Human Neuroscience

Citations in the corpus

Semi-analytical approximations to statistical moments of sigmoid and softmax mappings of normal variables (2017, background): “Note that, in the context of our own work, approximations of this sort have already found their way in many probabilistic models of learning and decision making (Daunizeau et al., 2010; Devaine et al., 2014; Lebreton et al., 2015; Mathys et al., 2011).”
Adolescent risk-taking in the context of exploration and social influence (2021, background): “Evidence suggests that learning to interact with novel environments can be characterized by some form of Bayesian learning (Daw et al., 2005, 2006; Dayan & Daw, 2008; Knill & Pouget, 2004; Marković & Kiebel, 2016; Mathys et al., 2011; Nassar et al., 2010).”
The Social Bayesian Brain: Does Mentalizing Make a Difference When We Learn? (2014, unknown): “We refer the interested reader to [21].”
The Predictive Coding Account of Psychosis (2018, background): “The resulting aberrant encoding of precision could lead to an abnormally strong weighting of PEs, which in turn leads to aberrant learning and the formation of delusional beliefs (53,58,59,64).”
Active interoceptive inference and the emotional brain (2016, background): “13 (20), free-energy accounts of perception and behaviour (4) and hierarchical Bayesian models of learning (78).”
How Do Expectations Shape Perception? (2018, result): “, [130]) to derive trial-by-trial quantitative estimates of variables of interest, and compare those to the data [87,128,131].”
CURIOUS: Intrinsically Motivated Multi-Task, Multi-Goal Reinforcement Learning (2018, unknown): “approximate Bayesian methods like in (Mathys et al., 2011)).”
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules (2018, result): “. Indeed, theories of surprise as well as hierarchical Bayesian models predict a faster change of model parameters for surprising stimuli than for known ones (Yu and Dayan, 2005; Nassar et al., 2010; Mathys et al., 2011, 2014; Faraji et al., 2018) similar to, but more general than, the well-known Ka”
Dopamine, Affordance and Active Inference (2012, background): “One link may be via variational Bayesian formulations of reinforcement-learning update equations (see below), in which precision-weighted prediction errors play a key role [120].”
The anatomy of choice: active inference and agency (2013, background): “…between distributions over the ﬁnal state) that leads to a small value and associated precision. surprise—exactly the same conclusions have been reached when examining predictive coding schemes (Friston et al., 2012b) and hierarchical inference using volatility models (Mathys et al., 2011).”
Observing the Observer (II): Deciding When to Decide (2010, background): “A full treatment of these issues will be presented in forthcoming work [33].”
Uncertainty in perception and the Hierarchical Gaussian Filter (2014, methodology): “Wecan now derive update equations using the variational inversion method introduced in Mathys et al. (2011).”
Novelty or Surprise? (2013, methodology): “Variational approximation plays a key role in the hierarchical architecture proposed by Mathys et al. (2011), who discuss the relationship of the resulting learning process to error-correction methods like the Rescorla-Wagner model.”