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The Depressed Brain: An Evolutionary Systems Theory
Abstract
Major depression is a debilitating condition characterised by diverse neurocognitive and behavioural deficits. Nevertheless, our species-typical capacity for depressed mood implies that it serves an adaptive function. Here we apply an interdisciplinary theory of brain function to explain depressed mood and its clinical manifestations. Combining insights from the free-energy principle (FEP) with evolutionary theorising in psychology, we argue that depression reflects an adaptive response to perceived threats of aversive social outcomes (e.g., exclusion) that minimises the likelihood of surprising interpersonal exchanges (i.e., those with unpredictable outcomes). We suggest that psychopathology typically arises from ineffectual attempts to alleviate interpersonal difficulties and/or hyper-reactive neurobiological responses to social stress (i.e., uncertainty), which often stems from early experience that social uncertainty is difficult to resolve.
... The influence of Darwinian dynamics and the key role played by adaptive priors in generating the form and function of living systems is decidedly under researched in the active inference literature. Beyond a smattering of our own contributions on adaptive priors [18,58,116,[122][123][124], other work is limited, which tends to focus on formal models of evolutionary processes tested in silico (e.g., [55,120,121]). A clear shortfall of these models is that they overlook the importance of intergenerational dynamics (e.g., epigenetic inheritance) [125], which lie at the intersection between phylogeny and ontogeny, providing the grist for microevolution [93]. ...
... Here, we take a leaf from the book of the enactivists by explaining free-energy-minimising processes in terms of the dynamic interplay of body-mind-environment relations [196][197][198]. Previously, we have lent this dynamical perspective a distinctly Darwinian lens, which directs attention to how these depressogenic neurocognitive mechanisms and behaviours have been designed by selection to allow us to adaptively minimise free energy by reducing the volatility or unpredictability of our local social ecology [122]. Why is it that we all have the capacity to become depressed? ...
... To address this oversight, we have previously leveraged insights gleaned from evolutionary psychology, along with a wealth of supportive findings spanning psychology, psychiatry, and neuroscience, to highlight the key role of social contexts in depression (for other reviews, see [199][200][201]). According to this evolutionary systems perspective, the mild-to-moderate levels of depressed mood that we all experience from time to time reflect an adaptive, socially risk-averse strategy that reduces socioenvironmental volatility when sensory cues suggest an increased likelihood of non-preferred (surprising) interpersonal outcomes, such as rejection, defeat, or interpersonal loss [122]. To achieve this function, the depressive response engenders adaptive changes in perception: it increases the precision afforded to incoming social stimuli, which facilitates perceptual inference and learning about the social world; while also reducing confidence in top-down social predictions, which has the effect of suppressing confident, reward-approach behaviours that run the risk of further exposure to deleterious outcomes (e.g., anhedonia and social withdrawal). ...
The free energy principle is a formal theory of adaptive self-organising systems that emerged from statistical thermodynamics, machine learning and theoretical neuroscience and has since been translated into biologically plausible ‘process theories’ of cognition and behaviour, which fall under the banner of ‘active inference’. Despite the promise this theory holds for theorising, research and practical applications in psychology and psychiatry, its impact on these disciplines has only now begun to bear fruit. The aim of this treatment is to consider the extent to which active inference has informed theoretical progress in psychology, before exploring its contributions to our understanding and treatment of psychopathology. Despite facing persistent translational obstacles, progress suggests that active inference has the potential to become a new paradigm that promises to unite psychology’s subdisciplines, while readily incorporating the traditionally competing paradigms of evolutionary and developmental psychology. To date, however, progress towards this end has been slow. Meanwhile, the main outstanding question is whether this theory will make a positive difference through applications in clinical psychology, and its sister discipline of psychiatry.
... Empirical phenomenological research utilises experience to obtain comprehensive descriptions that provide the basis for a reflective structural analysis to portray the essence of the phenomenon (Emiliussen et al., 2021;Moustakas, 1994). This approach aims to determine the meaning of an experience to the person who has gone through it and provides a comprehensive description (Arteaga and Cocker, 2022). From the individual description, general or universal meanings can be derived -the essences or structures of the experience (Mangan et al., 2004). ...
... Giorgi's approach is empirical because it is grounded on factual data and is performed systematically, although it lacks a moral dimension (Ehrich, 2005). The phenomenological methodology allows the researcher to recognise every experience in its singularity or individuality (Arteaga and Cocker, 2022;Helkkula et al., 2012). Unlike Giorgi's empirical phenomenological psychology, the hermeneutic phenomenology of the Utrecht School incorporates ideas from human science pedagogy and the Dutch movement of phenomenological pedagogy to give insight into the human experience (Müller and Jedličková, 2020). ...
... A phenomenological research approach allows managers to articulate their management experiences and become more aware of themselves and their management actions, particularly how their decisions constitute the subject of empirical examination and shape organisational policies (Arteaga and Cocker, 2022;Helkkula et al., 2012;Irarrázaval, 2020;Müller and Jedličková, 2020;Peñaflor and Juevesa, 2021;Shorey and Ng, 2022). Supervisors also stand to gain a deeper understanding of management phenomena and questions geared towards grasping everyday management practices and human encounters in the organisation. ...
This paper examines phenomenology as a research methodology and posits that management research could benefit from its focus on human action and experience. The paper is conceived as a reflective literature review on phenomenology augmented by case studies on its application. It is shown that management is intertwined in a web of competing and reciprocal human experiences and actions, making it challenging to understand without grasping the human element. This complexity pressures management researchers and practitioners to reconcile theory with practice. Consequently, management research requires a worldview that invites scrutiny of how individuals assign significance to their everyday management responsibilities and encounters in their natural as opposed to contrived settings. While phenomenological research has been applied extensively in understanding human-related experiences, its application in management research is limited. Accordingly, the paper adds to scholarly discourse by providing insights into the application of phenomenology in management research.
... Further, in human studies of adolescents, interpersonal stress (e.g., relational bullying) is more strongly associated with blunted reward sensitivity than other nonrelational stressors (e.g., [16]). Indeed, reduced motivation to pursue and learn from rewards may be an adaptive strategy in the face of interpersonal stress as a means of defusing interpersonal conflict and preventing rejection, which would have been catastrophic in humans' early evolutionary environment [17]. Therefore, we also hypothesized here that greater exposure to dependent-interpersonal life events in the past 6 months would be significantly associated with reduced response bias and reward learning. ...
... Contrary to hypotheses, there was no evidence for an association between dependent-interpersonal events and either reward learning or response bias. This stands in contrast to theoretical work positing a specific association between interpersonal stress and anhedonia [17]. One possible explanation for the lack of a significant association in the current sample is that the frequency and severity of interpersonal life events were low. ...
Background
Exposure to acute stress is associated with reduced reward processing in laboratory studies in animals and humans. However, less clear is the association between reward processing and exposure to naturalistic stressful life events. The goal of the current study was to provide a novel investigation of the relation between past 6-month stressful life events and reward processing, and the extent to which this relation was moderated by depression diagnostic status and state symptoms of anhedonia.
Methods
The current study included a secondary analysis of data from 107 adults (37 current-depressed, 25 past-depressed, 45 never-depressed; 75% women) drawn from two previous community studies. Past 6-month stressful life events were assessed with a rigorous contextual interview with independent ratings. Response to monetary reward was assessed with a probabilistic reward task.
Results
Among current-depressed participants, and among both current- and past-depressed participants with high levels of anhedonia, greater exposure to independent life events outside of individuals' control was significantly associated with poorer reward learning. In direct contrast, among those with low levels of anhedonia, greater exposure to independent life events was significantly associated with a greater overall bias toward the more frequently rewarded stimulus.
Conclusions
Results suggest that depression and anhedonia are uniquely associated with vulnerability to blunted reward learning in the face of uncontrollable stressors. In contrast, in the absence of anhedonia symptoms, heightened reward processing during or following independent stressful life event exposure may represent an adaptive response.
... Low central 5-HT states may be an adaptive, functional response to avoid harm that is learned in environments where punishment is common and then recruited when complex social issues are interpreted as chronic danger. Other scholars predict that hypervigilance is an adaptive response for a child raised in an unpredictable environment [32], and that social avoidance is an adaptive response to volatile social experiences [149]. ...
... Evolutionary psychologists tend to view short periods of stress, anxiety or depression as adaptive responses that can increase resiliency in certain contexts if the triggers are addressed [7,147,149,150]. For example, the Analytical Rumination Hypothesis posits that depressive symptoms of cognitive rumination and loss of interest (lack of concentration, appetite, sex drive or socialization) are coordinated as an evolutionary adaptation to solve complex social problems or traumatic experiences with sustained focus [7, 151,152]. ...
This chapter reviews the important neurophysiological mechanisms that drive symptoms characteristic of comorbid depression and metabolic disease. It outlines how insulin impairment in the periphery1 interferes with central 5-hydroxyindole metabolism and ultimately restricts central2 serotonin synthesis. More specifically, peripheral insulin impairment disrupts i) peripheral and central tryptophan stores, ii) tryptophan uptake into the brain, and iii) tryptophan hydroxylase-2 function. Central serotonin availability appears to be increasingly restricted by higher degree and duration of insulin impairment, which can lead to both physiological and behavioral positive feedback loops experienced by individuals as a spiral of deteriorating mental health and tryptophan metabolism. Serotonin and its metabolites are fundamentally homeostatic regulators that serve to enhance adaptive response to stress in all organisms. Considering this essential trait, this review proposes that: disruptions in normal 5-hydroxyindole metabolism of tryptophan during impaired insulin function will disrupt homeostatic adaptive capacity of central serotonin, thereby increasing vulnerability to emotional and energy disturbances, and limiting recovery from such disturbances.
... Computational psychiatry based on predictive coding framework generally understands anxiety as a psychological state grounded on the learned (second-order, or metacognitive) belief that the world is inherently unpredictable and uncertain, meaning that neither learning nor action policies can reduce expected surprise [37,40,41]. In healthy agents who maintain a good balance between surprise avoidance and novelty seeking [10,42], expected uncertainty can elicit positive emotions such as the excitement associated with the opportunity to learn and reduce uncertainty [43]. In anxious states, on the other hand, uncertainty is more negatively valenced [21] and the agent is not able to tolerate emotionally arousing uncertainty, because the agent does not have a good expectation that expected uncertainty can be reduced by learning [44]. ...
... R. Soc. B 379: 20220413 anterior insular cortex as a result of inadequate suppression of these signals [42,124,125]. Protracted maladaptive exteroception may cause adjustments in metacognitive (second order) beliefs resulting in pathological anxiety, in which neutral sensory and social stimuli are evaluated as threating and uncertainty is avoided. In short, anxious agents have learned that surprise cannot be reduced by learning and therefore try to avoid it [44,124]. ...
Pursuing new knowledge in the entropic environment is pivotal for survival. However, dealing with uncertainty is a costly challenge for the agent surrounded by the stochastic sensory world, giving rise to different epistemic emotions, such as curiosity and anxiety. We recently proposed that aesthetic appreciation may have the role of associating pleasant feedback with the update of predictive representations. According to this idea, aesthetic appreciation and its associated rewarding feeling could drive people to seek new knowledge over anxiety. However, the relationship between aesthetic appreciation, curiosity, and anxiety has been still under-examined in the literature. Here, we explore the relationship between these epistemic emotions in a series of three experiments. In study 1, we examined whether music-induced aesthetic appreciation would influence curiosity in a gambling task. In studies 2a and 2b, we explore the relationship between music-induced aesthetic appreciation and anxiety state. Overall, aesthetic appreciation promoted curiosity-driven behaviour while it was negatively associated with anxiety. These results were consistent with the idea that aesthetic appreciation could act as a ‘valve’, prompting the individual to perceive curiosity (i.e. to consider novelty as a valuable opportunity to acquire new knowledge) rather than anxiety (i.e. to consider novelty as a risk to be avoided).
This article is part of the theme issue ‘Art, aesthetics and predictive processing: theoretical and empirical perspectives’.
... For instance, the social risk hypothesis proposes that a core function of depression is to minimize the risk of social exclusion. This is achieved, in part, through several adaptations that aim to increase the predictability of the social environment, especially of the aversive stimuli from the social environment (Allen & Badcock, 2003;Badcock et al., 2017). These attempts to increase the predictability of social stimuli manifest through attentional biases towards negative social stimuli (e.g., Sanchez et al., 2017), but might also operate through an enhanced detection of the covariations and regularities that make these stimuli more predictable. ...
