Predictive processing models and affective neuroscience

Disentangling emotional source memory: a mega-analysis on the effects of emotion on item-context binding in episodic long-term memory

Introduction

It has long been known that highly arousing emotional single items are better recollected than low arousing neutral items. Despite the robustness of this memory advantage, emotional arousing events may not always promote the retrieval of source details (i.e., source memory) or associated neutral information.

Methods

To shed more light on these effects, we pooled data from seven different studies (N = 333) to investigate the role of emotion on item-context binding in episodic memory, as well as potential interacting factors (e.g., encoding instructions, type of retrieval task, or acute stress). In all studies, participants incidentally encoded common neutral objects (i.e., items), overlaid on different pleasant, neutral, or unpleasant background scenes (i.e., contexts). One week later, the encoded objects were presented intermixed with new ones and memory for item and source contextual details was tested, also considering the contribution of recollection and familiarity-based processes.

Results

Linear mixed models revealed a recollection-based retrieval advantage for unpleasant and pleasant source contextual details compared to neutral ones. Bayes hypothesis-testing analysis further indicated decisive evidence in favor of a relevant role of emotional arousal and recollection in source contextual memory. Regarding item memory, linear mixed models revealed enhanced recollection-based memory for items encoded in pleasant contexts compared to their neutral and unpleasant counterparts. However, Bayes analysis revealed strong to moderate evidence for models without affective category (or its interactions), indicating that the affective category of contexts in which objects were paired during encoding had little influence on item memory performance.

Discussion

The present results are discussed in relation to existing evidence and current neurobiological models of emotional episodic memory by also emphasizing the role of predictive processing as a useful conceptual framework to understand the effects of emotion on memory for source details and associated neutral information.

From perception to projection: Exploring neuroaffective advances in understanding optimism bias and belief updating

Why do individuals tend to discount bad news when making judgements about the likelihood of future events? In this short review, we explore recent research findings regarding this frequently observed and replicated phenomenon - optimism bias - with particular attention on how this bias affects the way individuals update or revise their beliefs. We begin by highlighting five interrelated frameworks for understanding optimism bias (utility maximization, active inference, dual systems, cognitive immunity, predictive processing). We then examine findings from affective and clinical domains that indicate that people with depression and other psychiatric disorders, as well as acutely sad or anxious individuals, have dampened optimistic bias when updating beliefs in response to good news. Finally, we consider the neural underpinnings of these phenomena through the free energy principle and discuss implications for clinical interventions and future research.

Predictive coding for the actions and emotions of others and its deficits in autism spectrum disorders

Traditionally, the neural basis of social perception has been studied by showing participants brief examples of the actions or emotions of others presented in randomized order to prevent participants from anticipating what others do and feel. This approach is optimal to isolate the importance of information flow from lower to higher cortical areas. The degree to which feedback connections and Bayesian hierarchical predictive coding contribute to how mammals process more complex social stimuli has been less explored, and will be the focus of this review. We illustrate paradigms that start to capture how participants predict the actions and emotions of others under more ecological conditions, and discuss the brain activity measurement methods suitable to reveal the importance of feedback connections in these predictions. Together, these efforts draw a richer picture of social cognition in which predictive coding and feedback connections play significant roles. We further discuss how the notion of predicting coding is influencing how we think of autism spectrum disorder.

Neural Predictors of Fear Depend on the Situation

The extent to which neural representations of fear experience depend on or generalize across the situational context has remained unclear. We systematically manipulated variation within and across three distinct fear-evocative situations including fear of heights, spiders, and social threats. Participants (n = 21; 10 females and 11 males) viewed ∼20 s clips depicting spiders, heights, or social encounters and rated fear after each video. Searchlight multivoxel pattern analysis was used to identify whether and which brain regions carry information that predicts fear experience and the degree to which the fear-predictive neural codes in these areas depend on or generalize across the situations. The overwhelming majority of brain regions carrying information about fear did so in a situation-dependent manner. These findings suggest that local neural representations of fear experience are unlikely to involve a singular pattern but rather a collection of multiple heterogeneous brain states.

