1 Running head: Free Energy and anxiety formation

Learned Uncertainty: A Perspective on Anxiety

McGovern, H.T.1, de Foe, A4, Leptourgos, P3., Corlett, P3., Bandara, K2 ., Biddell, H1., &

Hutchinson, B2

1The University of Queensland, Brisbane, Australia

2The Australian National University, Canberra, Australia

3Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United

States

4Royal Melbourne Institute of Technology, Melbourne, Australia

Author Note

Correspondence to be addressed to H.T. McGovern, School of Psychology, University of Queensland. [email protected]

2 Free Energy and Anxiety formation Abstract

Generalized Anxiety Disorder (GAD) is among the world’s most prevalent psychiatric disorders. Affecting an eighth of the world’s population, it often manifests as persistent apprehension which is difficult to control. Despite its prevalence, neuroscientific efforts to understand the cognitive mechanisms of GAD remain sparse. This has resulted in a fractured theoretical landscape, lacking a unitary framework. While prior theories of anxiety describe the cognitive, affective and behavioral dimensions of anxiety, a unified theory is lacking.

Here, we point out that postulates derived from the Free Energy Principle (FEP) may allow for a unified theory to emerge. We argue an approach focused on predictive modelling may afford opportunities to re-conceptualize anxiety within the framework of working generative models, rather than static beliefs. We suggest that a biological system—having had persistent uncertainty in its past—will form posteriors in line with uncertainty in its future, irrespective of whether that uncertainty is real. After discussing the FEP, we explain how anxiety develops through learning uncertainty before suggesting predictions for how the model can be tested.

Keywords: Anxiety, Free Energy Principle, , clinical, psychiatry 3 Free Energy and Anxiety formation

Learned Uncertainty

Nature consists of dynamic and complex systems (Friston, 2010; Zednik, 2011). For a biological system to exist, it must have the capacity to identify its own boundaries—lest it cannot be distinguished from other systems. A fundamental property of any biological system is therefore the requirement that it can differentiate itself from its environment. The free energy principle asserts that to do this, biological systems model their external states and themselves within those states (Friston, 2010; Friston et al., 2006). This occurs through a process where the system samples information from outside its boundaries, via its suite of sensory channels, and acts based on that sampled information. Ultimately, this is an iterative process in which the nature of what is perceived of the external state informs the system’s internal state. The system then generates a model of its external state, which informs how the system ought to act within its external state. Via action, the system influences its external state, which leads to feedback as to the precision of the biological system’s world model— and the cycle repeats.

The Free Energy Principle: A Primer

The free energy principle describes how biological systems resist dissipation and destruction (Friston, 2010; Friston et al., 2006). Under thermodynamic principles, all systems move, irreversibly, toward disorder (Hirsh et al., 2012; Schneider & Kay, 1994). Yet, biological systems resist such decay and instead carry impetus toward attainment of states allowing their survival (Hirsh et al., 2012; Ramstead et al., 2018). Biological systems move increasingly toward a lower number of such "attractor" states, the spectrum of which can be thought of as equivalent to homeostasis, i.e., a variable number of states within which the system can feasibly sustain its own existence. The crucial insight specified by the free energy principle is that this process—of moving toward a lower number of “attractor” states—is an 4 Free Energy and Anxiety formation intrinsic property of biological systems that emerges through modelling the world (Friston,

2010; Hirsh et al., 2012). The free energy principle is thus concerned with how biological systems self-fulfillingly define themselves as systems per se, and in doing so move away from destruction and toward attractor states.

What is Free Energy?

To "minimize free energy" is to minimize error (or surprise) in the biological system's modelling of its external states across time. Free energy can be considered a sort of proxy for surprise. By its nature, uncertainty is that which is not known. Yet no model will perfectly capture the observation it is attempting to model and will therefore have to deal with uncertainty. Uncertainty or “error” inherently contradicts the system’s goal of moving toward a lower number of attractor states: a failure to move toward attractor states means the organism will instead move toward dissipation, opposing it's imperative to survive (Badcock et al., 2017; 2019; Bruinberg et al., 2018; Bruinberg & Rietveld, 2014; Ramstead et al.,

2018). The free energy principle specifies that free energy is the internal system’s upper bound of the uncertainty of its external states (Friston, 2010; Ramstead et al., 2018). Put simply, free energy is the system’s “best guess” as to the uncertainty that exists “out there”, and minimizing free energy is akin to minimizing the error in the system's prediction about the world. To “minimize free energy” is thus to maximize precision in the system’s capacity to model its own world.

The idea of the mind modelling the world dates to the origins of psychological science. Models of rooted in prediction stem from Helmholtz’ (1860) notion of unconscious inference. Helmholtz (1860) proposed that we attempt to infer our environment via unconscious cues, and constructivist theorists including those within Jean Piaget’s (i.e., developmental schema theory, Beard, 2013; Feldman, 2004; Piaget, 2003) and George

Kelly’s (personal construct psychology, Kelly, 1955) school of thought later posited similar 5 Free Energy and Anxiety formation notions. More recent iterations of this basic idea have since been proposed (McClelland &

Rumelhart, 1981; Rao & Ballard, 1999) and are generally considered under the term

“”.

Predictive coding equates the brain to that of a scientist—it makes observations, takes in data, and generates and updates hypotheses based on that data (Hohwy, 2013; 2017).

Predictive coding recasts the classical notion of the brain as a feature detector, specifying instead that top-down and bottom-up neural networks are functionally driven toward signaling prediction and prediction error, respectively (Clark, 2013; Friston, 2010). Top-down neural activity associated with reentrant loops that provide feedback from higher levels to sensory processing regions are conceptualized as propagating a prediction about a given sensory input, whilst the bottom-up or feed-forward sweep of activity associated with that sensory input is conceptualized as carrying a prediction “error”. If the top-down prediction sufficiently accounts for the bottom-up signal, the error is “explained away”—for example, the signal is attenuated via inhibitory mechanisms (Howhy, 2013; 2017; Friston, 2012). If the prediction does not sufficiently account for the bottom-up signal, the error propagates up the hierarchy and the top-down predictive model is updated (Alexander & Brown, 2018; Bastos et al., 2012; Friston, 2008; Friston & Kiebel, 2009; Huang & Rao., 2011). In this way, signals cascading bidirectionally throughout the cortical hierarchy can be thought of as building generative models about sensory information.

