Introduction

It has recently been proposed that depressed mood reflects an adaptive, socially risk-averse psychobiological strategy that preserves social relationships (i.e., inclusion) when there is evidence for maladaptive instability in interpersonal exchanges (Badcock et al., 2017). This perspective follows an evolutionary systems theory (EST) of human biobehaviour called the hierarchically mechanistic mind, which combines insights drawn from research in psychology with the computational resources borrowed from the theory of active inference in theoretical neurobiology (Badcock, Friston, & Ramstead, 2019). This model rests on two fundamental claims. The first conforms to the theory of active inference by suggesting that the brain comprises hierarchically organized neurocognitive mechanisms that reduce the dispersion or decay of our sensory and phenotypic states—by generating action-perception cycles that minimize surprising exchanges with the world. The second claim ensues from an embodied perspective on neural form and function—that accommodates the broader evolutionary, developmental, and real-time processes that act on human phenotypes. The implication here is that to understand a phenotypic trait, we need approaches that synthesise findings from diverse fields of inquiry to explain both why that trait is adaptive, along with how it emerges from the nested dynamics across different timescales. In this spirit, the current study provides proof of principle for the EST of depression, using simulations of active inference. We conclude by discussing the clinical implications of our model.

Our proof of principle integrates two major schools of thought. The first is rooted in evolutionary psychological approaches to depression, rallied around the social risk hypothesis (SRH) proposed by Allen and Badcock (Allen & Badcock, 2003). Psychological symptoms of depression include feelings of sadness, emptiness, and hopelessness, along with systematic disinterest in activities (i.e., anhedonia), feelings of worthlessness, and inappropriate guilt. Typically, a diagnosis of depression is made when symptoms have been present for at least 14 days (American Psychiatric Association, 2013). Two important symptoms of depression are anhedonia and social withdrawal: the latter is commonly observed in depression as a clinical correlate of anhedonia, but is not a formal criterion (Buckner et al., 2008). Evolutionary models of depression explain the maintenance of genetic vulnerabilities to depressive symptomatology in terms of the selective advantage of these vulnerabilities in ancestral environments (R. M. Nesse, 1990). The adaptive properties of depression are thought to be restricted to the relatively transient, normative depressed mood states that we all experience from time to time, while more severe manifestations, like those observed in major depressive disorder, reflect a dysregulation of our species-typical capacity for mood variation (Nettle, 2004). The SRH suggests that depressive symptoms might have been selected as a strategy that prevents the deterioration of interpersonal relationships. Low mood reduces one’s propensity for social risk-taking, and increases implicit signalling for social support, which reduces competitive encounters (see Box 1 for background). Clinical depression occurs when this sequence becomes maladaptive; specifically, when it does not lead to a resumption of normal mood. This may be due to neurobiological or psychological deficits that maintain increased sensitivity to social instability, or to instabilities or the absence of support in the proximal environment towards which the depressed individual reacts. The increased sensitivity to social instability constitutes the basis of the neurocognitive processes leading to depressed mood, upon which selection can act (for a review see (Badcock, Friston, Ramstead, et al., 2019)).

