How does the emotional experience evolve? Feeling generation as evidence accumulation

Bumpy ride ahead: Anticipated effort as emotional evidence?

Conscious reportable (un)pleasantness feelings were shown to be successfully described by a process in which evidence favoring pleasant and unpleasant feelings accumulates until one response wins the race. This approach is challenged by (a) insufficient specification of "evidence," and (b) incomplete verification that participants report their truly experienced (un)pleasant feelings and not what they expect to feel. In each trial in this preregistered experiment, the (un)pleasant feeling reports regarding emotion evoking pictures was embedded in a period when participants expected a low-effort task (feature visual search) or a high-effort task (feature-conjunction search). Fitting the Linear Ballistic Accumulator model to the feeling report data shows that anticipated effort was associated with a higher rate of unpleasant evidence accumulation, but only when the emotion evoking pictures were normatively unpleasant and not when they were normatively pleasant. These results suggest that anticipated effort may be one source of "evidence," but only given a certain interpretation of the findings, and that genuinely felt emotions contribute to the emotion reports, assuming that participants intended to react to the pictures, as instructed, and not to the anticipated effort.

Self-Perceived Stress in Association with Emotional Experiences Following Patient Death and Coping Adequacy among Clinical Nurses in Lithuania: A Cross-Sectional Study

(1) Background: Stress is defined as a psychological sensation related to a change in both human physiology and behavior in response to a threat or challenge. As the prevalence of stress is increasing globally, nurses represent a risk group for exposure to stress-related psychological alterations. The aim of this study was to explore how clinical nurses in Lithuania cope with the perceived chronic stress in relation to additional emotional experiences following the death of a patient. (2) Methods: During a four-week period of October 2023, a total of 283 female nurses, aged between 20 and 70 years old, were enrolled in a single cross-sectional study. The PSS-10 (perceived stress scale) and the Brief-COPE (Coping Orientation to Problems Experienced) questionnaires were applied to assess the level of self-perceived stress and the use of coping styles along with the adequate strategies. Furthermore, the nurses' emotions, evoked following patient death, were classified depending on their valence. Both the multiple linear and logistic regression statistical analyses were conducted to examine the associations between dependent and independent variables. (3) Results: The study identified that more than half of the nurses experienced patient death several times a month. A moderate-to-high level of the symptoms of mental chronic stress were suffered by 76% of caregivers. The psychological arousal following the death of a patient may induce the occurrence of emotional complexity that refers to competitive feelings, namely, helplessness (adjusted odd ratio (AOR) 1.7, 95% confidence interval (95% CI) 1.1; 2.9), disappointment (AOR 1.9, 95% CI 1.1; 3.5), anxiety (AOR 1.9, 95% CI 1.2; 4.2), and guilt (ORadj 4.7, 95% CI 1.4; 5.7), and can serve as a potential trigger for chronic stress development in clinical nurses. In terms of emotion-focused stress control (β 0.1, 95% CI -0.1; -0.2, R2 = 0.12), Lithuanian nurses had low control of the perceived chronic stress. The use of problem-focused coping (β -0.2, 95% CI -0.3; -0.1, R2 = 0.09) was also not addressed to an appropriate extent. The use of the avoidance coping style was associated with moderate-high levels of chronic stress perceived by nurses (β 0.5, 95% CI 0.4; 0.7, R2 = 0.24). Furthermore, the avoidant coping strategy related to behavioral disengagement was significantly related to resilience to chronic stress in an adverse way. The cognitive process of self-blame was considered as a meaningful maladaptive behavior component for magnifying chronic stress in nurses. (4) Conclusions: The study highlights the need to implement the recommendations for boosting the nurses' mental health. Hospitals should contribute to psychological assistance along with providing necessary training on stress-coping strategies for clinical nurses. In order to promote the sustainability of mental health through additional social support interventions, it would be useful to upgrade the clinical nurses' daily routines with mindfulness-based trainings.

The spread of affective and semantic valence representations across states

In many decision problems, outcomes are not reached after a single action but rather after a series of events or states. To optimize decisions over multiple states, representations of how good or bad the outcomes are, that is, the outcomes' valence, should spread across states. One mechanism for valence spreading is a temporal, state-independent process in which a single valence representation is updated when an outcome is experienced and fades away afterwards. Each state's valence is based on its temporal proximity to the experienced outcome. An alternative, state-dependent mechanism relies on the structure of transitions between states, updating a separate valence representation for each state according to its spatial distance from the outcomes. We examined how these mechanistic accounts shape the spread of two formats of valence representation, feelings (affective valence) and knowledge (semantic valence), between states. In two pre-registered experiments (N = 585), we used a novel task in which participants move in a four-state maze, one of which contains an outcome. The participants provide self-reports of affective and semantic valence throughout the maze and after finishing it. Results show that the affective representation of negative valence is more localized in state-space than the semantic representation. We also found evidence for the relative reliance of the affective valence on a temporal, state-independent mechanism and of the semantic valence on a structured, state-dependent mechanism. Our findings provide mechanistic accounts for the differences between affective and semantic valence representations and indicate how such representations may play a role in associative learning and decision-making.