The point here is that both the free energy principle and predictive processing speak of the brain in terms of modelling: the brain is a system that is modelling the world in order to understand it. In this way, both describe and seek to explain the process by which the brain functions. Predictive processing is a particular cognitive implementation, and a prediction based on the axioms of the free energy principle. The free energy principle and predictive processing are thus complementary, deviating primarily in terms of literature and scope. The 6 Free Energy and Anxiety formation free energy principle is generally considered as a description of the fundamental laws underlying biological systems, whilst predictive coding is more typically leveraged as a framework to understand aspects of psychology and the mind (Bogacz, 2017; Friston &

Kiebel, 2009).

Whilst primarily concerned with system dynamics, the free energy principle can be applied to biological phenomena at every scale—from the microscopic to the psychological.

For example, under the free energy principle, belief formation may be thought of as a probability distribution of external states based on internal representations of those states (see

Figure 1). Beliefs stored at higher levels of the predictive hierarchy eventuate through sufficient iterations of the prediction and feedback (Friston, 2008; 2010). In order to form expectations about the world, agents must be able to pull together probability distributions for co-occurring sensory phenomena. The probability distributions associated with initially unique occurrences merge, and hence become part of the same expectation about the world

(see Friston, 2010; Hirsh et al., 2012). Little effort is needed to see how this conceptualization of belief formation emerges from the dynamic process described by the free energy principle, as it predicts that all cognitive systems will operate on a set of prior beliefs about the consequences of future action. Should this prediction be borne out, then the predictive processing account holds. This process enables cognitive systems to model their environment to get at the so-called “end goal” of minimizing free energy (Friston & Kiebel,

2009; Friston, 2010; Hirsh et al, 2012).

Predictive processing describes belief formation through the development of priors, in that beliefs are formed via the brain generating predictions about the world based upon prior observations of the world: my belief that water quenches thirst is formed based on prior observations that water quenched my thirst. Whilst much debate centers on how such predictions are optimized in a given model, recent research has seen a method of optimized 7 Free Energy and Anxiety formation prediction based on Bayes’ theorem, specifically within the context of the free energy principle (see Figure 1) (Friston & Kiebel, 2009; Spratling, 2016; Sterzer et al., 2018).

Figure 1. Bayes theorem

PE(H) = [P(H)/P(E)] PH(E)

"Bayes' Theorem relates the "direct" probability of a hypothesis conditional on a given body of data, PE(H), to the "inverse" probability of the data conditional on the hypothesis, PH(E)" (Joyce, 2019, pp. 2)

The key point here is that both the free energy principle and predictive processing are comparable in how they describe the psychological development of belief formation, but the free energy principle provides a framework that underlies and attempts to dissolve disciplinary boundaries (Friston, 2012; 2019; Rubin et al., 2019). These approaches thus provide an ecological analogue for the role of surprise in real-time model update. In all, these approaches together not only explain optimal model generation, but allow us to consider various contextual cues that may lead to optimized probabilities of a given state or outcome.

Anxiety

Traditional models of anxiety are underpinned by the notion that erroneous beliefs lead to perpetuated and often exaggerated anxiety responses to a situation or context. Early behavioral models of anxiety relate to conditioning or learning (Beck & Clark, 1988; Clark,

1986; 1997; 1999). Entering a situation where panic or anxiety has been learned leads to subsequent anticipation of anxiety upon re-entering that situation and provokes further anxiety (Clark, 1986). The work of Beck and Ellis established well-considered cognitive biases and filters that are thought to skew one’s and conceptions of various circumstances (Beck, 1970; Ellis, 1980). Common examples include catastrophizing, dualistic thinking, and exaggeration of anxiety-provoking stimuli (Beck & Weishaar, 1989; 8 Free Energy and Anxiety formation Benjamin et al., 2011). The later stimulus-response model of anxiety is compatible with the traditional cognitive framework, as entrained behaviors may exacerbate cognitions and vice versa. For example, Clark’s (1986) cognitive model proposes panic attacks are a “catastrophic misinterpretation” (p. 462) of bodily sensations that amounts to a cyclical feedback response in instances where anxiety is expected.

While the cognitive-behavioral model has informed clinical practice for several decades (see Behar et al., 2009 for overview), it is not without its critics. One of its major criticisms in relation to the treatment of anxiety disorders relates to the observation that panic sensations and stress-related responses can still persist despite the fact that erroneous beliefs had been adequately disputed at length (Beidel & Turner, 1986; Cartwright-Hatton et al.,

2004; Linden et al., 2005). For example, a therapist may have established that nobody is making fun of a client within a social situation, yet the client persists in their report of a of dread and anxiety when confronted with a range of social contexts.

We ought to first point out that numerous theorists have proposed unique means of conceptualizing beliefs centric to anxiety that challenge traditional models such as CBT and rational-emotive behavioral therapy. For instance, rather than interpreting beliefs as static filters that inform one’s perceptions in a similar manner in all situations, Kelly (1955) proposed that erroneous beliefs have a tangible “weight” and might be conceived of as tight/loose or brief/elaborate, among other corollaries. Conversely, emotion-focused therapies (EFT) argue that affect precedes belief formation, and hence is ultimately subject to those factors which determine emotion (Greenberg, 2004). Here, we argue an approach focused on predictive modelling may afford opportunities to re-conceptualize anxiety within the framework of working generative models, rather than static beliefs.

A Fresh Perspective Through the Free Energy Principle? 9 Free Energy and Anxiety formation The free energy principle provides a novel framework to understand the what and how of anxiety. Beginning from the free energy principle, anxiety can be described as the system’s degree of uncertainty about its external state, brought about by sufficient long-term surprise (also see Hirsh et al., 2012). That is, within a biological system that strives toward attractor states, anxiety is the psychological consequence of mismatch between a biological system’s world model and information sampled from the environment. Sufficiently long- lasting and persistent uncertainty impairs the system’s capacity to develop adaptive models for undertaking effective sampling in order to minimize free energy. When this occurs, the update from prior to posterior (in predictive processing terms) becomes dysfunctionally geared toward uncertainty, which consequently affirm and reinforce the now uncertain world model—and generalized anxiety disorder results (Dugas et al., 1997; 2005a; 2005b; 2007;

Ladouceur et al., 1999; McEvoy & Mahoney, 2012).

Keep in mind that, based on the free energy principle, the sustainable existence of a biological system is tantamount to it being driven toward a lower number of attractor states.