The second root of our simulation centres upon formal, testable models of depressive phenomena that borrow from the principles of computational psychiatry (Huys, Guitart-Masip, et al., 2015). Accordingly, a key aspect of the work reported in this paper is the attempt to model social inference – as it relates to the phenomenology of depression – from first principles. This is challenging, because of the many aetiological factors that underwrite the psychopathology and pathophysiology of depression. We try to formalise the normative aspects of depression as a Bayes optimal response to inference in the prosocial world, while considering both social and pharmacological interventions. To our knowledge, this is the first modelling work that addresses the interaction between social factors and pharmacotherapy within the same formalism. In this sense, the simulations reported here also provide a proof of principle for a model of the effects of drug treatment on neuronal computations that underlie belief updating and behaviour in depression. In brief, we make three basic assumptions that allow us to characterise the effect of drug treatment on social inference and subsequent behaviour. First, both inference and learning conform to the same (ideal Bayesian observer) principles of active inference; namely, belief updating and experience-dependent plasticity both optimise a variational free energy bound on (log) model evidence or marginal likelihood (Friston et al., 2016). Second, pharmacotherapy motivates neuromodulatory effects that, computationally, change the precision of sub-personal probabilistic beliefs (i.e., prior beliefs about states of affairs in the world or likelihood mappings between causes and consequences) (Parr et al., 2018). Finally, one cannot ignore the reciprocal coupling between an agent and her (prosocial) environment when modelling interpersonal exchanges. This requires an explicit consideration of how environmental (prosocial) contingencies respond to an agent’s behaviour (Badcock et al., 2017) (see Box 2 for background). Here, the embedded aspect of interventions on the social environment (Bruineberg et al., 2018; Constant et al., 2018) was modelled by an increase in social reliability following patterns of behaviour that can be construed as social signalling. Our hope was to show that functional responses to social adversity use the same inferential mechanisms seen in pathological depression – and that psychopathology can be remediated by a combination of social support and drug therapy.

Our numerical proof of principle is based on active inference for discrete states, using (Markovian) generative models (Friston, Parr, et al., 2017) – see method section. We present a series of simulations based on an augmented version of a two-armed bandit game from economics, in which the agent has to choose between a risky or safe social engagement (Schwartenbeck et al., 2019). The augmentation involved offering the agent a cue option that indicates whether the risky arm is low-risk or high-risk (i.e., it indicates the social context); this contextual state alternates every other trial. Narratively, the cue corresponds to social media that provides information or evidence that reduces uncertainty about the prevailing social context. This means that healthy agents will systematically sample the cue to make an informed decision. As they sample their social environment, agents will learn the probability of reward afforded by choosing one of the two arms. Narratively, this relates to checking people’s availability before choosing among social options.

Based on the learning process characteristic of active inference, agents exposed to social adversity (e.g., rejection by social partners) will learn the likelihood of being rejected. We simulate different phenotypes, with and without social support and pharmacotherapy, which reshape the agent’s (pessimistic) prior beliefs. Our numerical analyses speak to how pharmacotherapy and social support – triggered by social signalling on social media – allows the agent to regain a normal mood. Depending on the type of intervention (social, pharmaceutical, or the lack thereof), the agent typically experiences a phase of low mood, and either spirals into persistent depression (anhedonia and social withdrawal), or returns to various levels of normal functioning. We will quantify the responses of our synthetic agent in terms of task performance and associated synthetic mood (i.e., expected reward under a given action policy) and behaviour (action selection).

Our two-arm bandit social decision-making task (see Figure 1) involves choosing among three social engagement options, which vary in their risk. The first is a ‘safe’ option, but with low social preference (going to a well-known friend, Rudolph, who you know can be engaged with 100% success, but won’t provide the most fulfilling interaction). The second, ‘risky’ option has a high preference (going to see a new popular student, Caroline, whom you do not know, but were told is a lot of fun), but is risky because Caroline often forfeits, and the agent is averse to failed social encounters. On a ‘good day’ the agent has a 75% chance of successfully engaging Caroline, but on a ‘busy day’, only a 25% chance of catching her. The third, ‘socially epistemic’ option yields a null cost: under this option, the agent can turn to social media, to see if Caroline is having a ‘busy day’. To characterise prosocial and emotional inference that might underwrite depression, we considered belief updating and subsequent behavior under 8 different conditions – in a three-way factorial design involving the following factors: social adversity, social support, and pharmacotherapy (see Table 1).

Methods and materials

Active inference is a Bayesian framework that only uses local information (i.e., there is no external supervision) for belief-updating, in order to ensure biological plausibility. Markov Decision Processes (MDPs) can be used to simulate how agents infer which discrete hidden states (s) of the world provide the best explanation of observed sensory outcomes (o), under a given generative model. To generate predictions of sensory outcomes, an agent needs prior expectations about initial hidden states (an initial state prior, D), how states generate sensory outcomes (sensory mapping, A), and how states evolve over time (state transitions, B). The agent can infer states of the world by minimising the discrepancy between predicted and observed outcomes (a.k.a., variational free energy), or equivalently, by maximising Bayesian model evidence. For mathematical details, see (Parr & Friston, 2017).

