Clinical and cognitive insight in panic disorder: phenomenology and treatment effects in internet cognitive behavior therapy

Clinical observations suggest that individuals with panic disorder (PD) vary in their beliefs about the causes of their panic attacks. Some attribute these attacks to psychological factors, while others to physiological or medical factors. These beliefs also extend to whether individuals perceive panic attacks as dangerous. In other areas of psychiatric nosology, these phenomena are commonly called clinical insight (recognition of disorder and the need for treatment) and cognitive insight (the ability to reflect on one's beliefs). Despite its importance, limited research exists on insight in PD and its relation to symptoms and treatment outcomes. This study examines clinical and cognitive insight in 83 patients with PD who received internet-based cognitive behavioral therapy, investigating their relationship with symptoms, treatment outcomes, and changes in insight. We assessed patients using interview and self-report measures of insight and symptoms. Clinical and cognitive insight were correlated and both constructs improved significantly during treatment. Good clinical insight pretreatment was positively correlated with more severe pretreatment symptoms. Pretreatment clinical and cognitive insight were not correlated with symptom change or attrition. Greater change in clinical and cognitive insight was related to greater change in symptoms. The findings highlight the significance of clinical and cognitive insight in PD, and the importance of distinguishing between them. This suggests the need to develop interventions according to patients' level of insight, particularly focusing on those lacking insight. Further research is essential to advance our understanding of the relationship between insight and the phenomenology and treatment of PD.

Accumulating evidence for myriad alternatives: Modeling the generation of free association

The associative manner by which thoughts follow one another has intrigued scholars for decades. The process by which an association is generated in response to a cue can be explained by classic models of semantic processing through distinct computational mechanisms. Distributed attractor networks implement rich-get-richer dynamics and assume that stronger associations can be reached with fewer steps. Conversely, spreading activation models assume that a cue distributes its activation, in parallel, to all associations at a constant rate. Despite these models' huge influence, their intractability together with the unconstrained nature of free association have restricted their few previous uses to qualitative predictions. To test these computational mechanisms quantitatively, we conceptualize free association as the product of internal evidence accumulation and generate predictions concerning the speed and strength of people's associations. To this end, we first develop a novel approach to mapping the personalized space of words from which an individual chooses an association to a given cue. We then use state-of-the-art evidence accumulation models to demonstrate the function of rich-get-richer dynamics on the one hand and of stochasticity in the rate of spreading activation on the other hand, in preventing an exceedingly slow resolution of the competition among myriad potential associations. Furthermore, whereas our results uniformly indicate that stronger associations require less evidence, only in combination with rich-get-richer dynamics does this explain why weak associations are slow yet prevalent. We discuss implications for models of semantic processing and evidence accumulation and offer recommendations for practical applications and individual-differences research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Theory-Driven Analysis of Natural Language Processing Measures of Thought Disorder Using Generative Language Modeling

BACKGROUND

Natural language processing (NLP) holds promise to transform psychiatric research and practice. A pertinent example is the success of NLP in the automatic detection of speech disorganization in formal thought disorder (FTD). However, we lack an understanding of precisely what common NLP metrics measure and how they relate to theoretical accounts of FTD. We propose tackling these questions by using deep generative language models to simulate FTD-like narratives by perturbing computational parameters instantiating theory-based mechanisms of FTD.

METHODS

We simulated FTD-like narratives using Generative-Pretrained-Transformer-2 by either increasing word selection stochasticity or limiting the model's memory span. We then examined the sensitivity of common NLP measures of derailment (semantic distance between consecutive words or sentences) and tangentiality (how quickly meaning drifts away from the topic) in detecting and dissociating the 2 underlying impairments.

RESULTS

Both parameters led to narratives characterized by greater semantic distance between consecutive sentences. Conversely, semantic distance between words was increased by increasing stochasticity, but decreased by limiting memory span. An NLP measure of tangentiality was uniquely predicted by limited memory span. The effects of limited memory span were nonmonotonic in that forgetting the global context resulted in sentences that were semantically closer to their local, intermediate context. Finally, different methods for encoding the meaning of sentences varied dramatically in performance.

CONCLUSIONS

This work validates a simulation-based approach as a valuable tool for hypothesis generation and mechanistic analysis of NLP markers in psychiatry. To facilitate dissemination of this approach, we accompany the paper with a hands-on Python tutorial.

Hierarchical inference as a source of human biases

The finding that human decision-making is systematically biased continues to have an immense impact on both research and policymaking. Prevailing views ascribe biases to limited computational resources, which require humans to resort to less costly resource-rational heuristics. Here, we propose that many biases in fact arise due to a computationally costly way of coping with uncertainty-namely, hierarchical inference-which by nature incorporates information that can seem irrelevant. We show how, in uncertain situations, Bayesian inference may avail of the environment's hierarchical structure to reduce uncertainty at the cost of introducing bias. We illustrate how this account can explain a range of familiar biases, focusing in detail on the halo effect and on the neglect of base rates. In each case, we show how a hierarchical-inference account takes the characterization of a bias beyond phenomenological description by revealing the computations and assumptions it might reflect. Furthermore, we highlight new predictions entailed by our account concerning factors that could mitigate or exacerbate bias, some of which have already garnered empirical support. We conclude that a hierarchical inference account may inform scientists and policy makers with a richer understanding of the adaptive and maladaptive aspects of human decision-making.