... The present results are, prima facie, inconsistent with theories that postulate adaptive functions for depressive or dysphoric states, such as the social risk hypothesis (Allen & Badcock, 2003;Badcock et al., 2017), the depressive realism hypothesis (Alloy & Abramson, 1979;Moore & Fresco 2012), the analytical rumination hypothesis (Andrews & Thomson, 2009), or the affect as information theory (e.g., Forgas 2017;Gasper & Clore, 2002;Schwarz & Clore, 2003). Although they stress different facilitative mechanisms, all these models concur that negative moods are associated with an increase in the capacity for detection or analysis of information, especially related to social situations. ...
Past research has found that depression is associated with a multitude of deficits in processing social stimuli. These deficits might impair the implicit (unconscious) learning of socio-emotional regularities, an essential process for adaptive social functioning. In contrast, previous research on adaptive functions of depression suggests that dysphoric states can be associated, in some circumstances, with increased accuracy in detecting regularities. However, a direct assessment of implicit learning of socio-emotional regularities in depression has not yet been conducted.
In the present studies, we adapted the Artificial Grammar Learning task to induce implicit and explicit learning of regularities that govern social emotional stimuli (facial emotional expressions in Experiment 1) and social stimuli without explicit emotional content (body movements in Experiment 2). We assessed participants’ learning and awareness of these regularities, as well as their levels of depression. In both experiments, Bayesian analyses showed that the depressive symptomatology was neither associated with a learning deficit, nor with a learning advantage. This was the case for participants’ overall performance, as well as for their implicit and their explicit learning performance.
Our results contradict most previous findings and show that, even though depressive symptoms are associated with a variety of socio-cognitive deficits, they do not hinder the ability to implicitly or explicitly learn regularities within social contexts. Our results also show some constraints on the types of abilities that can be enhanced by depressive states.
... Because they are pivotal in our understanding of psychopathology, in this section we attempt to explicitly outline some preliminary ideas of how PP can be used to study how genetic and temperamental influences give rise to psychopathology. Broadly, the influence of genes in the PP framework can be described in terms of initial layout of the generative model, encoding initial preferences shaped by evolution and inherited individual differences (Badcock et al., 2017;Friston et al., 2006). One way to incorporate this genetic influence can be in terms of inherited precisions that influence what the individual is sensitive to at the beginning of their life, which can be modified by subsequent life experiences. ...
... Even if two individuals-identical twins, for example-have the same initial generative model, life experiences shape different trajectories (Badcock et al., 2017;Friston et al., 2006). Repeated stressful events can shape a generative model into one that is adaptive in a highly uncertain environment. ...
Most theories of psychopathology have focused on etiology at a specific level (e.g., genetic, neurobiological, psychological, or environmental) to explain specific symptoms or disorders. A few biopsychosocial theories have provided explanations that attempt to integrate different levels and disorders to some extent. However, these theories lack a framework in which different levels of analysis are integrated and thus do not explain the mechanism by which etiological factors interact and perturb neurobiology which in turn leads to psychopathology. We propose that predictive processing (PP), which originated in theoretical neurobiology literature, may provide a conceptually parsimonious and biologically plausible framework to achieve such integration. In PP, the human brain can be cast as implementing a generative model whose task is to minimize the surprise of sensory evidence by inferring its causes and actively controlling future sensory signals via action. This account offers a unifying model of perception, action, and emotion implicated in psychopathology. Furthermore, we show that PP can explain how different factors or levels result in psychopathology via updates of the generative model (the depth of the PP framework). Finally, we demonstrate the transdiagnostic appeal of PP by showing how perturbations within this framework can explain a broad range of psychopathology (the breadth of the PP framework), with a focus on bridging well-established psychosocial theories of psychopathology and PP.
... Stressful life events may promote the development of depression in individuals (109). To some extent, depression can be seen as an adaptive strategy that helps individuals cope with the challenges of uncertainty and unfavorable relationships in the social world (110). Not all individuals who experience stress in early life develop the disorder in subsequent trauma or stress, and similarly, not all adults with depression have experienced early life stress (24). ...
Insomnia and depression, both significantly impacting public health, are common psychosomatic illnesses that frequently co-occur in the same individual. Not only do these two conditions commonly co-occur, but they also exhibit a bidirectional link, where the existence of one may heighten the risk for the other. Latest research offers compelling evidence of significant overlap in biological, psychological, and sociological aspects in the comorbidity of insomnia and depression. Building on this, we aim to examine the pathophysiology of insomnia and depression, along with their comorbid mechanisms, encompassing biological routes (like genetics, HPA axis, immune-inflammatory activation, neuroendocrine regulation, microbiome alterations, and neural circuits integrating sleep and emotion regulation), as well as psychosocial routes. Consequently, proposing a self-perpetuating and mutually reinforcing “snowball effect” model of comorbid insomnia and depression, and examining corresponding preventative intervention strategies to rectify associated imbalances. Finally, this article encapsulates the challenges in this field of study and the directions for future research. Finally, the paper points out the limitations of current research (cross-sectional data being dominant, and the mechanism of multi-omics dynamics being unknown) and the future direction (longitudinal cohort combined with computational modeling to resolve temporal interactions), which will provide a theoretical basis for precision interventions.
... From the predictive processing perspective, depression is viewed as a disorder of inference and, more specifically, precision weighting. The depressive generative model is described as having hypoprecise phenotype-congruent priors about the world, perceiving it as an unmanageable hostile environment (for humans, mostly the social aspect of it), thus overweighting prediction errors signaling threat to the organism (Badcock et al., 2017;Clark et al., 2018;Fabry, 2019). Accordingly, such a model has low-precision phenotype-congruent action policies resulting in behavioral and social withdrawal instead of exploration and foraging. ...
The Bayesian brain hypothesis conceives of the brain as a generative model (GM) of its environment, where the model improves its accuracy by updating itself via Bayesian inferential statistics. This is accomplished by adjusting the model’s prior beliefs into posterior beliefs based on the sensory input from the environment. Thus, the Bayesian brain learns the causal structure of world events and refines its belief space. The Bayesian brain is thought to learn by updating its beliefs and the parameters of its GM. It is less clear whether Bayesian updating alone is sufficient to explain the genesis and evolution of belief space, as it appears challenging to explain the generation of truly novel original priors (structure learning) or the transition to the direct opposite of the initial prior through Bayesian updating. To address this challenge, we suggest integrating Bayesian updating with the principles of the dialectical development of thought as conceived in Hegel’s work. We argue that applying dialectics and the freeenergy principle to Bayesian inference makes the evolution of belief space both its emergent and imperative property. We naturalize this idea by illustrating how behaviors of different complexity from the molecular mechanisms of the simplest biological behavior, such as bacterial chemotaxis, through psychopathology can be viewed in the ‘dialectical Bayes’ framework. This framework is used to explore the cognitive dynamics of physical and emotional pain and to propose a mechanism of chronic suffering.
... That depression is closely associated with changes in the social world (Gotlib & Hammen, 2014) is empirically well supported (Slavich & Irwin, 2014;Sun et al., 2013;Vialou et al., 2013;Weissman et al., 2015). This is particularly interesting to investigate, as conceptual parallels exist between FEP and the social risk hypothesis, as social risk technically corresponds to uncertainty referring to expected surprise or free energy (Badcock et al., 2017). Thus, the interdisciplinary framework of the present paper, which utilizes concepts lent by the Heideggerian analysis and the FEP framework, can with good reason be extended to describe a social human world relation. ...
This paper concerns an interdisciplinary theoretical analysis of the underlying ways the depressed individual is related to the world. Specific aspects of this relation, namely time, mood and perception of affordances will be characterized. By understanding these relations from both an existential phenomenological Heideggerian approach and through the modern neuroscientific paradigm of the Free Energy Principle, we strive to formulate a complementary conceptualization of the depressed person's relation to the world. Furthermore, we argue that this interdisciplinary approach bears a synergic coupling, despite obvious epistemic and disciplinary differences. We argue that the theories rest upon interestingly compatible theoretical notions of how any individual is related to the world. We argue this interdisciplinary conceptualization constitutes an epistemically useful alternative based on dynamic assumptions, unlike the conceptualization offered by DSM-5, which we argue is based on dualistic assumptions. We argue these underlying assumptions are important to challenge, as recent research, in psychedelic treatment of "treatment resistant" depressed patients, provides interesting findings challenging our current view of depression. Because experiences of connectedness are the strongest predictor for positive clinical outcomes in these trials, we argue that a dynamic characterization of depression is beneficial. We argue that this dynamic characterization is beneficial to pursue, as it offers an interdisciplinary approach to understand how the depressed individual fundamentally is connected to the world.
... Thus, the importance of precision weighting to the healthy predictive dynamics of an agent cannot be overstated. Indeed, under the AIF, aberrant precision weighting is thought to be the key to modelling a wide range of psychopathologies including depression (Barrett et al., 2016;Badcock et al., 2017;Kiverstein et al., 2020;Smith et al., 2020;Ramstead et al., 2020;Fabry, 2020;Constant et al., 2021;Van de Cruys & Van Dessel, 2021;Ramos-Grille et al., 2022) and addiction (Schwartenbeck et al., 2015;Smith et al., 2020;Miller et al., 2020). ...
In this paper we assess the functionality and therapeutic potential of ambient smart environments. We argue that the language of affordances alone fails to do justice to the peculiar functionality of this ambient technology, and draw from theoretical approaches based on the free energy principle and active inference. We argue that ambient smart environments should be understood as playing an'upstream' role, shaping an agent's field of affordances in real time, in an adaptive way that supports an optimal grip on a field of affordances. We characterise this optimal grip using precision weighting, and in terms of allostatic control, drawing an analogy with the role of precision weighting in metacognitive processes. One key insight we present is that ambient smart environments may support allostatic control not only by simplifying an agent's problem space, but by increasing uncertainty, in order to destabilise calcified, sub-optimal, psychological and behavioural patterns. In short, we lay an empirically-grounded theoretical foundation for understanding ambient smart environments, and for answering related philosophical questions around agency, trust, and subjective wellbeing.
... This perspective is further elaborated in Friston and Parr's (2020) investigation into the role of active inference in clinical disorders, suggesting a fundamental reevaluation of pathologies like schizophrenia through the lens of failed predictive processes. The significance of predictive coding extends beyond pathological states, as highlighted by Badcock et al. (2017), who propose an evolutionary systems theory to account for the neurobiological basis of depression, suggesting that mood disorders may arise from maladaptive predictive models. 2 Smith and Friston's (2020) examination of attention and sensory processing through predictive coding provides insights into how the brain modulates its response to surprising events, underscoring the adaptive nature of neural computation. ...
Attention-Deficit/Hyperactivity Disorder (ADHD) is traditionally conceptualized within a framework emphasizing deficits in attention regulation and executive function. However, emerging evidence from the fields of predictive coding and active inference offers a novel lens through which to understand ADHD not merely as a disorder of attention but as a distinctive mode of neurodevelopmental variation. This article synthesizes current research and theoretical models to propose a redefined understanding of ADHD. By integrating insights from Ryan Smith, Paul B. Badcock, and Karl J. Friston (2020), we explore the premise that individuals with ADHD exhibit unique differences in how their brains generate and update predictions about the environment, leading to the characteristic behavioral patterns observed in ADHD. We argue that ADHD involves a divergence in the brain's predictive models and its ability to minimize prediction error, impacting sensory processing, attention allocation, and motor control. This perspective illuminates the adaptive aspects of ADHD, suggesting that what are often labeled as deficits could also reflect an adaptive fit to certain environmental contexts, underscoring the role of neurodiversity in human evolution. Furthermore, we discuss the implications of this reconceptualization for clinical practice, including diagnosis, treatment, and support for individuals with ADHD, emphasizing approaches that align with their unique neurocognitive profiles. This article calls for a shift in the narrative surrounding ADHD, advocating for a model that appreciates the complexity and adaptiveness of neurodevelopmental diversity.
... This perspective is further elaborated in Friston and Parr's (2020) investigation into the role of active inference in clinical disorders, suggesting a fundamental reevaluation of pathologies like schizophrenia through the lens of failed predictive processes. The significance of predictive coding extends beyond pathological states, as highlighted by Badcock et al. (2017), who propose an evolutionary systems theory to account for the neurobiological basis of depression, suggesting that mood disorders may arise from maladaptive predictive models. Smith and Friston's (2020) examination of attention and sensory processing through predictive coding provides insights into how the brain modulates its response to surprising events, underscoring the adaptive nature of neural computation. ...