When expectations of hidden states are conditioned upon the agent’s plan or policy (as encoded in the policy dependent B matrices), one has a generative model of action (see Figure 2). Without an external referee to say what is right or wrong, the agent will need to: (i) predict her course of action, based on the succession of states, expected under each policy; and (ii) select her action based on (posterior) beliefs about the best policy. To that end, we equip the agent with (self-referential) prior beliefs that are biased towards policies with stronger expected model evidence or, equivalently, lower expected free energy, G.

Mathematically, expected free energy can be decomposed into pragmatic and epistemic components for any given policy. On the one hand, pragmatic value (i.e., exploitation) biases policy selection towards obtaining preferred sensory outcomes (evolutionary prior preferences, C), much like utility in reinforcement learning. On the other hand, epistemic value (i.e., exploration) biases policy selection towards the (expected) minimisation of uncertainty about states of the world (a.k.a., artificial curiosity).

Uncertainty can be over beliefs about current hidden states or model parameters (as quantified in free energy F) or over beliefs about future hidden states and their associated outcomes under a given policy (as quantified in expected free energy G_π). In active inference, the latter guides action selection and can be decomposed in three distinct sources of uncertainty: (i) expected ambiguity, or anticipated uncertainty about hidden states (e.g., “how certain will I be about Caroline’s mood, given that I check social media?”), (ii) expected risk, or the anticipated uncertainty about whether future outcomes will align with preferences C (e.g., “how certain will I be that I obtain preferred outcomes, given that I visit Caroline?”), and (iii) the anticipated uncertainty about Dirichlet parameters of the likelihood mapping A (e.g., “how much might I learn about state-outcome mappings if I visit Rudolf?”) (Kaplan & Friston, 2018). Each of these sources of uncertainty can be manipulated directly with interventions on the model. Here, we focus on direct intervention on salience—via serotonergic and noradrenergic manipulation of initial states and state transitions—and on the indirect manipulation of extrinsic value via the manipulation of social observations, or outcomes (Figure 2). Thus, in our simulations, our agent will have a double incentive for social engagement: (i) fulfilling preferences for positive social outcomes; and (ii) the natural drive towards resolving her uncertainty over the various beliefs she has about the social world (c.f., curiosity about a new acquaintance). Crucially, it is this double incentive that we exploit to formalize the behavioural dynamics envisaged by the EST of depression; the first incentive relating to ‘evolutionary’ prior preferences for high social reward, and the second incentive relating to ‘developmental’ learning.

We limit the notion of social engagement to face-to-face encounters with Rudolph or Caroline.

The software to simulate belief updating and action selection, based on the specification of any generative model (as the one specified in Figure 2), is freely available as part of the academic software SPM; specifically, the Matlab routine spm_MDP_VB_X.m (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/).

The generative model and process used to simulate social inference – and ensuing changes in depressed mood – are described formally in Figure 2. In brief, this setup considers 5 (observable) outcomes: an outcome that sets the scene for a social choice (e.g., being at home), three levels of social reward (low, moderate and high), and an epistemic cue that reports the current context (this is a ‘Go’ or ‘no-Go’ social context) that determines Caroline’s availability.

Outcomes are generated by two kinds of external states called hidden factors. The first is the context with the two levels pertaining to Caroline’s availability. These hidden states are not under the agent’s control. Conversely, transitions among the states of the second factor reflect the agents choice or policy, with four levels; i.e., home, Rudolph, Caroline, social media. The two factors interact to generate outcomes. Specifically, the context (Caroline’s availability) determines whether the social media state generates an (epistemic) outcome that is ‘go’ or ‘no-go’. Put simply, this means the agent can choose to find out whether Caroline is available or not—or contact her directly—or not. The context alternates every other day, meaning that the context-sensitive outcome available to the agent changes every other day.

