Neuro-cognitive processes as mediators of psychological treatment effects

Reiter, Andrea M.F.; Atiya, Nadim A. A.; Isabel,; Huys, Quentin J. M.

doi:10.31234/osf.io/wz5rd

Cited by 6 publications

(7 citation statements)

References 19 publications

(5 reference statements)

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“…It would be of interest to assess whether successfully challenging these maladaptive beliefs, through techniques such as cognitive behavioural 86 or metacognitive 104 therapies, would result in corresponding changes in confidence criteria. As well as providing a useful neuro-computational outcome measure for clinical research 105 , this would help to elucidate a key open question of the causal direction of the relationship between symptoms and metacognitive bias: Do the biases arise prior to, and potentially confer risk for, the onset of symptomology; or are they rather concomitant symptoms themselves? Incorporating quantitative measurement of metacognitive bias into studies employing longitudinal and/or interventional designs could shed light on this question.…”

Section: Discussionmentioning

confidence: 99%

Psychiatrically relevant signatures of domain-general decision-making and metacognition in the general population

Benwell

Mohr

Wallberg

et al. 2022

npj Mental Health Res

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Human behaviours are guided by how confident we feel in our abilities. When confidence does not reflect objective performance, this can impact critical adaptive functions and impair life quality. Distorted decision-making and confidence have been associated with mental health problems. Here, utilising advances in computational and transdiagnostic psychiatry, we sought to map relationships between psychopathology and both decision-making and confidence in the general population across two online studies (N’s = 344 and 473, respectively). The results revealed dissociable decision-making and confidence signatures related to distinct symptom dimensions. A dimension characterised by compulsivity and intrusive thoughts was found to be associated with reduced objective accuracy but, paradoxically, increased absolute confidence, whereas a dimension characterized by anxiety and depression was associated with systematically low confidence in the absence of impairments in objective accuracy. These relationships replicated across both studies and distinct cognitive domains (perception and general knowledge), suggesting that they are reliable and domain general. Additionally, whereas Big-5 personality traits also predicted objective task performance, only symptom dimensions related to subjective confidence. Domain-general signatures of decision-making and metacognition characterise distinct psychological dispositions and psychopathology in the general population and implicate confidence as a central component of mental health.

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Section: Discussionmentioning

confidence: 99%

Psychiatrically relevant signatures of domain-general decision-making and metacognition in the general population

Benwell

Mohr

Wallberg

et al. 2022

npj Mental Health Res

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“…Figure 4: A graphical summary of the intended uses of computational assays. This summary is based on the most common proposals in the literature (Paulus et al, 2016;Paulus, 2017;Patzelt et al, 2018;Nair et al, 2020;Reiter et al, 2021;Yip et al, 2022;Hauser et al, 2022) and might not be exhaustive. Green denotes applications in the clinic while blue denotes research applications.…”

Section: Might Computational Measures Show Better Convergent Validity?mentioning

confidence: 99%

Individual Differences in Computational Psychiatry: A Review of Current Challenges

Karvelis¹,

Paulus²,

Diaconescu³

2022

Preprint

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Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. To contextualize this, we also provide a more general perspective on key developments that are needed for translating computational assays to clinical practice. Emerging evidence suggests that most computational measures show poor-to-moderate reliability and often provide little improvement over simple behavioral measures. Furthermore, behavioral and computational measures used to test computational accounts of mental disorders show a lack of convergent validity, which compromises their interpretability. Taken together, these issues pose a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We suggest that cross-sectional single-task designs, which currently dominate the research landscape, are partly to blame for these problems and therefore are not suitable for solving them. Instead, reliability and construct validity need to be studied more systematically using longitudinal designs with batteries of tasks. Finally, to enable clinical applications, it will be necessary to establish predictive and longitudinal validity, and to make the assays more efficient and less burdensome.

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“…This echoes with recent calls in computational psychiatry to characterize time-varying alterations of behavior, cognition and emotion in context [138][139][140]. It holds promise to re-validate the effectiveness of existing psychopharmacological and psychotherapeutic treatment strategies and to dissect heterogeneity in treatment response [38,141]. Such designs also allow for immediate interventions in real-life (via the patient's smartphone) when risk signs occur ( [142][143][144] 'ecological momentary intervention')…”

Section: Box 3 Implications For the Development Of Mental Health Prob...mentioning

confidence: 86%

A cognitive-computational account of mood swings in adolescence

Gregorova¹,

Eldar²,

Deserno³

et al. 2022

Preprint

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Teenagers have a reputation for being fickle, both in their choices and in their moods. This variability may help adolescents as they begin to independently navigate novel environments. Recently, however, adolescent moodiness has also been linked to psychopathology. Here we consider adolescents’ mood swings from a novel computational perspective, grounded in Reinforcement Learning. This model proposes that mood is determined by surprises about outcomes in the environment, and how much we learn from these surprises. It additionally suggests that, in a bidirectional manner, mood biases learning and choice. Integrating independent lines of research, we sketch a cognitive-computational account of how adolescents’ mood, learning, and choice dynamics influence each other, with implications for normative and psychopathological development.

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Neuro-cognitive processes as mediators of psychological treatment effects

Cited by 6 publications

References 19 publications

Psychiatrically relevant signatures of domain-general decision-making and metacognition in the general population

Psychiatrically relevant signatures of domain-general decision-making and metacognition in the general population

Individual Differences in Computational Psychiatry: A Review of Current Challenges

A cognitive-computational account of mood swings in adolescence

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