Despite the system developing an uncertain model, then, its goal is still toward the minimization of free energy—otherwise, as a system, it “collapses”. The biological system is consequently in a position where it is driven toward free energy minimization, yet its priors are based upon high entropy (i.e., highly uncertain) probability distributions. Anxiety can therefore be considered the expression of uncertainty within a biological system which is modelling its external states, whilst anxiety disorder is the outcome when, by virtue of seeking to minimize free energy, the biological system attempts attainment of a lower number of attractor states—yet within those states exist high entropy probability distributions. The existence of persistent anxious states as a characteristic of anxiety disorder is thus analogous to a system converging on high entropy states, in spite of the existence of low entropy alternative states. Put differently, given a sufficient number of times where the system’s 10 Free Energy and Anxiety formation model provided inaccurate representations of an uncertain world, the system “learns” that this uncertainty is true. From here, the process of model generation becomes paradoxically optimally geared toward seeking out uncertainty in its environment, even though low(er) entropy distributions might be theoretically attainable. Put simply, a biological system— having had persistent uncertainty in its past—will be more likely to form posteriors in line with uncertainty in its future, irrespective of whether that uncertainty is true.

A simple example from probability theory helps illustrate: Suppose a system observes ten flips of a coin. For simplicity, each flip can be represented in a single perception-action cycle (see Figure 2). For every observation, the system updates its model about how the coin might land in future. If the coin lands on heads eight times out of ten, the system will “learn” the coin is weighted and be positioned to make differential predictions about the coin’s action in future. But this occurs irrespective of whether the model was correct. That is, regardless of what was predicted, any prediction at all will inform the system of the accuracy of its model

—eight correct predictions for heads are equivalent to eight incorrect predictions for tails.

Either way, the system can update its model of the coin accordingly. This means that, over time, the system will be in a better position to make predictions regardless of its accuracy, and invariably its actions will be better positioned to minimize free energy (i.e., Bayesian optimized modelling; Friston & Kiebel, 2009). In this way, the system is given enough information consistent with its world model so that it can still obtain new information effectively. 11 Free Energy and Anxiety formation

Figure 2. The perception action cycle. Internal state generates a model that informs action.

Action leads to a change in external state. External state informs perception. Perception informs internal state model update.

Of course, the flip of a coin is best expressed as a discrete uniform distribution: only two possible outcomes exist, and the probability of heads or tails is equally likely. Thus, if the observation results in an equal proportion of heads and tails, the system will not have sufficient information to model one outcome over another. The only occasion where the system cannot adequately model the observation is therefore when it fits a uniform distribution, i.e., where the accuracy of the observation is at approximately 50 per cent. It is 12 Free Energy and Anxiety formation within this situation where the system “learns” that its generative model does not yield certainty or provide any degree of “useable” data concerning its environment. In other words, the environment is uncertain.

With respect to the coin, the only outcome in which the system develops “anxiety” is one which is uncertain i.e., leads to an equivalent amount of evidence for either observation, leading the system to be unable to generate an accurate model to inform its action. In free energy terms, the system’s predictions are guided by a highly uncertain probability distribution—one whose likelihood estimate across all possibilities is roughly equal—and thus the system’s actions are guided by a model that performs no better than random chance.

This can be described as a circumstance where the internal state has modelled a uniform distribution of its external state.

We consider this the initial lever for anxiety formation. It is well-known that persistent uncertainty is linked to higher formation of anxious beliefs across both short and enduring time periods (Burke et al., 2017; Chorpita & Barlow, 1998; Compton et al, 2008;

2010; Epstein & Roupenian, 1970; Grillon et al, 2004; Gutman & Nemeroff, 2003; Kendall et al., 2010; MacLeod & Cohen, 1993; Murray et al, 2009). For example, recent work by

Hayward et al. (2020) showed that more adversity in childhood was predictive of more severe anxiety symptoms in adulthood. We interpret this as illustrative of how uncertainty at an early stage of the model’s development instigates the proliferation of anxiety, which then has significant downstream effects on later generative models via inhibition of adaptive priors from which to operate. In other words, initial uncertainty within the environment lays the groundwork for how generalized anxiety forms.

Learning Uncertainty

To understand this process requires appeal to a higher-order layer in which the system makes “meta” predictions about its own predictions, equivalent to metacognitive belief 13 Free Energy and Anxiety formation formation or “hyperpriors” in predictive coding (Clauss et al., 2020; Lyndon & Corlett, 2020;

Papageorgiou & Wells, 2001; Smith & Hudson. 2013; Wells, 1995; 1999; 2004; 2005;

2006). Higher order predictive layering emerges naturally from the free energy principle, as the system models itself within the external states it is bound to (Boly et al., 2011).

Returning to our example, suppose we now have one hundred observations catalogued into ten observations of ten. Anxiety disorder forms when the probability of a “meta” observation can be predicted, i.e., its likelihood is increased (see Figure 3). We can understand this via reference to Bayes rule: priors update over time in accordance with new information, from which predictions are made about what will subsequently be observed (refer to Friston, 2012;

Mathys et al., 2014; Westbury, 2010). Anxiety disorder thus forms via Bayesian learning. 14 Free Energy and Anxiety formation

Figure 3. The process of learned uncertainty with reference to the coin toss example. Ten

“meta” observations of ten observations. White/black refer to heads/tails. The large circle denotes the entire sequence of flips, from which an agent makes a meta perceptual inference

(i.e., higher order beliefs). Uncertainty arises from a single sequence (5 out of 10 in 1 meta observation), whilst anxiety beliefs form when the agent is accurate in its modelling of uncertainty across a sufficient number of meta observations (8 out of 10 correct meta observations), i.e., so-called learning that uncertainty will persist. The agent can thus encode uncertainty which gives rise to feedback of an insufficient world model at anyone (or all) of these outcomes.

To be precise, momentary anxiety can be thought of as generated via high entropy probability distributions within a single perception-action cycle, whereas anxious beliefs 15 Free Energy and Anxiety formation form based upon low entropy probability distributions of those meta observations, which are themselves composed of high entropy probability distributions. This account shares similarities with a number of previous frameworks. For example, Lyndon and Corlett (2020) take the position that hallucinations experienced as a symptom of PTSD can be explained based upon predictive coding, whilst Chekroud (2015) suggests the formation of depression can be Bayes optimal (i.e., learned helplessness), where depressive beliefs form despite a match between information sampled and the system’s empirical priors (see Chekroud, 2015;

Holmes & Nolte, 2019). We suggest anxiety-based pathologies form because of sufficient and/or persistent mismatch between information sampled and the organism’s inability to internally model that information. Over time, the system models, in a Bayesian optimal way, its own priors as not adequately updating in a way that alters the probability of a future outcome. In so doing, anxious priors’ form. Despite this, as specified by the free energy principle, the system will still be driven toward minimizing free energy. The biological system therefore predicts that its modelling of the world is neither sufficient nor insufficient

—and yet is still necessary—and generates models that the inherent uncertainty will persist across time (see Dickstein et al., 2010; Fonzo & Etkin, 2016; Grupe & Nitschke, 2013;

Kannis-Dymand et al., 2020; LaFreniere et al., 2019). Because this occurs in a Bayesian optimal way, model precision increases, despite clearly not being adaptive—and learned uncertainty results.

