness
8and drop-out or avoidance. Importantly, the magnitude of a perceived mismatch between a prediction
9and an outcome is positively correlated with predictive learning and phasic DA release in the ventral
10striatum for both appetitive and aversive stimuli (Robinson et al. 2013), a mechanism that explains how
11this proposed conflict – between a high-magnitude actual value punishment and a high-magnitude
12expected value reward – can signal sufficient positive APE to motivate drop-out and avoidance. The
13expected reward of adaptive negative reinforcement is not deemed ‘worth’ the actual value of the
14‘punishment’ represented by exposure for many clients.
15A second and related mechanism via which exposure may induce positive APE signaling is via
16activation of the behavioral inhibition system. The behavioral inhibition system is a neural network that
17heightens arousal, increases attention to novel stimuli, and attenuates behavior for rapid adaptation
18that can be activated by an approach-approach conflict, where competing positive behavioral options
19invoke uncertainty and thus create the perception of a threat, creating anxiety (Anderson et al. 2019).
20The behavioral inhibition system is reliably activated during in-vivo exposure to fear-inducing stimuli
21(Wilhelm et al. 2005). In this regard, the cognitive conflict between immediate noxious states and an
22expected reward could generate sufficient cognitive uncertainty – puzzlement – as the client cannot be
23sure which of two conflicting ‘positive’ behavioral options – continuing to engage in adaptive negative
24reinforcement or dropping out of therapy – is the best course of action. Thus, repeated engagement
25with this approach-approach conflict, which is documented to induce APE (Anderson et al. 2019), and
9 Inhibitory Learning Prediction Error Feedback Loop
1the uncertainty generated by the mismatch between an expected reward and the subjective state
2endured during exposure, may also induce positive APE signaling as a step subsequent to negative
3RPE signaling.
4Phenomenologically, the uncertainty experienced by the client during exposure may be described by
5the concept cognitive dissonance (CD). CD theory claims that individuals are: a) incentivized to create
6consistency in cognition, behavior, and environment and b) may suffer psychological discomfort –
7dissonance – when new evidence or re-evaluated decisions reveal inconsistency (Festinger 1957). CD
8theory is highly compatible with recent developments in the field of predictive processing and indicates
9that prediction errors may evoke distressing differences between predicted and perceived outcomes, or
10predictive dissonance (Kaaronen 2018). Predictive dissonance may induce attempts to avoid situations
11and information that increase dissonance, such as by changing 1) the related dissonant behavior or
12cognition; or 2) the value of the dissonant cognition by either: a) conscious devaluation of the
13cognition’s salience; or b) acquiring new information that reduces the magnitude of the dissonance
14(Festinger 1957). Directional negative RPE→positive APE signaling may therefore be a neural
15substrate of phenomenological CD and uncertainty during exposure.
16Though clients may be uncertain about the benefits of exposure initially, the process is effective and
17leads to gradual negative reinforcement of avoidant behaviors via relief (Leyro, Zvolensky, & Bernstein
182010), as well as reduced amygdala activation as a potential core mechanism for the effects of
19exposure (Björkstrand et al. 2020; Zhu et al. 2018). This could indicate that negative APE signaling,
20where an expected punishment is omitted or found not to be as bad as expected, is also a component
21of exposure that may emerge subsequently to positive APE signaling.
22Lastly, to the extent that exposure therapy eventually works better than the client expected — which a
23client may tend to experience given the paradoxical phenomenology of exposure — positive RPE
24signaling may be the last step in an algorithmic process of operant unlearning of maladaptive
10 Inhibitory Learning Prediction Error Feedback Loop
1associations created as a result of stressful life events. Evidence for the timing of positive RPE in this
2sequence may be found in several studies. Papalini, Beckers, and Vervliet (2020) found that positive
3RPE-induced dopaminergic activity is linked to learning safe memories, while Cisler et al. (2020) found
4that augmenting exposure with the dopamine precursor L-DOPA at 45 minutes post exposure can
5decrease reinstatement of fear memories at 24 hours post administration in PTSD. Additionally, deficits
6in midbrain and prefrontal dopaminergic activity can inhibit positive RPE-induced learning of safe
7memories (Papalini et al. 2020). Taken together, these studies provide support for the timing of positive
8RPE signaling as a final step in what may be termed the inhibitory learning prediction error feedback
9loop.
