Embracing integration and complexity: placing emotion within a science of brain and behaviour

The conceptualisation and measurement of engagement in digital health

Digital tools are an increasingly important component of healthcare, but their potential impact is commonly limited by a lack of user engagement. Digital health evaluations of engagement are often restricted to system usage metrics, which cannot capture a full understanding of how and why users engage with an intervention. This study aimed to examine how theory-based, multifaceted measures of engagement with digital health interventions capture different components of engagement (affective, cognitive, behavioural, micro, and macro) and to consider areas that are unclear or missing in their measurement. We identified and compared two recently developed measures that met these criteria (the Digital Behaviour Change Intervention Engagement Scale and the TWente Engagement with Ehealth Technologies Scale). Despite having similar theoretical bases and being relatively strongly correlated, there are key differences in how these scales aim to capture engagement. We discuss the implications of our analysis for how affective, cognitive, and behavioural components of engagement can be conceptualised and whether there is value in distinguishing between them. We conclude with recommendations for the circumstances in which each scale may be most useful and for how future measure development could supplement existing scales.

In Context: A Developmental Model of Reward Processing, With Implications for Autism and Sensitive Periods

OBJECTIVE

Differences in reward processing have been associated with numerous psychiatric disorders, including autism and attention-deficit/hyperactivity disorder (ADHD). Many attempts to understand reward processing characterize differences in clinical populations after disorder onset; however, divergence may begin much earlier. In fact, the typical developmental progression of reward processing in infancy and early childhood is poorly understood. We re-conceptualize classic infant developmental constructs such as preferential looking into a Six-Component Developmental Model of Reward Processing: an infant- and young child-focused framework to guide research and assessment of reward processing across development.

METHOD

The extant developmental literature including recent textbooks, systematic reviews, and meta-analyses was reviewed to build a conceptual framework. We describe experimental paradigms to assess each developmental component of reward processing longitudinally from infancy. A timeline of each component's emergence was estimated.

RESULTS

Six components of reward processing were identified-association, discrimination, preference/valuation, effort, anticipation, and response. Selected evidence suggests emergence between birth and 6 months. Application of this model to autism led to a reinterpretation of existing disparate results, and illuminated a path to study the developmental processes underlying a popular hypothesis of autism, the motivation hypothesis. Current evidence further suggests that a sensitive period may exist for the emergence of reward processing.

CONCLUSION

The proposed framework offers a useful reconceptualization of the extant literature. Future longitudinal work using the suggested experimental paradigms with high-risk populations could elucidate the developmental trajectory of the components and timing of potential sensitive period(s) for each component.

Dissecting the depressed mood criterion in adult depression: The heterogeneity of mood disturbances in major depressive episodes

BACKGROUND

Mood disturbances have historically remained a core criterion in diagnosing major depressive episode. DSMs have illustrated this criterion with depressed, hopeless, discouraged, cheerless, and irritable mood, suggesting interchangeability. Extant research has examined individual forms of mood disturbance to depression severity. Less examined is the heterogeneity in mood disturbances and its implication to their association to depression presentations and outcomes.

METHOD

The current study used a nationally representative sample of U.S. adults with unipolar major depressive disorder to study the association between specific forms of mood disturbances to depression severity, chronicity, or symptoms, above and beyond other forms, as well as their relations to functional impairment, suicidal outcomes, and psychiatric comorbidity via generalized linear models.

RESULTS

Cheerless and hopeless mood were associated with depression severity. Hopeless and irritable mood were associated with depression chronicity. Different forms of mood disturbance showed differential relations to depressive symptoms. Cheerless, hopeless, and irritable mood were associated with depression-specific functional interference, incremental to depression severity. Cheerless, hopeless, and discouraged mood were associated with passive suicidal ideation. Hopeless mood was associated with active suicidal ideation. Hopeless and irritable mood were associated with both suicide plan and suicide attempt. Different forms of mood disturbance demonstrated differential associations to comorbid psychiatric conditions.

DISCUSSION

The relations between different forms of mood disturbances and various aspects of depression are nuanced. Theoretically, these relations highlight the potential utility in acknowledging the complexity and heterogeneity in mood disturbances. Clinically, our results suggest potential utility in routinely monitoring mood disturbances.

Room for Feelings: A “Working Memory” Account of Affective Processing

In the past decades, affective science has overwhelmingly demonstrated the unique properties of affective information to bias our attention, memory, and decisions. At the same time, accumulating evidence suggests that neutral and affective representations rely on the same working memory substrates for the selection and computation of information and that they are therefore restricted by the same capacity limitations that these substrates impose. Here, we integrate these insights into a working memory model of affective processing (WMAP). Drawing on competitive access models of working memory, we discuss its role in the various stages of affective processing, from attentional selection to maintenance and memory storage, and resulting feelings and actions. We end our overview with some open questions and future directions.