Attention-Deficit/Hyperactivity Disorder (ADHD) is traditionally conceptualized within a framework emphasizing deficits in attention regulation and executive function. However, emerging evidence from the fields of predictive coding and active inference offers a novel lens through which to understand ADHD not merely as a disorder of attention but as a distinctive mode of neurodevelopmental variation. This article synthesizes current research and theoretical models to propose a redefined understanding of ADHD. By integrating insights from Ryan Smith, Paul B. Badcock, and Karl J. Friston (2020), we explore the premise that individuals with ADHD exhibit unique differences in how their brains generate and update predictions about the environment, leading to the characteristic behavioral patterns observed in ADHD. We argue that ADHD involves a divergence in the brain's predictive models and its ability to minimize prediction error, impacting sensory processing, attention allocation, and motor control. This perspective illuminates the adaptive aspects of ADHD, suggesting that what are often labeled as deficits could also reflect an adaptive fit to certain environmental contexts, underscoring the role of neurodiversity in human evolution. Furthermore, we discuss the implications of this reconceptualization for clinical practice, including diagnosis, treatment, and support for individuals with ADHD, emphasizing approaches that align with their unique neurocognitive profiles. This article calls for a shift in the narrative surrounding ADHD, advocating for a model that appreciates the complexity and adaptiveness of neurodevelopmental diversity.
... Furthermore, traditional research approaches have had limited success in explaining and predicting how depressed individuals' interpersonal experiences and behaviors adapt dynamically in response to changing interpersonal environments (Wichers, 2014). For example, there is evidence that depressed individuals struggle to adjust negative interpersonal beliefs in response to positive interpersonal experiences (Kube, 2023), favoring experiences that align with their existing beliefs (e.g., "As I said, no one talked to me at the party!") (Badcock, Davey, Whittle, Allen, & Friston, 2017). 3 A more precise (i.e., mathematical) description and examination of how interpersonal experiences are integrated into existing belief-systems and how these belief-systems change dynamically over time in response to new social experiences would provide an opportunity for more accurate understanding of how biased belief-systems and pathological interpersonal behaviors in depression can be altered progressively through treatment. ...
Depression is characterized by different distortions in interpersonal experience and behavior, ranging from social withdrawal to overt hostility. However, clinical psychological research has largely neglected the need for an integrative framework to operationalize these different phenomena and their dynamic change more accurately in depression. In this article, we draw on active inference theory, a comprehensive theory of perception, action, and learning, to provide a formal model explaining how variations in patients' internal belief-systems lead to differences in social experience and behavior. In this context, we assume that individuals cannot directly grasp the characteristics of their social environment. Instead, they must infer them indirectly from ambiguous social observations , which they themselves generate and alter through their actions. Differences in interpersonal experience and behavior arise from the interplay of patients' prior expectations, their propensity to infer particular social states from certain observations, and their beliefs in their ability to influence these situations through specific actions. We then use concrete examples to demonstrate how future research can take our approach to identify systematic differences in interpersonal experiences and behaviors among depressed patients (or patient subgroups) and to investigate their changes in response to new social experiences. We also discuss potential applications of our approach in diagnosing and treating depression. This work is a move towards understanding the interpersonal aspects of depression in more detail, recognizing their importance in etiology, diagnosis, and treatment.
... Successfully building social connections is crucial to humans' survival, health, and happiness (e.g., Badcock et al., 2017;Cacioppo et al., 2015). Although social structures such as family and community provide a foundation for social connection, the growth of larger social networks depends on our ability to form relationships with new people. ...
A common conjecture is that social success relies on “theory of mind”—the everyday skill of imputing mental states to others. We test the hypothesis that individuals with stronger theory of mind skills and motivation garner more positive first impressions because of how they interact with others. Participants included 334 young adults who were paired with a peer for a first-time meeting. Dyads completed a cooperative Lego-building task, which was videotaped and later coded for behavioral manifestations of theory of mind by independent raters. Theory of mind accuracy and motivation were assessed with validated laboratory tasks and a self-report questionnaire, respectively. First impressions were assessed based on partner’s ratings of participant likeability, enjoyment of the interaction, and changes in positive affect. Results of actor–partner interdependence mediation models revealed that the associations between theory of mind and first impressions are indirect and mediated through behaviors. Specifically, participants with stronger theory of mind demonstrated greater cognitive sensitivity and pragmatic conversational skills. However, only cognitive sensitivity subsequently predicted more favorable first impressions. This research shows that social-cognitive skills can affect others’ social impressions through their behavioral manifestations.
... Depression is a debilitating mental illness characterized by emotional numbness and a generalized lack of motivation or pleasure in life [1,2]. Depressed patients often exhibit patterns of constant negative rumination about themselves and others, as well as social isolation [1][2][3]. Depression is associated with dysregulation in the brain's mesocortical and mesolimbic reward pathways, particularly in areas like the ventral tegmental area (VTA) and the nucleus accumbens (NAc) [4,5]. These areas, rich in dopaminergic neurons, are crucial for experiencing pleasure and motivation [4,6]. ...
Background
Depression is a major global health challenge, affecting over 300 million people worldwide. Current pharmacological and psychotherapeutic interventions have limited efficacy, underscoring the need for novel approaches. Emerging evidence suggests that peak emotional experiences characterized by awe, transcendence, and meaning hold promise for rapidly shifting maladaptive cognitive patterns in depression. Aesthetic chills, a peak positive emotion characterized by physical sensations such as shivers and goosebumps, may influence reward-related neural pathways and hold promise for modifying core maladaptive beliefs rooted in early adverse experiences.
Methods
We enrolled 96 patients diagnosed with major depressive disorder. A validated database of multimedia known to elicit chills responses (ChillsDB) was used for stimulus presentation. Participants’ emotional responses were assessed using the Emotional Breakthrough Inventory (EBI), while shifts in self-schema were measured via the Young Positive Schema Questionnaire (YSPQ).
Results
The study found that chill-inducing stimuli have the potential to positively influence the core schema of individuals with depression, impacting areas of self-related beliefs. The associated phenomenology triggered by chills appears to share similarities with the altered states of consciousness induced by psychedelic substances like psilocybin.
Conclusions
These preliminary results suggest that the biological processes involved in aesthetic chills could be harnessed as a non-pharmacological intervention for depression. However, further investigation is necessary to comprehensively understand the neurophysiological responses to chills and to evaluate the practicality, effectiveness, and safety of utilizing aesthetic chills as a preventive measure in mental health care.
... A correlation (though not a causation), between symptoms of loneliness and symptoms of low mood has also been perceived, while detailing the evolutionary theory of loneliness (Cacioppo et al., 2006). Research across several studies has been gathered to demonstrate how depression can help with problem solving as an adaptive feature (Andrews & Thomson, 2009) as depressed moods help us to target and avoid exclusion from social groups, which is a danger when it comes to survival, particularly in the wild (Badcock et al., 2017). Given the remnants of these traits, it may be natural for adults to experience the sensations of addiction, anxiety, or depression in our ever-changing environment, requiring on-going management through models which are accessible to them. ...
Self-efficacy is described as people's beliefs regarding their capabilities to produce designated levels of performance that affect their lives and is considered important for self-regulation of mental health disorders. Biofeedback has demonstrated that knowledge of one’s physiology can help regulate mental health disorders, such as anxiety (McKe, 2008). Neuroplasticity is defined as the capacity for the brain to rewire its structure and create new neural pathways to make up for lost functions due to brain injury. There is limited research in how neuroplasticity can be used as an agent for behavioral change. This qualitative study examined if knowledge of the brain’s malleability may affect adults’ perception of self-efficacy in recovery from evolutionary-based mental health disorders, with an aim to lay a foundation of general themes for further quantitative studies. Following 12 interviews, themes were recorded regarding perceived self-efficacy at time points during mental health recovery from an adult group who was knowledgeable about neuroplasticity versus a group that wasn’t knowledgeable. In addition to other differences, the Superordinate theme of will was mentioned 64 times across the Knowledgeable group, versus only 11 times in the Non-knowledgeable group. As variations between the groups were perceived, future quantitative research may determine if educational programs can assist adults who are turning to self-regulation as a means of recovery from said afflictions.
... Empirically, disruptions in precision weighting imbalance are suggested to explain many psychiatric conditions like autism [21,22], ADHD [23], psychosis [24][25][26], PTSD [27], anxiety [28,29], personality disorders [30], and depression [31]. ...
In response to shortcomings of the current classification system in translating discoveries from basic science to clinical applications, NIMH offers a new framework for studying mental health disorders called Research Domain Criteria (RDoC). This framework holds a multidimensional outlook on psychopathologies focusing on functional domains of behavior and their implementing neural circuits. In parallel, the Predictive Processing (PP) framework stands as a leading theory of human brain function, offering a unified explanation for various types of information processing
in the brain. While both frameworks share an interest in studying psychopathologies based on
pathophysiology, their integration still needs to be explored. Here, we argued in favor of the explanatory power of PP to be a groundwork for the RDoC matrix in validating its constructs and creating testable hypotheses about mechanistic interactions between molecular biomarkers and clinical traits. Together, predictive processing may serve as a foundation for achieving the goals of the RDoC framework
... Empirically, disruptions in precision weighting imbalance are suggested to explain many psychiatric conditions like autism [21,22], ADHD [23], psychosis [24][25][26], PTSD [27], anxiety [28,29], personality disorders [30], and depression [31]. ...
... J. Friston et al., 2014). Empirically, precision weighting imbalance is suggested to explain many psychiatric conditions like autism (Lawson et al., 2017;Van de Cruys et al., 2014), ADHD (Richards et al., 2020), psychosis (Adams et al., 2013;Powers et al., 2017;Sterzer et al., 2018), PTSD (Homan et al., 2019), anxiety (Hein et al., 2023;Paulus et al., 2019), personality disorders (Moutoussis et al., 2014), and depression (Badcock et al., 2017). ...
In response to shortcomings of the current classification system in translating discoveries from basic science to clinical applications, NIMH offers a new framework for studying mental health disorders called Research Domain Criteria (RDoC). This framework holds a multidimensional outlook on psychopathologies focusing on functional domains of behavior and their implementing neural circuits. In parallel, the Predictive Processing (PP) framework stands as a leading theory of human brain function, offering a unified explanation for various types of information processing in the brain. While both 1 Corresponding author: Anahita Khorrami Banarak, Institute for Cognitive Science Studies, KHORRAMIAANAHITA@GMAIL.COM frameworks share an interest in studying psychopathologies based on pathophysiology, their integration still needs to be explored. Here, we argued in favor of the explanatory power of PP to be a groundwork for the RDoC matrix in validating its constructs, creating testable hypotheses about mechanistic interactions between molecular biomarkers and clinical traits, and finally, considering developmental trajectories and environmental factors in psychopathologies. Together, predictive processing may serve as a foundation for achieving the goals of the RDoC framework.
... Predictive processing theory holds that the brain generates anticipatory top-down models that organize environmental encounters and experiences, but which also get updated when expectations conflict with sensory input from the world (Clark, 2016). The theory has been applied to psychiatric conditions, with commentators concluding that depression and PTSD are maladaptive cases of epistemic breakdown that follow from mismatches between predictive models and incoming information flows (e.g., Barrett et al., 2016;Clark et al., 2018;Fabry, 2020;Kube et al., 2020a, b;Linson & Friston, 2019;Smith et al., 2021; for exception, see Badcock et al., 2017). But what if accurately anticipating disasters to avoid them counterfactually means overpredicting bad results? ...
Predictive processing theorists have claimed PTSD and depression are maladaptive and epistemically distorting because they entail wide gaps between top-down models and bottom-up information inflows. Without denying this is sometimes so, the "maladaptive" label carries questionable normative assumptions. For instance, trauma survivors facing significant risk of subsequent attacks may overestimate threats to circumvent further trauma, "bringing forth" concretely safer personal spaces, to use enactive terminology, ensuring the desired gap between predicted worries and outcomes. The violation of predictive processing can go in the opposite direction too, as when depression coincides with energy-depletion, and hence objectively strenuous situations in which things look farther away because they are (accurately anticipated to be) harder to reach. These examples partly encapsulate what predictive processing theorists call "active inference," yet with differences. In the first case, actions fruitfully obviate predictions rather than fulfilling them. In the second, mental models do not dysregulate bodily processes, making coping harder. Instead, problems (e.g., personal obstacles, gastric illness) deplete energy, eliciting a depressive and adaptive slowdown. Some predictive proponents narrowly apply correspondence criteria when alleging mismatches between internal models and the world, while incongruously asserting that the brain did not evolve to see things veridically, but to execute actions. An alternative is to adopt pluralistic, pragmatic epistemologies suited to the complexity of mind. The upshot is that mental outlooks can depart from the norm without epistemically being distorted and that mismatches between anticipatory worries and outcomes, when they actually exist, can be a measure of adaptive and epistemic success.