Criticisms and limitations

We must stress that our proposition is not intended as an exhaustive account of anxiety disorder. Still, there are a number of key objections we anticipate, and which require addressing. The first is why anxiety disorder develops in some—but not all—when uncertainty is equated between individuals (Zuckerman, 1999). We agree that not all individuals will have the same likelihood of developing anxiety disorder, even in the face of 16 Free Energy and Anxiety formation an equally uncertain environment. The diathesis stress model provides a popular account to explain such variability, suggesting pathologies arise via mutual feedback between genetic predispositions combined with environmental stressors (Zuckerman, 1999). This implies that an individual with any given genetic “set” will interact differently with their environment

(Schiele & Domschke, 2018), and thus differing degrees of uncertainty (or stressor events) will be needed for pathological anxiety to form (Frank et al., 2006). Further, protective environmental factors such as a stable and safe family environment, supportive relationships, and the presence of role models can offset or “buffer” against the experienced uncertainty which would otherwise result in subsequent pathology (Tyler et al., 2018). We suggest that these factors will protect against the formation of those internalized metacognitive beliefs

(i.e., “hyperpriors”) regarding the individual’s inability to alter the probability of a future outcome. Because our account specifies that this metacognitive belief formation is intrinsic to the development of pathological anxiety, this provides a tentative rationale for why anxiety disorder will develop in some—but not all—individuals when uncertainty is equated.

For an illustrative case, consider Lyndon and Corlett’s (2020) account of the formation of PTSD. They posit that PTSD is characterized by a reduced confidence in one’s ability to resolve prediction errors, given the failure of resolving these errors when experiencing a traumatic event (Lyndon & Corlett, 2020). In the case of anxiety, we suggest that the traumatic event/s is not necessary for its formation - merely a sufficient presence of uncertainty (or prediction error under Lyndon and Corlett’s model). Via this process, the agent learns an “inability” to resolve prediction errors. In this way, anxiety formation is analogous to PTSD formation, but without the accompanying high precision gleaned from experiencing a traumatic event (and thus learning its own catastrophic failure to resolve prediction errors). Thus, we should not expect that given the same uncertainty, two individuals would both develop anxiety. Put simply, we are arguing that background factors 17 Free Energy and Anxiety formation would lead to substantial variability in how much uncertainty is needed, however the experience, modelling, and learning of uncertainty itself nevertheless remains the underlying factor for the development of anxiety disorder between individuals.

Another potential objection is that we have not adequately accounted for active inference. In models of free energy and predictive processing, active inference provides a key method for an agent to minimize free energy through sampling data to confirm predictions.

Various policies for action, perception, and learning are predicated on inference testing

(Friston et al., 2016; 2017). Broadly, the generative process of accumulating models of one’s experience centers on various possibilities, inclusive of outcomes, actions, hidden states, and time-sensitive factors. Spurious prediction errors pertaining to any of these factors can give rise to “surprise”: a disparity between one’s implicit world model and one’s explicit experience (Friston, 2010).

In normative populations, surprise is resolved in vivo via updating one’s model, such that more accurate inferences can be made in future instances, whilst in populations with impaired working models, like those with anxiety disorder, spurious prediction errors persist at the meta or “hyperprior” level, impacting the way in which information is sampled and how the generative model is updated (Hohwy, 2013). An anxious person should thus adopt hyper priors which predict that they will be uncertain in perceptual inference, and thus more likely to “overweight” the precision (or informative value) of priors (see for example differences in safety signal responding whereby anxious people take longer to learn that once dangerous environments are now safe; Lohr et al., 2007). In other words, the hyperprior sets the parameters for future instances of perception - setting a parameter of expectant uncertainty in future perception. While active inference is important in the proposed model

(for example there is notable bias in information sampling in anxiety such as avoidance behaviors; see Abend et al., 2018; Dedeney et al., 2015), this is not the focus of this paper. 18 Free Energy and Anxiety formation We merely aim to describe altered processes in agent’s internal world models, and how they are differentially formed in GAD formation.

Predictions

Several predictions flow from the basic suppositions laid out by our model. Overall, our model supposes that given sufficient uncertainty, agents should “learn” this uncertainty at higher levels of the cortical hierarchy. This offers several specific predictions for both the neurobiology and the behavioral correlates of those with anxiety.

At the neurobiological level, this should mean that given sufficient environmental uncertainty, anxious agents should be more rigid in their belief in the uncertainty of their world model, and thus be more impervious to learning and integrating more “certain” information into their world model. Although perhaps too simple an example to illustrate this effect, this could mean that given sufficient flips of a coin being 50:50 probability for heads or tails (say, 1000), that even though the probability will switch to 80:20 in favor of heads, their higher-level priors will not shift optimally toward this new probability and instead show increased rigidity. Some neurobiological evidence supports this claim, with anxious people exhibiting differences in integrating new information (Makovac et al., 2015; 2016; 2018), potentially underwritten by disruptions between brain regions responsible for perception and integration of new information (Andreescu et al., 2017; Basten et al., 2011; Blair et al.,

2012a. 2012b; Calhoon & Tye, 2015; Chavanne & Robinson, 2021; Eckstrand et al., 2019;

Etkin, 2009; Etkin et al., 2009; Felix-Ortiz et al., 2016; Fonzo et al., 2016; Gray et al., 1982;

Hamm et al., 2020; Hilbert et al., 2014; Imperatori et al., 2019; Kim et al., 2011a; 2011b;

LeDoux, 2003; 2007; Li et al., 2016; Lueken et al., 2016; Nitschke et al., 2009; Ochsner &

Gross, 2005; Qin et al., 2014; Sylvester et al., 2012; Tovoto et al., 2015; Zald, 2003). At the behavioral level we thus expect cognitive rigidity, underwritten by decreased efficacy in information and signal transmission between brain regions. 19 Free Energy and Anxiety formation We suggest that this model offers interesting avenues for further exploration in both computational and experimental research. The primary strength of the proposed framework allows for an explanation of anxiety formation operating from first principles. This allows anxiety to be nested within a broad and deep theoretic framework offered by the free energy principle. Optimization of action and representation are the two primary methods by which human agents minimize variational free energy (Chekroud, 2015; Friston, 2010; Friston et al.,

2006). Given we have only sought to describe the process of anxiety formation with the optimization of representation, future iterations of this framework should describe the role of altered active inference strategies in patients with anxiety disorder. We suspect that generalized anxiety forms when these processes (optimization of action and representation) become dysfunctionally tuned to high entropy information, impairing the ability of the organism to form adaptive generative models of the world via these processes. Future iterations of this theoretical framework would be well served by explicit consideration of both these processes.

