Unwanted reminders: The effects of emotional memory suppression on subsequent neuro-cognitive processing

Tackling addictive behaviors through memory suppression: A scoping review and perspective

Forgetting is often viewed as a human frailty. However, over the years, it has been considered an adaptive process that allows people to avoid retrieval of undesirable memories, preventing them from suffering and discomfort. Evidence shows that the ability to suppress memories is affected by several psychopathological conditions characterized by persistent unwanted thoughts, including anxiety and posttraumatic stress disorders. Nevertheless, memory suppression (MS) mechanisms in addiction-a clinical condition characterized by recurrent drug-related thoughts that contribute to repeated drug use-have received little attention so far. Addiction theories reveal that drugs change behavior by working on memory systems, particularly on declarative memory, which is related to the retrieval and encoding of drug-related memories. In this review, the main behavioral and neurofunctional findings concerning the Think/No-Think task-an adaptation of the classical Go/No-Go tasks typically used to evaluate the suppression of motor response-are presented. We then show how the memory system can be involved in the craving or anticipation/preoccupation stage of the addiction cycle. Subsequently, the study of MS in the context of addictive behaviors is highlighted as a promising approach for gaining knowledge about the mechanisms contributing to the continuation of addiction. Finally, we discuss how interventions aiming to strengthen this ability could impact the anticipation/preoccupation stage by (i) reducing the accessibility of drug-related memories, (ii) decreasing craving and attention toward drug-related stimuli, and (iii) improving overall inhibition abilities. In conclusion, this review aims to illustrate how the study of MS may be a valuable approach to enhance our understanding of substance use disorders by unveiling the underlying cognitive and neural mechanisms involved, which could have important implications for addiction treatment.

The Active Inference Model of Coherence Therapy

Coherence Therapy is an empirically derived experiential psychotherapy based on Psychological Constructivism. Symptoms are viewed as necessary output from an implicit model of the world. The therapist curates experiences and directs attention toward discovering the model. Rendered explicit, the model is juxtaposed with contradictory knowledge driving memory re-consolidation with resolution of the symptom. The Bayesian Brain views perception and action as inferential processes. Prior beliefs are combined in a generative model to explain the hidden causes of sensations through a process of Active Inference. Prior beliefs that are poor fits to the real world are suboptimal. Suboptimal priors with optimal inference produce Bayes Optimal Pathology with behavioral symptoms. The Active Inference Model of Coherence Therapy posits that Coherence Therapy is a dyadic act of therapist guided Active Inference that renders the (probable) hidden causes of a client's behavior conscious. The therapist's sustained attention on the goal of inference helps to overcome memory control bias against retrieval of the affectively charged suboptimal prior. Serial experiences cue memory retrieval and re-instantiation of the physiological/affective state that necessitates production of the symptom in a particular context. As this process continues there is a break in modularity with assimilation into broader networks of experience. Typically, the symptom produced by optimal inference with the suboptimal prior is experienced as unnecessary/inappropriate when taken out of the particular context. The implicit construct has been re-represented and rendered consciously accessible, by a more complex but more accurate model in which the symptom is necessary in some contexts but not others. There is an experience of agency and control in symptom creation, accompanied by the spontaneous production of context appropriate behavior. The capacity for inference has been restored. The Active Inference Model of Coherence Therapy provides a framework for Coherence Therapy as a computational process which can serve as the basis for new therapeutic interventions and experimental designs integrating biological, cognitive, behavioral, and environmental factors.

