Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
MA Myszczynska, PN Ojamies, AMB Lacoste… - Nature reviews …, 2020 - nature.com
Globally, there is a huge unmet need for effective treatments for neurodegenerative
diseases. The complexity of the molecular mechanisms underlying neuronal degeneration …
diseases. The complexity of the molecular mechanisms underlying neuronal degeneration …
[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] Revealing neurocomputational mechanisms of reinforcement learning and decision-making with the hBayesDM package
Reinforcement learning and decision-making (RLDM) provide a quantitative framework and
computational theories with which we can disentangle psychiatric conditions into the basic …
computational theories with which we can disentangle psychiatric conditions into the basic …
[HTML][HTML] A tutorial on bridge sampling
The marginal likelihood plays an important role in many areas of Bayesian statistics such as
parameter estimation, model comparison, and model averaging. In most applications …
parameter estimation, model comparison, and model averaging. In most applications …
The drift diffusion model as the choice rule in reinforcement learning
Current reinforcement-learning models often assume simplified decision processes that do
not fully reflect the dynamic complexities of choice processes. Conversely, sequential …
not fully reflect the dynamic complexities of choice processes. Conversely, sequential …
A causal account of the brain network computations underlying strategic social behavior
During competitive interactions, humans have to estimate the impact of their own actions on
their opponent's strategy. Here we provide evidence that neural computations in the right …
their opponent's strategy. Here we provide evidence that neural computations in the right …
Altered learning under uncertainty in unmedicated mood and anxiety disorders
Anxiety is characterized by altered responses under uncertain conditions, but the precise
mechanism by which uncertainty changes the behaviour of anxious individuals is unclear …
mechanism by which uncertainty changes the behaviour of anxious individuals is unclear …
Theoretically informed generative models can advance the psychological and brain sciences: Lessons from the reliability paradox
Theories of individual differences are foundational to psychological and brain sciences, yet
they are traditionally developed and tested using superficial summaries of data (eg, mean …
they are traditionally developed and tested using superficial summaries of data (eg, mean …
A brain network supporting social influences in human decision-making
L Zhang, J Gläscher - Science advances, 2020 - science.org
Humans learn from their own trial-and-error experience and observing others. However, it
remains unknown how brain circuits compute expected values when direct learning and …
remains unknown how brain circuits compute expected values when direct learning and …
Hierarchical Bayesian parameter estimation for cumulative prospect theory
H Nilsson, J Rieskamp, EJ Wagenmakers - Journal of Mathematical …, 2011 - Elsevier
Cumulative prospect theory (CPT Tversky & Kahneman, 1992) has provided one of the most
influential accounts of how people make decisions under risk. CPT is a formal model with …
influential accounts of how people make decisions under risk. CPT is a formal model with …