Given some observations, the agent can predict outcomes under a set of plans or policies, given her beliefs about (policy-dependent) transitions among different states. This enables her to evaluate the expected free energy of each policy – and use the expected free energy as prior beliefs to form posterior beliefs, given what she has already observed. An action is generated by selecting the most likely action from the resulting posterior. And so, the cycle of perception and action continues. Notice that the coupling between the agent and the world is mediated by observable outcomes and action. The interventions corresponding to the conditions above can be modelled, either by changing the prior beliefs of the agent (about initial conditions, likelihoods or state transitions), or by changing the prosocial world in a way that responds to her choices.

Results

We used belief updating to simulate perception, action, and learning under different levels of social adversity, support and antidepressant treatment (i.e., pharmacologically induced changes in prior beliefs about states and contingencies). The results of these simulations are summarised in Figures 4, 5, 6. Behavioural outcomes and choices were assessed using the criteria listed in Table 2. In what follows, we described the responses to different scenarios or conditions in turn.

Baseline

0 Baseline (Figure 4): The first 28 trials are equivalent across all simulations. In the absence of adversity, the agent skillfully responds to contextual changes by shifting between action policies that yield a (risky) high social reward and a (safe) moderate social reward. After the 7^th trial, the agent always engages epistemic policies; foraging on social media first, followed by exploitative behaviour resulting in positive or negative outcomes. Before the 7^th trial, the agent is still learning her prior beliefs about social partners and figuring out what policy will best suit her preferences; hence the different policies (6,9,8,4, see Figure 4, bottom right for a visual description of each policy). After the 23^rd day, the agent misreads the situation: Caroline was having a good day, but the agent perceived a negative outcome (e.g., by misinterpreting Caroline’s behavior during the encounter). The expected utility remains high over all (above the baseline; the pink line), and crucially, there are no consecutive days of anhedonia. Figures 5 and 6 use the same format as the upper panel in Figure 4 to show the effects of various interventions on social adversity and support, with or without pharmacological interventions.

Figure 3 

This figure details the likelihood and prior transition probabilities for our generative model of prosocial exchanges. The variables pertaining to the generative model are shown in light blue boxes, while the corresponding parameters of the generative process (i.e., the social world) are shown in light pink. The states and outcomes in this model are generated under two contexts pertaining to Caroline’s availability: available or not available. For ease of visualization, we have shown context-sensitive outcome likelihoods. In other words, there are six potential outcomes, but we have conditioned the epistemic (‘go’ and ‘no-go’) outcome on the context (to generate five outcomes). This simplifies the graphics and is licensed by the fact that only the epistemic outcome is context-sensitive. The top-left section corresponds to the contingencies during the initial exchanges (days 0–28) and corresponds with the narrative description in Figure 1. The adverse life event on the 28^th day amounts to Rudolph and Caroline (on a good day) now yielding negative outcomes, and Caroline, even on a good day, affording negative outcomes. Adversity happens when the agent is sensitive to (i.e., prone to learn) the social environment. We implemented this by reinitialising the counts over the sensory prior beliefs of the agent (a). Social adversity and support are modelled by changing the precision or reliability of social outcomes in the generative process – in response to social signals. This is a subtle aspect of this model; namely, the generative process or social environment responds adaptively to the agent’s behaviour. As of the 30^th day (for the conditions involving social support), we implement social support by adding counts (+10) to the likelihood of the environment counts (+10) for the cells corresponding to the mappings ‘Rudolph and positive outcomes’, ‘Caroline good day context and positive outcome’, and ‘Caroline busy day context and negative outcome’. The n_i corresponds to the number of times the agent visited the location a_i. The increase in counts has the ultimate consequence of driving the probability mapping in the (A) of the generative process towards and beyond their initial values more. A ‘+10’ is added to the cells every time the agent solicits the epistemic cue (i.e., social media). This implements the social signalling characteristic of adaptive low mood. Pharmacological interventions on the 35^th day include the following: Serotonin provides an optimistic bias by changing prior beliefs about the initial states, in favour of the ‘Go’ context (from .5;.5 to .99;.01). Noradrenaline decreases the precision of the transition probability matrices B (i.e., it increases uncertainty about future states), which leads to a gradual accumulation of uncertainty about unvisited states. Through the expected ambiguity component of expected free energy G, it tends to motivate exploratory behaviours. The agent continues to learn the state transition after we administer noradrenaline.