Comprehensive investigation of predictive processing: A cross- and within-cognitive domains fMRI meta-analytic approach

Predictive processing (PP) stands as a predominant theoretical framework in neuroscience. While some efforts have been made to frame PP within a cognitive domain-general network perspective, suggesting the existence of a "prediction network," these studies have primarily focused on specific cognitive domains or functions. The question of whether a domain-general predictive network that encompasses all well-established cognitive domains exists remains unanswered. The present meta-analysis aims to address this gap by testing the hypothesis that PP relies on a large-scale network spanning across cognitive domains, supporting PP as a unified account toward a more integrated approach to neuroscience. The Activation Likelihood Estimation meta-analytic approach was employed, along with Meta-Analytic Connectivity Mapping, conjunction analysis, and behavioral decoding techniques. The analyses focused on prediction incongruency and prediction congruency, two conditions likely reflective of core phenomena of PP. Additionally, the analysis focused on a prediction phenomena-independent dimension, regardless of prediction incongruency and congruency. These analyses were first applied to each cognitive domain considered (cognitive control, attention, motor, language, social cognition). Then, all cognitive domains were collapsed into a single, cross-domain dimension, encompassing a total of 252 experiments. Results pertaining to prediction incongruency rely on a defined network across cognitive domains, while prediction congruency results exhibited less overall activation and slightly more variability across cognitive domains. The converging patterns of activation across prediction phenomena and cognitive domains highlight the role of several brain hubs unfolding within an organized large-scale network (Dynamic Prediction Network), mainly encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. Additionally, the crucial role played at a cross-domain, multimodal level by the anterior insula, as evidenced by the conjunction and Meta-Analytic Connectivity Mapping analyses, places it as the major hub of the Dynamic Prediction Network. Results support the hypothesis that PP relies on a domain-general, large-scale network within whose regions PP units are likely to operate, depending on the context and environmental demands. The wide array of regions within the Dynamic Prediction Network seamlessly integrate context- and stimulus-dependent predictive computations, thereby contributing to the adaptive updating of the brain's models of the inner and external world.

The neurobiology of interoception and affect

Scholars have argued for centuries that affective states involve interoception, or representations of the state of the body. Yet, we lack a mechanistic understanding of how signals from the body are transduced, transmitted, compressed, and integrated by the brains of humans to produce affective states. We suggest that to understand how the body contributes to affect, we first need to understand information flow through the nervous system's interoceptive pathways. We outline such a model and discuss how unique anatomical and physiological aspects of interoceptive pathways may give rise to the qualities of affective experiences in general and valence and arousal in particular. We conclude by considering implications and future directions for research on interoception, affect, emotions, and human mental experiences.

Systematic review and meta-analysis of the visual mismatch negativity in schizophrenia

Mismatch negativity (MMN) is an event-related potential component automatically elicited by events that violate predictions based on prior events. To elicit this component, researchers use stimulus repetition to induce predictions, and the MMN is obtained by subtracting the brain response to rare or unpredicted stimuli from that of frequent stimuli. Under the Predictive Processing framework, one increasingly popular interpretation of the mismatch response postulates that MMN represents a prediction error. In this context, the reduced MMN amplitude to auditory stimuli has been considered a potential biomarker of Schizophrenia, representing a reduced prediction error and the inability to update the mental model of the world based on the sensory signals. It is unclear, however, whether this amplitude reduction is specific for auditory events or if the visual MMN reveals a similar pattern in schizophrenia spectrum disorder. This review and meta-analysis aimed to summarise the available literature on the vMMN in schizophrenia. A systematic literature search resulted in 10 eligible studies that resulted in a combined effect size of g = -.63, CI [-.86, -.41], reflecting lower vMMN amplitudes in patients. These results are in line with the findings in the auditory domain. This component offers certain advantages, such as less susceptibility to overlap with components generated by attentional demands. Future studies should use vMMN to explore abnormalities in the Predictive Processing framework in different stages and groups of the SSD and increase the knowledge in the search for biomarkers in schizophrenia.