10The Inhibitory Learning Prediction Error Feedback Loop 11The inhibitory learning prediction error feedback loop (see Fig. 1 below) appears to be a
12neuroeconomic algorithm — a self-sustaining code of neural activity reflecting rational opportunity-cost
13decision-making — that could either constitute a neural pathway for developing control over fear
14responses as a result of inhibitory learning due to exposure or be a neural correlate of that process.
15The inhibitory learning prediction error feedback loop appears to promote a permanent cognitive shift
16away from viewing negative reinforcement as an immediate reward (i.e., from a maladaptive
17perspective) to one that views negative reinforcement as a delayed reward (i.e., from an adaptive
18perspective).
19 Figure 1: Model of the Inhibitory Learning Prediction Error Feedback Loop
11 Inhibitory Learning Prediction Error Feedback Loop
1. NegativeRPE Positive4. RPE Positive2. APE Negative3. APE
1
2Extinction as a Process of Association of Disparate Prediction Errors 3Though it is not known how the inhibitory learning prediction error feedback loop may function in short-
4or long-term resolutions, the inhibitory learning prediction error feedback loop may create associations
5between otherwise disparate prediction errors, leading to the ‘collapse’ of the inhibitory learning
6feedback loop as an essential element of fear extinction. This ‘collapse’ of the feedback loop may be a
7promising avenue for research for improved psychotherapeutic interventions.
8In this regard, avoidant reactions (negative RPE and positive APE), which may motivate avoidance and
9drop-out initially in exposure interventions, may paradoxically be interpreted as a motivation to
10approach inhibitory learning as a behavior after some duration of exposure and a reduction in negative
11reactions in latter stages of the inhibitory learning process. In other words, after some time in exposure
12therapy and a reduction in negative responses is attained, positive APE (anxiety) may become
13associated with or even replaced by negative APE (relief) in the feedback loop. This relief may then be
14associated with positive RPE. Thus, negative RPE could in time be associated with positive RPE as the
15client realizes the short-term noxious effort is the right first step toward the client’s long-term goal of
16adaptive negative reinforcement. Importantly, rewards that are received in reference to well-conditioned
17or predicted stimuli that are exactly as rewarding as expected do not elicit changes in mesolimbic DA
18neuron firing rates (Abler et al. 2006; Postle 2015; Schultz 2016). Thus, extinction may be definable
19neurally as an absence of prediction error signaling relative to a stimulus that previously induced fear.
12 Inhibitory Learning Prediction Error Feedback Loop
1‘Short-circuiting’ the Feedback Loop 2Human and animal studies provide evidence for the ability to ‘short-circuit’ the inhibitory learning
3prediction error feedback loop by immediately pairing negative RPE followed by positive RPE as a
4method of fear reduction, as suggested above. Taschereau-Dumouchel et al. (2018) utilized a
5procedure known as ‘hyperalignment’ that used fMRI scans to decode a ‘template’ of neural activation
6patterns in the ventral temporal cortex in response to a fear-provoking animal in ‘surrogate’ participants
7that did not include actual study participants. This template was then compared to fMRI scans in a
8cohort of actual participants who performed a cognitive task that authors termed ‘neural reinforcement’.
9The goal of this task was only to enlarge the size of an image of a disk while being scanned.
10Participants were not instructed how to enlarge the image of the disk, but when the scanner detected
11hyperalignment patterns reflecting those of a naturally feared animal in the original ‘template’ sample,
12participants were then monetarily rewarded in a manner positively correlated with the activation of the
13template network at the end of each block of trials. The authors found a negative correlation between
14participants learning to activate the ‘template’ network and post-task amygdala activation and skin
15conductance response when subsequently shown phobic imagery, the effect size of which was similar
16to exposure therapy. A similar study by this group (Koizumi et al. 2017) found a negative correlation
17between learning to activate the ‘template’ network and post-task amygdala activation and skin
18conductance response relative to a reinforced fear stimulus (CS+) in participants who previously
19performed the ‘neural reinforcement’ task. Relatedly, Redondo et al. (2014) found that in mice, fear
20responses could be reduced by pairing a CS+ (in the form of a fear conditioned location that had been
21optogenetically reactivated) with a reward. Specifically, Redondo et al. found that the valence of a CS+
22response in neurons in the dorsolateral gyrus of the amygdala can be reversed by following this CS+
23with an oppositely-valenced unconditioned stimulus (UCS).
