Preserved representations and decodability of diverse cognitive functions across the cortex, cerebellum, and subcortex

Which part of the brain contributes to our complex cognitive processes? Studies have revealed contributions of the cerebellum and subcortex to higher-order cognitive functions; however it is unclear whether such functional representations are preserved across the cortex, cerebellum, and subcortex. In this study, we used functional magnetic resonance imaging data with 103 cognitive tasks and constructed three voxel-wise encoding and decoding models independently using cortical, cerebellar, and subcortical voxels. Representational similarity analysis revealed that the structure of task representations is preserved across the three brain parts. Principal component analysis visualized distinct organizations of abstract cognitive functions in each part of the cerebellum and subcortex. More than 90% of the cognitive tasks were decodable from the cerebellum and subcortical activities, even for the novel tasks not included in model training. Furthermore, we discovered that the cerebellum and subcortex have sufficient information to reconstruct activity in the cerebral cortex.

Multi-task representations in human cortex transform along a sensory-to-motor hierarchy

Human cognition recruits diverse neural processes, yet the organizing computational and functional architectures remain unclear. Here, we characterized the geometry and topography of multi-task representations across human cortex using functional MRI during 26 cognitive tasks in the same subjects. We measured the representational similarity across tasks within a region, and the alignment of representations between regions. We found a cortical topography of representational alignment following a hierarchical sensory-association-motor gradient, revealing compression-then-expansion of multi-task dimensionality along this gradient. To investigate computational principles of multi-task representations, we trained multi-layer neural network models to transform empirical visual to motor representations. Compression-then-expansion organization in models emerged exclusively in a training regime where internal representations are highly optimized for sensory-to-motor transformation, and not under generic signal propagation. This regime produces hierarchically structured representations similar to empirical cortical patterns. Together, these results reveal computational principles that organize multi-task representations across human cortex to support flexible cognition.

Similarity in evoked responses does not imply similarity in macroscopic network states across tasks

There is an ongoing debate as to whether cognitive processes arise from a group of functionally specialized brain modules (modularism) or as the result of a distributed nonlinear process (dynamical systems theory). The former predicts that tasks that recruit similar brain areas should have an equivalent degree of similarity in their connectivity. The latter allows for differential connectivity, even when the areas recruited are largely the same. Here we evaluated both views by comparing activation and connectivity patterns from a large sample of healthy subjects (N=242) that performed two executive control tasks, color-word Stroop task and Multi-Source Interference Task (MSIT), known to recruit similar brain areas. Using a measure of instantaneous connectivity based on edge time series as outcome variables, we estimated task-related network profiles as connectivity changes between incongruent and congruent information conditions. The degree of similarity of such profiles at the group level between both tasks was substantially smaller than their overlapping activation responses. A similar finding was observed at the subject level and when employing a different method for defining task-related connectivity. Our results are consistent with the perspective of the brain as a dynamical system, suggesting that task representations should be understood at both node and edge (connectivity) levels.

Medial temporal and prefrontal cortices encode structural task representations at different levels of abstraction

Memory generalisations may be underpinned by either encoding- or retrieval-based mechanisms. We used a transitive inference task to investigate whether these generalisation mechanisms are influenced by progressive vs randomly interleaved training, and overnight consolidation. On consecutive days, participants learnt pairwise discriminations from two transitive hierarchies before being tested during fMRI. Inference performance was consistently better following progressive training, and for pairs further apart in the transitive hierarchy. BOLD pattern similarity correlated with hierarchical distances in the medial temporal lobe (MTL) and medial prefrontal cortex (MPFC). These results are consistent with the use of representations that directly encode structural relationships between different task features. Furthermore, BOLD patterns in MPFC were similar across the two independent hierarchies. We conclude that humans preferentially employ encoding-based mechanisms to store map-like relational codes that can be used for memory generalisation. While both MTL and MPFC support these representations, the MPFC encodes more abstract relational information.

Cognitive Capacity, Representation, and Instruction

The central argument of the present article is that Cognitive Psychology’s problems in dealing with the concept of “cognitive capacity” is intimately linked with Cognitive Psychology’s long-lasting failure of coming to terms with the concept of “representation” in general, and “task representation” in particular. From this perspective, the role of instructions in psychological experiments is emphasised. It is argued that both a careful conceptual analysis of instruction-induced task representations as well as an experimental variation of instructions promises to broaden our understanding of the role of task representations as a determinant of limited cognitive capacity.

Neural Correlates of Task-order Preparation in Dual Tasks: An EEG Study

Dual-task scenarios require a coordinated regulation of the processing order of component tasks in light of capacity limitations during response selection. A number of behavioral and neuroimaging findings suggest a distinct set of control processes involved in preparing this task order. In this study, we investigated electrophysiological correlates of task-order preparation in a variant of the overlapping dual-task paradigm with cue-determined task order that resulted in trials with blockwise fixed task order as well as trials with repeated and switched task order in blocks with variable task order. During the cue–stimulus interval, we found an earlier centroparietal order-mixing positivity and a later parietal order-switch positivity. A decoding approach based on multivariate pattern analysis showed that the order-mixing positivity is a necessary prerequisite for successful order selection, whereas the order-switch positivity appears to facilitate the implementation of a new task order after its selection. These correlates of order preparation share striking similarities to commonly found potentials involved in the preparation of individual tasks in the (single-)task-switching paradigm, which is strong empirical support for the account that the underlying preparatory processes are to be considered as higher-level control signals that are implemented independently of specific task representations.

Majority Decision-Making Works Best Under Conditions of Leadership Ambiguity and Shared Task Representations

The effectiveness of decision-making teams depends largely on their ability to integrate and make sense of information. Consequently, teams which more often use majority decision-making may make better quality decisions, but particularly so when they also have task representations which emphasize the elaboration of information relevant to the decision, in the absence of clear leadership. In the present study we propose that (a) majority decision-making will be more effective when task representations are shared, and that (b) this positive effect will be more pronounced when leadership ambiguity (i.e., team members’ perceptions of the absence of a clear leader) is high. These hypotheses were put to the test using a sample comprising 81 teams competing in a complex business simulation for seven weeks. As predicted, majority decision-making was more effective when task representations were shared, and this positive effect was more pronounced when there was leadership ambiguity. The findings extend and nuance earlier research on decision rules, the role of shared task representations, and leadership clarity.

Using top-down gating to optimally balance shared versus separated task representations

Human adaptive behavior requires continually learning and performing a wide variety of tasks, often with very little practice. To accomplish this, it is crucial to separate neural representations of different tasks in order to avoid interference. At the same time, sharing neural representations supports generalization and allows faster learning. Therefore, a crucial challenge is to find an optimal balance between shared versus separated representations. Typically, models of human cognition employ top-down gating signals to separate task representations, but there exist surprisingly little systematic computational investigations of how such gating is best implemented. We identify and systematically evaluate two crucial features of gating signals. First, top-down input can be processed in an additive or multiplicative manner. Second, the gating signals can be adaptive (learned) or non-adaptive (random). We cross these two features, resulting in four gating models which are tested on a variety of input datasets and tasks with different degrees of stimulus-action mapping overlap. The multiplicative adaptive gating model outperforms all other models in terms of accuracy. Moreover, this model develops hidden units that optimally share representations between tasks. Specifically, different than the binary approach of currently popular latent state models, it exploits partial overlap between tasks.