Temporally organized representations of reward and risk in the human brain

The value and uncertainty associated with choice alternatives constitute critical features along which decisions are made. While the neural substrates supporting reward and risk processing have been investigated, the temporal organization by which these computations are encoded remains elusive. Here we leverage the high spatiotemporal precision of intracranial electroencephalography (iEEG) to uncover how representations of decision-related computations unfold in time. We present evidence of locally distributed representations of reward and risk variables that are temporally organized across multiple regions of interest. Reward outcome representations across wide-spread regions follow a temporally cascading order along the anteroposterior axis of the brain. In contrast, expected value can be decoded from multiple regions at the same time, and error signals in both reward and risk domains reflect a mixture of sequential and parallel encoding. We highlight the role of the anterior insula in generalizing between reward prediction error (RePE) and risk prediction error (RiPE), within which the encoding of RePE in the distributed iEEG signal predicts RiPE. Together our results emphasize the utility of uncovering temporal dynamics in the human brain for understanding how computational processes critical for value-based decisions under uncertainty unfold.

Specificity in the Study of Mixed Emotions: A Theoretical Framework

Research on mixed emotions is yet to consider emotion-specificity, the idea that same-valenced emotions have distinctive characteristics and functions. We review two decades of research on mixed emotions, focusing on evidence for the occurrence of mixed emotions and the effects of mixed emotions on downstream outcomes. We then propose a novel theoretical framework of mixed-emotion-specificity with three foundational tenets: (a) Mixed emotions are distinguishable from single-valenced emotions and other mixed emotions based on their emotion-appraisal relationships; (b) Mixed emotions can further be characterized by four patterns that describe relationships between simultaneous appraisals or appraisals that are unique to mixed emotions; and (c) Carryover effects occur only on outcomes that are associated with the appraisal characteristics of mixed emotion. We outline how mixed-emotion-specific effects can be predicted based on the appraisal tendency framework. Temporal dynamics, the application of mixed-emotion-specificity to individual difference research, methodological issues, and future directions are also discussed.

Naturalistic Stimuli in Affective Neuroimaging: A Review

Naturalistic stimuli such as movies, music, and spoken and written stories elicit strong emotions and allow brain imaging of emotions in close-to-real-life conditions. Emotions are multi-component phenomena: relevant stimuli lead to automatic changes in multiple functional components including perception, physiology, behavior, and conscious experiences. Brain activity during naturalistic stimuli reflects all these changes, suggesting that parsing emotion-related processing during such complex stimulation is not a straightforward task. Here, I review affective neuroimaging studies that have employed naturalistic stimuli to study emotional processing, focusing especially on experienced emotions. I argue that to investigate emotions with naturalistic stimuli, we need to define and extract emotion features from both the stimulus and the observer.

Bittersweet: The Neuroscience of Ambivalent Affect

Ambivalent affective states, such as bittersweetness, nostalgia, and longing, which are felt as having both positive and negative aspects, are an important component of human experience but have received little attention. The most influential theoretical frameworks in affective neuroscience focus largely on univalenced states. As a result, it is not known whether ambivalent affect corresponds to a simultaneously positive and negative valenced state or whether it results from a rapid vacillation between positive and negative states. Here we hypothesize that ambivalent affect involves both mechanisms, that is, rapid vacillation and simultaneity of positive and negative affect, albeit at different neurobiological levels. Rapidly vacillating univalent emotions could give rise to an ambivalent feeling, a mechanism that depends on brainstem nuclei that facilitate rapid action programs of emotional behavior while inhibiting opposing behaviors. This reciprocal inhibition prevents organisms from simultaneously implementing responses to conflicting emotions but also allows for rapid switching between emotions triggered by counterfactual thinking and rapid reappraisal of situations. We propose that as these transitions occur and respective interoceptive information reaches the insular cortex, further processing of this "emotional moment" would allow separate emotional events to be experienced as one "mixed" and integrated feeling.

Multiple scales of valence processing in the brain

Psychological theories posit that affective experiences can be decomposed into component constituents, yet disagree on the level of representation of these components. Affective experiences have been previously described as emerging from core dimensions of valence and arousal. However, this view needs to be reconciled with accounts of valence processing in appetitive and aversive circuits from the neuroscience literature. Here we offer an account of affect that allows for both perspectives but compares across levels of analysis. At one level of analysis, valence and arousal are observed already in the properties of encountered stimuli and the appetitive and aversive neural circuits that engage accordingly. At another level of analysis, the explicit experiential aspect of affective processes are compressed and appraised in a manner that allows these experiences to be organized along valence and arousal axes. We review both the behavioral neuroscience evidence on appetitive and aversive circuits as well as the cognitive neuroscience literature on compression in information coding across multiple domains of processing. We argue that these processes are domain-general and adapt these principles to provide a perspective on how valence can be represented at multiple scales in the brain.

Challenge and Threat: A Critical Review of the Literature and an Alternative Conceptualization

In this article, the authors describe a new theory, the Evaluative Space Approach to Challenge and Threat (ESACT). Prompted by the Biopsychosocial model of challenge and threat (BPS: Blascovich and Tomaka, 1996) and the development of the Theory of Challenge and Threat States in Athletes (Jones et al., 2009), recent years have witnessed a considerable increase in research examining challenge and threat in sport. This manuscript provides a critical review of the literature examining challenge and threat in sport, tracing its historical development and some of the current empirical ambiguities. To reconcile some of these ambiguities, and utilizing neurobiological evidence associated with approach and avoidance motivation (c.f. Elliot and Covington, 2001), this paper draws upon the Evaluative Space Model (ESM; Cacioppo et al., 1997) and considers the implications for understanding challenge and threat in sport. For example, rather than see challenge and threat as opposite ends of a single bipolar continuum, the ESM implies that individuals could be (1) challenged, (2) threatened, (3) challenged and threatened, or (4) neither challenged or threatened by a particular stimulus. From this perspective, it could be argued that the appraisal of some sport situations as both challenging and threatening could be advantageous, whereas the current literature seems to imply that the appraisal of stress as a threat is maladaptive for performance. The ESACT provides several testable hypotheses for advancing understanding of challenge and threat (in sport) and we describe a number of measures that can be used to examine these hypotheses. In sum, this paper provides a significant theoretical, empirical, and practical contribution to our understanding of challenge and threat (in sport).