24A possible mechanism for fear reduction in the absence of an explicit fear stimulus may be suggested
25by an ‘artificial’ or ‘engineered’ collapse of the inhibitory learning prediction error feedback loop. Notably
13 Inhibitory Learning Prediction Error Feedback Loop
1in the ‘neural realignment’ task, the creation of a reward task with no instructions was likely to induce
2negative RPE signaling (failure to obtain an expected reward) and make the task seem ‘difficult’,
3inducing uncertainty – puzzlement – as to how to attain the reward. Usually, people do not wish to
4engage in an effort that cannot be reasonably justified to produce a reward, much less one for which
5there is no known method of attaining it. Nevertheless, this encoding may have then been followed by
6the monetary reward, which was, by design, unexpected, and thus a positive RPE (which itself may
7also have been puzzling to participants). Additionally, Redondo et al. likely induced negative RPE (and
8possibly positive APE) upon optogenetic reactivation of the conditioned fear location in mice, and then
9followed this encoding immediately by positive RPE in the form of a positively-valenced UCS.
10Thus, it may be possible to ‘short-circuit’ the feedback loop by immediately pairing negative RPE
11followed by positive RPE (and thereby maintaining uncertainty induction). ‘Short-circuiting’ the feedback
12loop may also aid in ‘short-cutting’ the ‘temporal distance’ between punishment and reward in a
13theoretical hybrid exposure/’short-circuit’ intervention, thereby permitting a client to update the null
14value (ø) in the timing component of the actual value of the reward of adaptive negative reinforcement
15more quickly, improving retention. This effect of the ‘short-circuit’ is especially important given that
16factors such as early life trauma have been demonstrated to inhibit adult use of negative APE to reduce
17fear in response to uncertainty (Wright et al. 2015). Thus, eliminating this step in the feedback loop may
18prove a fruitful method of improving clinical fear-reduction protocols.
19Horizons & Recommendations
20The inhibitory learning prediction error feedback loop could serve as a platform for transdiagnostic
21neuropsychiatric research within the National Institute of Mental Health’s Research and Domain Criteria
22Frustrative Nonreward Construct in the Negative Valence Systems Domain or under the Arousal
23Construct of the Arousal and Regulatory Systems Domain. CBGTC circuits are essential to reward
24learning and play a central role in psychiatric morbidity and treatment (Maia & Frank 2011; Peters et al.
14 Inhibitory Learning Prediction Error Feedback Loop
12016). Thus, the hypothesis may also inform an expanded scope for inhibitory learning theory that
2includes potential ‘unlearning’ of epigenetic reward network dysfunction. This conceptualization could
3be experimentally tested in a number of ways. fMRI or EEG could be used to evaluate related
4processes before, during and after episodic or continuous inhibitory learning protocols and to link those
5results to subjectively expected outcomes of the interventions.
6Conclusion
7This paper proposes that inhibitory learning during exposure involves a cognitive shift away from
8viewing negative reinforcement as a maladaptive, immediate reward to an adaptive, delayed reward
9that potentially utilizes or is correlated with a gestalt – a prediction error feedback loop, i.e., a
10neuroeconomic algorithm of self-promoting mismatches between expected and perceived outcomes. If
11correct, this hypothesis could indicate that inhibitory learning may heavily rely on
12puzzlement/uncertainty and gaining knowledge to update the probability component of the actual value
13of the reward of adaptive negative reinforcement, as well as the association or replacement of
14otherwise disparate prediction errors in the feedback loop, to promote emotion regulation. The
15hypothesis also provides a framework for how some experimental human and animal studies
16demonstrate reduced fear in the absence of any specific fear-inducing stimulus. If validated, the
17inhibitory learning prediction error feedback loop may aid in the development of new studies with the
18potential to improve outcomes.