Fear-related psychophysiological patterns are situation and individual dependent: A Bayesian model comparison approach

Is there a universal mapping of physiology to emotion, or do these mappings vary substantially by person or situation? Psychologists, philosophers, and neuroscientists have debated this question for decades. Most previous studies have focused on differentiating emotions on the basis of accompanying autonomic responses using analytical approaches that often assume within-category homogeneity. In the present study, we took an alternative approach to this question. We determined the extent to which the relationship between subjective experience and autonomic reactivity generalizes across, or depends upon, the individual and situation for instances of a single emotion category, specifically, fear. Electrodermal activity and cardiac activity-two autonomic measures that are often assumed to show robust relationships with instances of fear-were recorded while participants reported fear experience in response to dozens of fear-evoking videos related to three distinct situations: spiders, heights, and social encounters. We formally translated assumptions from diverse theoretical models into a common framework for model comparison analyses. Results exceedingly favored a model that assumed situation-dependency in the relationship between fear experience and autonomic reactivity, with subject variance also significant but constrained by situation. Models that assumed generalization across situations and/or individuals performed much worse by comparison. These results call into question the assumption of generalizability of autonomic-subjective mappings across instances of fear, as required in translational research from nonhuman animals to humans, and advance a situated approach to understanding the autonomic correlates of fear experience. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Amplified gut feelings under inflammation and depressed mood: A randomized fMRI trial on interoceptive pain in healthy volunteers

BACKGROUND

Inflammation and depressed mood constitute clinically relevant vulnerability factors for enhanced interoceptive sensitivity and chronic visceral pain, but their putative interaction remains untested in human mechanistic studies. We tested interaction effects of acute systemic inflammation and sad mood on the expectation and experience of visceral pain by combining experimental endotoxemia with a mood induction paradigm.

METHODS

The double-blind, placebo-controlled, balanced crossover fMRI-trial in N=39 healthy male and female volunteers involved 2 study days with either intravenous administration of low-dose lipopolysaccharide (LPS, 0.4ng/kg body weight; inflammation condition) or saline (placebo condition). On each study, day two scanning sessions were conducted in an experimentally induced negative (i.e., sad) and in a neutral mood state, accomplished in balanced order. As a model of visceral pain, rectal distensions were implemented, which were initially calibrated to be moderately painful. In all sessions, an identical series of visceral pain stimuli was accomplished, signaled by predictive visual conditioning cues to assess pain anticipation. We assessed neural activation during the expectation and experience of visceral pain, along with unpleasantness ratings in a condition combining an inflammatory state with sad mood and in control conditions. All statistical analyses were accomplished using sex as covariate.

RESULTS

LPS administration led to an acute systemic inflammatory response (inflammation X time interaction effects for TNF-α, IL-6, and sickness symptoms, all p<.001). The mood paradigm effectively induced distinct mood states (mood X time interaction, p<.001), with greater sadness in the negative mood conditions (both p<.001) but no difference between LPS and saline conditions. Significant main and interaction effects of inflammation and negative mood were observed for pain unpleasantness (all p<.05). During cued pain anticipation, a significant inflammation X mood interaction emerged for activation of the bilateral caudate nucleus and right hippocampus (all p_FWE<.05). Main effects of both inflammation and mood were observed in multiple regions, including insula, midcingulate cortex, prefrontal gyri, and hippocampus for inflammation, and midcingulate, caudate, and thalamus for mood (all p_FWE<0.05).

CONCLUSIONS

Results support an interplay of inflammation and sad mood on striatal and hippocampal circuitry engaged during visceral pain anticipation as well as on pain experience. This may reflect a nocebo mechanism, which may contribute to altered perception and interpretation of bodily signals. At the interface of affective neuroscience and the gut-brain axis, concurrent inflammation and negative mood may be vulnerability factors for chronic visceral pain.

Role of Culture in Meaning Making: Bridging Semiotic Cultural Psychology and Active Inference

This essay takes up the framework of Semiotic Cultural Psychology, which in last decade was very productive in analyzing societal phenomena. Digging into complex sensemaking mechanisms, underlying the interaction between individual and his surrounding environment, has consolidated a base of empirical data gathered from different societies. Aiming to better understand the role of culture in this interaction, the current paper proposes to bridge these findings of observable macro-processes with the ones from the Active inference theory and cognitive science. Similarities between the two frameworks, as well as possible benefits of employing them for one goal, are discussed in this paper.

