Neural superstatistics for Bayesian estimation of dynamic cognitive models
Mathematical models of cognition are often memoryless and ignore potential fluctuations of
their parameters. However, human cognition is inherently dynamic. Thus, we propose to …
their parameters. However, human cognition is inherently dynamic. Thus, we propose to …
An extension of the shifted Wald model of human response times: Capturing the time dynamic properties of human cognition: Trial-varying Wald model
Despite the ubiquitous nature of evidence accumulation models in cognitive and
experimental psychology, there has been a comparatively limited uptake of such techniques …
experimental psychology, there has been a comparatively limited uptake of such techniques …
Validation and comparison of non-stationary cognitive models: A diffusion model application
Cognitive processes undergo various fluctuations and transient states across different
temporal scales. Superstatistics are emerging as a flexible framework for incorporating such …
temporal scales. Superstatistics are emerging as a flexible framework for incorporating such …
The Bayesian Mutation Sampler explains distributions of causal judgments
IR Kolvoort, N Temme, L van Maanen - Open Mind, 2023 - direct.mit.edu
One consistent finding in the causal reasoning literature is that causal judgments are rather
variable. In particular, distributions of probabilistic causal judgments tend not to be normal …
variable. In particular, distributions of probabilistic causal judgments tend not to be normal …
Self-reported mind wandering reflects executive control and selective attention
Mind wandering is ubiquitous in everyday life and has a pervasive and profound impact on
task-related performance. A range of psychological processes have been proposed to …
task-related performance. A range of psychological processes have been proposed to …
New estimation approaches for the hierarchical Linear Ballistic Accumulator model
Abstract The Linear Ballistic Accumulator (LBA: Brown and Heathcote, 2008) model is used
as a measurement tool to answer questions about applied psychology. The analyses based …
as a measurement tool to answer questions about applied psychology. The analyses based …
What Happens After a Fast Versus Slow Error, and How Does It Relate to Evidence Accumulation?
KAM Damaso, PG Williams, A Heathcote - Computational Brain & Behavior, 2022 - Springer
It has traditionally been assumed that responding after an error is slowed because
participants try to improve their accuracy by increasing the amount of evidence required for …
participants try to improve their accuracy by increasing the amount of evidence required for …
Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive Models
Mathematical models of cognition are often memoryless and ignore potential fluctuations of
their parameters. However, human cognition is inherently dynamic. Thus, we propose to …
their parameters. However, human cognition is inherently dynamic. Thus, we propose to …
Grounding computational cognitive models
Cognitive scientists and neuroscientists are increasingly deploying computational models to
develop testable theories of psychological functions and make quantitative predictions about …
develop testable theories of psychological functions and make quantitative predictions about …
Consistency is the key! Learning to adapt in a multi-context predictive inference task.
Predictive inference is an important cognitive function and there are many tasks which
measure it, and the error driven learning that underpins it. Context is a key contribution to …
measure it, and the error driven learning that underpins it. Context is a key contribution to …