19Acknowledgements
20The author would like to thank Dr. Thomas Meyer, Professor of Psychiatry, Faillace Department of
21Psychiatry and Behavioral Sciences, McGovern Medical School at UTHealth, Dr. Christopher Frueh,
22Professor of Psychology, University of Hawaii, Dr. Melba Hernandez-Tejada, Associate Professor of
23Psychiatry, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School at
24UTHealth, Dr. Katherine Loveland, Professor of Psychiatry, Faillace Department of Psychiatry and
15 Inhibitory Learning Prediction Error Feedback Loop
1Behavioral Sciences, McGovern Medical School at UTHealth, and Ms. Lisa Getz, LCSW, Memorial
2Hermann Health System, for their advice and suggesting changes to the manuscript.
3References
4Abler, B., Walter, H., Erk, S., Kammerer, H., & Spitzer, M. 2006. Prediction error as a linear function of reward 5 probability is coded in human nucleus accumbens. NeuroImage, 31(2), 790-795. 6 doi:10.1016/j.neuroimage.2006.01.001 7Abramowitz, J. S., & Arch, J. J. 2014. Strategies for improving long-term outcomes in cognitive behavioral 8 therapy for obsessive-compulsive disorder: Insights from learning theory. Cognitive and Behavioral 9 Practice, 21(1), 20-31. doi:10.1016/j.cbpra.2013.06.004 10Akram, U., Gardani, M., Irvine, K., Allen, S., Ypsilanti, A., Lazuras, L., . . . Akram, A. 2020. Emotion dysregulation 11 mediates the relationship between nightmares and psychotic experiences: results from a student 12 population.npj Schizophrenia, 6(1). doi:10.1038/s41537-020-0103-y 13American Psychological Association. 2017. Clinical practice guideline for the treatment of posttraumatic stress 14 disorder (PTSD) in adults. American Psychological Association Guideline Development Panel for the 15 Treatment of PTSD in Adults. Retrieved from https://www.apa.org/ptsd-guideline/ptsd.pdf 16Anderson, E. C., Carleton, R. N., Diefenbach, M., & Han, P. K. J. 2019. The relationship between uncertainty and 17 affect. Frontiers in Psychology, 10(2504). doi:10.3389/fpsyg.2019.02504 18Andreatta, M., Mühlberger, A., Yarali, A., Gerber, B., & Pauli, P. 2010. A rift between implicit and explicit 19 conditioned valence in human pain relief learning. Proceedings of the Royal Society B: Biological 20 Sciences, 277(1692), 2411-2416. doi:10.1098/rspb.2010.0103 21Björkstrand, J., Agren, T., Frick, A., Hjorth, O., Furmark, T., Fredrikson, M., & Åhs, F. 2020. Decrease in amygdala 22 activity during repeated exposure to spider images predicts avoidance behavior in spider fearful 23 individuals. Translational Psychiatry, 10(1). doi:10.1038/s41398-020-00887-2 24Blakey, S. M., & Abramowitz, J. S. 2016. The effects of safety behaviors during exposure therapy for anxiety: 25 Critical analysis from an inhibitory learning perspective. Clinical Psychology Review, 49, 1-15. 26 doi:10.1016/j.cpr.2016.07.002 27Boswell, J. F., Thompson-Hollands, J., Farchione, T. J., & Barlow, D. H. 2013. Intolerance of uncertainty: a 28 common factor in the treatment of emotional disorders. Journal of Clinical Psychology, 69(6), 630-645. 29 doi:10.1002/jclp.21965 30Carleton, R. N. 2016. Into the unknown: A review and synthesis of contemporary models involving uncertainty. 31 Journal of Anxiety Disorders, 39, 30-43. doi:10.1016/j.janxdis.2016.02.007 32Cisler, J. M., Esbensen, K., Sellnow, K., Ross, M., Weaver, S., Sartin-Tarm, A., . . . Kilts, C. D. 2018. Differential 33 roles of the salience network during prediction error encoding and facial emotion processing among 34 female adolescent assault victims. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 4(4), 35 371-380. doi:10.1016/j.bpsc.2018.08.014 36Cisler, J. M., Privratsky, A. A., Sartin-Tarm, A., Sellnow, K., Ross, M., Weaver, S., . . . Kilts, C. D. 2020. l-DOPA and 37 consolidation of fear extinction learning among women with posttraumatic stress disorder. Translational 38 Psychiatry, 10(1), 287. doi:10.1038/s41398-020-00975-3 39Der-Avakian, A., & Markou, A. 2012. The neurobiology of anhedonia and other reward-related deficits. Trends in 40 Neurosciences, 35(1), 68-77. doi:10.1016/j.tins.2011.11.005 41Dvir, Y., Ford, J. D., Hill, M., & Frazier, J. A. 2014. Childhood Maltreatment, Emotional Dysregulation, and 42 Psychiatric Comorbidities. Harvard Review of Psychiatry, 22(3), 149-161. 43 doi:10.1097/hrp.0000000000000014
16 Inhibitory Learning Prediction Error Feedback Loop
1Eskelund, K., Karstoft, K.-I., & Andersen, S. B. 2018. Anhedonia and emotional numbing in treatment-seeking 2 veterans: behavioural and electrophysiological responses to reward. European journal of 3 psychotraumatology, 9(1), 1446616-1446616. doi:10.1080/20008198.2018.1446616 4Festinger, L. 1957. A Theory of Cognitive Dissonance. Stanford, CA: Stanford University Press. 5Fouragnan, E., Retzler, C., & Philiastides, M. G. 2018. Separate neural representations of prediction error valence 6 and surprise: Evidence from an fMRI meta-analysis. Human Brain Mapping, 39(7), 2887-2906. 7 doi:10.1002/hbm.24047 8Frömer, R., Nassar, M., Stürmer, B., Sommer, W., & Yeung, N. 2018. I knew that! Confidence in outcome 9 prediction and its impact on feedback processing and learning. bioRxiv 442822. Retrieved from 10 https://www.biorxiv.org/content/10.1101/442822v1 11Garfield, J. B., Lubman, D. I., & Yücel, M. 2014. Anhedonia in substance use disorders: a systematic review of its 12 nature, course and clinical correlates. Australian & New Zealand Journal of Psychiatry, 48(1), 36-51. 13Gradin, V. B., Kumar, P., Waiter, G., Ahearn, T., Stickle, C., Milders, M., . . . Steele, J. D. 2011. Expected value and 14 prediction error abnormalities in depression and schizophrenia. Brain, 134(6), 1751-1764. 15 doi:10.1093/brain/awr059 16Gross, J. J. 2001. Emotion Regulation in Adulthood: Timing Is Everything. Current Directions in Psychological 17 Science, 10(6), 214-219. doi:10.1111/1467-8721.00152 18Gross, J. J. 2015. Emotion Regulation: Conceptual and Empirical Foundations. InHandbook of Emotion 19 Regulation, Second edition ed., (J. J. Gross ed.,^eds.), pp. xviii, 669. New York: The Guilford Press. 20Grupe, D. W., & Nitschke, J. B. 2013. Uncertainty and anticipation in anxiety: An integrated neurobiological and 21 psychological perspective. Nature Reviews Neuroscience, 14(7), 488-501. doi:10.1038/nrn3524 22Grzyb, B. J., Boedecker, J., Asada, M., Pobil, A. P. D., & Smith, L. B. 2011. Between frustration and elation: sense 23 of control regulates the lntrinsic motivation for motor learning. Paper presented at the Proceedings of 24 the 15th AAAI Conference on Lifelong Learning. 25Hauner, K. K., Mineka, S., Voss, J. L., & Paller, K. A. 2012. Exposure therapy triggers lasting reorganization of 26 neural fear processing. 109(23), 9203-9208. doi:10.1073/pnas.1205242109 27Hayes, S. C., Wilson, K. G., Gifford, E. V., Follette, V. M., & Strosahl, K. 1996. Experiential avoidance and 28 behavioral disorders: A functional dimensional approach to diagnosis and treatment. Journal of 29 Consulting and Clinical Psychology, 64(6), 1152–1168. doi:10.1037/0022-006x.64.6.1152 30Kaaronen, R. O. 2018. A theory of predictive dissonance: Predictive processing presents a new take on cognitive 31 dissonance. Frontiers in Psychology, 9. doi:10.3389/fpsyg.2018.02218 32Knight, F. H. 1921. Risk, Uncertainty, and Profit. Boston, MA: Houghton Mifflin. 