Summary and Conclusion

Here we have looked to account for how anxiety forms through appeal to the free energy principle. We suggest that, from principles derived from the free energy framework, anxiety and anxiety disorder can be understood as a process of learning uncertainty. We must stress that our intention here is not to provide an exhaustive account of anxiety disorder, for example its long-term clinical presentation. Still, conceptualizing anxiety in this way situates its genesis at a fundamental principles level and therefore provides a solid grounding to understand the necessary conditions for how and why anxiety develops in the first place. We suggest a free energy framework offers new avenues for understanding anxiety formation, proliferation, and maintenance. Broadly, we posit that humans must be psychologically motivated toward attractor states, those states in which they can sustain their own existence. 20 Free Energy and Anxiety formation When the degree of uncertainty within these states persists for long enough, the organism generates world models that this uncertainty is inherent to the world. The agent is thus left with expecting uncertainty in the updating of its world model.

We suggest adoption of this framework could bring together a range of neurobiological and clinical findings and could yield novel insights into the formation and operation of generalized anxiety. Understanding the multifaceted and reciprocal nature between action and beliefs may lead to increased understanding of their reciprocal and mutually reinforcing relationship in generalized anxiety.

Authorship Contribution Statement

The project was conceived by H.T. McGovern and supervised by B Hutchinson and A

De Foe. The manuscript was principally drafted by B Hutchinson, H.T. McGovern, and A De

Foe. Review and intellectual contributions were made by P Corlett, P Leptgourgus, K

Bandara, and H Biddell. 21 Free Energy and Anxiety formation

References

Abend, R., de Voogd, L., Salemink, E., Wiers, R. W., Pérez‐ Edgar, K., Fitzgerald, A., ... &

Bar‐ Haim, Y. (2018). Association between attention bias to threat and anxiety

symptoms in children and adolescents. Depression and Anxiety, 35(3), 229-238.

Alexander, W. H., & Brown, J. W. (2018). Frontal cortex function as derived from

hierarchical predictive coding. Scientific reports, 8(1), 1-11.

Andreescu, C., Tudorascu, D., Sheu, L. K., Rangarajan, A., Butters, M. A., Walker, S., ... &

Aizenstein, H. (2017). Brain structural changes in late-life generalized anxiety

disorder. Psychiatry Research: Neuroimaging, 268, 15-21.

Badcock, P. B., Davey, C. G., Whittle, S., Allen, N. B., & Friston, K. J. (2017). The depressed

brain: An evolutionary systems theory. Trends in Cognitive Sciences, 21(3), 182-194.

Badcock, P. B., Friston, K. J., Ramstead, M. J., Ploeger, A., & Hohwy, J. (2019). The

hierarchically mechanistic mind: an evolutionary systems theory of the human brain,

cognition, and behavior. Cognitive, Affective, & Behavioral Neuroscience, 19(6),

1319-1351.

Basten, U., Stelzel, C., & Fiebach, C. J. (2011). Trait anxiety modulates the neural efficiency

of inhibitory control. Journal of , 23(10), 3132-3145.

Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012).

Canonical microcircuits for predictive coding. Neuron, 76(4), 695-711.

Beard, R. M. (2013). An outline of Piaget's developmental psychology. Routledge.

Beck, A. T. (1970). Cognitive therapy: Nature and relation to behavior therapy. Behavior

therapy, 1(2), 184-200.

Beck, A. T., & Weishaar, M. (1989). Cognitive therapy. In Comprehensive handbook of

cognitive therapy (pp. 21-36). Springer, New York, NY. 22 Free Energy and Anxiety formation Beck, A. T., & Clark, D. A. (1997). An information processing model of anxiety: Automatic

and strategic processes. Behaviour research and therapy, 35(1), 49-58.

Beck, A. T., & Clark, D. A. (1988). Anxiety and depression: An information processing

perspective. Anxiety research, 1(1), 23-36.

Behar, E., DiMarco, I. D., Hekler, E. B., Mohlman, J., & Staples, A. M. (2009). Current

theoretical models of generalized anxiety disorder (GAD): Conceptual review and

treatment implications. Journal of anxiety disorders, 23(8), 1011-1023.

Beidel, D. C., & Turner, S. M. (1986). A critique of the theoretical bases of cognitive

behavioral theories and therapy. Clinical psychology review, 6(2), 177-197.

Bogacz, R. (2017). A tutorial on the free-energy framework for modelling perception and

learning. Journal of mathematical psychology, 76, 198-211.

Cartwright‐ Hatton, S., Roberts, C., Chitsabesan, P., Fothergill, C., & Harrington, R. (2004).

Systematic review of the efficacy of cognitive behaviour therapies for childhood and

adolescent anxiety disorders. British journal of clinical psychology, 43(4), 421-436.

Chavanne, A. V., & Robinson, O. J. (2021). The overlapping neurobiology of induced and

pathological anxiety: a meta-analysis of functional neural activation. American

Journal of Psychiatry, 178(2), 156-164.

Benjamin, C. L., Beidas, R. S., Comer, J. S., Puliafico, A. C., & Kendall, P. C. (2011).

Generalized Anxiety Disorder in youth: diagnostic considerations. Depression and

anxiety, 28(2), 173-182.

Blair, K. S., & Blair, R. J. R. (2012). A cognitive neuroscience approach to generalized

anxiety disorder and social phobia. Emotion Review, 4(2), 133-138.

Blair, K. S., Geraci, M., Smith, B. W., Hollon, N., DeVido, J., Otero, M., ... & Pine, D. S.

(2012). Reduced dorsal anterior cingulate cortical activity during emotional

regulation and top-down attentional control in generalized social phobia, generalized 23 Free Energy and Anxiety formation anxiety disorder, and comorbid generalized social phobia/generalized anxiety

disorder. Biological Psychiatry, 72(6), 476-482.

Bogacz, R. (2017). A tutorial on the free-energy framework for modelling perception and

learning. Journal of mathematical psychology, 76, 198-211.

Boly, M., Garrido, M. I., Gosseries, O., Bruno, M. A., Boveroux, P., Schnakers, C., & Friston,

K. (2011). Preserved feedforward but impaired top-down processes in the vegetative

state. Science, 332(6031), 858-862.

Bruineberg, J., & Rietveld, E. (2014). Self-organization, free energy minimization, and

optimal grip on a field of affordances. Frontiers in human neuroscience, 8, 599.

Bruineberg, J., Kiverstein, J., & Rietveld, E. (2018). The anticipating brain is not a scientist:

the free-energy principle from an ecological-enactive perspective. Synthese, 195(6),

2417-2444.

