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, regulation, intolerance of uncertainty 23 24 25 26 27 28 29 30 31 32 33

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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 (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 , schizophrenia, obsessive-

11compulsive disorder (OCD), posttraumatic stress disorder (PTSD), chronic , depression,

12anhedonia, and (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 &

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 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 , 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 , 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

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