33Knowles, K. A., & Olatunji, B. O. 2019. Enhancing Inhibitory Learning: The Utility of Variability in Exposure. 34 Cognitive and Behavioral Practice, 26(1), 186-200. doi:10.1016/j.cbpra.2017.12.001 35Koizumi, A., Amano, K., Cortese, A., Shibata, K., Yoshida, W., Seymour, B., . . . Lau, H. 2017. Fear reduction 36 without fear through reinforcement of neural activity that bypasses conscious exposure. Nature Human 37 Behaviour, 1(1), 0006. doi:10.1038/s41562-016-0006 38Koran, L. M., Hanna, G. L., Hollander, E., Nestadt, G., Simpson, H. B., & Association, A. P. 2007. Practice guideline 39 for the treatment of patients with obsessive-compulsive disorder. American Journal of Psychiatry, 164(7 40 Suppl), 5-53. Retrieved from 41 https://www.researchgate.net/publication/5990301_Practice_Guideline_for_the_Treatment_of_Patient 42 s_with_Obsessive-Compulsive_Disorder 43Laposa, J. M., Collimore, K. C., Hawley, L. L., & Rector, N. A. 2015. Distress tolerance in OCD and anxiety 44 disorders, and its relationship with anxiety sensitivity and intolerance of uncertainty. Journal of Anxiety 45 Disorders, 33, 8-14. doi:10.1016/j.janxdis.2015.04.003
17 Inhibitory Learning Prediction Error Feedback Loop
1Lauriola, M., Mosca, O., Trentini, C., Foschi, R., Tambelli, R., & Carleton, R. N. 2018. The Intolerance of 2 Uncertainty Inventory: Validity and Comparison of Scoring Methods to Assess Individuals Screening 3 Positive for Anxiety and Depression. Frontiers in Psychology, 9. doi:10.3389/fpsyg.2018.00388 4Lee, J. K., Orsillo, S. M., Roemer, L., & Allen, L. B. 2010. Distress and Avoidance in Generalized Anxiety Disorder: 5 Exploring the Relationships with Intolerance of Uncertainty and Worry. Cognitive Behaviour Therapy, 6 39(2), 126-136. doi:10.1080/16506070902966918 7Leyro, T. M., Zvolensky, M. J., & Bernstein, A. 2010. Distress tolerance and psychopathological symptoms and 8 disorders: A review of the empirical literature among adults. Psychological Bulletin, 136(4), 576–600. 9 doi:10.1037/a0019712 10Luhmann, C. C., Ishida, K., & Hajcak, G. 2011. Intolerance of uncertainty and decisions about delayed, 11 probabilistic rewards. Behav Ther, 42(3), 378-386. doi:10.1016/j.beth.2010.09.002 12Maia, T. V., & Frank, M. J. 2011. From reinforcement learning models to psychiatric and neurological disorders. 13 Nature Neuroscience, 14(2), 154-162. doi:10.1038/nn.2723 14Marchand, R. W. 2012. Mindfulness-based stress reduction, mindfulness-based cognitive therapy, and Zen 15 meditation for depression, anxiety, pain, and psychological distress. Journal of Psychiatric Practice, 16 18(4), 233-252. doi:10.1097/01.pra.0000416014.53215.86 17McNally, G. P., Johansen, J. P., & Blair, H. T. 2011. Placing prediction into the fear circuit. 34(6), 283-292. 18 doi:10.1016/j.tins.2011.03.005 19Miller, R. R., Barnet, R. C., & Grahame, N. J. 1995. Assessment of the Rescorla-Wagner model. Psychological 20 Bulletin, 117(3), 363-386. doi:10.1037/0033-2909.117.3.363 21Morriss, J., Christakou, A., & van Reekum, C. M. 2015. Intolerance of uncertainty predicts fear extinction in 22 amygdala-ventromedial prefrontal cortical circuitry. Biology of Mood & Anxiety Disorders, 5(1), 4. 23 doi:10.1186/s13587-015-0019-8 24Najavits, L. M. 2015. The problem of dropout from “gold standard” PTSD therapies. F1000Prime Reports, 7(43). 