Burke, A. R., McCormick, C. M., Pellis, S. M., & Lukkes, J. L. (2017). Impact of adolescent

social experiences on behavior and neural circuits implicated in mental illnesses.

Neuroscience & Biobehavioral Reviews, 76, 280-300.

Calhoon, G. G., & Tye, K. M. (2015). Resolving the neural circuits of anxiety. Nature

neuroscience, 18(10), 1394-1404.

Cartwright‐ Hatton, S., Roberts, C., Chitsabesan, P., Fothergill, C., & Harrington, R. (2004).

Systematic review of the efficacy of cognitive behaviour therapies for childhood and

adolescent anxiety disorders. British journal of clinical psychology, 43(4), 421-436.

Chekroud, A. M. (2015). Unifying treatments for depression: an application of the Free

Energy Principle. Frontiers in Psychology, 6, 153.

Chorpita, B. F., & Barlow, D. H. (1998). The development of anxiety: The role of control in

the early environment. Psychological Bulletin, 124(1), 3-21. doi:10.1037/0033-

2909.124.1.3 24 Free Energy and Anxiety formation Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of

. Behavioral and brain sciences, 36(3), 181-204.

Clark, D. M. (1986). A cognitive approach to panic. Behaviour research and therapy, 24(4),

461-470.

Clark, D. M. (1999). Anxiety disorders: Why they persist and how to treat them. Behaviour

research and therapy, 37, S5-S27.

Clauss, K., Bardeen, J. R., Thomas, K., & Benfer, N. (2020). The interactive effect of

emotional reactivity and maladaptive metacognitive beliefs on anxiety. Cognition

and Emotion, 34(2), 393-401.

Compton, R. J., Robinson, M. D., Ode, S., Quandt, L. C., Fineman, S. L., & Carp, J. (2008).

Error-monitoring ability predicts daily stress regulation. Psychological

Science, 19(7), 702-708.

Compton, R.J., Dainer-Best, J., Fineman, S.L., Freedman, G., Mutso, A., & Rohwer, J.

(2010). Anxiety and expectancy violations: Neural response to false feedback is

exaggerated in worriers, Cognition and Emotion, 24:3, 465-479.

Dudeney, J., Sharpe, L., & Hunt, C. (2015). Attentional bias towards threatening stimuli in

children with anxiety: A meta-analysis. Clinical psychology review, 40, 66-75.

Dickstein, D. P., Finger, E. C., Brotman, M. A., Rich, B. A., Pine, D. S., Blair, J. R., &

Leibenluft, E. (2010). Impaired probabilistic reversal learning in youths with mood

and anxiety disorders. Psychological medicine, 40(7), 1089.

Dugas, M. J., Savard, P., Gaudet, A., Turcotte, J., Laugesen, N., Robichaud, M., ... &

Koerner, N. (2007). Can the components of a cognitive model predict the severity of

generalized anxiety disorder?. Behavior therapy, 38(2), 169-178. 25 Free Energy and Anxiety formation Dugas, M.J., Hedayati, M., Karavidas, A., Buhr, K., Francis, K., & Phillips, N.A. (2005).

Intolerance of uncertainty and information processing: Evidence of biased recall and

interpretations. Cognitive Therapy and Research, 29(2), 57-70.

Dugas, M. J., Freeston, M. H., & Ladouceur, R. (1997). Intolerance of uncertainty and

problem orientation in worry. Cognitive therapy and research, 21(6), 593-606.

Dugas, M.J., & Koerner, N. (2005). Cognitive-behavioral treatment for generalized anxiety

disorder: Current status and future directions. Journal of Cognitive Psychotherapy,

19(1), 61.

Eckstrand, K. L., Hanford, L. C., Bertocci, M. A., Chase, H. W., Greenberg, T., Lockovich, J.,

... & Forbes, E. E. (2019). Trauma-associated anterior cingulate connectivity during

reward learning predicts affective and anxiety states in young adults. Psychological

medicine, 49(11), 1831-1840.

Ellis, A. (1980). Rational-emotive therapy and cognitive behavior therapy: Similarities and

differences. Cognitive Therapy and Research, 4(4), 325-340.

Etkin, A. (2009). Functional neuroanatomy of anxiety: a neural circuit perspective. In

Behavioral neurobiology of anxiety and its treatment (pp. 251-277). Springer,

Berlin, Heidelberg.

Etkin, A., Prater, K. E., Schatzberg, A. F., Menon, V., & Greicius, M. D. (2009). Disrupted

amygdalar subregion functional connectivity and evidence of a compensatory

network in generalized anxiety disorder. Archives of general psychiatry, 66(12),

1361-1372.

Epstein, S., & Roupenian, A. (1970). Heart rate and skin conductance during experimentally

induced anxiety: The effect of uncertainty about receiving a noxious stimulus.

Journal of Personality and Social Psychology, 16(1), 20–28. 26 Free Energy and Anxiety formation Felix-Ortiz, A. C., Burgos-Robles, A., Bhagat, N. D., Leppla, C. A., & Tye, K. M. (2016).

Bidirectional modulation of anxiety-related and social behaviors by amygdala

projections to the medial prefrontal cortex. Neuroscience, 321, 197-209.

Feldman, D. H. (2004). Piaget's stages: the unfinished symphony of cognitive development.

New Ideas in Psychology, 22(3), 175-231.Fox, C. W., & Roberts, S. J. (2012). A

tutorial on variational . review, 38(2), 85-

95.

Fonzo, G. A., & Etkin, A. (2016). Brain connectivity reflects mental and physical states in

generalized anxiety disorder. Biological psychiatry, 80(10), 733-735.

Frank, E., Salchner, P., Aldag, J. M., Salomé, N., Singewald, N., Landgraf, R., & Wigger, A.

(2006). Genetic predisposition to anxiety-related behavior determines coping style,

neuroendocrine responses, and neuronal activation during social defeat. Behavioral

neuroscience, 120(1), 60.

Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G. (2017). Active

inference: a process theory. Neural computation, 29(1), 1-49.

Friston, K. (2019). A free energy principle for a particular physics. arXiv preprint

arXiv:1906.10184.

Friston, K., & Kiebel, S. (2009). Predictive coding under the free-energy

principle. Philosophical Transactions of the Royal Society B: Biological

Sciences, 364(1521), 1211-1221.

Friston, K., Kilner, J., & Harrison, L. (2006). A free energy principle for the brain. Journal of

Physiology-Paris, 100(1-3), 70-87.

Friston, K. (2008). Hierarchical models in the brain. PLoS Comput Biol, 4(11), e1000211.

