Recent work has highlighted that multi-voxel pattern analysis (MVPA) can be severely biased when BOLD response estimation involves systematic imbalance in model regressor correlations. This problem occurs in situations where trial types of interest are temporally dependent and the associated BOLD activity overlaps. For example, in learning paradigms early and late learning stage trials are inherently ordered. It has been shown empirically that MVPAs assessing consecutive learning stages can be substantially biased especially when stages are closely spaced. Here, we propose a simple technique that ensures zero bias in item-specific multi-voxel activation patterns for consecutive learning stages with stage being defined by the incremental number of individual item occurrences. For the simpler problem, when MVPA is computed irrespective of learning stage over all item occurrences within a trial sequence, our results confirm that a sufficiently large, randomly selected subset of all possible trial sequence permutations ensures convergence to zero bias – but only when different trial sequences are generated for different subjects. However, this does not help to solve the harder problem to obtain bias-free results for learning-related activation patterns regarding consecutive learning stages. Randomization over all item occurrences fails to ensure zero bias when the full trial sequence is retrospectively divided into item occurrences confined to early and late learning stages. To ensure bias-free MVPA of consecutive learning stages, trial-sequence randomization needs to be done separately for each consecutive learning stage.
Forward planning is often essential to achieve goals over extended time periods. However, forward planning is typically computationally costly for the brain and should only be employed when necessary. The explicit calculation of how necessary forward planning will be, is in itself computationally costly. We therefore assumed that the brain generates a mapping from a particular situation to a proxy of planning value to make fast decisions about whether to use forward planning, or not. Moreover, since the state space of real world decision problems can be large, we hypothesized that such a mapping will rely on mechanisms that generalize sets of situations based on shared demand for planning. We tested this hypothesis in an fMRI study using a novel complex sequential task. Our results indicate that participants abstracted from the set of task features to more generalized control contexts that govern the balancing between forward planning and a simple response strategy. Strikingly, we found that correlations of conflict with response time and with activity in the dorsal anterior cingulate cortex were dependent on context. This context-dependency might reflect that the cognitive control system draws on category-based cognition, harnessing regularities in control demand across task space to generate control contexts that help reduce the complexity of control allocation decisions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.