Context-Dependence and Context-Invariance in the Neural Coding of Intentional Action

Adaptive coding of stimulus information in human frontoparietal cortex during visual classification

The neural mechanisms of how frontal and parietal brain regions support flexible adaptation of behavior remain poorly understood. Here, we used functional magnetic resonance imaging (fMRI) and representational similarity analysis (RSA) to investigate frontoparietal representations of stimulus information during visual classification under varying task demands. Based on prior research, we predicted that increasing perceptual task difficulty should lead to adaptive changes in stimulus coding: task-relevant category information should be stronger, while task-irrelevant exemplar-level stimulus information should become weaker, reflecting a focus on the behaviorally relevant category information. Counter to our expectations, however, we found no evidence for adaptive changes in category coding. We did find weakened coding at the exemplar-level within categories however, demonstrating that task-irrelevant information is de-emphasized in frontoparietal cortex. These findings reveal adaptive coding of stimulus information at the exemplar-level, highlighting how frontoparietal regions might support behavior even under challenging conditions.

The effect of context and reason on the neural correlates of intentions

Many studies have identified networks in parietal and prefrontal cortex that are involved in intentional action. Yet, our understanding of the way these networks are involved in intentions is still very limited. In this study, we investigate two characteristics of these processes: context- and reason-dependence of the neural states associated with intentions. We ask whether these states depend on the context a person is in and the reasons they have for choosing an action. We used a combination of functional magnetic resonance imaging (fMRI) and multivariate decoding to directly assess the context- and reason-dependency of the neural states underlying intentions. We show that action intentions can be decoded from fMRI data based on a classifier trained in the same context and with the same reason, in line with previous decoding studies. Furthermore, we found that intentions can be decoded across different reasons for choosing an action. However, decoding across different contexts was not successful. We found anecdotal to moderate evidence against context-invariant information in all regions of interest and for all conditions but one. These results suggest that the neural states associated with intentions are modulated by the context of the action.

Functional magnetic resonance imaging multivoxel pattern analysis reveals neuronal substrates for collaboration and competition with myopic and predictive strategic reasoning

Competition and collaboration are strategies that can be used to optimize the outcomes of social interactions. Research into the neuronal substrates underlying these aspects of social behavior has been limited due to the difficulty in distinguishing complex activation via univariate analysis. Therefore, we employed multivoxel pattern analysis of functional magnetic resonance imaging to reveal the neuronal activations underlying competitive and collaborative processes when the collaborator/opponent used myopic/predictive reasoning. Twenty‐four healthy subjects participated in 2 × 2 matrix‐based sequential‐move games. Searchlight‐based multivoxel patterns were used as input for a support vector machine using nested cross‐validation to distinguish game conditions, and identified voxels were validated via the regression of the behavioral data with bootstrapping. The left anterior insula (accuracy = 78.5%) was associated with competition, and middle frontal gyrus (75.1%) was associated with predictive reasoning. The inferior/superior parietal lobules (84.8%) and middle frontal gyrus (84.7%) were associated with competition, particularly in trials with a predictive opponent. The visual/motor areas were related to response time as a proxy for visual attention and task difficulty. Our results suggest that multivoxel patterns better represent the neuronal substrates underlying the social cognition of collaboration and competition intermixed with myopic and predictive reasoning than do univariate features.

Shared Neural Representations of Cognitive Conflict and Negative Affect in the Medial Frontal Cortex

Influential theories of Medial Frontal Cortex (MFC) function suggest that the MFC registers cognitive conflict as an aversive signal, but no study directly tested this idea. Instead, recent studies suggested that nonoverlapping regions in the MFC process conflict and affect. In this preregistered human fMRI study (male and female), we used MVPAs to identify which regions respond similarly to conflict and aversive signals. The results reveal that, of all conflict- and value-related regions, only the ventral pre-supplementary motor area (or dorsal anterior cingulate cortex) showed a shared neural pattern response to different conflict and affect tasks. These findings challenge recent conclusions that conflict and affect are processed independently, and provide support for integrative views of MFC function.SIGNIFICANCE STATEMENT Multiple theories propose that the MFC, and the dorsal ACC in particular, integrates information related to suboptimal outcomes from different psychological domains (e.g., cognitive control and negative affect) with the aim of adaptively steering behavior. In contrast to recent studies in the field, we provide evidence for the idea that cognitive control and negative affect are integrated in the MFC by showing that a classification algorithm trained on discerning cognitive control (conflict vs no conflict) can predict affect (negative vs positive) in the voxel pattern response of the dorsal ACC/pre-SMA.

Decoding Changes of Mind in Voluntary Action-Dynamics of Intentional Choice Representations

Voluntary actions rely on appropriate flexibility of intentions. Usually, we should pursue our goals, but sometimes we should change goals if they become too costly to achieve. Using functional magnetic resonance imaging, we investigated the neural dynamics underlying the capacity to change one's mind based on new information after action onset. Multivariate pattern analyses revealed that in visual areas, neural representations of intentional choice between 2 visual stimuli were unchanged by additional decision-relevant information. However, in fronto-parietal cortex, representations changed dynamically as decisions evolved. Precuneus, angular gyrus, and dorsolateral prefrontal cortex encoded new externally cued rewards/costs that guided subsequent changes of mind. Activity in medial frontal cortex predicted changes of mind when participants detached from externally cued evidence, suggesting a role in endogenous decision updates. Finally, trials with changes of mind were associated with an increase in functional connectivity between fronto-parietal areas, allowing for integration of various endogenous and exogenous decision components to generate a distributed consensus about whether to pursue or abandon an initial intention. In conclusion, local and global dynamics of choice representations in fronto-parietal cortex allow agents to maintain the balance between adapting to changing environments versus pursuing internal goals.

Outcome contingency selectively affects the neural coding of outcomes but not of tasks

Value-based decision-making is ubiquitous in every-day life, and critically depends on the contingency between choices and their outcomes. Only if outcomes are contingent on our choices can we make meaningful value-based decisions. Here, we investigate the effect of outcome contingency on the neural coding of rewards and tasks. Participants performed a reversal-learning paradigm in which reward outcomes were contingent on trial-by-trial choices, and performed a ‘free choice’ paradigm in which rewards were random and not contingent on choices. We hypothesized that contingent outcomes enhance the neural coding of rewards and tasks, which was tested using multivariate pattern analysis of fMRI data. Reward outcomes were encoded in a large network including the striatum, dmPFC and parietal cortex, and these representations were indeed amplified for contingent rewards. Tasks were encoded in the dmPFC at the time of decision-making, and in parietal cortex in a subsequent maintenance phase. We found no evidence for contingency-dependent modulations of task signals, demonstrating highly similar coding across contingency conditions. Our findings suggest selective effects of contingency on reward coding only, and further highlight the role of dmPFC and parietal cortex in value-based decision-making, as these were the only regions strongly involved in both reward and task coding.