Friston, K. (2010). The free-energy principle: a unified brain theory?. Nature reviews

neuroscience, 11(2), 127-138. 27 Free Energy and Anxiety formation Friston, K. (2012). A free energy principle for biological systems. Entropy, 14(11), 2100-

2121.

Friston, K. (2012). Predictive coding, precision and synchrony. Cognitive Neuroscience, 3(3-

4), 238-239

Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G. (2016). Active

inference and learning. Neuroscience & Biobehavioral Reviews, 68, 862-879.

Gray, J. A. (1982). Précis of The of anxiety: An enquiry into the functions

of the septo-hippocampal system. Behavioral and brain sciences, 5(3), 469-484.

Greenberg, L. S. (2004). Emotion–focused therapy. Clinical Psychology & Psychotherapy:

An International Journal of Theory & Practice, 11(1), 3-16.

Grillon, C., Baas, J. P., Lissek, S., Smith, K., & Milstein, J. (2004). Anxious Responses to

Predictable and Unpredictable Aversive Events. Behavioral Neuroscience, 118(5),

916–924.

Grupe, D. W., & Nitschke, J. B. (2013). Uncertainty and anticipation in anxiety: an integrated

neurobiological and psychological perspective. Nature reviews. Neuroscience, 14(7),

488–501. https://doi.org/10.1038/nrn3524

Gutman, D. A., & Nemeroff, C. B. (2003). Persistent central nervous system effects of an

adverse early environment: clinical and preclinical studies. Physiology & behavior,

79(3), 471-478.

Hamm, A. O. (2020). Fear, anxiety, and their disorders from the perspective of

psychophysiology. Psychophysiology, 57(2), e13474.

Hayward, L. E., Vartanian, L. R., Kwok, C., & Newby, J. M. (2020). How might childhood

adversity predict adult psychological distress? Applying the Identity Disruption

Model to understanding depression and anxiety disorders. Journal of Affective

Disorders, 265, 112-119. 28 Free Energy and Anxiety formation Helmholtz, H. (1860) Handbuch der physiologischen optik (Southall, J. P. C. (Ed.), English

trans.Vol. 3. New York: Dover.

Hilbert, K., Lueken, U., & Beesdo-Baum, K. (2014). Neural structures, functioning and

connectivity in Generalized Anxiety Disorder and interaction with neuroendocrine

systems: a systematic review. Journal of affective disorders, 158, 114-126

Hirsh, J. B., Mar, R. A., & Peterson, J. B. (2012). Psychological entropy: A framework for

understanding uncertainty-related anxiety. Psychological review, 119(2), 304.

Hohwy, J. (2013). The predictive mind. Oxford University Press.

Hohwy, J. (2017). Priors in perception: Top-down modulation, Bayesian perceptual learning

rate, and prediction error minimization. Consciousness and Cognition, 47, 75-85.

Holmes, J., & Nolte, T. (2019). “Surprise” and the Bayesian Brain: Implications for

Psychotherapy Theory and Practice. Frontiers in Psychology, 10, 592.

Huang, Y., & Rao, R. P. (2011). Predictive coding. Wiley Interdisciplinary Reviews:

Cognitive Science, 2(5), 580-593.

Imperatori, C., Farina, B., Adenzato, M., Valenti, E. M., Murgia, C., Della Marca, G., ... &

Ardito, R. B. (2019). Default mode network alterations in individuals with high-

trait-anxiety: an EEG functional connectivity study. Journal of Affective Disorders,

246, 611-618.

Joyce, James, "Bayes’ Theorem", The Stanford Encyclopedia of Philosophy (Spring 2019

Edition), Edward N. Zalta (ed.). Available at:

https://plato.stanford.edu/archives/spr2019/entries/bayes-theorem/>.

LaFreniere, L. S., & Newman, M. G. (2019). Probabilistic learning by positive and negative

reinforcement in generalized anxiety disorder. Clinical Psychological Science, 7(3),

502-515.

LeDoux, J. (2007). The amygdala. Current biology, 17(20), R868-R874. 29 Free Energy and Anxiety formation LeDoux, J. (2003). The emotional brain, fear, and the amygdala. Cellular and molecular

neurobiology, 23(4-5), 727-738.

Linden, M., Zubraegel, D., Baer, T., Franke, U., & Schlattmann, P. (2005). Efficacy of

cognitive behaviour therapy in generalized anxiety disorders. Psychotherapy and

psychosomatics, 74(1), 36-42.

Lohr, J. M., Olatunji, B. O., & Sawchuk, C. N. (2007). A functional analysis of danger and

safety signals in anxiety disorders. Clinical psychology review, 27(1), 114-126

Lueken, U., Zierhut, K. C., Hahn, T., Straube, B., Kircher, T., Reif, A., ... & Domschke, K.

(2016). Neurobiological markers predicting treatment response in anxiety disorders:

A systematic review and implications for clinical application. Neuroscience &

Biobehavioral Reviews, 66, 143-162.

Kannis-Dymand, L., Hughes, E., Mulgrew, K., Carter, J. D., & Love, S. (2020). Examining

the roles of metacognitive beliefs and maladaptive aspects of perfectionism in

depression and anxiety. Behavioural and Cognitive Psychotherapy, 1-12.

Kelly, G. (1955). Personal construct psychology. Nueva York: Norton.

Kendall, P. C., Compton, S. N., Walkup, J. T., Birmaher, B., Albano, A. M., Sherrill, J., ... &

Keeton, C. (2010). Clinical characteristics of anxiety disordered youth. Journal of

anxiety disorders, 24(3), 360-365.

Kim, M. J., Gee, D. G., Loucks, R. A., Davis, F. C., & Whalen, P. J. (2011). Anxiety

dissociates dorsal and ventral medial prefrontal cortex functional connectivity with

the amygdala at rest. Cerebral cortex, 21(7), 1667-1673.

Kim, M. J., Loucks, R. A., Palmer, A. L., Brown, A. C., Solomon, K. M., Marchante, A. N.,

& Whalen, P. J. (2011). The structural and functional connectivity of the amygdala:

from normal emotion to pathological anxiety. Behavioural brain research, 223(2),

403-410. 30 Free Energy and Anxiety formation Ladouceur, R., Dugas, M. J., Freeston, M. H., Rhéaume, J., Blais, F., Boisvert, J. M., ... &

Thibodeau, N. (1999). Specificity of generalized anxiety disorder symptoms and

processes. Behavior Therapy, 30(2), 191-207.

Li, W., Cui, H., Zhu, Z., Kong, L., Guo, Q., Zhu, Y., ... & Jiang, J. (2016). Aberrant functional

connectivity between the amygdala and the temporal pole in drug-free generalized

anxiety disorder. Frontiers in human neuroscience, 10, 549.

