Bayesian methods can be used in general data-analytic models, in psychometric models, and in models of mind. What is the difference? In all three applications, there is Bayesian estimation of parameter values in a model. What differs between models is the source of the data and the meaning (semantic referent) of the parameters, as described in the diagram below:
As an example of a generic data-analytic model, consider data about ice cream sales and sleeve lengths, measured at different times of year. A linear regression model might show a negative slope for the line that describes a trend in the scatter of points. But the slope does not necessarily describe anything in the processes that generated the ice cream sales and sleeve lengths.
As an example of a psychometric model, consider multidimensional scaling (MDS). The data are similarity ratings (or confusion matrices) from a human observer, and the parameters are coordinates of items in a geometric representation of mental constructs that produced the ratings. Note that if the MDS model is applied to non-behavioral data, such as inter-city road distances, then it is not a psychometric model.
As an example of a Bayesian model of mind, consider models of visual perception of concavity. The data are the light beams reflecting off the curved object in the world and falling on the retina. The model in the mind has parameters that represent the shape of the object in the world being viewed and the angle of the light falling on it. The prior has strong bias for light falling from above (e.g., from the sun and sky). The posterior estimate of shape and lighting then tends to produce interpretations of shapes consistent with overhead lighting, unless there is strong evidence to the contrary. Notice that the parameter values are inside the head, so there must be additional assumptions regarding how to measure those parameter values.