Figure 4 

Baseline. Top panel: The upper images show the posterior expectation of each of 10 policies (see method, Figure 2) as they evolve from day to day (64 in total). The small circles in the upper part of these panels indicate the observed outcomes (context in the first panel, and outcome in the second). The context changes every other day. The pragmatic value of these outcomes is shown as a (black) bar chart in the second panel. The lower panel describes the interventions that depend on the condition, and the symptoms, which are: (i) anhedonia when pragmatic value or reward (black bars) are below the pink bar over multiple days (duration, black shaded rounded rectangles), and (ii) social withdrawal, expressed by policies 1,4,7, and 10. The lower left panel provides a legend (upper) and a graphical description of the policies (lower). The intensity component of anhedonia corresponds inversely to the expected utility of a policy, or the extent to which it will yield preferred outcomes. Narratively speaking, this amounts to expecting socially rewarding outcomes when engaging a certain action. The intensity component of anhedonia is thus defined as low appetitive action. We assume that normal levels of appetitive action correspond to the expected utility experienced on most days, for a healthy (baseline) agent (pink line). The duration component of anhedonia corresponds to the number of consecutive days. A normative assessment of anhedonia thus would involve 14 consecutive days, as is the case in the condition of severe depression below.

Figure 5 

Responses to intervention. This figure uses the same format as the upper panels of Figure 4. Interventions are indicated by the solid lines (red line: social adversity; blue line: social support; orange line: pharmacotherapy). The plots report the simulated responses to social adversity (red lines in all quadrants), and the remedial effects of social support (blue line in the second quadrant). Quadrants with orange lines show the corresponding effects of pharmacotherapy (serotonin or noradrenergic). The four treatment conditions show the same behavior over the first 28 days as the baseline scenario. This figure reports the results of conditions 1 to 4.

Figure 6 

Responses to pharmacotherapy and social support. This figure uses the same format as Figure 4 and 5. Here, we report the responses to the final three conditions; namely, responses to serotonin and noradrenaline and combinations of drug treatment (yellow line), after social support (blue line).

Discussion

Using active inference, we have reproduced (artificial) anhedonia and social withdrawal to provide a numerical analysis of the EST of depression. We specified a generative model, involving multiple components that conspired to generate context-sensitive responses to social uncertainty; particularly, the prior preferences for socially rewarding outcomes (e.g., encounters with Caroline). Our results provide support for our hypothesis that depressed mood reflects an adaptive response to interpersonal adversity. Following an adverse life event, our synthetic agent resolved interpersonal uncertainty via social signaling, thereby alleviating her depressed mood. Except for scenarios involving social support, all the conditions we simulated resulted in an above-average duration of episodes of anhedonia and social withdrawal, speaking to unresolved uncertainty. Crucially, we do not claim that depressive psychopathology is adaptive. Indeed, unlike our ‘social support’ condition, the ‘severe depression’ scenario proved to be maladaptive, characterised by unresolvable episodes of low mood and social withdrawal. This may either occur when signalling is defective (e.g., due to personality difficulties, rendering a person unable to deliver the appropriate signals), or when it fails to be received (e.g., cues provided by someone who is socially isolated).

