Safety learning in anxiety, Pavlovian conditioned inhibition and Covid concerns

Experimental studies of fear conditioning have identified the effectiveness of safety signals in inhibiting fear and maintaining fear-motivated behaviours. In fear conditioning procedures, the presence of safety signals means that the otherwise expected feared outcome will not now occur. Differences in the inhibitory learning processes needed to learn safety are being identified in various psychological and psychiatric conditions. However, despite early theoretical interest, the role of conditioned inhibitors as safety signals in anxiety has been under-investigated to date, in part because of the stringent test procedures required to confirm the demonstration of conditioned inhibition as such. Nonetheless, the theoretical implications of an inhibitory learning perspective continue to influence clinical practice. Moreover, our understanding of safety signals is of additional importance in the context of the increased health anxiety and safety behaviours generated by the Covid-19 pandemic.

The Associative Learning Prediction Error Feedback Loop as Reversible Driving Belt of the Bioenergetics of Longevity

Leukocyte telomere shortening is a useful biomarker of biological and cellular age that occurs at an accelerated rate in anxiety disorders and posttraumatic stress disorder (PTSD). Intriguingly, inhibitory learning — the systematic exposure to noxious stimuli that serves as a basis for many treatments for anxiety, phobia, and PTSD —reduces relative telomeres attrition rates and increases protective telomerase activity in a manner predictive of treatment response. How does inhibitory learning, a behavioral strategy, modulate organismal chromosomal activity? Inhibitory learning may induce repeated mismatch between treatment expectations, intrasession states, and eventual outcome. Nevertheless, inhibitory learning can incentivize repetition of the behavior. Thus, this paper aims to conceptualize inhibitory learning as involving a ‘prediction error feedback loop’, i.e., a series of self-perpetuating prediction errors — mismatches between expectations and outcomes — that enhances neural inhibitory regulation to effectuate extinction. Inhibitory learning is necessarily predicated upon an opposing process – excitatory learning – that may be conceptualized as a prediction error feedback loop that operates in reverse to inhibitory learning and enhances neural excitability as arousal. Together, excitatory and inhibitory learning may be elements of an associative learning prediction error feedback loop responsible for modulating neural bioenergetic rates, leading to changes in downstream cellular signaling that could explain reduced or increased rates of leukocyte telomere shortening and telomerase activity from each behavioral strategy, respectively.

Examining Differences in Fear Learning in Patients With Obsessive-Compulsive Disorder With Pupillometry, Startle Electromyography and Skin Conductance Responses

Obsessive-compulsive disorder (OCD) is characterized by recurrent, persistent thoughts and repetitive behaviors causing stress and anxiety. In the associative learning model of OCD, mechanisms of fear extinction are supposed to partly underlie symptom development, maintenance and treatment of OCD, proposing that OCD patients suffer from rigid memory associations and inhibitory learning deficits. To test these assumptions, previous studies have used skin conductance and subjective ratings as readouts in fear conditioning paradigms, finding impaired fear extinction learning, impaired fear extinction recall or no differences between individuals with OCD and healthy controls. Against this heterogeneous background, we tested fear acquisition and extinction in 37 OCD patients and 56 healthy controls, employing skin conductance as well as pupillometry and startle electromyography. Extinction recall was also included in a subsample. We did not observe differences between groups in any of the task phases, except a trend toward higher startle amplitudes during extinction for OCD. Overall, sensitive readouts such as pupillometry and startle responses did not provide evidence for moderate-to-large inhibitory learning deficits using classical fear conditioning, challenging the assumption of generically impaired extinction learning and memory in OCD.

Inhibitory Learning as Prediction Error Feedback Loop

Inhibitory learning promotes emotion regulation via systematic exposure to fear-inducing stimuli. Given that inconsistencies between expectations, states, and outcomes may be experienced as elements of inhibitory learning, to what extent are prediction errors – mismatches between expectations and outcomes – a core neural element of inhibitory learning? This paper takes a complex systems approach to prediction errors and postulates that a prediction error feedback loop – a series of self-perpetuating disparities between expected and perceived outcomes – could be a correlate of or responsible for improved emotion regulation from inhibitory learning. The inhibitory learning prediction error feedback loop may additionally elucidate how human and animal studies demonstrate improved emotion regulation in the form of reduced fear responses without exposure to specific fear-inducing stimuli.

The Inhibitory Learning Approach to Exposure and Response Prevention for Obsessive-Compulsive Disorder

Exposure and response prevention (ERP) is the most effective treatment for obsessive-compulsive disorder (OCD), with robust symptom gains consistently observed. Yet, both research and clinical practice indicate ERP is not equally efficacious for all individuals with OCD, and a considerable portion fail to achieve full symptom remission or experience relapse despite substantial short-term gains. To this end, inhibitory learning theory (ILT) has emerged as an empirically driven conceptual framework for implementing ERP with the goal of optimizing the efficiency and durability of treatment gains. This chapter reviews the conceptual framework for ERP, illustrates ERP through a case example in which common pitfalls were encountered, defines ILT, and illustrates the use of ILT to address these pitfalls.

Merging Our Understanding of Anxiety and Exposure: Using Inhibitory Learning to Target Anxiety Sensitivity in Exposure Therapy

Exposure-based therapies are the gold standard treatment for anxiety disorders, and recent advancements in basic and clinical research point to the need to update the implementation of exposure. Recent research has highlighted the importance of transdiagnostic factors such as anxiety sensitivity (AS), or fear of anxiety-related sensations. Elevated AS is common among all anxiety disorders and contains three dimensions, or expectancies, that can be used to guide treatment. Recently, treatments directly targeting AS have shown potential in reducing symptoms of anxiety. In addition, inhibitory learning theory (ILT) provides an alternative explanation of exposure processes based on basic learning research. ILT extends the current framework by accounting for renewal of fear, which is important given the substantial number of individuals who experience a return of symptoms following treatment. The current paper will provide an overview of ILT and discuss several ILT techniques that can be used to target AS. These two converging bodies of research hold strong potential for optimizing treatment for anxiety.

Orchestrated Excitatory and Inhibitory Learning Rules Lead to the Unsupervised Emergence of Up-states and Balanced Network Dynamics

ABSTRACTSelf-sustaining dynamics maintained through recurrent connections are of fundamental importance to cortical function. We show that Up-states—an example of self-sustained network dynamics—autonomously emerge in cortical circuits across three weeks of ex vivo development, establishing the presence of unsupervised synaptic learning rules that lead to globally stable emergent dynamics. Computational models of excitatory-inhibitory networks have established that four sets of weights (WE←E, WE←I, WI←E, WI←I) cooperate to generate stable self-sustained dynamics, but have not addressed how a family of learning rules can operate in parallel at all four weight classes to achieve self-sustained inhibition-stabilized regimes. Using numerical and analytical methods we show that standard homeostatic rules cannot account for the emergence of self-sustained dynamics due to the paradoxical effect. We derived a novel family of homeostatic learning rules that operate in parallel at all four synaptic classes, which robustly lead to the emergence of Up-states and balanced excitation-inhibition.