... Heightened sensitivity to social rejection cues and processing of these cues feeds into state levels of self-esteem, which thus serves as a gauge of interpersonal relationship status 1 . The Social Risk Hypothesis (SRH) of depressed mood 3,7 extends this notion to propose that heightened sensitivity to social cues is a cardinal feature of depressed mood. It argues that depressed individuals adopt a risk-averse approach to social interactions with the overall aim of reducing the threat of social exclusion 3 , engaging in social withdrawal and/or signalling submissiveness and subordination to conspecifics to avoid defeat, or to elicit assistance 3,8 . ...
Cues of social rejection and affiliation represent proximal risk and protective factors in the onset and maintenance of depression. Such cues are thought to activate an evolutionarily primed neuro-cognitive alarm system, alerting the agent to the benefits of inclusion or the risk of social exclusion within social hierarchies focused on ensuring continued access to resources. In tandem, autobiographical memory is thought to be over-general and negatively biased in Major Depressive Disorder (MDD) which can contribute to maintenance and relapse. How memories of social rejection and affiliation are experienced and processed in MDD remains unexplored. Eighteen participants with recurrent and chronic MDD and 18 never-depressed controls listened to and vividly revisited autobiographical social experiences in an ecologically valid script-driven imagery paradigm using naturalistic memory narratives in an fMRI paradigm. Memories of Social Inclusion and Social Rejection broadly activated a common network of regions including the bilateral insula, thalamus and pre/postcentral gyrus across both groups. However, having a diagnosis of MDD was associated with an increased activation of the right middle frontal gyrus irrespective of memory type. Changes in positive affect were associated with activity in the dorsal ACC in the MDD group and in the insular cortex of the Control group. Our findings add to the evidence for complex representations for both positive and negative social signals in MDD and suggest neural sensitivity in MDD towards any socially salient information as opposed to selective sensitivity towards negative social experiences.
... The principle of MG could serve as a neuronal gauge theory linking the moment-tomoment experience of depression (and other manifestations of MG) to human lifespan development and cultural or biological evolution [132,133]. The MG theory of depression covers multiple timescales of gravity-like experiences from the development of "outside-in" empirical priors to the long-term signs and symptoms of depression, listed in Table 1, that manifest as a feeling of personal descent [36], perceived bodily heaviness [23], subjective time dilation [9,80], an altered sense of self in relation to narrative gravity [37,65], and distorted neural spatiotemporal dynamics [109,134,135]. ...
The principle of mental gravity contends that the mind uses physical gravity as a mental model or simulacrum to express the relation between the inner self and the outer world in terms of “UP”-ness and “DOWN”-ness. The simulation of increased gravity characterises a continuum of mental gravity which states includes depression as the paradigmatic example of being down, low, heavy, and slow. The physics of gravity can also be used to model spacetime curvature in depression, particularly gravitational time dilation as a property of MG analogous to subjective time dilation (i.e., the slowing of temporal flow in conscious experience). The principle has profound implications for the Temporo-spatial Theory of Consciousness (TTC) with regard to temporo-spatial alignment that establishes a “world-brain relation” that is centred on embodiment and the socialisation of conscious states. The principle of mental gravity provides the TTC with a way to incorporate the structure of the world into the structure of the brain, conscious experience, and thought. In concert with other theories of cognitive and neurobiological spacetime, the TTC can also work towards the “common currency” approach that also potentially connects the TTC to predictive processing frameworks such as free energy, neuronal gauge theories, and active inference accounts of depression. It gives the up/down dimension of space, as defined by the gravitational field, a unique status that is connected to both our embodied interaction with the physical world, and also the inverse, reflective, emotional but still embodied experience of ourselves.
... In another study, Krishnan-Barman and Hamilton (2019) showed that movement patterns were more likely to be imitated by a follower when the leader could watch the follower's action-a phenomenon that was, it was argued, due to a desire to affiliate. Similarly, kinematic patterns have been shown to be distinct when one is acting in isolation compared with the same action in a social context (Becchio et al., 2010). 5. Interestingly, the experience of volatility within the social environment and the subsequent preference for risk-averse behavior have been argued to play an important role in the emergence of depressive symptoms (but see Badcock et al., 2017, for a comprehensive review). An example of this riskaverse response to volatility might be withdrawing from the volatile environment and consequently experiencing more predictable outcomes. ...
Social neuroscience has often been criticized for approaching the investigation of the neural processes that enable social interaction and cognition from a passive, detached, third-person perspective, without involving any real-time social interaction. With the emergence of second-person neuroscience, investigators have uncovered the unique complexity of neural-activation patterns in actual, real-time interaction. Social cognition that occurs during social interaction is fundamentally different from that unfolding during social observation. However, it remains unclear how the neural correlates of social interaction are to be interpreted. Here, we leverage the active-inference framework to shed light on the mechanisms at play during social interaction in second-person neuroscience studies. Specifically, we show how counterfactually rich mutual predictions, real-time bodily adaptation, and policy selection explain activation in components of the default mode, salience, and frontoparietal networks of the brain, as well as in the basal ganglia. We further argue that these processes constitute the crucial neural processes that underwrite bona fide social interaction. By placing the experimental approach of second-person neuroscience on the theoretical foundation of the active-inference framework, we inform the field of social neuroscience about the mechanisms of real-life interactions. We thereby contribute to the theoretical foundations of empirical second-person neuroscience.
... Subsequent elaborations of these interoceptive-inference accounts have sought to explain how aberrant interoceptive processing and autonomic regulation might engender various psychopathologies (Owens et al., 2018;Paulus et al., 2019;Quadt et al., 2018;R. Smith et al., 2020), including depression (Arnaldo et al., 2022;Badcock et al., 2017;Barrett et al., 2016;Seth & Friston, 2016;Stephan et al., 2016) and anxiety-related disorders ( J. E. Clark et al., 2018;Gerrans & Murray, 2020;Linson et al., 2020;Peters et al., 2017). ...
Embodied cognition—the idea that mental states and processes should be understood in relation to one’s bodily constitution and interactions with the world—remains a controversial topic within cognitive science. Recently, however, increasing interest in predictive processing theories among proponents and critics of embodiment alike has raised hopes of a reconciliation. This article sets out to appraise the unificatory potential of predictive processing, focusing in particular on embodied formulations of active inference. Our analysis suggests that most active-inference accounts invoke weak, potentially trivial conceptions of embodiment; those making stronger claims do so independently of the theoretical commitments of the active-inference framework. We argue that a more compelling version of embodied active inference can be motivated by adopting a diachronic perspective on the way rhythmic physiological activity shapes neural development in utero. According to this visceral afferent training hypothesis, early-emerging physiological processes are essential not only for supporting the biophysical development of neural structures but also for configuring the cognitive architecture those structures entail. Focusing in particular on the cardiovascular system, we propose three candidate mechanisms through which visceral afferent training might operate: (a) activity-dependent neuronal development, (b) periodic signal modeling, and (c) oscillatory network coordination.
... Based on the existing theories and findings related to body-brain interaction in typically developing individuals, as well as literature regarding atypical neurophysiological reactivity and social information processing in individuals with ASD, we hypothesize that the characteristics of persons diagnosed with ASD may reflect altered ANS and CNS coupling during processing of sensory input, especially processing of socially relevant information. This assumption builds on the suggested role of ANS signalling in modulating initial perception of incoming stimuli and contributing to the cognitive top-down reappraisal of information flow also during social interaction (Badcock et al., 2017;Craig, 2014;Critchley & Harrison, 2013). Since ANS is evolutionarily older and automatically adjusts to internally or externally driven requirements (McEwen, 1998), our underlying assumption was that ANS activation modulates the processing of social information in the CNS. ...
Body-brain interaction provides a novel approach to understand neurodevelopmental conditions such as autism spectrum disorder (ASD). In this systematic review, we analyse the empirical evidence regarding coexisting differences in autonomic (ANS) and central nervous system (CNS) responses to social stimuli between individuals with ASD and typically developing individuals. Moreover, we review evidence of deviations in body-brain interaction during processing of socially relevant information in ASD. We conducted systematic literature searches in PubMed, Medline, PsychInfo, PsychArticles, and Cinahl databases (until 12.1.2022). Studies were included if individuals with ASD were compared with typically developing individuals, study design included processing of social information, and ANS and CNS activity were measured simultaneously. Out of 1892 studies identified based on the titles and abstracts, only six fulfilled the eligibility criteria to be included in synthesis. The quality of these studies was assessed using a quality assessment checklist. The results indicated that individuals with ASD demonstrate atypicalities in ANS and CNS signalling which, however, are context dependent. There were also indications for altered contribution of ANS-CNS interaction in processing of social information in ASD. However, the findings must be considered in the context of several limitations, such as small sample sizes and high variability in (neuro)physiological measures. Indeed, the methodological choices varied considerably, calling for a need for unified guidelines to improve the interpretability of results. We summarize the current experimentally supported understanding of the role of socially relevant body-brain interaction in ASD. Furthermore, we propose developments for future studies to improve incremental knowledge building across studies of ANS-CNS interaction involving individuals with ASD.
Background:
A sub-anesthetic dose of ketamine, an N-methyl-D-aspartate receptor (NMDAR) antagonist, produces robust antidepressant effects in treatment-resistant major depressive disorder (MDD). The mismatch negativity (MMN) is reliant on glutamatergic neurotransmission and reduced by NMDAR antagonists. The MMN may characterise the neural mechanisms underlying ketamine's effects.
Aims:
This study examined the acute effects of ketamine and midazolam on the MMN and its relationship to early and sustained decreases in depressive symptoms.
Methods:
Treatment-resistant MDD patients (N = 24), enrolled in a multi-phase clinical ketamine trial, received two intravenous infusions within an initial double-blind crossover phase: ketamine (0.5 mg/kg) and midazolam (30 μg/kg). Three recordings were carried out per session (pre-, immediately post- and 2 h post-infusion). Peak MMN amplitude (μV), latency (ms), theta event-related oscillations (EROs), theta phase locking factor (PLF) and source-localised MMN generator activity were assessed. Relationships between changes in MMN indices and early (Phase 1: double-blind, cross-over phase) and sustained (Phases 2, 3: open-label repeated and maintenance phases, respectively) changes in depressive symptoms (Montgomery-Åsberg Depression Rating Scale score) were examined.
Results:
Ketamine reduced frontal MMN amplitudes, theta ERO immediately post- and 2 h post-infusion and source-localised peak MMN frontal generator activity. Select baseline and ketamine-induced MMN decreases correlated and predicted greater early (left frontal MMN decreases in amplitude and theta ERO, baseline left PLF) and sustained (baseline left PLF, right inferior temporal activity) symptom reductions.
Conclusions:
Acute NMDARs blockade reduced frontal MMN, with larger MMN reductions predicting greater symptom improvement. The MMN may serve as a non-invasive biomarker predicting antidepressant response to glutamatergic agents.
Depression is vastly heterogeneous in its symptoms, neuroimaging data, and treatment responses. As such, describing how it develops at the network level has been notoriously difficult. In an attempt to overcome this issue, a theoretical “negative prediction mechanism” is proposed. Here, eight key brain regions are connected in a transient, state-dependent, core network of pathological communication that could facilitate the development of depressive cognition. In the context of predictive processing, it is suggested that this mechanism is activated as a response to negative/adverse stimuli in the external and/or internal environment that exceed a vulnerable individual’s capacity for cognitive appraisal. Specifically, repeated activation across this network is proposed to update an individual’s brain so that it increasingly predicts and reinforces negative experiences over time—pushing an individual at-risk for or suffering from depression deeper into mental illness. Within this, the negative prediction mechanism is poised to explain various aspects of prognostic outcome, describing how depression might ebb and flow over multiple timescales in a dynamically changing, complex environment.
Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through the mechanistic modeling of this disorder. Using the Kolmogorov theory (KT) of consciousness, we developed a foundational model where algorithmic agents interact with the world to maximize an Objective Function evaluating affective valence. Depression, defined in this context by a state of persistently low valence, may arise from various factors—including inaccurate world models (cognitive biases), a dysfunctional Objective Function (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, we map the agent model to brain circuits and functional networks, framing potential etiological routes and linking with depression biotypes. Finally, we explore how brain stimulation, psychotherapy, and plasticity-enhancing compounds such as psychedelics can synergistically repair neural circuits and optimize therapies using personalized computational models.
In response to shortcomings of the current classification system in translating discoveries from basic science to clinical applications, NIMH offers a new framework for studying mental health disorders called Research Domain Criteria (RDoC). This framework holds a multidimensional outlook on psychopathologies focusing on functional domains of behavior and their implementing neural circuits. In parallel, the Predictive Processing (PP) framework stands as a leading theory of human brain function, offering a unified explanation for various types of information processing in the brain. While both frameworks share an interest in studying psychopathologies based on pathophysiology, their integration still needs to be explored. Here, we argued in favor of the explanatory power of PP to be a groundwork for the RDoC matrix in validating its constructs and creating testable hypotheses about mechanistic interactions between molecular biomarkers and clinical traits. Together, predictive processing may serve as a foundation for achieving the goals of the RDoC framework.