Examining the relationship between cognitive factors and insight in panic disorder before and during treatment

Individuals with Panic Disorder (PD) often have impaired insight, which can impede their willingness to seek treatment. Cognitive processes, including metacognitive beliefs, cognitive flexibility, and jumping to conclusions (JTC) may influence the degree of insight. By understanding the relationship between insight and these cognitive factors in PD, we can better identify individuals with such vulnerabilities to improve their insight. The aim of this study is to examine the relationships between metacognition, cognitive flexibility, and JTC with clinical and cognitive insight at pretreatment. We investigate the association among those factors' changes and changes in insight over treatment. Eighty-three patients diagnosed with PD received internet-based cognitive behavior therapy. Analyses revealed that metacognition was related to both clinical and cognitive insight, and cognitive flexibility was related to clinical insight at pre-treatment. Greater changes in metacognition were correlated with greater changes in clinical insight. Also, greater changes in cognitive flexibility were related to greater changes in cognitive insight. The current study extends previous studies suggesting potential relationships among insight, metacognition, and cognitive flexibility in PD. Determining the role of cognitive concepts in relation to insight may lead to new avenues for improving insight and can have implications for engagement and treatment-seeking behaviors.

If you don’t let it in, you don’t have to get it out: Thought preemption as a method to control unwanted thoughts

To attain goals, people must proactively prevent interferences and react to interferences once they occur. Whereas most research focuses on how people deal with external interferences, here we investigate the use of proactive and reactive control in dealing with unwanted thoughts. To examine this question, we asked people to generate an association to each of several repeating cue words, while forbidding the repetition of associations. Reactively rejecting and replacing unwanted repeated associations after they occur entails slower response times. Conversely, proactive control entails constricting the search space and thus faster response times. To gain further insight into different potential proactive thought control mechanisms, we augmented the analysis of raw response times with a novel, hypothesis-based, tractable computational model describing how people serially sample associations. Our results indicate that people primarily react to unwanted thoughts after they occur. Yet, we found evidence for two latent proactive control mechanisms: one that allows people to mitigate the episodic strengthening of repeated thoughts, and another that helps avoid looping in a repetitive thought. Exploratory analysis showed a relationship between model parameters and self-reported individual differences in the control over unwanted thoughts in daily life. The findings indicate the novel task and model can advance our understanding of how people can and cannot control their thoughts and memories, and benefit future research on the mechanisms responsible for unwanted thought in different psychiatric conditions. Finally, we discuss implications concerning the involvement of associative thinking and various control processes in semantic fluency, decision-making and creativity.

Anxious attachment improves and is predicted by anxiety sensitivity in internet-based, guided self-help cognitive behavioral treatment for panic disorder

The purpose of this study was to examine whether anxious and avoidant attachment styles improve during guided internet-based cognitive behavioral treatment (ICBT) for panic disorder, and if so, to identify potential theoretically driven mechanisms related to the change. We examined changes in anxious and avoidant attachment and their time-lagged (1 week), longitudinal relationship with panic-related constructs in patients participating in ICBT (n = 79) in an open trial. Anxious attachment scores improved significantly with a medium effect during ICBT, d = 0.76 [0.45, 1.08]. According to benchmark analyses, changes were similar to the magnitude of change in face-to-face CBT and final scores to values of a nonclinical sample. Additionally, similar to findings in face-to-face CBT for panic disorder, longitudinal time analyses revealed that anxiety sensitivity scores predicted later improvement in anxious attachment scores, but not vice versa. Counter to our hypothesis, avoidant attachment did not significantly change during treatment, d = 0.15 [0.02, 0.46]; however, pretreatment level of avoidant attachment in ICBT was similar to the nonclinical sample. Also counter to our hypotheses, agoraphobic avoidant behaviors when alone did not predict changes in anxious attachment. These results suggest that anxious attachment can improve in ICBT for panic disorder even though the focus of the treatment is not on interpersonal relationships. These changes appear to follow changes in anxiety sensitivity. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Patient alliance with the program predicts treatment outcomes whereas alliance with the therapist predicts adherence in internet-based therapy for panic disorder

This study examines relationships among different aspects of therapeutic alliance with treatment outcome, adherence and attrition in internet delivered cognitive behavioral therapy (ICBT) for panic disorder.

We examined alliance-outcome relationships in ICBT (N = 74) using a newly developed self-report alliance measure that disentangles alliance with program content (Internet Patient's Experience of Attunement and Responsiveness with the program; I-PEARp) and with the therapist (I-PEARt). We compared ICBT outcomes of patient rated and therapist-rated alliance with conventional alliance scales (WAI-6 and WAI-T).

Consistent with our hypothesis, I-PEARp and I-PEARt distinguished between different aspects of the alliance and predicted outcomes better than standard alliance scales. Furthermore, higher ratings of I-PEARp were associated with subsequent lower symptoms and lower symptoms were associated with higher subsequent alliance. In contrast, I-PEARt predicted adherence, but not symptoms. Although therapists' ratings of alliance (thI-PEAR) improved significantly during treatment, they did not predict subsequent symptoms, adherence, or dropout.