Affective valence does not reflect progress prediction errors in perceptual decisions

Affective valence and intensity form the core of our emotional experiences. It has been proposed that affect reflects the prediction error between expected and actual states, such that better/worse-than-expected discrepancies result in positive/negative affect. However, whether the same principle applies to progress prediction errors remains unclear. We empirically and computationally evaluate the hypothesis that affect reflects the difference between expected and actual progress in forming a perceptual decision. We model affect within an evidence accumulation framework where actual progress is mapped onto the drift-rate parameter and expected progress onto an expected drift-rate parameter. Affect is computed as the difference between the expected and actual amount of accumulated evidence. We find that expected and actual progress both influence affect, but in an additive manner that does not align with a prediction error account. Our computational model reproduces both task behavior and affective ratings, suggesting that sequential sampling models provide a promising framework to model progress appraisals. These results show that although affect is sensitive to both expected and actual progress, it does not reflect the computation of a progress prediction error.

Framing Subjective Emotion Reports as Dynamic Affective Decisions

Self-reports remain affective science's only direct measure of subjective affective experiences. Yet, little research has sought to understand the psychological process that transforms subjective experience into self-reports. Here, we propose that by framing these self-reports as dynamic affective decisions, affective scientists may leverage the computational tools of decision-making research, sequential sampling models specifically, to better disentangle affective experience from the noisy decision processes that constitute self-report. We further outline how such an approach could help affective scientists better probe the specific mechanisms that underlie important moderators of affective experience (e.g., contextual differences, individual differences, and emotion regulation) and discuss how adopting this decision-making framework could generate insight into affective processes more broadly and facilitate reciprocal collaborations between affective and decision scientists towards a more comprehensive and integrative psychological science.

Temporal dynamics of the semantic versus affective representations of valence during reversal learning

Valence, the representation of a stimulus in terms of good or bad, plays a central role in models of affect, value-based learning theories, and value-based decision-making models. Previous work used Unconditioned Stimulus (US) to support a theoretical division between two different types of valence representations for a stimulus: the semantic representation of valence, i.e., stored accumulated knowledge about the value of the stimulus, and the affective representation of valence, i.e., the valence of the affective response to this stimulus. The current work extended past research by using a neutral Conditioned Stimulus (CS) in the context of reversal learning, a type of associative learning. The impact of expected uncertainty (the variability of rewards) and unexpected uncertainty (reversal) on the evolving temporal dynamics of the two types of valence representations of the CS was tested in two experiments. Results show that in an environment presenting the two types of uncertainty, the adaptation process (learning rate) of the choices and of the semantic valence representation is slower than the adaptation of the affective valence representation. In contrast, in environments with only unexpected uncertainty (i.e., fixed rewards), there is no difference in the temporal dynamics of the two types of valence representations. Implications for models of affect, value-based learning theories, and value-based decision-making models are discussed.

Whose emotion is it? Perspective matters to understand brain-body interactions in emotions

Feeling happy, or judging whether someone else is feeling happy are two distinct facets of emotions that nevertheless rely on similar physiological and neural activity. Differentiating between these two states, also called Self/Other distinction, is an essential aspect of empathy, but how exactly is it implemented? In non-emotional cognition, the transient neural response evoked at each heartbeat, or heartbeat evoked response (HER), indexes the self and signals Self/Other distinction. Here, using electroencephalography (n = 32), we probe whether HERs' role in Self/Other distinction extends also to emotion - a domain where brain-body interactions are particularly relevant. We asked participants to rate independently validated affective scenes, reporting either their own emotion (Self) or the emotion expressed by people in the scene (Other). During the visual cue indicating to adopt the Self or Other perspective, before the affective scene, HERs distinguished between the two conditions, in visual cortices as well as in the right frontal operculum. Physiological reactivity (facial electromyogram, skin conductance, heart rate) during affective scene co-varied as expected with valence and arousal ratings, but also with the Self- or Other- perspective adopted. Finally, HERs contributed to the subjective experience of valence in the Self condition, in addition to and independently from physiological reactivity. We thus show that HERs represent a trans-domain marker of Self/Other distinction, here specifically contributing to experienced valence. We propose that HERs represent a form of evidence related to the 'I' part of the judgement 'To which extent do I feel happy'. The 'I' related evidence would be combined with the affective evidence collected during affective scene presentation, accounting at least partly for the difference between feeling an emotion and identifying it in someone else.

Can Feelings "Feel" Wrong? Similarities Between Counter-Normative Emotion Reports and Perceptual Errors

In popular belief, emotions are regarded as deeply subjective and thus as lacking truth value. Is this reflected at the behavioral or brain level? This work compared counter-normative emotion reports with perceptual-decision errors. Participants (university students; N = 29, 16, 40, and 60 in Experiments 1-4, respectively) were given trials comprising two tasks and were asked to (a) report their pleasant or unpleasant feelings in response to emotion-invoking pictures (emotion report) and (b) indicate the gender of faces (perceptual decision). Focusing on classical error markers, we found that the results of both tasks indicated (a) post-error slowing, (b) speed/accuracy trade-offs, (c) a heavier right tail of the reaction time distribution for errors or counter-normative responses relative to correct or normative responses, and (d) inconclusive evidence for error-related negativity in electroencephalograms. These results suggest that at both the behavioral and the brain levels, the experience of reporting counter-normative emotions is remarkably similar to that accompanying perceptual-decision errors.

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.