A key aspect of simulations similar to ours is the explicit and formal modelling of the aetiological factors that underwrite the selection of, or inference about, prosocial behaviour; ranging from Bayesian belief updating (i.e., perceptual inference), through to experience-dependent plasticity (i.e., perceptual learning), and to the social and encultured responses of the environment. The active inference framework has an explicit (neuronal) process theory, which could allow future studies to simulate the selective effects of neuromodulatory interventions on the encoding of precision or uncertainty, and its consequences for the agent’s social behaviour. Having a complete model of (aberrant) social inference means that in the future, one could simulate neuronal processes that lend themselves to empirical measurement. Studies along these lines could simulate dopamine responses in order to provide qualitative predictions that could be tested with functional magnetic resonance imaging, e.g.,(D’Ardenne et al., 2008; Schwartenbeck et al., 2015). In this setting, dopamine responses are usually associated with updates to the expected precision of Bayesian beliefs about the policy in play (see Appendix E in Friston, FitzGerald, et al., 2017; Sales et al., 2019)

In our simulations, uncertainty over contingencies decreased every time the agent referred to social media. Narratively, this could be interpreted as the agent signaling (implicitly or explicitly) to Caroline and Rudolph that they should be more consistent in order to provide more support. Note that this does not imply any qualitative change in prosocial responses; it simply corresponds to an increase in the consistency or reliability of responses that may or may not be affiliative. Computationally, this amounts to repairing the environment, such that the prior beliefs of a phenotype are fit for purpose. In other words, the (social) environment changes to match the prior beliefs of its incumbents; thereby reversing the suboptimality implicit in maladaptive depression. Recovery then depends on the sensitivity of the subject’s social environment and on how often she consults social media. Here, the positive effect of combining pharmacotherapy with social support is thought to be attributable to the optimistic bias associated with serotonin (Harmer, 2008; Harmer et al., 2017), coupled with the effect of noradrenaline, which motivates the exploration of states associated with rewards (Aston-Jones & Cohen, 2005).

Given the parametrization of our subject, the best intervention was the combination of social support and serotonin, while the worst outcome—in terms of social withdrawal—was the intervention with serotonin alone (see results table). How these two interventions work together in real participants remains open to question Table 3. Computationally, serotonin provides an optimism bias while social support confirms that bias by returning the social environment to its normal setting (i.e., the setting matches the non-pessimistic expectations of the agent). However, when social support is lacking, serotonin leads to repeated, failed social encounters and social isolation. It is unclear whether serotonergic antidepressants are direct mood enhancers. Rather, it is suggested that antidepressants work by augmenting positive emotional processing, which then has positive effects on other psychological factors (Harmer et al., 2009). Our simulation results highlight this more complex systemic interaction between the psychosocial and neurocognitive aspects of depression and stresses the importance of social support. Indeed, social support in older adults is known to have alleviating, bidirectional effects on symptoms of depression and anxiety. Social disconnectedness appears to predict perceived isolation, which itself predicts higher depressive symptoms, and vice versa (Santini et al., 2020). Adolescents who self-report higher perceived social support at age 19 are less likely to show depressive symptoms one year later (Scardera et al., 2020), and reviews emphasise the significant protective effects of perceived emotional and instrumental support, as well as social network diversity in the general population (Santini et al., 2015). The strength of the positive effect of social support, of course, rests on the subject-specific parametrization, which we can expect to vary across real subjects. For instance, we initialized our subjects as ‘blank slates’ with respect to state transitions. However, this would be expected to vary across participants based on their individual experiences and development. This may also vary based on the volatility of the (prosocial) environment prior to the occurrence of social adversity. Again, our proposal is a proof of principle, and is only meant as a general portrait of what is feasible, when considering the social environment in computational phenotyping.