Parent–child relationships play a crucial role in shaping adolescent depression. However, existing research has predominantly focused on isolated time points when examining the impact of parent–child relationship quality, overlooking the effect of parent–child relationship changes on depression. Using a four‐wave longitudinal study of 951 adolescents, the current study investigated the impact of parent–child relationship quality and its changes on adolescent depression, while also examining the moderating role of interpersonal sensitivity and potential gender differences in these moderation patterns. Herein, we report that higher parent–child relationship quality is associated with lower levels of adolescent depression, while parent–child relationship changes are linked to higher depression. Interpersonal sensitivity moderates the association between parent–child relationship quality, relationship changes, and depression, with the moderation varying by gender. Specifically, in males with high interpersonal sensitivity, parent–child relationships negatively predict depression, while relationship fluctuations positively predict depression. In females with high interpersonal sensitivity, a rapid decline in the quality of the parent–child relationship seems to be associated with an increase in depression. The present study highlights the impact and mechanisms of parent–child relationship quality and its effects on depression, and provides a reference for the intervention of depression based on parent–child relationship changes.
Michelle Maiese and Robert Hanna’s The Mind-Body Politic sets out to combine both the philosophy of essentially embodied cognition (EEM) and emancipatory political theory to put forth a “new critique of contemporary social institutions.” There remains, however, an explanatory gap between the general, normative foundations of their approach in EEM, and the particular critiques of neoliberalism and the alternative presented. This paper explores an alternative totalitarian pathway (as understood by Arendt) that such general EEM normativity might lead us, as a friendly invitation for further elaboration on the normative basis of MBP’s critiques of—and proposed alternative to—neoliberal institutions.
This paper reviews biophysical models of psychotherapeutic change based on synergetics and the free energy principle. These models suggest that introducing sensory surprise into the patient-therapist system can lead to self-organization and the formation of new attractor states, disrupting entrenched patterns of thoughts, emotions, and behaviours. We propose that the therapist can facilitate this process by cultivating epistemic trust and modulating embodied attention to allow surprising affective states to enter shared awareness. Transient increases in free energy enable the update of generative models, expanding the range of experiences available within the patient-therapist phenomenal field. We hypothesize that patterns of disorganization at behavioural and physiological levels, indexed by increased entropy, complexity, and lower determinism, are key markers and predictors of psychotherapeutic gains. Future research should investigate how the therapist's openness to novelty shapes therapeutic outcomes.
Negative social expectations are a key symptom of depression. It has been suggested that individuals with depressive symptoms tend to maintain these expectations by devaluing new experiences that do not fit prior expectations. However, our understanding of the role of such “immunizing” cognition in response to unexpected social feedback in depression, as well as the cognitive mechanisms involved, remains limited. This study investigated the association between depressive symptoms and the cognitive processing of unexpected positive or negative social feedback using a novel, video-based approach featuring naturalistic social stimuli in a subclinical online sample (N = 155). We also examined how surprise levels, feedback acceptance and immunizing cognition relate to other cognitive processes, such as attributional style and rumination, using cross-sectional network analyses. Robust multiple linear regression analyses revealed that depressive symptoms were associated with higher surprise levels (R²adj. = .27), lower feedback acceptance (R²adj. = .19), and higher levels of immunizing cognition (R²adj. = .09) in response to unexpected positive social feedback, but only partially to unexpected negative social feedback. The network analysis suggested that self-efficacy expectations for coping with negative feelings and acceptance of positive social feedback had the strongest expected influence on the different cognitive processes. Our study highlights the challenges that individuals with depressive symptoms face in utilizing positive social feedback to modify negative expectations. For clinicians, our findings suggest the importance of promoting acceptance of positive social feedback, while simultaneously inhibiting immunizing cognition and avoiding the use of overly positive feedback.
Introduction: Dissociative experiences are considered undesirable ketamine’s adverse events. However, they might be crucial for ketamine’s antidepressant effects, at least in some depression subtypes. Current understandings of ketamine’s therapeutic potentials converge on the so-called “relaxed prior hypothesis,” suggesting that glutamatergic blockage up-weights bottom-up surprising somatosensory/affective states. As a result, ketamine improves short-term plasticity in depression by enhancing sensitivity to interoceptive signals. Methods: We selected 2 case studies for their paradigmatic description of “depersonalized depression” (Entfremdungsdepression) symptoms. Patients were included in a 6-month-long esketamine program for treatment resistant depression, during which we collected their spontaneous experience with esketamine. According to a neurophenomenological approach, we combined subjective reports from unstructured clinical interviews and the review of previous objective neuroimaging results and neurocomputational models to unveil the relation between esketamine antidepressant effects and interoceptive sensitivity. Results: According to our clinical observations, esketamine-induced dissociation might be particularly effective in the depersonalized depression subtype, in which interoceptive awareness and interaffectivity are particularly compromised. Ketamine and esketamine’s dissociative effects and particularly disembodiment might suspend previously acquired patterns of feeling, sensing, and behaving. Conclusions: Coherently with previous research, we suggest that esketamine-induced disembodiment allows for a transient window of psychological plasticity and enhanced sensitivity, where the body recovers its permeability to affective affordances.
Although Postpartum depression (PPD) and PPD with anxiety (PPD-A) have been well characterized as functional disruptions within or between multiple brain systems, however, how to quantitatively delineate brain functional system irregularity and the molecular basis of functional abnormalities in PPD and PPD-A remains unclear. Here, brain sample entropy (SampEn), resting-state functional connectivity (RSFC), tran-scriptomic and neurotransmitter density data were used to investigate brain functional system irregularity, functional connectivity abnormalities and associated molecular basis for PPD and PPD-A. PPD-A exhibited higher SampEn in medial pre-frontal cortex (MPFC) and posterior cingulate cortex (PPC) than healthy postnatal women (HPW) and PPD while PPD showed lower SampEn in PPC compared to HPW and PPD-A. The functional connectivity analysis with MPFC and PPC as seed areas revealed decreased functional couplings between PCC and paracentral lobule and between MPFC and angular gyrus in PPD compared to both PPD-A and HPW. Moreover , abnormal SampEn and functional connectivity were associated with estrogenic
Social media is implicated today in an array of mental health concerns. While concerns around social media have become mainstream, little is known about the specific cognitive mechanisms underlying the correlations seen in these studies or why we find it so hard to stop engaging with these platforms when things obviously begin to deteriorate for us. New advances in computational neuroscience, however, are now poised to shed light on this matter. In this paper, we approach the phenomenon of social media addiction through the lens of the active inference framework. According to this framework, predictive agents like us use a ‘generative model’ of the world to predict our own incoming sense data and act to minimize any discrepancy between the prediction and incoming signal (prediction error). In order to live well and be able to act effectively to minimize prediction error, it is vital that agents like us have a generative model, which not only accurately reflects the regularities of our complex environment but is also flexible and dynamic and able to stay accurate in volatile and turbulent circumstances. In this paper, we propose that some social media platforms are a spectacularly effective way of warping an agent’s generative model and of arresting the model’s ability to flexibly track and adapt to changes in the environment. We go on to investigate cases of digital tech, which do not have these adverse effects and suggest—based on the active inference framework—some ways to understand why some forms of digital technology pose these risks, while others do not.
For the past decade, ketamine, an N-methyl-D-aspartate receptor (NMDAr) antagonist, has been considered a promising treatment for major depressive disorder (MDD). Unlike the delayed effect of monoaminergic treatment, ketamine may produce fast-acting antidepressant effects hours after a single administration at subanesthetic dose. Along with these antidepressant effects, it may also induce transient dissociative (disturbing of the sense of self and reality) symptoms during acute administration which resolve within hours. To understand ketamine’s rapid-acting antidepressant effect, several biological hypotheses have been explored, but despite these promising avenues, there is a lack of model to understand the timeframe of antidepressant and dissociative effects of ketamine. In this article, we propose a neurocomputational account of ketamine’s antidepressant and dissociative effects based on the Predictive Processing (PP) theory, a framework for cognitive and sensory processing. PP theory suggests that the brain produces top-down predictions to process incoming sensory signals, and generates bottom-up prediction errors (PEs) which are then used to update predictions. This iterative dynamic neural process would relies on N-methyl-D-aspartate (NMDAr) and α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic receptors (AMPAr), two major component of the glutamatergic signaling. Furthermore, it has been suggested that MDD is characterized by over-rigid predictions which cannot be updated by the PEs, leading to miscalibration of hierarchical inference and self-reinforcing negative feedback loops. Based on former empirical studies using behavioral paradigms, neurophysiological recordings, and computational modeling, we suggest that ketamine impairs top-down predictions by blocking NMDA receptors, and enhances presynaptic glutamate release and PEs, producing transient dissociative symptoms and fast-acting antidepressant effect in hours following acute administration. Moreover, we present data showing that ketamine may enhance a delayed neural plasticity pathways through AMPAr potentiation, triggering a prolonged antidepressant effect up to seven days for unique administration. Taken together, the two sides of antidepressant effects with distinct timeframe could constitute the keystone of antidepressant properties of ketamine. These PP disturbances may also participate to a ketamine-induced time window of mental flexibility, which can be used to improve the psychotherapeutic process. Finally, these proposals could be used as a theoretical framework for future research into fast-acting antidepressants, and combination with existing antidepressant and psychotherapy.
Reinforcement sensitivity theory (RST) is a major neuropsychological personality theory. However, a detailed introduction is lacking in Japanese. Therefore, this paper provides an overview of RST and discusses future perspectives. First, this paper introduces the original form of RST, a personality theory that introduced punishment sensitivity and rewards sensitivity. Second, this paper describes RST and Gray and McNaughton’s (2000) revised version of the RST. Third, this paper summarizes the relationship between RST and emotional disorders. Fourth, review of the Japanese literature in this study revealed that a comparison of the changes made by the revised RST with descriptions of the current RST aimed at Japanese speakers tended to show a lack of explanation of the major changes in the revised RST. Based on these findings, the author proposed a Japanese-translation of the key concepts in RST. Finally, future directions for RST research are discussed.
We review a recent shift in conceptions of interoception and its relationship to hierarchical inference in the brain. The notion of interoceptive inference means that bodily states are regulated by autonomic reflexes that are enslaved by descending predictions from deep generative models of our internal and external milieu. This re-conceptualization illuminates several issues in cognitive and clinical neuroscience with implications for experiences of selfhood and emotion. We first contextualize interoception in terms of active (Bayesian) inference in the brain, highlighting its enactivist (embodied) aspects. We then consider the key role of uncertainty or precision and how this might translate into neuromodulation. We next examine the implications for understanding the functional anatomy of the emotional brain, surveying recent observations on agranular cortex. Finally, we turn to theoretical issues, namely, the role of interoception in shaping a sense of embodied self and feelings. We will draw links between physiological homoeostasis and allostasis, early cybernetic ideas of predictive control and hierarchical generative models in predictive processing. The explanatory scope of interoceptive inference ranges from explanations for autism and depression, through to consciousness. We offer a brief survey of these exciting developments.
This article is part of the themed issue ‘Interoception beyond homeostasis: affect, cognition and mental health’.
In this paper, we integrate recent theoretical and empirical developments in predictive coding and active inference accounts of interoception (including the Embodied Predictive Interoception Coding model) with working hypotheses from the theory of constructed emotion to propose a biologically plausible unified theory of the mind that places metabolism and energy regulation (i.e. allostasis), as well as the sensory consequences of that regulation (i.e. interoception), at its core. We then consider the implications of this approach for understanding depression. We speculate that depression is a disorder of allostasis, whose myriad symptoms result from a ‘locked in’ brain that is relatively insensitive to its sensory context. We conclude with a brief discussion of the ways our approach might reveal new insights for the treatment of depression.
This article is part of the themed issue ‘Interoception beyond homeostasis: affect, cognition and mental health’.
Depression is associated with social risk factors, social impairments and poor social functioning. This paper gives an overview of these social aspects using the NIMH Research and Domain Criteria ‘Systems for Social Processes’ as a framework. In particular, it describes the bio-psycho-social interplay regarding impaired affiliation and attachment (social anhedonia, hyper-sensitivity to social rejection, competition avoidance, increased altruistic punishment), impaired social communication (impaired emotion recognition, diminished cooperativeness), impaired social perception (reduced empathy, theory-of-mind deficits) and their impact on social networks and the use of social media. It describes these dysfunctional social processes at the behavioural, neuroanatomical, neurochemical and genetic levels, and with respect to animal models of social stress. We discuss the diagnostic specificity of these social deficit constructs for depression and in relation to depression severity. Since social factors are importantly involved in the pathogenesis and the consequences of depression, such research will likely contribute to better diagnostic assessments and concepts, treatments and preventative strategies both at the diagnostic and transdiagnostic level.