Emotional states as distinct configurations of functional brain networks

The conceptualization of emotional states as patterns of interactions between large-scale brain networks has recently gained support. Yet, few studies have directly examined the brain’s network structure during emotional experiences. Here, we investigated the brain’s functional network organization during experiences of sadness, amusement, and neutral states elicited by movies, in addition to a resting-state. We tested the effects of the experienced emotion on individual variability in the brain’s functional connectome. Next, for each state, we defined a community structure of the brain and quantified its segregation and integration. We found that sadness, relative to amusement, was associated with higher modular integration and increased connectivity of cognitive control networks: the salience and fronto-parietal networks. Moreover, in both the functional connectome and the emotional report, the similarity between individuals was dependent on the sex. Our results suggest that the experience of emotion is linked to a reconfiguration of whole-brain distributed, not emotion-specific, functional networks and that the brain’s topological structure carries information about the subjective emotional experience.

Quantum core affect. Color-emotion structure of semantic atom

Psychology suffers from the absence of mathematically-formalized primitives. As a result, conceptual and quantitative studies lack an ontological basis that would situate them in the company of natural sciences. The article addresses this problem by describing a minimal psychic structure, expressed in the algebra of quantum theory. The structure is demarcated into categories of emotion and color, renowned as elementary psychological phenomena. This is achieved by means of quantum-theoretic qubit state space, isomorphic to emotion and color experiences both in meaning and math. In particular, colors are mapped to the qubit states through geometric affinity between the HSL-RGB color solids and the Bloch sphere, widely used in physics. The resulting correspondence aligns with the recent model of subjective experience, producing a unified spherical map of emotions and colors. This structure is identified as a semantic atom of natural thinking-a unit of affectively-colored personal meaning, involved in elementary acts of a binary decision. The model contributes to finding a unified ontology of both inert and living Nature, bridging previously disconnected fields of research. In particular, it enables theory-based coordination of emotion, decision, and cybernetic sciences, needed to achieve new levels of practical impact.

Temporal dynamics of affect in the brain: Evidence from human imaging and animal models

Emotions are time-varying internal states that promote survival in the face of dynamic environments and shifting homeostatic needs. Research in non-human organisms has recently afforded specific insights into the neural mechanisms that support the emergence, persistence, and decay of affective states. Concurrently, a separate affective neuroscience literature has begun to dissect the neural bases of affective dynamics in humans. However, the circuit-level mechanisms identified in animals lack a clear mapping to the human neuroscience literature. As a result, critical questions pertaining to the neural bases of affective dynamics in humans remain unanswered. To address these shortcomings, the present review integrates findings from humans and non-human organisms to highlight the neural mechanisms that govern the temporal features of emotional states. Using the theory of affective chronometry as an organizing framework, we describe the specific neural mechanisms and modulatory factors that arbitrate the rise-time, intensity, and duration of emotional states.

Emotions as Abstract Evaluation Criteria in Biological and Artificial Intelligences

Biological as well as advanced artificial intelligences (AIs) need to decide which goals to pursue. We review nature's solution to the time allocation problem, which is based on a continuously readjusted categorical weighting mechanism we experience introspectively as emotions. One observes phylogenetically that the available number of emotional states increases hand in hand with the cognitive capabilities of animals and that raising levels of intelligence entail ever larger sets of behavioral options. Our ability to experience a multitude of potentially conflicting feelings is in this view not a leftover of a more primitive heritage, but a generic mechanism for attributing values to behavioral options that can not be specified at birth. In this view, emotions are essential for understanding the mind. For concreteness, we propose and discuss a framework which mimics emotions on a functional level. Based on time allocation via emotional stationarity (TAES), emotions are implemented as abstract criteria, such as satisfaction, challenge and boredom, which serve to evaluate activities that have been carried out. The resulting timeline of experienced emotions is compared with the “character” of the agent, which is defined in terms of a preferred distribution of emotional states. The long-term goal of the agent, to align experience with character, is achieved by optimizing the frequency for selecting individual tasks. Upon optimization, the statistics of emotion experience becomes stationary.