25 doi:10.12703/p7-43 26Papalini, S., Beckers, T., & Vervliet, B. 2020. Dopamine: from prediction error to psychotherapy. Translational 27 Psychiatry, 10(1), 164. doi:10.1038/s41398-020-0814-x 28Peters, S. K., Dunlop, K., & Downar, J. 2016. Cortico-striatal-thalamic loop circuits of the salience network: A 29 central pathway in psychiatric disease and treatment. Frontiers in Systems Neuroscience, 10(104). 30 doi:10.3389/fnsys.2016.00104 31Ploner, M., Sorg, C., & Gross, J. 2017. Brain rhythms of pain. Trends in Cognitive Sciences, 21(2), 100-110. 32 doi:10.1016/j.tics.2016.12.001 33Postle, B. R. 2015. Essentials of Cognitive Neuroscience. Chichester, West Sussex, UK: Wiley Blackwell. 34Rauch, S. A. M., Eftekhari, A., & Ruzek, J. I. 2012. Review of exposure therapy: A gold standard for PTSD 35 treatment. The Journal of Rehabilitation Research and Development, 49(5), 679. 36 doi:10.1682/jrrd.2011.08.0152 37Redondo, R. L., Kim, J., Arons, A. L., Ramirez, S., Liu, X., & Tonegawa, S. 2014. Bidirectional switch of the valence 38 associated with a hippocampal contextual memory engram. Nature, 513(7518), 426-430. 39 doi:10.1038/nature13725 40Riquino, M. R., Priddy, S. E., Howard, M. O., & Garland, E. L. 2018. Emotion dysregulation as a transdiagnostic 41 mechanism of opioid misuse and suicidality among chronic pain patients. Borderline Personality Disorder 42 and Emotion Dysregulation, 5(1). doi:10.1186/s40479-018-0088-6 43Robinson, O. J., Overstreet, C., Charney, D. R., Vytal, K., & Grillon, C. 2013. Stress increases aversive prediction 44 error signal in the ventral striatum. Proceedings of the National Academy of Sciences, 110(10), 4129- 45 4133. doi:10.1073/pnas.1213923110
18 Inhibitory Learning Prediction Error Feedback Loop
1Roy, M., Shohamy, D., Daw, N., Jepma, M., Wimmer, G. E., & Wager, T. D. 2014. Representation of aversive 2 prediction errors in the human periaqueductal gray. Nature Neuroscience, 17(11), 1607-1612. 3 doi:10.1038/nn.3832 4Schmaal, L., Van Harmelen, A.-L., Chatzi, V., Lippard, E. T. C., Toenders, Y. J., Averill, L. A., . . . Blumberg, H. P. 5 2019. Imaging suicidal thoughts and behaviors: A comprehensive review of 2 decades of neuroimaging 6 studies. Molecular Psychiatry, 25, 408–427(2020). doi:10.1038/s41380-019-0587-x 7Schultz, W. 2016. Dopamine reward prediction error coding. Dialogues in Clinical Neuroscience, 18(1), 23-32. 8 Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/27069377 9Schultz, W. 2017. Reward prediction error. Current Biology, 27(10), R369-R371. 10Servatius, R. J. 2016. Editorial: Avoidance: From Basic Science to Psychopathology. Front Behav Neurosci, 10, 15. 11 doi:10.3389/fnbeh.2016.00015 12Seth, A. K., & Critchley, H. D. 2013. Extending predictive processing to the body: Emotion as interoceptive 13 inference. Behavioral and Brain Sciences, 36(3), 227-228. doi:10.1017/S0140525X12002270 14Sewart, A. R., & Craske, M. G. 2020. Inhibitory Learning. In Clinical Handbook of Fear and Anxiety: Maintenance 15 Processes and Treatment Mechanisms, (J. S. Abramowitz & S. M. Blakey ed.,^eds.). Washington, DC: 16 American Psychological Association. 17Shin, L. M., & Liberzon, I. 2009. The neurocircuitry of fear, stress, and anxiety disorders. 18 Neuropsychopharmacology, 35(1), 169–191. doi:10.1038/npp.2009.83 19Simons, J. S., & Gaher, R. M. 2005. The Distress Tolerance Scale: Development and Validation of a Self-Report 20 Measure. Motivation and Emotion, 29(2), 83-102. doi:10.1007/s11031-005-7955-3 21Skinner, B. F. 1938. The Behavior of Organisms: An Experimental Analysis. Oxford, England: Appleton-Century. 