(This post is part 4 of a series of blog posts discussing Bayesian estimation of Signal Detection models.) In this blog post, I describe how to estimate the unequal variances Gaussian signal detection (UVSDT) model for confidence rating responses, for multiple participants simultaneously. I provide software code for the hierarchical Bayesian model in R.
(This post is part 3 in a series of blog posts discussing Bayesian estimation of Signal Detection models.) In this post, we extend the EVSDT model to confidence rating responses, and estimate the resulting model as an ordinal probit regression. I also describe how to estimate the unequal variance Gaussian SDT model for a single participant. I provide a software implementation in R.
This is a part of a series of blog posts discussing Bayesian estimation of Signal Detection models. In this post, I describe how to estimate the equal variance Gaussian SDT model’s parameters for multiple participants simultaneously, using Bayesian generalized linear and nonlinear hierarchical models. I provide a software implementation in R.
Signal Detection Theory (SDT) is a common framework for modeling memory and perception. Calculating point estimates of equal variance Gaussian SDT parameters is easy using widely known formulas. More complex SDT models, such as the unequal variance SDT model, require more complicated modeling techniques. These models can be estimated using Bayesian (nonlinear and/or hierarchical) regression methods, which are sometimes difficult to implement in practice. In this post, I describe how to estimate the equal variance Gaussian SDT model’s parameters for a single participant with a Generalized Linear Model, and a nonlinear model. I describe the software implementation in R.
Assessing the correlations between psychological variabless, such as abilities and improvements, is one essential goal of psychological science. However, psychological variables are usually only available to the researcher as estimated parameters in mathematical and statistical models. The parameters are often estimated from small samples of observations for each research participant, which results in uncertainty (aka sampling error) about the participant-specific parameters. Ignoring the resulting uncertainty can lead to suboptimal inferences, such as asserting findings with too much confidence. Hierarchical models alleviate this problem by accounting for each parameter’s uncertainty at the person- and average levels. However, common maximum likelihood estimation methods can have difficulties converging and finding appropriate values for parameters that describe the person-level parameters’ spread and correlation. In this post, I discuss how Bayesian hierarchical models solve this problem, and advocate their use in estimating psychological variables and their correlations.
In this blog post, I use metadata from 70k Psychology journal articles, published in 25 journals from Scopus, to visualize ‘consciousness hubs’, or academic institutions that publish (more than other institutions) research on consciousness.
Last spring, at The Science of Consciousness conference in Tucson (previously known as Toward a Science of Consciousness), I was fortunate to be asked to participate in a discussion panel on Consciousness and Free Will. I only now found out that they have uploaded the videos from all the plenary talks and panels on YouTube.
The plenary talk before the panel was given by Aaron Schurger, on a computational model of the controversial results from Benjamin Libet’s experiments (a really great talk about a very nice paper, I might add).
Only 6 months after initial submission, our paper on the phenomenology of agency and the flow state is out in Consciousness and Cognition.
In the experiments, volunteer participants played an arcade-style computer game, and provided judgments of how in control they felt, or judgments of flow, after each 20-second round of the game. The phenomenology of flow is interesting because it is a very common experience, although people might not know this verbal label for it.
We recently ran a Scientific Practices Workshop, and one of us later collected several links for follow-up materials for the interested. I thought the list of links was a fantastic source of materials, so I post it here: Why this is important? (New) A publication reform is needed Would you like to take the recommended Statistical Rethinking course? Statistical Rethinking: The course, the lectures, the textbook. Would you like to learn more about Bayesian statistics?