[HTML][HTML] Individual differences in computational psychiatry: A review of current challenges
Bringing precision to the understanding and treatment of mental disorders requires
instruments for studying clinically relevant individual differences. One promising approach is …
instruments for studying clinically relevant individual differences. One promising approach is …
[HTML][HTML] Identifying transdiagnostic mechanisms in mental health using computational factor modeling
Most psychiatric disorders do not occur in isolation, and most psychiatric symptom
dimensions are not uniquely expressed within a single diagnostic category. Current …
dimensions are not uniquely expressed within a single diagnostic category. Current …
[HTML][HTML] Reliability of decision-making and reinforcement learning computational parameters
Computational models can offer mechanistic insight into cognition and therefore have the
potential to transform our understanding of psychiatric disorders and their treatment. For …
potential to transform our understanding of psychiatric disorders and their treatment. For …
Self-judgment dissected: A computational modeling analysis of self-referential processing and its relationship to trait mindfulness facets and depression symptoms
Cognitive theories of depression, and mindfulness theories of well-being, converge on the
notion that self-judgment plays a critical role in mental health. However, these theories have …
notion that self-judgment plays a critical role in mental health. However, these theories have …
Consistency within change: Evaluating the psychometric properties of a widely used predictive-inference task
Rapid adaptation to sudden changes in the environment is a hallmark of flexible human
behaviour. Many computational, neuroimaging, and even clinical investigations studying this …
behaviour. Many computational, neuroimaging, and even clinical investigations studying this …
Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts
Active reinforcement learning enables dynamic prediction and control, where one should not
only maximize rewards but also minimize costs such as of inference, decisions, actions, and …
only maximize rewards but also minimize costs such as of inference, decisions, actions, and …
Improving the Reliability of the Pavlovian Go/no-go Task
Background: The Pavlovian go/no-go task is commonly used to measure individual
differences in Pavlovian biases and their interaction with instrumental learning. However …
differences in Pavlovian biases and their interaction with instrumental learning. However …
The selective serotonin reuptake inhibitor sertraline alters learning from aversive reinforcements in patients with depression: evidence from a randomized controlled …
J Malamud, G Lewis, M Moutoussis, L Duffy… - Psychological …, 2024 - cambridge.org
BackgroundSelective serotonin reuptake inhibitors (SSRIs) are first-line pharmacological
treatments for depression and anxiety. However, little is known about how pharmacological …
treatments for depression and anxiety. However, little is known about how pharmacological …
Individual differences in computational psychiatry: A review of current challenges
P Karvelis, M Paulus, A Diaconescu - 2022 - osf.io
Bringing precision to the understanding and treatment of mental disorders requires
instruments for studying clinically relevant individual differences. One promising approach is …
instruments for studying clinically relevant individual differences. One promising approach is …
The factor structure of reinforcement learning behaviors
SE Zorowitz - 2023 - search.proquest.com
Reinforcement learning (RL) is a cognitive and algorithmic framework that specifies how
agents (eg, humans, animals, machines) can learn reward-maximizing policies through trial …
agents (eg, humans, animals, machines) can learn reward-maximizing policies through trial …