22Skinner, B. F. 1963. Operant behavior. American Psychologist, 18(8), 503-515. doi:10.1037/h0045185 23Suri, G., Sheppes, G., & Gross, J. J. 2015. The role of action readiness in motivated behavior. Journal of 24 Experimental Psychology: General, 144(6), 1105-1113. doi:10.1037/xge0000114 25Tanaka, S., O’Doherty, J. P., & Sakagami, M. 2019. The cost of obtaining rewards enhances the reward prediction 26 error signal of midbrain dopamine neurons. Nature Communications, 10(1), 3674. doi:10.1038/s41467- 27 019-11334-2 28Taschereau-Dumouchel, V., Cortese, A., Chiba, T., Knotts, J., Kawato, M., & Lau, H. 2018. Towards an 29 unconscious neural reinforcement intervention for common fears. Proceedings of the National Academy 30 of Sciences, 115(13), 3470-3475. doi:10.1073/pnas.1721572115 31Toates, F. 1988. Motivation and Emotion from a Biological Perspective. InCognitive Perspectives on Emotion and 32 Motivation, Vol. 44 of Nato Science Series D, (V. Hamilton, G. H. Bower, & N. H. Frijda ed.,^eds.). Boston, 33 MA; London, UK: Kluwer Academic Publishing. 34Ubl, B., Kuehner, C., Kirsch, P., Ruttorf, M., Diener, C., & Flor, H. 2015. Altered neural reward and loss processing 35 and prediction error signalling in depression. Social Cognitive and Affective Neuroscience, 10(8), 1102- 36 1112. doi:10.1093/scan/nsu158 37Wei, W., & Wang, X.-J. 2016. Inhibitory control in the cortico-basal ganglia-thalamocortical loop: Complex 38 regulation and interplay with memory and decision processes. Neuron, 92(5), 1093-1105. 39 doi:10.1016/j.neuron.2016.10.031 40Wever, M., Smeets, P., & Sternheim, L. 2015. Neural Correlates of Intolerance of Uncertainty in Clinical 41 Disorders. The Journal of Neuropsychiatry and Clinical Neurosciences, 27(4), 345-353. 42 doi:10.1176/appi.neuropsych.14120387 43Wilhelm, F. H., Pfaltz, M. C., Gross, J. J., Mauss, I. B., Kim, S. I., & Wiederhold, B. K. 2005. Mechanisms of Virtual 44 Reality Exposure Therapy: The Role of the Behavioral Activation and Behavioral Inhibition Systems. 45 Applied Psychophysiology and Biofeedback, 30(3), 271-284. doi:10.1007/s10484-005-6383-1
19 Inhibitory Learning Prediction Error Feedback Loop
1Winer, E. S., Bryant, J., Bartoszek, G., Rojas, E., Nadorff, M. R., & Kilgore, J. 2017. Mapping the relationship 2 between anxiety, anhedonia, and depression. Journal of affective disorders, 221, 289-296. 3 doi:10.1016/j.jad.2017.06.006 4Wolitzky-Taylor, K. B., Horowitz, J. D., Powers, M. B., & Telch, M. J. 2008. Psychological approaches in the 5 treatment of specific phobias: A meta-analysis. Clinical Psychology Review, 28(6), 1021-1037. 6 doi:10.1016/j.cpr.2008.02.007 7Wright, K., DiLeo, A., & McDannald, M. 2015. Early adversity disrupts the adult use of aversive prediction errors 8 to reduce fear in uncertainty. Frontiers in Behavioral Neuroscience, 9(227). 9 doi:10.3389/fnbeh.2015.00227 10Zhu, X., Suarez-Jimenez, B., Lazarov, A., Helpman, L., Papini, S., Lowell, A., . . . Neria, Y. 2018. Exposure-based 11 therapy changes amygdala and hippocampus resting-state functional connectivity in patients with 12 posttraumatic stress disorder. Depression and anxiety, 35(10), 974-984. doi:10.1002/da.22816 13Zvolensky, M. J., Leyro, T. M., Bernstein, A., & Vujanovic, A. A. 2011. Historical Perspectives, Theory, and 14 Measurement of Distress Tolerance. In Distress Tolerance: Theory, Research, and Clinical Applications, 15 (M. J. Zvolensky, A. Bernstein, & A. A. Vujanovic ed.,^eds.), pp. 3-27. New York, NY; London, UK: Guilford 16 Press.
17
18
20