My undergraduate and graduate training in psychology and cognitive neuroscience focused on computational modeling and behavioral experimentation: implementing concrete models to test cognitive theories by simulation and evaluating predictions from those models with behavioral experiments. During this time, good ol’ t-test was enough statistics for me. I continued this sort of work during my post-doctoral fellowship, but as I became more interested in studying the time course of cognitive processing, I had to learn about statistical modeling, specifically, growth curve analysis (multilevel regression) for time series data. These two kinds of modeling – computational/cognitive and statistical – are often conflated, but I believe they are very different and serve complementary purposes in cognitive science and cognitive neuroscience.
It will help to have some examples of what I mean when I say that statistical and cognitive models are sometimes conflated. I have found that computational modeling talks sometimes provoke a certain kind of skeptic to ask “With a sufficient number of free parameters it is possible to fit any data set, so how many parameters does your model have?” The first part of that question is true in a strictly mathematical sense: for example, a Taylor series polynomial can be used to approximate any function with arbitrary precision. But this is not how cognitive modeling works. Cognitive models are meant to implement theoretical principles, not arbitrary mathematical functions, and although they always have some flexible parameters, these parameters are not “free” in the way that the coefficients of a Taylor series are free.
On the other hand, when analyzing behavioral data, it can be tempting to use a statistical model with parameters that map in some simple way onto theoretical constructs. For example, assuming Weber’s Law holds (a power law relationship between physical stimulus magnitude and perceived intensity), one can collect data in some domain of interest, fit a power law function, and compute the Weber constant for that domain. However, if you happen to be studying a domain where Weber’s law does not quite hold, your Weber constant will not be very informative.
In other words, statistical and computational models have different, complementary goals. The point of statistical models is to describe or quantify the observed data. This is immensely useful because extracting key effects or patterns allows us to talk about large data sets in terms of a small number of “effects” or differences between conditions. Such descriptions are best when they focus on the data themselves and are independent of any particular theory – this allows researchers to evaluate any and all theories against the data. Statistical models need to worry about number of free parameters and this is captured by standard goodness-of-fit statistics such as AIC, BIC, and log-likelihood.
In contrast, cognitive models are meant to test a specific theory, so fidelity to the theory is more important than counting the number of parameters. Ideally, the cognitive model’s output can be compared directly to the observed behavioral data, using more or less the same model comparison techniques (R-squared, log-likelihood, etc.). However, because cognitive models are usually simplified, that kind of quantitative fit is not always possible (or even advisable) and a qualitative comparison of model and behavioral data must suffice. This qualitative comparison critically depends on an accurate – and theory-neutral – description of the behavioral data, which is provided by the statistical model. (A nice summary of different methods of evaluating computational models against behavioral data is provided by Pitt et al.,2006).
Jim Magnuson, J. Dixon, and I advocated this kind of two-pronged approach – using statistical models to describe the data and computational models to evaluate theories – when we adapted growth curve analysis to eye-tracking data (Mirman etal., 2008). Then, working with Eiling Yee and Sheila Blumstein, we used this approach to study phonological competition in spoken word recognition in aphasia (Mirman etal., 2011). To my mind, this is the optimal way to simultaneously maximize accurate description of behavioral data and theoretical impact of the research.