How the brain negotiates divergent executive processing demands: Evidence of network reorganization in fleeting brain states

During performance in everyday contexts, multiple networks draw from shared executive resources to maintain attention, regulate arousal, and solve problems. At times, requirements for attention and self-regulation appear to be in competition. How does the brain attempt to resolve conflicts arising from such divergent processing demands? Here we demonstrate that the brain is capable of managing multiple processes via rapidly cycling between functional brain states over time, as it is typically regarded. Treating the brain as a complex system, comprising relationships within and between functional networks, we implemented Hidden Markov Modeling (HMM) on electroencephalographic (EEG) data to identify nonlinear brain states in both intra and internetwork synchrony that produced better performance for women subjects who were tasked with solving difficult problems under autobiographically-relevant, evaluative stress. Prior work often found that emotion-regulation and default-mode network (ERN and DMN) activity conflicted with the frontoparietal network's (FPN) ability to facilitate executive functioning necessary for problem solving. Contrastingly, we discovered that fleeting, nonlinear states dominated by FPN and ERN internetwork synchrony supported optimum performance generally, while during stress, states dominated by ERN and DMN intranetwork synchrony were more important for performance. These results imply that the brain may be capable of resolving competing processes through networks' cooperative dynamics. Further, data suggests a novel role for DMN as a mechanism for integrating external threats with internal, self-referent processing during evaluative stress within the observed population.

A Systems View of Emotion in Socio-political Context

Most work to date in psychology and related sciences has examined simple, unidirectional causal processes of emotion affecting socio-political context or vice versa. In this classic, mechanistic view of science, each empirical observation stands on its own as a piece of some grander, not yet understandable, puzzle of nature. There have been repeated calls to eschew classic approaches in favor of systems meta-theory in psychology and related sciences. In this paper, we join these calls by arguing that systems meta-theory can better enable the study of emotions in socio-political contexts. We offer a brief primer on systems meta-theory, delineating three key beneficial features: multi-leveled, complex, and dynamic. Viewing emotion as a system of systems-within the person, their relationships (to others), and within the world (locally and globally)-enables fresh theory, method, and statistical analysis well suited to the study of emotion in a socio-political context.

Psychological mechanisms and functions of 5-HT and SSRIs in potential therapeutic change: Lessons from the serotonergic modulation of action selection, learning, affect, and social cognition

Uncertainty regarding which psychological mechanisms are fundamental in mediating SSRI treatment outcomes and wide-ranging variability in their efficacy has raised more questions than it has solved. Since subjective mood states are an abstract scientific construct, only available through self-report in humans, and likely involving input from multiple top-down and bottom-up signals, it has been difficult to model at what level SSRIs interact with this process. Converging translational evidence indicates a role for serotonin in modulating context-dependent parameters of action selection, affect, and social cognition; and concurrently supporting learning mechanisms, which promote adaptability and behavioural flexibility. We examine the theoretical basis, ecological validity, and interaction of these constructs and how they may or may not exert a clinical benefit. Specifically, we bridge crucial gaps between disparate lines of research, particularly findings from animal models and human clinical trials, which often seem to present irreconcilable differences. In determining how SSRIs exert their effects, our approach examines the endogenous functions of 5-HT neurons, how 5-HT manipulations affect behaviour in different contexts, and how their therapeutic effects may be exerted in humans - which may illuminate issues of translational models, hierarchical mechanisms, idiographic variables, and social cognition.

Impulsive Responses to Positive and Negative Emotions: Parallel Neurocognitive Correlates and Their Implications

Theory about the conceptual basis of psychiatric disorders has long emphasized negative emotionality. More recent ideas emphasize roles for positive emotionality and impulsivity as well. This review examines impulsive responses to positive and negative emotions, which have been labeled as urgency. Urgency is conceptually and empirically distinct from other forms of impulsivity. A large body of work indicates that urgency is more robustly related to psychopathology than are other forms of impulsivity. Researchers have considered 4 neurocognitive models of urgency: excessive emotion generation, poor emotion regulation, risky decision making, and poor cognitive control. Little evidence supports emotion generation or risky decision making as the core issues driving urgency. Rather, urgency appears related to dysfunction in key hubs implicated in the integration of cognitive control and emotion regulation (e.g., the orbitofrontal cortex and anterior insula), expressed as response inhibition deficits that emerge most robustly in high arousal contexts. These neurocognitive processes appear remarkably parallel for positive and negative urgency. We provide methodological suggestions and theoretical hypotheses to guide future research.

Neural dynamics of emotion and cognition: From trajectories to underlying neural geometry

How can we study, characterize, and understand the neural underpinnings of cognitive-emotional behaviors as inherently dynamic processes? In the past 50 years, Stephen Grossberg has developed a research program that embraces the themes of dynamics, decentralized computation, emergence, selection and competition, and autonomy. The present paper discusses how these principles can be heeded by experimental scientists to advance the understanding of the brain basis of behavior. It is suggested that a profitable way forward is to focus on investigating the dynamic multivariate structure of brain data. Accordingly, central research problems involve characterizing "neural trajectories" and the associated geometry of the underlying "neural space." Finally, it is argued that, at a time when the development of neurotechniques has reached a fever pitch, neuroscience needs to redirect its focus and invest comparable energy in the conceptual and theoretical dimensions of its research endeavor. Otherwise we run the risk of being able to measure "every atom" in the brain in a theoretical vacuum.