Results indicate that the patient experience of the alliance in ICBT includes two aspects, each of which uniquely contributes to outcomes; patient connection to the program is related to symptom outcomes whereas the dyadic relationship with the therapist serves as the glue to allow the treatment to hold.

Modelling Pathological Narcissism Using the Brief PNI in Terms of Structure and Convergent and Divergent Validity: A New Perspective

The Pathological Narcissism Inventory (PNI) is a popular measure and the first to attempt to tap into both grandiose and vulnerable narcissism (GN/VN). However, data have raised questions as to whether it appropriately formulates GN and differentiates it from VN. In this study, we examined the Brief-PNI's structure and construct validity, by using a novel model based on the theoretical notion of GN and VN sharing core features. Participants (N = 1,061) completed the Brief-PNI, and psychological distress and social avoidance questionnaires. Results showed a better fit and differentiation of GN and VN for a model with cross-loading of shared first-order factors on GN and VN, compared with the traditional PNI structure. We concluded that the Brief-PNI can offer an appropriate formulation and differentiation of VN and GN, provided that their shared variance is considered.

Doubting what you already know: Uncertainty regarding state transitions is associated with obsessive compulsive symptoms

Obsessive compulsive (OC) symptoms involve excessive information gathering (e.g., checking, reassurance-seeking), and uncertainty about possible, often catastrophic, future events. Here we propose that these phenomena are the result of excessive uncertainty regarding state transitions (transition uncertainty): a computational impairment in Bayesian inference leading to a reduced ability to use the past to predict the present and future, and to oversensitivity to feedback (i.e. prediction errors). Using a computational model of Bayesian learning under uncertainty in a reversal learning task, we investigate the relationship between OC symptoms and transition uncertainty. Individuals high and low in OC symptoms performed a task in which they had to detect shifts (i.e. transitions) in cue-outcome contingencies. Modeling subjects' choices was used to estimate each individual participant's transition uncertainty and associated responses to feedback. We examined both an optimal observer model and an approximate Bayesian model in which participants were assumed to attend (and learn about) only one of several cues on each trial. Results suggested the participants were more likely to distribute attention across cues, in accordance with the optimal observer model. As hypothesized, participants with higher OC symptoms exhibited increased transition uncertainty, as well as a pattern of behavior potentially indicative of a difficulty in relying on learned contingencies, with no evidence for perseverative behavior. Increased transition uncertainty compromised these individuals' ability to predict ensuing feedback, rendering them more surprised by expected outcomes. However, no evidence for excessive belief updating was found. These results highlight a potential computational basis for OC symptoms and obsessive compulsive disorder (OCD). The fact the OC symptoms predicted a decreased reliance on the past rather than perseveration challenges preconceptions of OCD as a disorder of inflexibility. Our results have implications for the understanding of the neurocognitive processes leading to excessive uncertainty and distrust of past experiences in OCD.

Why check? A meta-analysis of checking in obsessive-compulsive disorder: Threat vs. distrust of senses

Compulsive checking is the most common ritual among individuals with obsessive-compulsive disorder (OCD). Yet, other than uncertainty, the variables prompting checking are not fully understood. Laboratory studies suggest that task conditions - whether threatening (anxiety-relevant) or neutral, and task type - whether requiring perceptual or reasoning decision-making - may be influential. The purpose of our meta-analysis was to compare OCD participants and healthy controls on experimental tasks involving uncertainty in which a behavioral measure of checking was obtained. Four databases were searched. Twenty-two studies met the inclusion criteria, including 43 conditions comparing 663 OCD participants to 614 healthy controls. Due to the dependent structure of the data a robust variance estimation analysis approach was used. Overall effects were similar for neutral and threatening conditions. However, OCD participants responded with greater checking compared to controls on perceptual tasks, but not on reasoning tasks. Results support previous reports suggesting that OCD checking can be observed in neutral conditions, possibly posing as a risk factor for a checking vicious cycle. In addition, our results support OCD models which focus on checking as stemming from interference with automatic processes and distrust of sensory modalities.

Are stronger memories forgotten more slowly? No evidence that memory strength influences the rate of forgetting

Information stored in visual short-term memory is used ubiquitously in daily life; however, it is forgotten rapidly within seconds. When more items are to be remembered, they are forgotten faster, potentially suggesting that stronger memories are forgotten less rapidly. Here we tested this prediction with three experiments that assessed the influence of memory strength on the rate of forgetting of visual information without manipulating the number of items. Forgetting rate was assessed by comparing the accuracy of reports in a delayed-estimation task following relatively short and long retention intervals. In the first experiment, we compared the forgetting rate of items that were directly fixated, to items that were not. In Experiments 2 and 3 we manipulated memory strength by extending the exposure time of one item in the memory array. As expected, direct fixation and longer exposure led to better accuracy of reports, reflecting stronger memory. However, in all three experiments, we did not find evidence that increased memory strength moderated the forgetting rate.