The development of new technologies for mapping structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks can be examined and analyzed with a large variety of graph theory tools. Methods for detecting modules, or network communities, are of particular interest because they uncover major building blocks or subnetworks that are particularly densely connected, often corresponding to specialized functional components. A large number of methods for community detection have become available and are now widely applied in network neuroscience. This article first surveys a number of these methods, with an emphasis on their advantages and shortcomings; then it summarizes major findings on the existence of modules in both structural and functional brain networks and briefly considers their potential functional roles in brain evolution, wiring minimization, and the emergence of functional specialization and complex dynamics. Expected final online publication date for the Annual Review of Psychology Volume 67 is January 03, 2016. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.
Computational Psychiatry aims to describe the relationship between the brain's neurobiology, its environment and mental symptoms in computational terms. In so doing, it may improve psychiatric classification and the diagnosis and treatment of mental illness. It can unite many levels of description in a mechanistic and rigorous fashion, while avoiding biological reductionism and artificial categorisation. We describe how computational models of cognition can infer the current state of the environment and weigh up future actions, and how these models provide new perspectives on two example disorders, depression and schizophrenia. Reinforcement learning describes how the brain can choose and value courses of actions according to their long-term future value. Some depressive symptoms may result from aberrant valuations, which could arise from prior beliefs about the loss of agency ('helplessness'), or from an inability to inhibit the mental exploration of aversive events. Predictive coding explains how the brain might perform Bayesian inference about the state of its environment by combining sensory data with prior beliefs, each weighted according to their certainty (or precision). Several cortical abnormalities in schizophrenia might reduce precision at higher levels of the inferential hierarchy, biasing inference towards sensory data and away from prior beliefs. We discuss whether striatal hyperdopaminergia might have an adaptive function in this context, and also how reinforcement learning and incentive salience models may shed light on the disorder. Finally, we review some of Computational Psychiatry's applications to neurological disorders, such as Parkinson's disease, and some pitfalls to avoid when applying its methods.
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Intuition suggests that perception follows sensation and therefore bodily feelings originate in the body. However, recent evidence goes against this logic: interoceptive experience may largely reflect limbic predictions about the expected state of the body that are constrained by ascending visceral sensations. In this Opinion article, we introduce the Embodied Predictive Interoception Coding model, which integrates an anatomical model of corticocortical connections with Bayesian active inference principles, to propose that agranular visceromotor cortices contribute to interoception by issuing interoceptive predictions. We then discuss how disruptions in interoceptive predictions could function as a common vulnerability for mental and physical illness.
Hierarchical organization -- the recursive composition of sub-modules -- is
ubiquitous in biological networks, including neural, metabolic, ecological, and
genetic regulatory networks, and in human-made systems, such as large
organizations and the Internet. To date, most research on hierarchy in networks
has been limited to quantifying this property. However, an open, important
question in evolutionary biology is why hierarchical organization evolves in
the first place. It has recently been shown that modularity evolves because of
the presence of a cost for network connections. Here we investigate whether
such connection costs also tend to cause a hierarchical organization of such
modules. In computational simulations, we find that networks without a
connection cost do not evolve to be hierarchical, even when the task has a
hierarchical structure. However, with a connection cost, networks evolve to be
both modular and hierarchical, and these networks exhibit higher overall
performance and evolvability (i.e. faster adaptation to new environments).
Additional analyses confirm that hierarchy independently improves adaptability
after controlling for modularity. Overall, our results suggest that the same
force--the cost of connections--promotes the evolution of both hierarchy and
modularity, and that these properties are important drivers of network
performance and adaptability. In addition to shedding light on the emergence of
hierarchy across the many domains in which it appears, these findings will also
accelerate future research into evolving more complex, intelligent
computational brains in the fields of artificial intelligence and robotics.
Sociality permeates each of the fundamental motives of human existence and plays a critical role in evolutionary fitness across the lifespan. Evidence for this thesis draws from research linking deficits in social relationship-as indexed by perceived social isolation (i.e. loneliness)-with adverse health and fitness consequences at each developmental stage of life. Outcomes include depression, poor sleep quality, impaired executive function, accelerated cognitive decline, unfavourable cardiovascular function, impaired immunity, altered hypothalamic pituitary-adrenocortical activity, a pro-inflammatory gene expression profile and earlier mortality. Gaps in this research are summarized with suggestions for future research. In addition, we argue that a better understanding of naturally occurring variation in loneliness, and its physiological and psychological underpinnings, in non-human species may be a valuable direction to better understand the persistence of a 'lonely' phenotype in social species, and its consequences for health and fitness.
© 2015 The Author(s) Published by the Royal Society. All rights reserved.
Major Depressive Disorder is a debilitating and increasingly prevalent psychiatric condition (Compton et al., 2006; Andersen et al., 2011). At present, its primary treatments are antidepressant medications and psychotherapy. Curiously, although the pharmacological effects of antidepressants manifest within hours, remission of clinical symptoms takes a number of weeks—if at all. Independently, support has grown for an idea—proposed as early as Helmholtz (von Helmholtz, 1924)—that the brain is a prediction machine, holding generative models¹ for the purpose of inferring causes of sensory information (Dayan et al., 1995; Rao and Ballard, 1999; Knill and Pouget, 2004; Friston et al., 2006; Friston, 2010). If the brain does indeed represent a collection of beliefs about the causal structure of the world, then the depressed phenotype may emerge from a collection of depressive beliefs. These beliefs are modified gradually through successive combinations of expectations with observations. As a result, phenotypic remission ought to take some time as the brain's relevant statistical structures become less pessimistic.
We offer a formal treatment of choice behaviour based on the premise that agents minimise the expected free energy of future outcomes. Crucially, the negative free energy or quality of a policy can be decomposed into extrinsic and epistemic (or intrinsic) value. Minimising expected free energy is therefore equivalent to maximising extrinsic value or expected utility (defined in terms of prior preferences or goals), while maximising information gain or intrinsic value (reducing uncertainty about the causes of valuable outcomes). The resulting scheme resolves the exploration-exploitation dilemma: epistemic value is maximised until there is no further information gain, after which exploitation is assured through maximisation of extrinsic value. This is formally consistent with the Infomax principle, generalising formulations of active vision based upon salience (Bayesian surprise) and optimal decisions based on expected utility and risk-sensitive (Kullback-Leibler) control. Furthermore, as with previous active inference formulations of discrete (Markovian) problems, ad hoc softmax parameters become the expected (Bayes-optimal) precision of beliefs about, or confidence in, policies. This article focuses on the basic theory, illustrating the ideas with simulations. A key aspect of these simulations is the similarity between precision updates and dopaminergic discharges observed in conditioning paradigms.
Increase in the area and neuron number of the cerebral cortex over evolutionary time systematically changes its computational properties. One of the fundamental developmental mechanisms generating the cortex is a conserved rostrocaudal gradient in duration of neuron production, coupled with distinct asymmetries in the patterns of axon extension and synaptogenesis on the same axis. A small set of conserved sensorimotor areas with well-defined thalamic input anchors the rostrocaudal axis. These core mechanisms organize the cortex into two contrasting topographic zones, while systematically amplifying hierarchical organization on the rostrocaudal axis in larger brains. Recent work has shown that variation in 'cognitive control' in multiple species correlates best with absolute brain size, and this may be the behavioral outcome of this progressive organizational change.
Copyright © 2014 Elsevier Ltd. All rights reserved.
Stressors are imminent or perceived challenges to homeostasis. The stress response is an innate, stereotypic, adaptive response to stressors that has evolved in the service of restoring the nonstressed homeostatic set point. It is encoded in specific neuroanatomical sites that activate a specific repertoire of cognitive, behavioral and physiologic phenomena. Adaptive responses, though essential for survival, can become dysregulated and result in disease. A clear example is autoimmune disease. I postulate that depression, like autoimmunity, represents a dysregulated adaptive response: a stress response that has gone awry. The cardinal manifestation of the normal stress response is anxiety. Cognitive programs shift from complex associative operations to rapid retrieval of unconscious emotional memories acquired during prior threatening situations. These emerge automatically to promote survival. To prevent distraction during stressful situations, the capacity to seek and experience pleasure is reduced, food intake is diminished and sexual activity and sleep are held in abeyance. Monoamines, cytokines, glutamate, GABA and other central mediators have key roles in the normal stress response. Many central loci are involved. The subgenual prefrontal cortex restrains the amygdala, the corticotropin-releasing hormone/hypothalamic-pituitary-adrenal (CRH/HPA) axis and the sympathomedullary system. The function of the subgenual prefrontal cortex is moderately diminished during normal stress to disinhibit these loci. This disinhibition promotes anxiety and physiological hyperarousal, while diminishing appetite and sleep. The dorsolateral prefrontal cortex is downregulated, diminishing cognitive regulation of anxiety. The nucleus accumbens is also downregulated, to reduce the propensity for distraction by pleasurable stimuli or the capacity to experience pleasure. Insulin resistance, inflammation and a prothrombotic state acutely emerge. These provide increased glucose for the brain and establish premonitory, proinflammatory and prothrombotic states in anticipation of either injury or hemorrhage during a threatening situation. Essential adaptive intracellular changes include increased neurogenesis, enhancement of neuroplasticity and deployment of a successful endoplasmic reticulum stress response. In melancholic depression, the activities of the central glutamate, norepinephrine and central cytokine systems are significantly and persistently increased. The subgenual prefrontal cortex is functionally impaired, and its size is reduced by as much as 40%. This leads to sustained anxiety and activations of the amygdala, CRH/HPA axis, the sympathomedullary system and their sequella, including early morning awakening and loss of appetite. The sustained activation of the amygdala, in turn, further activates stress system neuroendocrine and autonomic functions. The activity of the nucleus accumbens is further decreased and anhedonia emerges. Concomitantly, neurogenesis and neuroplasticity fall significantly. Antidepressants ameliorate many of these processes. The processes that lead to the behavioral and physiological manifestations of depressive illness produce a significant decrease in lifespan, and a doubling of the incidence of premature coronary artery disease. The incidences of premature diabetes and osteoporosis are also substantially increased. Six physiological processes that occur during stress and that are markedly increased in melancholia set into motion six different mechanisms to produce inflammation, as well as sustained insulin resistance and a prothrombotic state. Clinically, melancholic and atypical depression seem to be antithesis of one another. In melancholia, depressive systems are at their worst in the morning when arousal systems, such as the CRH/HPA axis and the noradrenergic systems, are at their maxima. In atypical depression, depressive symptoms are at their worst in the evening, when these arousal systems are at their minima. Melancholic patients experience anorexia and insomnia, whereas atypical patients experience hyperphagia and hypersomnia. Melancholia seems like an activation and persistence of the normal stress response, whereas atypical depression resembles a stress response that has been excessively inhibited. It is important that we stratify clinical studies of depressed patients to compare melancholic and atypical subtypes and establish their differential pathophysiology. Overall, it is important to note that many of the major mediators of the stress response and melancholic depression, such as the subgenual prefrontal cortex, the amygdala, the noradrenergic system and the CRH/HPA axis participate in multiple reinforcing positive feedback loops. This organization permits the establishment of the markedly exaggerated, persistent elevation of the stress response seen in melancholia. Given their pronounced interrelatedness, it may not matter where in this cascade the first abnormality arises. It will spread to the other loci and initiate each of their activations in a pernicious vicious cycle.
The neural criticality hypothesis states that the brain may be poised in a critical state at a boundary between different types of dynamics. Theoretical and experimental studies show that critical systems often exhibit optimal computational properties, suggesting the possibility that criticality has been evolutionarily selected as a useful trait for our nervous system. Evidence for criticality has been found in cell cultures, brain slices, and anesthetized animals. Yet, inconsistent results were reported for recordings in awake animals and humans, and current results point to open questions about the exact nature and mechanism of criticality, as well as its functional role. Therefore, the criticality hypothesis has remained a controversial proposition. Here, we provide an account of the mathematical and physical foundations of criticality. In the light of this conceptual framework, we then review and discuss recent experimental studies with the aim of identifying important next steps to be taken and connections to other fields that should be explored.
