Computational advances towards linking BOLD and behavior

 

Serences and Saproo, Neuropsychologia (in press).

 

Traditionally, fMRI studies have focused on analyzing the mean response amplitude within a cortical area. However, the mean response is blind to many important patterns of cortical modulation, which severely limits the formulation and evaluation of linking hypotheses between neural activity, BOLD responses, and behavior. More recently, multivariate pattern classification analysis (MVPA) has been applied to fMRI data to evaluate the information content of spatially distributed activation patterns. This approach has been remarkably successful at detecting the presence of specific information in targeted brain regions, and provides an extremely flexible means of extracting that information without a precise generative model for the underlying neural activity. However, this flexibility comes at a cost: since MVPA relies on pooling information across voxels that are selective for many different stimulus attributes, it is difficult to infer how specific sub-sets of tuned neurons are modulated by an experimental manipulation. In contrast, recently developed encoding models can produce more precise estimates of feature-selective tuning functions, and can support the creation of explicit linking hypotheses between neural activity and behavior. Although these encoding models depend on strong – and often untested – assumptions about the response properties of underlying neural generators, they also provide a unique opportunity to evaluate population-level computational theories of perception and cognition that have previously been difficult to assess using either single-unit recording or conventional neuroimaging techniques.

New paper accepted at Trends in Cogntive Science: B. Forstmann, E.J. Wagenmakers, T. Eichele, S. Brown, J. Serences

Cognitive neuroscientists study how the brain implements particular cognitive processes such as perception, learning, and decision-making. Traditional approaches in which experiments are designed to target a specific cognitive process have been supplemented by two recent innovations. First, formal cognitive models can decompose observed behavioral data into multiple latent cognitive processes, allowing brain measurements to be associated with a particular cognitive process more precisely and more confidently. Second, cognitive neuroscience can provide additional data to inform the development of formal cognitive models, providing greater constraint than behavioral data alone. We argue that these fields are mutually dependent: not only can models guide neuroscientific endeavors, but understanding neural mechanisms can provide key insights into formal models of cognition.