Temporally remote destabilization of prediction after rare breaches of expectancy

The significance of structural rich club hubs for the processing of hierarchical stimuli

The brain's structural network follows a hierarchy that is described as rich club (RC) organization, with RC hubs forming the well-interconnected top of this hierarchy. In this study, we tested whether RC hubs are involved in the processing of hierarchically higher structures in stimulus sequences. Moreover, we explored the role of previously suggested cortical gradients along anterior-posterior and medial-lateral axes throughout the frontal cortex. To this end, we conducted a functional magnetic resonance imaging (fMRI) experiment and presented participants with blocks of digit sequences that were structured on different hierarchically nested levels. We additionally collected diffusion weighted imaging data of the same subjects to identify RC hubs. This classification then served as the basis for a region of interest analysis of the fMRI data. Moreover, we determined structural network centrality measures in areas that were found as activation clusters in the whole-brain fMRI analysis. Our findings support the previously found anterior and medial shift for processing hierarchically higher structures of stimuli. Additionally, we found that the processing of hierarchically higher structures of the stimulus structure engages RC hubs more than for lower levels. Areas involved in the functional processing of hierarchically higher structures were also more likely to be part of the structural RC and were furthermore more central to the structural network. In summary, our results highlight the potential role of the structural RC organization in shaping the cortical processing hierarchy.

Exploitation of local and global information in predictive processing

While prediction errors have been established to instigate learning through model adaptation, recent studies have stressed the role of model-compliant events in predictive processing. Specifically, probabilistic information at critical points in time (so-called checkpoints) has been suggested to be sampled in order to evaluate the internal model, particularly in uncertain contexts. This way, initial model-based expectations are iteratively reaffirmed under uncertainty, even in the absence of prediction errors. Using electroencephalography (EEG), the present study aimed to investigate the interplay of such global uncertainty information and local adjustment cues prompting on-line adjustments of expectations. Within a stream of single digits, participants were to detect ordered sequences (i.e., 3-4-5-6-7) that had a regular length of five digits and were occasionally extended to seven digits. Over time, these extensions were either rare (low irreducible uncertainty) or frequent (high uncertainty) and could be unexpected or indicated by incidental colour cues. Accounting for cue information, an N400 component was revealed as the correlate of locally unexpected (vs expected) outcomes, reflecting effortful integration of incongruous information. As for model-compliant information, multivariate pattern decoding within the P3b time frame demonstrated effective exploitation of local (adjustment cues vs non-informative analogues) and global information (high vs low uncertainty regular endings) sampled from probabilistic events. Finally, superior fit of a global model (disregarding local adjustments) compared to a local model (including local adjustments) in a representational similarity analysis underscored the precedence of global reference frames in hierarchical predictive processing. Overall, results suggest that just like error-induced model adaptation, model evaluation is not limited to either local or global information. Following the hierarchical organisation of predictive processing, model evaluation too can occur at several levels of the processing hierarchy.

Being right matters: Model-compliant events in predictive processing

While prediction errors (PE) have been established to drive learning through adaptation of internal models, the role of model-compliant events in predictive processing is less clear. Checkpoints (CP) were recently introduced as points in time where expected sensory input resolved ambiguity regarding the validity of the internal model. Conceivably, these events serve as on-line reference points for model evaluation, particularly in uncertain contexts. Evidence from fMRI has shown functional similarities of CP and PE to be independent of event-related surprise, raising the important question of how these event classes relate to one another. Consequently, the aim of the present study was to characterise the functional relationship of checkpoints and prediction errors in a serial pattern detection task using electroencephalography (EEG). Specifically, we first hypothesised a joint P3b component of both event classes to index recourse to the internal model (compared to non-informative standards, STD). Second, we assumed the mismatch signal of PE to be reflected in an N400 component when compared to CP. Event-related findings supported these hypotheses. We suggest that while model adaptation is instigated by prediction errors, checkpoints are similarly used for model evaluation. Intriguingly, behavioural subgroup analyses showed that the exploitation of potentially informative reference points may depend on initial cue learning: Strict reliance on cue-based predictions may result in less attentive processing of these reference points, thus impeding upregulation of response gain that would prompt flexible model adaptation. Overall, present results highlight the role of checkpoints as model-compliant, informative reference points and stimulate important research questions about their processing as function of learning und uncertainty.

Frontostriatal Contribution to the Interplay of Flexibility and Stability in Serial Prediction

Surprising events may be relevant or irrelevant for behavior, requiring either flexible adjustment or stabilization of our model of the world and according response strategies. Cognitive flexibility and stability in response to environmental demands have been described as separable cognitive states, associated with activity of striatal and lateral prefrontal regions, respectively. It so far remains unclear, however, whether these two states act in an antagonistic fashion and which neural mechanisms mediate the selection of respective responses, on the one hand, and a transition between these states, on the other. In this study, we tested whether the functional dichotomy between striatal and prefrontal activity applies for the separate functions of updating (in response to changes in the environment, i.e., switches) and shielding (in response to chance occurrences of events violating expectations, i.e., drifts) of current predictions. We measured brain activity using fMRI while 20 healthy participants performed a task that required to serially predict upcoming items. Switches between predictable sequences had to be indicated via button press while sequence omissions (drifts) had to be ignored. We further varied the probability of switches and drifts to assess the neural network supporting the transition between flexible and stable cognitive states as a function of recent performance history in response to environmental demands. Flexible switching between models was associated with activation in medial pFC (BA 9 and BA 10), whereas stable maintenance of the internal model corresponded to activation in the lateral pFC (BA 6 and inferior frontal gyrus). Our findings extend previous studies on the interplay of flexibility and stability, suggesting that different prefrontal regions are activated by different types of prediction errors, dependent on their behavioral requirements. Furthermore, we found that striatal activation in response to switches and drifts was modulated by participants' successful behavior toward these events, suggesting the striatum to be responsible for response selections following unpredicted stimuli. Finally, we observed that the dopaminergic midbrain modulates the transition between different cognitive states, thresholded by participants' individual performance history in response to temporal environmental demands.

Action observers implicitly expect actors to act goal-coherently, even if they do not: an fMRI study

Actions observed in everyday life normally consist of one person performing sequences of goal-directed actions. The present fMRI study tested the hypotheses that observers are influenced by the actor's identity, even when this information is task-irrelevant, and that this information shapes their expectation on subsequent actions of the same actor. Participants watched short video clips of action steps that either pertained to a common action with an overarching goal or not, and were performed by either one or by varying actors (2 × 2 design). Independent of goal coherence, actor coherence elicited activation in dorsolateral and ventromedial frontal cortex, together pointing to a spontaneous attempt to integrate all actions performed by one actor. Interestingly, watching an actor performing unrelated actions elicited additional activation in left inferior frontal gyrus, suggesting a search in semantic memory in an attempt to construct an overarching goal that can reconcile the disparate action steps with a coherent intention. Post-experimental surveys indicate that these processes occur mostly unconsciously. Findings strongly suggest a spontaneous expectation bias toward actor-related episodes in action observers, and hence to the immense impact of actor information on action observation.