Our results speak to the Darwinian models of depression synthesised by the Social Risk Hypothesis (SRH) (cf. Box 1). Following the attachment model, simulated agents – displaying anhedonia and social withdrawal – inhibited social risk-taking under social uncertainty. Following the social competition hypothesis, the adverse life event reduced social uncertainty by producing social withdrawal. Consistent with the resource conservation model, after the adverse event, the agent progressively returned to Caroline, so long as the agent knew exactly when to approach her. From the point of view of the SRH, the explanations of the attachment model, the social competition model, and the resource conservation model are all grounded in the dynamics we simulated. The dynamics we simulated were the increase in social uncertainty leading to behavioural and psychological symptoms that either lead to depression—when the social environment is not responsive—or to the restabilisation of the social network—when the social environment is responsive. The Evolutionary System Theory (EST) of depression, which is the recent neurocomputational reinterpretation of the SRH, frames the adaptive mood dynamics integrated by the SRH as an attunement dynamic between evolutionary adaptive priors (here prior preferences), plastic developmental priors (here B and a likelihood A), and a social environment (here a generative process). These would have been selected to conspire to generate adaptive symptoms of depression in order to trigger social network restabilisation (ex. condition 2); social network stability having been crucial to evolutionary success throughout human history (see Box 1). When the social environment fails to respond to the social signalling represented by depressive symptoms, the behaviourally adaptive pessimistic beliefs that produced this signalling spirals into the maladaptive beliefs characteristic of depressive illness.

Conclusion: future directions

Simulation studies such as ours can be used to simulate both the symptoms and underlying processes of inference in silico. Note, however, that our generative model only had one level. By adding levels to the generative model, as in hierarchical (deep) active inference (Friston, Parr, et al., 2017), one could further finetune these affective dynamics. For instance, one could keep lower-level preferences fixed, reflecting their evolutionary origins, while allowing learning in higher-level preferences to change as a function of life experiences (e.g., learning to prefer Rudolph’s underwhelming calm over Caroline’s extravagance). Furthermore, generative models – of the kind used above – can be fitted to individual and population level clinical data; involving some general-purpose tasks related to a disorder of interest (e.g., social decision-making in depression), thereby yielding a novel avenue for computational phenotyping, prognosis, and diagnostic nosology. The idea here is that clinicians could then predict psychiatric trajectories in specific individuals, when conditioned on different available treatment options. The latter could then be used to generate a prognosis and course of treatment tailored for any client, which we believe is perhaps the most exciting promise of generative modelling in clinical psychiatry.

However, before achieving this, there are many conceptual and technical limitations to overcome, which chiefly relate to the treatment of clinical data using computationally meaningful generative models. Behavioral measurements such as hits and misses and associated social withdrawal can be measured in experimental designs that track behaviour in a decision-making task, with a given narrative (e.g., based on vignettes of real-life scenarios). The challenge lies in fitting individual and environmental initial conditions for both the generative model and generative process (see method, Figure 3). For instance, assuming that preferences are endowed by (encultured) evolution, one should provide a reliable estimate of population-level preferences for social encounters. Then, one should assess the degree of precision of empirical priors and measure the expected utility for each action policy. Crucially, in order to implement the effect of social support, one could also gather and translate information about environmental responsivity. This could be done via task-specific questionnaires (e.g., on a Likert scale, how desirable is an encounter with Rudolph versus Caroline? How reliable do you consider Caroline? Etc.). Alternatively, these questions could be answered by data captured by various technologies. Smartphone-based, passive sensing technologies, which can capture behavioural data (e.g., distances travelled, exercise, sleep, social media activity) and psychological data (e.g., affective tone of text entered), might help in this regard (Sapiro et al., 2019). More generally, the specification of environmental components might be achieved by using various local cultural factors (e.g., cultural norms) regarding the responsivity to idioms of distress; i.e., culturally specific ways of expressing illness experience (Kirmayer & Young, 1998).

In short, to achieve clinical utility, generative models of depression should summarise the client’s neurocognitive disposition to learning as well as her social situation, in terms of the environmental responsivity to her signalling. The role of the clinician, then, would be to map the evolutionary (e.g., adaptive priors), neurocognitive (e.g., empirical priors), and social (e.g., environmental responsivity) portrait of specific clients in terms of a generative (phenotypic) model – a Computational Evolutionary Social assessment of sorts. This opens a novel avenue for research, which attempts to quantify both generative models and processes, by bringing together the expertise of cultural, evolutionary, and computational psychiatrists and psychologists. If such an approach proves reliable – and robust predictions can be made regarding the course of illness experience and optimal treatment options – using computational (social and neurocognitive) phenotyping to improve psychiatric assessment, diagnosis, and tailored interventions might become commonplace.