Affect and Decision Making: Insights and Predictions from Computational Models

In recent years interest in integrating the affective and decision sciences has skyrocketed. Immense progress has been made, but the complexities of each field, which can multiply when combined, present a significant obstacle. A carefully defined framework for integration is needed. The shift towards computational modeling in decision science provides a powerful basis and a path forward, but one whose synergistic potential will only be fully realized by drawing on the theoretical richness of the affective sciences. Reviewing research using a popular computational model of choice (the drift diffusion model), we discuss how mapping concepts to parameters reduces conceptual ambiguity and reveals novel hypotheses.

Emotional response in schizophrenia to the "36 questions that lead to love": Predicted and experienced emotions regarding a live social interaction

Evidence suggests that individuals with schizophrenia (SZ) report anticipatory pleasure deficits compared to controls and that these deficits are linked to decreased motivation to engage socially. However, these deficits have been identified via self-report measures of hypothetical pleasant stimuli, leaving it unclear whether they exist in reference to actual social situations. To address this issue, we created a live social interaction that minimized the reliance of higher-order cognitive processes. SZ and control participants were told that they would be playing an "enjoyable sharing game" with another study participant (who was actually a confederate) that involved asking and answering questions (36 interpersonal closeness generation questions; Aron et al., 1997). Participants then reported their current mood and the emotions they anticipated experiencing during the pleasant social interaction. Immediately following the interaction, they reported their experienced emotions. We found that the SZ group anticipated more negative emotion (d = 1.0), but were less accurate in forecasting negative emotion (d = .81), than controls, and these effects were large. There were small, non-significant group differences in anticipation, experience, and accuracy in forecasting of positive emotion (all ds < .29). Also, social anhedonia was positively correlated with anticipated negative affect and negatively associated with experienced positive emotion. At the same time, controls reported finding the interaction to be a more positive emotional experience overall, d = 0.75. This is the first study to show that "anticipatory pleasure deficits" in SZ might actually be heightened anticipated negative emotion and that inaccurate forecasting could be linked to decreased social motivation.

An Appraisal-Driven Componential Approach to the Emotional Brain

This article suggests that methodological and conceptual advancements in affective sciences militate in favor of adopting an appraisal-driven componential approach to further investigate the emotional brain. Here we propose to operationalize this approach by distinguishing five functional networks of the emotional brain: (a) the elicitation network, (b) the expression network, (c) the autonomic reaction network, (d) the action tendency network, and (e) the feeling network, and discuss these networks in the context of the affective neuroscience literature. We also propose that further investigating the “appraising brain” is the royal road to better understand the elicitation network, and may be key to revealing the neural causal mechanisms underlying the emotion process as a whole.

A Mathematical Model Captures the Structure of Subjective Affect

Although it is possible to observe when another person is having an emotional moment, we also derive information about the affective states of others from what they tell us they are feeling. In an effort to distill the complexity of affective experience, psychologists routinely focus on a simplified subset of subjective rating scales (i.e., dimensions) that capture considerable variability in reported affect: reported valence (i.e., how good or bad?) and reported arousal (e.g., how strong is the emotion you are feeling?). Still, existing theoretical approaches address the basic organization and measurement of these affective dimensions differently. Some approaches organize affect around the dimensions of bipolar valence and arousal (e.g., the circumplex model), whereas alternative approaches organize affect around the dimensions of unipolar positivity and unipolar negativity (e.g., the bivariate evaluative model). In this report, we (a) replicate the data structure observed when collected according to the two approaches described above, and reinterpret these data to suggest that the relationship between each pair of affective dimensions is conditional on valence ambiguity, and (b) formalize this structure with a mathematical model depicting a valence ambiguity dimension that decreases in range as arousal decreases (a triangle). This model captures variability in affective ratings better than alternative approaches, increasing variance explained from ~60% to over 90% without adding parameters.

Discrimination between smiling faces: Human observers vs. automated face analysis

This study investigated (a) how prototypical happy faces (with happy eyes and a smile) can be discriminated from blended expressions with a smile but non-happy eyes, depending on type and intensity of the eye expression; and (b) how smile discrimination differs for human perceivers versus automated face analysis, depending on affective valence and morphological facial features. Human observers categorized faces as happy or non-happy, or rated their valence. Automated analysis (FACET software) computed seven expressions (including joy/happiness) and 20 facial action units (AUs). Physical properties (low-level image statistics and visual saliency) of the face stimuli were controlled. Results revealed, first, that some blended expressions (especially, with angry eyes) had lower discrimination thresholds (i.e., they were identified as "non-happy" at lower non-happy eye intensities) than others (especially, with neutral eyes). Second, discrimination sensitivity was better for human perceivers than for automated FACET analysis. As an additional finding, affective valence predicted human discrimination performance, whereas morphological AUs predicted FACET discrimination. FACET can be a valid tool for categorizing prototypical expressions, but is currently more limited than human observers for discrimination of blended expressions. Configural processing facilitates detection of in/congruence(s) across regions, and thus detection of non-genuine smiling faces (due to non-happy eyes).