Reward-processing behavior in depressed participants relative to healthy volunteers: A systematic review and meta-analysis
Importance Dysfunctional reward processing is a leading candidate mechanism for the
development of certain depressive symptoms, such as anhedonia. However, to our …
development of certain depressive symptoms, such as anhedonia. However, to our …
Computational models of subjective feelings in psychiatry
Research in computational psychiatry is dominated by models of behavior. Subjective
experience during behavioral tasks is not well understood, even though it should be relevant …
experience during behavioral tasks is not well understood, even though it should be relevant …
Cognitive model discovery via disentangled RNNs
Computational cognitive models are a fundamental tool in behavioral neuroscience. They
embody in software precise hypotheses about the cognitive mechanisms underlying a …
embody in software precise hypotheses about the cognitive mechanisms underlying a …
Dynamic inverse reinforcement learning for characterizing animal behavior
Understanding decision-making is a core goal in both neuroscience and psychology, and
computational models have often been helpful in the pursuit of this goal. While many models …
computational models have often been helpful in the pursuit of this goal. While many models …
Modelling human behaviour in cognitive tasks with latent dynamical systems
Response time data collected from cognitive tasks are a cornerstone of psychology and
neuroscience research, yet existing models of these data either make strong assumptions …
neuroscience research, yet existing models of these data either make strong assumptions …
Sensitivity to intrinsic rewards is domain general and related to mental health
Humans frequently engage in intrinsically rewarding activities (for example, consuming art,
reading). Despite such activities seeming diverse, we show that sensitivity to intrinsic …
reading). Despite such activities seeming diverse, we show that sensitivity to intrinsic …
Concept drift adaptation methods under the deep learning framework: A literature review
Q Xiang, L Zi, X Cong, Y Wang - Applied Sciences, 2023 - mdpi.com
With the advent of the fourth industrial revolution, data-driven decision making has also
become an integral part of decision making. At the same time, deep learning is one of the …
become an integral part of decision making. At the same time, deep learning is one of the …
Using deep learning to predict human decisions and using cognitive models to explain deep learning models
Deep neural networks (DNNs) models have the potential to provide new insights in the study
of cognitive processes, such as human decision making, due to their high capacity and data …
of cognitive processes, such as human decision making, due to their high capacity and data …
Predictive and interpretable: Combining artificial neural networks and classic cognitive models to understand human learning and decision making
Quantitative models of behavior are a fundamental tool in cognitive science. Typically,
models are hand-crafted to implement specific cognitive mechanisms. Such “classic” models …
models are hand-crafted to implement specific cognitive mechanisms. Such “classic” models …
Harnessing the flexibility of neural networks to predict dynamic theoretical parameters underlying human choice behavior
Reinforcement learning (RL) models are used extensively to study human behavior. These
rely on normative models of behavior and stress interpretability over predictive capabilities …
rely on normative models of behavior and stress interpretability over predictive capabilities …