An Adaptive Motivation Approach to Understanding the 'How' and 'Why' of Wellbeing

A new model provides insight into the 'how' and 'why' of wellbeing to better understand the 'what'. Informed by evolutionary psychology and neuroscience, it proposes that systems for adaptive motivation underpin experiential and reflective wellbeing. The model proposes that the brain learns to predict situations, and errors arise between the predictions and experience. These prediction errors drive emotional experience, learning, motivation, decision-making, and the formation of wellbeing-relevant memories. The model differentiates four layers of wellbeing: objective, experiential, reflective, and narrative, which relate to the model in different ways. Constituents of wellbeing, human motives, and specific emotions integrate into the model. A simple computational implementation of the model reproduced several established wellbeing phenomena, including: the greater frequency of pleasant to unpleasant emotions, the stronger emotional salience of unpleasant emotions, hedonic adaptation to changes in circumstances, heritable influences on wellbeing, and affective forecasting errors. It highlights the importance of individual differences, and implies that high wellbeing will correlate with the experience of infrequent, routine, and predictable avoidance cues and frequent, varied, and novel approach cues. The model suggests that wellbeing arises directly from a system for adaptive motivation. This system functions like a mental dashboard that calls attention to situational changes and motivates the kinds of behaviours that gave humans a relative advantage in their ancestral environment. The model offers a set of fundamental principles and processes that may underlie diverse conceptualisations of wellbeing.

Constructing Craving: Applying the Theory of Constructed Emotion to Urge States

Craving (a strong desire to ingest a substance or engage in an activity) is an important topic of study in the field of psychology. Along with being a key symptom of addiction, craving is a potent source of motivation for a wide range of appetitive behaviors. In this article, I offer a perspective regarding the nature of craving that is rooted in the theory of constructed emotion, a contemporary model of how emotions are created by the brain. According to this perspective, craving states emerge when the brain makes predictions that categorize sensory inputs as an instance of craving based on prior experience and the context in which the inputs occur. Using the theory of constructed emotion as a guiding framework, I review various lines of evidence that provide support for this idea. In addition, I offer recommendations for future research that stem from the hypothesis that instances of craving are constructed by the brain in an experience-dependent and situation-specific manner.

Interoception as modeling, allostasis as control

The brain regulates the body by anticipating its needs and attempting to meet them before they arise - a process called allostasis. Allostasis requires a model of the changing sensory conditions within the body, a process called interoception. In this paper, we examine how interoception may provide performance feedback for allostasis. We suggest studying allostasis in terms of control theory, reviewing control theory's applications to related issues in physiology, motor control, and decision making. We synthesize these by relating them to the important properties of allostatic regulation as a control problem. We then sketch a novel formalism for how the brain might perform allostatic control of the viscera by analogy to skeletomotor control, including a mathematical view on how interoception acts as performance feedback for allostasis. Finally, we suggest ways to test implications of our hypotheses.

A Computational Neural Model for Mapping Degenerate Neural Architectures

Degeneracy in biological systems refers to a many-to-one mapping between physical structures and their functional (including psychological) outcomes. Despite the ubiquity of the phenomenon, traditional analytical tools for modeling degeneracy in neuroscience are extremely limited. In this study, we generated synthetic datasets to describe three situations of degeneracy in fMRI data to demonstrate the limitations of the current univariate approach. We describe a novel computational approach for the analysis referred to as neural topographic factor analysis (NTFA). NTFA is designed to capture variations in neural activity across task conditions and participants. The advantage of this discovery-oriented approach is to reveal whether and how experimental trials and participants cluster into task conditions and participant groups. We applied NTFA on simulated data, revealing the appropriate degeneracy assumption in all three situations and demonstrating NTFA's utility in uncovering degeneracy. Lastly, we discussed the importance of testing degeneracy in fMRI data and the implications of applying NTFA to do so.