We provide a novel computational framework on how biological and artificial agents can learn to flexibly couple and decouple neural task modules for cognitive processing. In this way, they can address the stability-plasticity dilemma. For this purpose, we combine two prominent computational neuroscience principles, namely Binding by Synchrony and Reinforcement Learning. The model learns to synchronize task-relevant modules, while also learning to desynchronize currently task-irrelevant modules. As a result, old (but currently task-irrelevant) information is protected from overwriting (stability) while new information can be learned quickly in currently task-relevant modules (plasticity). We combine learning to synchronize with task modules that learn via one of several classical learning algorithms (Rescorla-Wagner, backpropagation, Boltzmann machines). The resulting combined model is tested on a reversal learning paradigm where it must learn to switch between three different task rules. We demonstrate that our combined model has significant computational advantages over the original network without synchrony, in terms of both stability and plasticity. Importantly, the resulting models’ processing dynamics are also consistent with empirical data and provide empirically testable hypotheses for future MEG/EEG studies.

In recent years, several hierarchical extensions of well-known learning algorithms have been proposed. For example, when stimulus-action mappings vary across time or context, the brain may learn two or more stimulus-action mappings in separate modules, and additionally (at a hierarchically higher level) learn to appropriately switch between those modules. However, how the brain mechanistically coordinates neural communication to implement such hierarchical learning remains unknown. Therefore, the current study tests a recent computational model that proposed how midfrontal theta oscillations implement such hierarchical learning via the principle of binding by synchrony (Sync model). More specifically, the Sync model uses bursts at theta frequency to flexibly bind appropriate task modules by synchrony. The 64-channel EEG signal was recorded while 27 human subjects (female: 21, male: 6) performed a probabilistic reversal learning task. In line with the Sync model, postfeedback theta power showed a linear relationship with negative prediction errors, but not with positive prediction errors. This relationship was especially pronounced for subjects with better behavioral fit (measured via Akaike information criterion) of the Sync model. Also consistent with Sync model simulations, theta phase-coupling between midfrontal electrodes and temporoparietal electrodes was stronger after negative feedback. Our data suggest that the brain uses theta power and synchronization for flexibly switching between task rule modules, as is useful, for example, when multiple stimulusaction mappings must be retained and used.

A classic example of discriminatory behavior is keeping spatial distance from an out-group member. To explain this social behavior, the literature offers two alternative theoretical options that we label as the "threat hypothesis" and the "shared-experience hypothesis". The former relies on studies showing that out-group members create a sense of alertness. Consequently, potentially threatening out-group members are represented as spatially close allowing the prevention of costly errors. The latter hypothesis suggests that the observation of out-group members reduces the sharing of somatosensory experiences and, thus, increases the perceived physical distance between oneself and others. In the present paper, we pitted the two hypotheses against each other. In Experiment 1, Caucasian participants expressed multiple implicit "Near/Far" spatial categorization judgments from a Black-African Avatar and a White-Caucasian Avatar located in a 3D environment. Results indicate that the Black-African Avatar was categorized as closer to oneself, as compared with the White-Caucasian Avatar, providing support for "the threat hypothesis". In Experiment 2, we tested to which degree perceived threat contributes to this categorization bias by manipulating the avatar's perceived threat orthogonally to group membership. The results indicate that irrespective of group membership, threatening avatars were categorized as being closer to oneself as compared with no threatening avatars. This suggests that provided information about a person and not the mere group membership influences perceived distance to the person.

Cognitive control is supported by theta band (4-7Hz) neural oscillations that coordinate distant neural populations for task implementation. Task performance has been shown to depend on theta amplitude but a second critical aspect of theta oscillations, its peak frequency, has mostly been overlooked. Using modelling, behavioral and electrophysiological recordings, we show that theta oscillations adapt to task demands by shifting towards the optimal frequency.

Cognitive control is supported by theta band (4-7Hz) neural oscillations coordinating neural populations for task implementation. Task performance has been shown to depend on theta amplitude but a second critical aspect of theta oscillations, its peak frequency, has mostly been overlooked. Using modelling, behavioral and electrophysiological recordings, we show that theta oscillations adapt to task demands by shifting towards the optimal frequency..