Meta-analytic evidence has supported a gene-environment interaction between life stress and the serotonin transporter-linked polymorphism (5-HTTLPR) on depression, but few studies have examined factors that influence detection of this effect, despite years of inconsistent results. We propose that the candidate environment (akin to a candidate gene) is key. Theory and evidence have implicated major stressful life events (SLEs)-particularly major interpersonal SLEs-as well as chronic family stress. A total of 400 participants from the Youth Emotion Project (which began with 627 high school juniors oversampled for high neuroticism) completed up to five annual diagnostic and stress interviews and provided DNA samples. A significant gene-environment effect for major SLEs and S-carrier genotype was accounted for significantly by major interpersonal SLEs but not significantly by major noninterpersonal SLEs. S-carrier genotype and chronic family stress also significantly interacted. Identifying such candidate environments may facilitate future gene-environment research in depression and psychopathology more broadly.
This paper considers goal-directed decision-making in terms of embodied or active inference. We associate bounded rationality with approximate Bayesian inference that optimizes a free energy bound on model evidence. Several constructs such as expected utility, exploration or novelty bonuses, softmax choice rules and optimism bias emerge as natural consequences of free energy minimization. Previous accounts of active inference have focused on predictive coding. In this paper, we consider variational Bayes as a scheme that the brain might use for approximate Bayesian inference. This scheme provides formal constraints on the computational anatomy of inference and action, which appear to be remarkably consistent with neuroanatomy. Active inference contextualizes optimal decision theory within embodied inference, where goals become prior beliefs. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (associated with softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution. Crucially, this sensitivity corresponds to the precision of beliefs about behaviour. The changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses-and they may provide a new perspective on the role of dopamine in assimilating reward prediction errors to optimize decision-making.
Viewing the brain as an organ of approximate Bayesian inference can help us understand how it represents the self. We suggest that inferred representations of the self have a normative function: to predict and optimise the likely outcomes of social interactions. Technically, we cast this predict-and-optimise as maximising the chance of favourable outcomes through active inference. Here the utility of outcomes can be conceptualised as prior beliefs about final states. Actions based on interpersonal representations can therefore be understood as minimising surprise – under the prior belief that one will end up in states with high utility. Interpersonal representations thus serve to render interactions more predictable, while the affective valence of interpersonal inference renders self-perception evaluative. Distortions of self-representation contribute to major psychiatric disorders such as depression, personality disorder and paranoia. The approach we review may therefore operationalise the study of interpersonal representations in pathological states.
A central question in brain evolution is how species-typical behaviors, and the neural function-structure mappings supporting them, can be acquired and inherited. Advocates of brain modularity, in its different incarnations across scientific subfields, argue that natural selection must target domain-dedicated, separately modifiable neural subsystems, resulting in genetically-specified functional modules. In such modular systems, specification of neuron number and functional connectivity are necessarily linked. Mounting evidence, however, from allometric, developmental, comparative, systems-physiological, neuroimaging and neurological studies suggests that brain elements are used and reused in multiple functional systems. This variable allocation can be seen in short-term neuromodulation, in neuroplasticity over the lifespan and in response to damage. We argue that the same processes are evident in brain evolution. Natural selection must preserve behavioral functions that may co-locate in variable amounts with other functions. In genetics, the uses and problems of pleiotropy, the re-use of genes in multiple networks have been much discussed, but this issue has been sidestepped in neural systems by the invocation of modules. Here we highlight the interaction between evolutionary and developmental mechanisms to produce distributed and overlapping functional architectures in the brain. These adaptive mechanisms must be robust to perturbations that might disrupt critical information processing and action selection, but must also recognize useful new sources of information arising from internal genetic or environmental variability, when those appear. These contrasting properties of “robustness” and “evolvability” have been discussed for the basic organization of body plan and fundamental cell physiology. Here we extend them to the evolution and development, “evo-devo,” of brain structure.
Major life stressors, especially those involving interpersonal stress and social rejection, are among the strongest proximal risk factors for depression. In this review, we propose a biologically plausible, multilevel theory that describes neural, physiologic, molecular, and genomic mechanisms that link experiences of social-environmental stress with internal biological processes that drive depression pathogenesis. Central to this social signal transduction theory of depression is the hypothesis that experiences of social threat and adversity up-regulate components of the immune system involved in inflammation. The key mediators of this response, called proinflammatory cytokines, can in turn elicit profound changes in behavior, which include the initiation of depressive symptoms such as sad mood, anhedonia, fatigue, psychomotor retardation, and social-behavioral withdrawal. This highly conserved biological response to adversity is critical for survival during times of actual physical threat or injury. However, this response can also be activated by modern-day social, symbolic, or imagined threats, leading to an increasingly proinflammatory phenotype that may be a key phenomenon driving depression pathogenesis and recurrence, as well as the overlap of depression with several somatic conditions including asthma, rheumatoid arthritis, chronic pain, metabolic syndrome, cardiovascular disease, obesity, and neurodegeneration. Insights from this theory may thus shed light on several important questions including how depression develops, why it frequently recurs, why it is strongly predicted by early life stress, and why it often co-occurs with symptoms of anxiety and with certain physical disease conditions. This work may also suggest new opportunities for preventing and treating depression by targeting inflammation.
Neural ensembles oscillate across a broad range of frequencies and are transiently coupled or "bound" together when people attend to a stimulus, perceive, think, and act. This is a dynamic, self-assembling process, with parts of the brain engaging and disengaging in time. But how is it done? The theory of Coordination Dynamics proposes a mechanism called metastability, a subtle blend of integration and segregation. Tendencies for brain regions to express their individual autonomy and specialized functions (segregation, modularity) coexist with tendencies to couple and coordinate globally for multiple functions (integration). Although metastability has garnered increasing attention, it has yet to be demonstrated and treated within a fully spatiotemporal perspective. Here, we illustrate metastability in continuous neural and behavioral recordings, and we discuss theory and experiments at multiple scales, suggesting that metastable dynamics underlie the real-time coordination necessary for the brain's dynamic cognitive, behavioral, and social functions.
Adolescence is a period of formative biological and social transition. Social cognitive processes involved in navigating increasingly complex and intimate relationships continue to develop throughout adolescence. Here, we describe the functional and structural changes occurring in the brain during this period of life and how they relate to navigating the social environment. Areas of the social brain undergo both structural changes and functional reorganization during the second decade of life, possibly reflecting a sensitive period for adapting to one's social environment. The changes in social environment that occur during adolescence might interact with increasing executive functions and heightened social sensitivity to influence a number of adolescent behaviors. We discuss the importance of considering the social environment and social rewards in research on adolescent cognition and behavior. Finally, we speculate about the potential implications of this research for society. Expected final online publication date for the Annual Review of Psychology Volume 65 is January 03, 2014. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.
This paper presents a heuristic proof (and simulations of a primordial soup) suggesting that life-or biological self-organization-is an inevitable and emergent property of any (ergodic) random dynamical system that possesses a Markov blanket. This conclusion is based on the following arguments: if the coupling among an ensemble of dynamical systems is mediated by short-range forces, then the states of remote systems must be conditionally independent. These independencies induce a Markov blanket that separates internal and external states in a statistical sense. The existence of a Markov blanket means that internal states will appear to minimize a free energy functional of the states of their Markov blanket. Crucially, this is the same quantity that is optimized in Bayesian inference. Therefore, the internal states (and their blanket) will appear to engage in active Bayesian inference. In other words, they will appear to model-and act on-their world to preserve their functional and structural integrity, leading to homoeostasis and a simple form of autopoiesis.
The free-energy principle has recently been proposed as a unified Bayesian account of perception, learning and action. Despite the inextricable link between emotion and cognition, emotion has not yet been formulated under this framework. A core concept that permeates many perspectives on emotion is valence, which broadly refers to the positive and negative character of emotion or some of its aspects. In the present paper, we propose a definition of emotional valence in terms of the negative rate of change of free-energy over time. If the second time-derivative of free-energy is taken into account, the dynamics of basic forms of emotion such as happiness, unhappiness, hope, fear, disappointment and relief can be explained. In this formulation, an important function of emotional valence turns out to regulate the learning rate of the causes of sensory inputs. When sensations increasingly violate the agent's expectations, valence is negative and increases the learning rate. Conversely, when sensations increasingly fulfil the agent's expectations, valence is positive and decreases the learning rate. This dynamic interaction between emotional valence and learning rate highlights the crucial role played by emotions in biological agents' adaptation to unexpected changes in their world.
Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. This target article critically examines this "hierarchical prediction machine" approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency.
The history of the sciences of the human brain and mind has been characterized from the beginning by two parallel traditions. The prevailing theory that still influences the way current neuroimaging techniques interpret brain function, can be traced back to classical localizational theories, which in turn go back to early phrenological theories. The other approach has its origins in the hierarchical neurological theories of Hughlings-Jackson, which have been influenced by the philosophical conceptions of Herbert Spencer. Another hallmark of the hierarchical tradition, which is also inherent to psychoanalytic metapsychology, is its deeply evolutionary perspective by taking both ontogenetic and phylogenetic trajectories into consideration. This article provides an outline on hierarchical concepts in brain and mind sciences, which contrast with current cognitivistic and non-hierarchical theories in the neurosciences.
A proposal for a fully post-phrenological neuroscience that details the evolutionary roots of functional diversity in brain regions and networks.
The computer analogy of the mind has been as widely adopted in contemporary cognitive neuroscience as was the analogy of the brain as a collection of organs in phrenology. Just as the phrenologist would insist that each organ must have its particular function, so contemporary cognitive neuroscience is committed to the notion that each brain region must have its fundamental computation. In After Phrenology, Michael Anderson argues that to achieve a fully post-phrenological science of the brain, we need to reassess this commitment and devise an alternate, neuroscientifically grounded taxonomy of mental function.
Anderson contends that the cognitive roles played by each region of the brain are highly various, reflecting different neural partnerships established under different circumstances. He proposes quantifying the functional properties of neural assemblies in terms of their dispositional tendencies rather than their computational or information-processing operations. Exploring larger-scale issues, and drawing on evidence from embodied cognition, Anderson develops a picture of thinking rooted in the exploitation and extension of our early-evolving capacity for iterated interaction with the world. He argues that the multidimensional approach to the brain he describes offers a much better fit for these findings, and a more promising road toward a unified science of minded organisms.
Bradford Books imprint
The subtle relationship between feeling and thinking, affect and cognition has fascinated philosophers and writers since time immemorial, yet, empirical research on this topic was relatively neglected by psychologists until recently. There have been many claims emphasising the beneficial cognitive and behavioural consequences of positive affect. Many recent works suggest that negative affect may also facilitate optimal performance in many situations, consistent with evolutionary theories suggesting the adaptive signalling function of various affective states. This paper reviews traditional and current psychological theories linking affect to social thinking and behaviour. A variety of empirical studies from our laboratory will also be presented, demonstrating that in many situations, negative affect promotes optimal performance in cognitive and social tasks, including tasks such as memory, social judgements, motivation, and strategic interpersonal behaviours. These results will be interpreted in terms of a dual-process theory that predicts that negative affect promotes a more accommodative, vigilant, and externally focused thinking strategy. The relevance of these findings for recent affect–cognition theories will be discussed, and the practical implications of negative affect promoting improved social thinking and performance in a number of applied fields will be considered.
Most research on sensitive periods has focussed on early sensory, motor, and language development, but it has recently been suggested that adolescence might represent a second 'window of opportunity' in brain development. Here, we explore three candidate areas of development that are proposed to undergo sensitive periods in adolescence: memory, the effects of social stress, and drug use. We describe rodent studies, neuroimaging, and large-scale behavioural studies in humans that have yielded data that are consistent with heightened neuroplasticity in adolescence. Critically however, concrete evidence for sensitive periods in adolescence is mostly lacking. To provide conclusive evidence, experimental studies are needed that directly manipulate environmental input and compare effects in child, adolescent, and adult groups. Recently the idea that adolescence may be a sensitive period of development has gained traction in the literature.Adolescence is characterised by changes in brain structure and function, particularly in regions of the cortex that are involved in higher-level cognitive processes such as memory, for which capacity may be heightened in adolescence.Heightened plasticity may not only result in increased opportunities for development but also in increased vulnerabilities. Data from rodents show effects of social isolation and reduced fear extinction that are consistent with adolescence as a sensitive period for the development of mental illness.Adolescent sensitive periods are likely to be characterised by large individual differences. Rodent data indicate that individuals who are exposed to drugs such as cannabis during adolescence may experience detrimental effects on cognitive functioning.