MacLeod, C., & Cohen, I. L. (1993). Anxiety and the interpretation of ambiguity: A text

comprehension study. Journal of Abnormal Psychology, 102(2), 238–247.

Makovac, E., Meeten, F., Watson, D. R., Garfinkel, S. N., Critchley, H. D., & Ottaviani, C.

(2015). Neurostructural abnormalities associated with axes of emotion dysregulation

in generalized anxiety. NeuroImage. Clinical, 10, 172–181.

https://doi.org/10.1016/j.nicl.2015.11.022

Makovac, E., Watson, D. R., Meeten, F., Garfinkel, S. N., Cercignani, M., Critchley, H. D., &

Ottaviani, C. (2016). Amygdala functional connectivity as a longitudinal biomarker

of symptom changes in generalized anxiety. Social cognitive and affective

neuroscience, 11(11), 1719-1728

Makovac, E., Mancini, M., Fagioli, S., Watson, D. R., Meeten, F., Rae, C. L., ... & Ottaviani,

C. (2018). Network abnormalities in generalized anxiety pervade beyond the

amygdala-pre-frontal cortex circuit: Insights from graph theory. Psychiatry

Research: Neuroimaging, 281, 107-116.

Mathys, C. D., Lomakina, E. I., Daunizeau, J., Iglesias, S., Brodersen, K. H., Friston, K. J., &

Stephan, K. E. (2014). Uncertainty in perception and the Hierarchical Gaussian

Filter. Frontiers in human neuroscience, 8, 825. 31 Free Energy and Anxiety formation McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context

effects in letter perception: I. An account of basic findings. Psychological review,

88(5), 375.

McEvoy, P. M., & Mahoney, A. E. (2012). To be sure, to be sure: Intolerance of uncertainty

mediates symptoms of various anxiety disorders and depression. Behavior therapy,

43(3), 533-545.

Murray, L., Creswell, C., & Cooper, P. J. (2009). The development of anxiety disorders in

childhood: an integrative review. Psychological medicine, 39(9), 1413-1423.

Nitschke, J. B., Sarinopoulos, I., Oathes, D. J., Johnstone, T., Whalen, P. J., Davidson, R. J.,

& Kalin, N. H. (2009). Anticipatory activation in the amygdala and anterior

cingulate in generalized anxiety disorder and prediction of treatment response. The

American journal of psychiatry, 166(3), 302–310.

Ochsner, K. N., & Gross, J. J. (2005). The cognitive control of emotion. Trends in cognitive

sciences, 9(5), 242-249.

Papageorgiou, C., & Wells, A. (2001). Metacognitive beliefs about rumination in recurrent

major depression. Cognitive and Behavioral Practice, 8(2), 160-164.

Piaget, J. (2003). Part I: Cognitive Development in Children--Piaget Development and

Learning. Journal of research in science teaching, 40.

Qin, S., Young, C. B., Duan, X., Chen, T., Supekar, K., & Menon, V. (2014). Amygdala

subregional structure and intrinsic functional connectivity predicts individual

differences in anxiety during early childhood. Biological psychiatry, 75(11), 892-

900.

Ramstead, M. J. D., Badcock, P. B., & Friston, K. J. (2018). Answering Schrödinger's

question: A free-energy formulation. Physics of life reviews, 24, 1-16. 32 Free Energy and Anxiety formation Rao, R. P., & Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional

interpretation of some extra-classical receptive-field effects. Nature neuroscience,

2(1), 79-87.

Rubin, S., Parr, T., Da Costa, L., & Friston, K. (2020). Future climates: Markov blankets and

active inference in the biosphere. Journal of the Royal Society Interface, 17(172),

20200503.

Schiele, M. A., & Domschke, K. (2018). Epigenetics at the crossroads between genes,

environment and resilience in anxiety disorders. Genes, Brain and Behavior, 17(3),

e12423.

Schneider, E. D., & Kay, J. J. (1994). Life as a manifestation of the second law of

thermodynamics. Mathematical and computer modelling, 19(6-8), 25-48.

Smith, K. E., & Hudson, J. L. (2013). Metacognitive beliefs and processes in clinical anxiety

in children. Journal of Clinical Child & Adolescent Psychology, 42(5), 590-602.

Sterzer, P., Adams, R. A., Fletcher, P., Frith, C., Lawrie, S. M., Muckli, L., ... & Corlett, P. R.

(2018). The predictive coding account of psychosis. Biological psychiatry, 84(9),

634-643.

Spratling, M. W. (2016). Predictive coding as a model of cognition. Cognitive

processing, 17(3), 279-305.

Sylvester, C. M., Corbetta, M., Raichle, M. E., Rodebaugh, T. L., Schlaggar, B. L., Sheline, Y.

I., Zorumski, C. F., & Lenze, E. J. (2012). Functional network dysfunction in anxiety

and anxiety disorders. Trends in , 35(9), 527–535.

Tovote, P., Fadok, J. P., & Lüthi, A. (2015). Neuronal circuits for fear and anxiety. Nature

Reviews Neuroscience, 16(6), 317-331. 33 Free Energy and Anxiety formation Tyler, K. A., Schmitz, R. M., & Ray, C. M. (2018). Role of social environmental protective

factors on anxiety and depressive symptoms among Midwestern homeless youth.

Journal of Research on Adolescence, 28(1), 199-210

Westbury, C. F. (2010). Bayes’ rule for clinicians: an introduction. Frontiers in psychology, 1,

192.

Wells, A. (1995). Meta-cognition and worry: a cognitive model of generalized anxiety

disorder. Behavioural and Cognitive Psychotherapy, 23, 301–320.

Wells, A. (1999). A cognitive model of generalized anxiety disorder. Behaviour Modification,

23, 526–555.

Wells, A. (2004). A cognitive model ofGAD: metacognitions and pathological worry. In:

R. G. Heimberg, C. L. Turk, & D. S. Mennin (Eds.), Generalized anxiety disorder:

advances in research and practice (pp. 164–186). New York: Guilford.

Wells, A. (2005). The metacognitive model of GAD: assessment of meta-worry and

relationship with DSM-IV generalized anxiety disorder. Cognitive Therapy and

Research, 29, 107–121.

Wells, A., & King, P. (2006). Metacognitive therapy for generalized anxiety disorder: an

open trial. Journal of Behavior Therapy and Experimental Psychiatry, 37, 206–212.

Zald, D. H. (2003). The human amygdala and the emotional evaluation of sensory stimuli.

Brain Research Reviews, 41(1), 88-123.

Zednik, C. (2011). The nature of dynamical explanation. Philosophy of Science, 78(2), 238-

263.

Zuckerman, M. (1999). Diathesis-stress models