Kalisch et al. provide an impressive model that considers resilience in terms of “quantitative outcome variables,” resulting from events. Accordingly, events elicit mental representations, which generate primary and secondary appraisals, the latter comprising coping potential (CP). Here we wish to underscore the possible regulatory value of “event-free” resting-states regarding implicit CP self-models and their role in mediating appraisal styles and resilience factors. Resting-state is a critical period, when we disengage from cognitively/affectively demanding environmental cues and tasks, and where the brain is “free” to process information from previous environmental interactions (Spreng & Grady Reference Spreng and Grady2010) and to prepare for future engagements. Neuroimaging data have implicated resting-state in the role of maintaining coherent and cohesive conceptual representations of self, or self-models (e.g., D'Argembeau et al. Reference D'Argembeau, Collette, Van der Linden, Laureys, Del Fiore, Degueldre, Luxen and Salmon2005; Schneider et al. Reference Schneider, Bermpohl, Heinzel, Rotte, Walter, Tempelmann, Wiebking, Dobrowolny, Heinze and Northoff2008). We propose that CP self-models assist in organizing resource allocation strategies for adaptive appraisal styles and in constructing value-based resilience factor representations. Resting-state may therefore permit automatic recursive affirmations and updating of implicit self- and world-representations per retrospective/prospective mental projections (Ostby et al. Reference Ostby, Walhovd, Tamnes, Grydeland, Westlye and Fjell2012). These representations then organize appraisal styles and attribute self-relevant value to resilience factors for future event engagement.

Using a Bayesian approach (Friston Reference Friston2012; Helmholtz & Southall Reference Helmholtz and Southall1962), we infer that during resting-state, implicit CP self-models incorporate beliefs generated posterior to experiencing the world. These self-models are then updated to maximize prediction accuracy for future events (Bengtsson & Penny Reference Bengtsson and Penny2013). This prospective mental projection can occur explicitly, but also implicitly (Gerrans & Sander Reference Gerrans and Sander2014). From Bayesian learning theory, it follows that implicit CP self-models may emerge from active inference of the world's potential to elicit affective self-states, promote/prevent goal achievement, and confer reward/punishment (Moutoussis et al. Reference Moutoussis, Fearon, El-Deredy, Dolan and Friston2014). Accordingly, active inference uses previous experiences to generate implicit (and explicit) representations of “efficacy expectancies.”

These representations, or empirical priors (Friston et al. Reference Friston, Schwartenbeck, Fitzgerald, Moutoussis, Behrens and Dolan2013), hence constitute implicit self-models of CP and include representations of others' capacity to help effectively in resolving conflict (“other-efficacy”). Implicit CP self-models reflect non-conscious attitudes and predictions toward one's capacity to cope/adjust to a situation. Behavioral and neuroimaging data illustrate the existence of implicit self-models (Back et al. Reference Back, Schmukle and Egloff2009; Rameson et al. Reference Rameson, Satpute and Lieberman2010), which may organize appropriate action tendencies and cognitive strategies necessary for elaborating positive protective appraisal/reappraisals. In alignment with the authors' PASTOR model, implicit CP self-models and world-models (e.g., other-efficacy, outcome expectancies) would organize beliefs, attitudes, and interpretive biases, which define appraisal styles, into a coherent narrative to minimize inconsistent future predictions (e.g., Bengtsson & Penny Reference Bengtsson and Penny2013). According to Kalisch et al., appraisal styles represent important variables predicting a resilient outcome and are mediated by unconscious (and conscious) processes. Likewise, we suggest that appraisal styles arise extensively from implicit CP self-model representations.

Event-free resting-states provide a unique opportunity for implicit CP self-models to renew and update per retrospective and prospective self-state projections, although this renewal/updating could not occur without appraisal/reappraisal mechanisms. Whereas bottom-up active inference may give rise to empirical priors of self, resting-state would equally permit top-down appraisals, evaluating expected outcomes of self, as an effective agent in the world. This dovetails nicely with neuroimaging evidence suggesting iterative resting-state mental time travel, from retrospective to prospective self-states (Ostby et al. Reference Ostby, Walhovd, Tamnes, Grydeland, Westlye and Fjell2012) as well as resting-state default mode network comprising self-referential substrates (Qin & Northoff Reference Qin and Northoff2011).

Hence, CP self-models may rely on the very appraisal styles they previously elaborated. For instance, appraisals of empirical priors and ensuing efficacy expectancies (Ellsworth & Scherer Reference Ellsworth, Scherer, Davidson, Scherer and Goldsmith2003) may yield “pessimistic” predictions (e.g., punishment), thus eliciting negative affective states. Consequently, reappraisal operations may ensue, where empirical priors are re-evaluated/re-interpreted and positive self-memories of coping and self-efficacy are incorporated. This parallels the authors' proposal for a memory-based “positive situation classification” process. Consequently, effective reappraisals would update empirical priors, which give rise to self- and world-models, and would minimize negative affective states experienced during rest. Hence, implicit CP self-models may be maintained thanks to appraisal styles elaborated previously by earlier self-models.

In order to benefit from positive appraisal styles, however, one must equally benefit from the value of resilience factors, an additional variable predicting a resilient outcome. CP self-models may serve as a valuation mechanism for resilience factors such as social support. Specifically, empirical priors may predict “other-efficacy,” as ensuing implicit CP self-models determine its self-relevant value. It is not sufficient to acknowledge one's network of close others; one must also appreciate the value in relying upon these close others. The authors' discussion of social support as a resilience factor is very relevant to the ongoing neurocognitive research in social anxiety disorder (SAD). SAD's symptomatology is distinguished by elevated fear and avoidance of future social interactions, and may reveal inadequate implicit CP self-models in social settings. In a review paper currently under preparation, we present published SAD event-free neuroimaging data to inform our theory of discrepant generative SAD CP self-models (Murray et al., in preparation). We highlight key self-related neural substrates (e.g., pregenual anterior cingulate (Murray et al. Reference Murray, Schaer and Debbane2012) and putative social-valuation regions (e.g., orbitofrontal cortex [Ruff & Fehr Reference Ruff and Fehr2014]), which illustrate structural/connectional aberrancies in SAD during event-free states. Our preliminary framework articulates SAD symptomatology as arising from static and poorly defined implicit event-free CP self-models, the validation of which may depend disproportionately on social information. Nevertheless, these self-models may prove incapable of exploiting positive social feedback in order to update empirical priors. This may potentially result from deficient valuation and appraisals/reappraisal mechanisms effectuated during event-free states (Murray et al., in preparation). Our recent work reviewing neurocognitive evidence of SAD symptomatology would therefore lend support to our claim that event-free implicit CP self-models may mediate the self-relevant value of resilience factors such as social support.

Today, there exists increasing neurocognitive literature validating appraisal theory predictions (cf. Brosch & Sander Reference Brosch and Sander2013). Although efforts to elucidate the neural substrates underlying CP are still in their infancy, Kalisch and colleagues have set forth a pragmatic framework for its future testing and analysis. We hope to contribute to this discussion by promoting event-free resting-state as an area of focus for the renewal and updating of implicit CP self-models.