Sexual differentiation of the nervous system and behavior occurs through organizational effects of gonadal hormones acting during early neural development and again during puberty. In rodents, a transient elevation in testosterone around the time of birth masculinizes and defeminizes the male brain, creating structural sexual dimorphisms and programming sex-typical responses to gonadal hormones in adulthood. A second wave of sexual differentiation occurs when levels of gonadal hormones are elevated at the time of puberty. At this time, both testicular and ovarian hormones further masculinize and feminize the male and female brain, respectively, fine-tuning sex differences in adult behavior. To test the hypothesis that the peripubertal period is a sensitive period for hormone-dependent sexual differentiation that is separate and distinct from the perinatal period, exposure to testosterone was experimentally manipulated in male Syrian hamsters to occur either prepubertally, during puberty, or in young adulthood. This experiment revealed that the perinatal and peripubertal periods of masculinization of male sexual behavior are not two separate critical periods of sensitivity to organizing effects of testosterone. Instead, the two periods of masculinization are driven by the two naturally occurring elevations in gonadal hormones. To explore possible neural mechanisms of peripubertal organizational effects of gonadal hormones, cell birthdating experiments in male and female rats revealed sex differences in the addition of new cells, including both neurons and glia, to sexually dimorphic cell groups in the hypothalamus and medial amygdala. These sex differences in cell addition were positively correlated with sex differences in the volume of these cell groups. Prepubertal gonadectomy abolished sex differences in the pubertal addition of new cells. These experiments provide evidence that gonadal hormone-dependent sex differences in pubertal cytogenesis contribute to the establishment or maintenance of sexual dimorphisms in the adult brain. The transition from childhood to adulthood begins with the onset of puberty and the ensuing rise in sex steroid hormones, and it is completed by the end of adolescence. This period of development comprises extraordinary gain of function: individuals acquire the capacity to procreate, function independently within their social realm, and provide for themselves and their offspring. The metamorphosis of behavior is the product of a metamorphosis of underlying neural circuits, which necessarily occurs along different trajectories in females and males. In fact, adolescence can be regarded as a period of further sexual differentiation of brain and behavior, which is mediated in part by the actions of sex steroid hormones in the brain. This paper highlights research from my laboratory that has uncovered roles for gonadal hormones in shaping sex-specific behavioral and brain development during puberty and adolescence. I will first review experiments that establish that testicular hormones, acting during puberty, organize neural circuits underlying male social behaviors. Next I will present evidence that hormonal regulation of pubertal neuro- and gliogenesis is a potential mechanism for the establishment or maintenance of structural sex differences in the brain during adolescence.
We review a theory of homeostatic regulation and adaptive behavioural control within the Active Inference framework. Our aim is to connect two research streams that are usually considered independently; namely, Active Inference and associative learning theories of animal behaviour. The former uses a probabilistic (Bayesian) formulation of perception and action, while the latter calls on multiple (Pavlovian, habitual, goal-directed) processes for homeostatic and behavioural control. We offer a synthesis these classical processes and cast them as successive hierarchical contextualisations of sensorimotor constructs, using the generative models that underpin Active Inference. This dissolves any apparent mechanistic distinction between the optimisation processes that mediate classical control or learning. Furthermore, we generalise the scope of Active Inference by emphasising interoceptive inference and homeostatic regulation. The ensuing homeostatic (or allostatic) perspective provides an intuitive explanation for how priors act as drives or goals to enslave action, and emphasises the embodied nature of inference.
Evolutionary psychology has been proposed as a metatheoretical framework for psychology. We argue that evolutionary psychology should be expanded if it is to offer new insights regarding the major issues in psychology. Evolutionary developmental biology can provide valuable new insights into issues such as the domain-specificity of the human mind, the nature–nurture debate, stages in development, and the origin of individual differences. Evolutionary developmental biology provides evidence for the hypotheses that domain-general and domain-specific abilities co-occur, that nature and nurture interact in a dynamic and nonadditive way, that stages occur in development, and that individual differences are the result of pleiotropic effects during development.
We propose a general hypothesis that integrates affective and cognitive processing with neuroanatomy to explain anxiety pronenes. The premise is that individuals who are prone to anxiety show an altered interoceptive prediction signal, i.e., manifest augmented detection of the difference between the observed and expected body state. As a consequence, the increased prediction signal of a prospective aversive body state triggers an increase in anxious affect, worrisome thoughts and other avoidance behaviors. The anterior insula is proposed to play a key role in this process. Further testing of this model--which should include investigation of genetic and environmental influences--may lead to the development of novel treatments that attenuate this altered interoceptive prediction signal in patients with anxiety disorders.
In this Review, we discuss advances in computational neuroscience that relate to psychiatry. We review computational psychiatry in terms of the ambitions of investigators, emerging domains of application, and future work. Our focus is on theoretical formulations of brain function that put subjective beliefs and behaviour within formal (computational) frameworks—frameworks that can be grounded in neurophysiology down to the level of synaptic mechanisms. Understanding the principles that underlie the brain's functional architecture might be essential for an informed phenotyping of psychopathology in terms of its pathophysiological underpinnings. We focus on active (Bayesian) inference and predictive coding. Specifically, we show how basic principles of neuronal computation can be used to explain psychopathology, ranging from impoverished theory of mind in autism to abnormalities of smooth pursuit eye movements in schizophrenia.
Investigated the relationship between change over time in severity of depression symptoms and facial expression. Depressed participants were followed over the course of treatment and video recorded during a series of clinical interviews. Facial expressions were analyzed from the video using both manual and automatic systems. Automatic and manual coding were highly consistent for FACS action units, and showed similar effects for change over time in depression severity. For both systems, when symptom severity was high, participants made more facial expressions associated with contempt, smiled less, and those smiles that occurred were more likely to be accompanied by facial actions associated with contempt. These results are consistent with the "social risk hypothesis" of depression. According to this hypothesis, when symptoms are severe, depressed participants withdraw from other people in order to protect themselves from anticipated rejection, scorn, and social exclusion. As their symptoms fade, participants send more signals indicating a willingness to affiliate. The finding that automatic facial expression analysis was both consistent with manual coding and produced the same pattern of depression effects suggests that automatic facial expression analysis may be ready for use in behavioral and clinical science.
A new theory is taking hold in neuroscience. It is the theory that the brain is essentially a hypothesis-testing mechanism, one that attempts to minimise the error of its predictions about the sensory input it receives from the world. It is an attractive theory because powerful theoretical arguments support it, and yet it is at heart stunningly simple. Jakob Hohwy explains and explores this theory from the perspective of cognitive science and philosophy. The key argument throughout The Predictive Mind is that the mechanism explains the rich, deep, and multifaceted character of our conscious perception. It also gives a unified account of how perception is sculpted by attention, and how it depends on action. The mind is revealed as having a fragile and indirect relation to the world. Though we are deeply in tune with the world we are also strangely distanced from it.
The first part of the book sets out how the theory enables rich, layered perception. The theory's probabilistic and statistical foundations are explained using examples from empirical research and analogies to different forms of inference. The second part uses the simple mechanism in an explanation of problematic cases of how we manage to represent, and sometimes misrepresent, the world in health as well as in mental illness. The third part looks into the mind, and shows how the theory accounts for attention, conscious unity, introspection, self and the privacy of our mental world.
Depression is a significant public health problem, but its etiology and pathophysiology remain poorly understood. Such incomplete understanding likely arises from the fact that depression encompasses a heterogeneous set of disorders. To overcome these limitations, renewed interest in intermediate phenotypes (endophenotypes) has resurfaced, and anhedonia has emerged as one of the most promising endophenotypes of depression. Here, a heuristic model is presented postulating that anhedonia arises from dysfunctional interactions between stress and brain reward systems. To this end, we review and integrate three bodies of independent literature investigating the role of (a) anhedonia, (b) dopamine, and (c) stress in depression. In a fourth section, we summarize animal data indicating that stress negatively affects mesocorticolimbic dopaminergic pathways critically implicated in incentive motivation and reinforcement learning. In the last section, we provide a synthesis of these four literatures, present initial evidence consistent with our model, and discuss directions for future research. Expected final online publication date for the Annual Review of Clinical Psychology Volume 10 is March 20, 2014. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.
Developmental psychopathology is not characterized by adherence to one specific theory but instead serves as an organizational framework in which research is driven by a number of key assumptions. In the developmental psychopathology approach, two primary assumptions emphasize the importance of systems thinking and the utility of multilevel analyses. As will be illustrated here, these emphases are inextricably linked: a systems approach necessitates a multilevel approach, such that a level of organization must bring coherence to a level of mechanisms. Given this assumption, coming to an integrative understanding of the relation between levels is of central importance. One broad framework for this endeavor is relational developmental systems, which has been proposed by certain theorists as a new paradigm for developmental science. The implications of embracing this framework include the potential to connect developmental psychopathology with other approaches that emphasize systems thinking and that take an integrative perspective on the problem of levels of analysis.
A new paper shows that a characteristic feature of the arrangement of brain networks, their modular organization across several scales, is responsible for an expanded range of critical neural dynamics. This finding solves several puzzles in computational neuroscience and links fundamental aspects of neural network organization and brain dynamics.
The human cerebral cortex is vastly expanded relative to other primates and disproportionately occupied by distributed association regions. Here we offer a hypothesis about how association networks evolved their prominence and came to possess circuit properties vital to human cognition. The rapid expansion of the cortical mantle may have untethered large portions of the cortex from strong constraints of molecular gradients and early activity cascades that lead to sensory hierarchies. What fill the gaps between these hierarchies are densely interconnected networks that widely span the cortex and mature late into development. Limitations of the tethering hypothesis are discussed as well as its broad implications for understanding critical features of the human brain as a byproduct of size scaling.
How rich functionality emerges from the invariant structural architecture of the brain remains a major mystery in neuroscience.
Recent applications of network theory and theoretical neuroscience to large-scale brain networks have started to dissolve
this mystery. Network analyses suggest that hierarchical modular brain networks are particularly suited to facilitate local
(segregated) neuronal operations and the global integration of segregated functions. Although functional networks are constrained
by structural connections, context-sensitive integration during cognition tasks necessarily entails a divergence between structural
and functional networks. This degenerate (many-to-one) function-structure mapping is crucial for understanding the nature
of brain networks. The emergence of dynamic functional networks from static structural connections calls for a formal (computational)
approach to neuronal information processing that may resolve this dialectic between structure and function.
The Social risk hypothesis contends that mild to moderate depression has evolved to promote belonging in small communities by making members sensitive to signs of rejection and motivated to restore their social value (Allen & Badcock, 2003). Using self-report data from 397 working adults, structural equation modeling examined the relationships between secure attachment, social comparison, defeat, depression, submissive behaviors, interpersonal sensitivity, and self-esteem. The analysis provided empirical support for an evolved adaptive mechanism functioning in mild to moderate depression. However, the moderating impact of social investment potential as an internal gauge measuring one's ratio of social value and social burden was only partially supported. Overall, the results of this study support the adaptive nature of mild to moderate depression as a mechanism that evolved to help sustain crucial restorative relationships and to prevent dangerous social risks.
Interactions between evolutionary psychologists and developmental systems theorists have been largely antagonistic. This is unfortunate because potential synergies between the two approaches remain unexplored. This article presents a method that may help to bridge the divide, and that has proven fruitful in biology: dynamic optimization. Dynamic optimization integrates developmental systems theorists' focus on dynamics and contingency with the 'design stance' of evolutionary psychology. It provides a theoretical framework as well as a set of tools for exploring the properties of developmental systems that natural selection might favor, given particular evolutionary ecologies. We also discuss limitations of the approach.
Humans have an intrinsic need for social connection; thus, it is crucial to understand depression in an interpersonal context. Interpersonal theories of depression posit that depressed individuals tend to interact with others in a way that elicits rejection, which increases their risk for future depression. In this review, we summarize the interpersonal characteristics, risk factors, and consequences of depression in the context of the relevant theories that address the role of interpersonal processes in the onset, maintenance, and chronicity of depression. Topics reviewed include social skills, behavioral features, communication behaviors, interpersonal feedback seeking, and interpersonal styles as they relate to depression. Treatment implications are discussed in light of the current research on interpersonal processes in depression, and the following future directions are discussed: developing integrative models of depression, improving measurement of interpersonal constructs, examining the association between interpersonal processes in depression and suicide, and tailoring interventions to target interpersonal processes in depression. Expected final online publication date for the Annual Review of Clinical Psychology Volume 9 is March 26, 2013. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.
This Perspective considers the influential notion of a canonical (cortical) microcircuit in light of recent theories about neuronal processing. Specifically, we conciliate quantitative studies of microcircuitry and the functional logic of neuronal computations. We revisit the established idea that message passing among hierarchical cortical areas implements a form of Bayesian inference-paying careful attention to the implications for intrinsic connections among neuronal populations. By deriving canonical forms for these computations, one can associate specific neuronal populations with specific computational roles. This analysis discloses a remarkable correspondence between the microcircuitry of the cortical column and the connectivity implied by predictive coding. Furthermore, it provides some intuitive insights into the functional asymmetries between feedforward and feedback connections and the characteristic frequencies over which they operate.