Deeply Felt Affect: The Emergence of Valence in Deep Active Inference

The positive-negative axis of emotional valence has long been recognized as fundamental to adaptive behavior, but its origin and underlying function have largely eluded formal theorizing and computational modeling. Using deep active inference, a hierarchical inference scheme that rests on inverting a model of how sensory data are generated, we develop a principled Bayesian model of emotional valence. This formulation asserts that agents infer their valence state based on the expected precision of their action model-an internal estimate of overall model fitness ("subjective fitness"). This index of subjective fitness can be estimated within any environment and exploits the domain generality of second-order beliefs (beliefs about beliefs). We show how maintaining internal valence representations allows the ensuing affective agent to optimize confidence in action selection preemptively. Valence representations can in turn be optimized by leveraging the (Bayes-optimal) updating term for subjective fitness, which we label affective charge (AC). AC tracks changes in fitness estimates and lends a sign to otherwise unsigned divergences between predictions and outcomes. We simulate the resulting affective inference by subjecting an in silico affective agent to a T-maze paradigm requiring context learning, followed by context reversal. This formulation of affective inference offers a principled account of the link between affect, (mental) action, and implicit metacognition. It characterizes how a deep biological system can infer its affective state and reduce uncertainty about such inferences through internal action (i.e., top-down modulation of priors that underwrite confidence). Thus, we demonstrate the potential of active inference to provide a formal and computationally tractable account of affect. Our demonstration of the face validity and potential utility of this formulation represents the first step within a larger research program. Next, this model can be leveraged to test the hypothesized role of valence by fitting the model to behavioral and neuronal responses.

Recent advances in the application of predictive coding and active inference models within clinical neuroscience

Research in clinical neuroscience is founded on the idea that a better understanding of brain (dys)function will improve our ability to diagnose and treat neurological and psychiatric disorders. In recent years, neuroscience has converged on the notion that the brain is a 'prediction machine,' in that it actively predicts the sensory input that it will receive if one or another course of action is chosen. These predictions are used to select actions that will (most often, and in the long run) maintain the body within the narrow range of physiological states consistent with survival. This insight has given rise to an area of clinical computational neuroscience research that focuses on characterizing neural circuit architectures that can accomplish these predictive functions, and on how the associated processes may break down or become aberrant within clinical conditions. Here, we provide a brief review of examples of recent work on the application of predictive processing models of brain function to study clinical (psychiatric) disorders, with the aim of highlighting current directions and their potential clinical utility. We offer examples of recent conceptual models, formal mathematical models, and applications of such models in empirical research in clinical populations, with a focus on making this material accessible to clinicians without expertise in computational neuroscience. In doing so, we aim to highlight the potential insights and opportunities that understanding the brain as a prediction machine may offer to clinical research and practice.

Attempts at memory control induce dysfunctional brain activation profiles in Generalized Anxiety Disorder: An exploratory fMRI study

Suppression of aversive memories through memory control has historically been proposed as a central psychological defense mechanism. Inability to suppress memories is considered a central psychological trait in several psychiatric disorders, including Generalized Anxiety Disorder (GAD). Yet, few studies have attempted the focused identification of dysfunctional brain activation profiles when patients with Generalized Anxiety Disorders attempt memory control. Using a well-characterized behavioral paradigm we studied brain activation profiles in a group of adult GAD patients and well-matched healthy controls (HC). Participants learned word-association pairs before imaging. During fMRI when presented with one word of the pair, they were instructed to either suppress memory of, or retrieve the paired word. Subsequent behavioral testing indicated both GAD and HC were able to engage in the task, but attempts at memory control (suppression or retrieval) during fMRI revealed vastly different activation profiles. GAD were characterized by substantive hypo-activation signatures during both types of memory control, with effects particularly strong during suppression in brain regions including the dorsal anterior cingulate and the ventral prefrontal cortex. Attempts at memory control in GAD fail to engage brain regions to the same extent HC, providing a putative neuronal signature for a well-established psychological characteristic of the illness.

Successful Goal-Directed Memory Suppression is Associated With Increased Inter-Hemispheric Coordination Between Right and Left Frontoparietal Control Networks

The neural basis of suppressing conscious access to one’s own memories has recently received considerable attention, with several studies suggesting this process engages frontal-parietal cognitive control regions. However, researchers to date have not examined the way right and left hemisphere cognitive control networks coordinate with one another to accomplish this. We had 48 participants (25 female) complete a Think/No Think (T/NT) task for memories of emotionally unpleasant visual scenes while undergoing functional magnetic resonance imaging. We used generalized psychophysiologic interaction analyses to examine functional connectivity between right and left hemisphere frontal-parietal regions during memory suppression. Participants who were better at memory suppression, as assessed by greater numbers of forgotten memories in the NT than T conditions, also showed greater functional connectivity between multiple right and left hemisphere control regions. This suggests that individual differences in memory suppression ability may be partially explained by differences in task-specific inter-hemispheric coordination.