Unimodal and cross-modal prediction is enhanced in musicians

Musical training involves exposure to complex auditory and visual stimuli, memorization of elaborate sequences, and extensive motor rehearsal. It has been hypothesized that such multifaceted training may be associated with differences in basic cognitive functions, such as prediction, potentially translating to a facilitation in expert musicians. Moreover, such differences might generalize to non-auditory stimuli. This study was designed to test both hypotheses. We implemented a cross-modal attentional cueing task with auditory and visual stimuli, where a target was preceded by compatible or incompatible cues in mainly compatible (80% compatible, predictable) or random blocks (50% compatible, unpredictable). This allowed for the testing of prediction skills in musicians and controls. Musicians showed increased sensitivity to the statistical structure of the block, expressed as advantage for compatible trials (disadvantage for incompatible trials), but only in the mainly compatible (predictable) blocks. Controls did not show this pattern. The effect held within modalities (auditory, visual), across modalities, and when controlling for short-term memory capacity. These results reveal a striking enhancement in cross-modal prediction in musicians in a very basic cognitive task

Adaptive effort investment in cognitive and physical tasks: a neurocomputational model

Despite its importance in everyday life, the computational nature of effort investment remains poorly understood. We propose an effort model obtained from optimality considerations, and a neurocomputational approximation to the optimal model. Both are couched in the framework of reinforcement learning. It is shown that choosing when or when not to exert effort can be adaptively learned, depending on rewards, costs, and task difficulty. In the neurocomputational model, the limbic loop comprising anterior cingulate cortex (ACC) and ventral striatum in the basal ganglia allocates effort to cortical stimulus-action pathways whenever this is valuable. We demonstrate that the model approximates optimality. Next, we consider two hallmark effects from the cognitive control literature, namely proportion congruency and sequential congruency effects. It is shown that the model exerts both proactive and reactive cognitive control. Then, we simulate two physical effort tasks. In line with empirical work, impairing the model's dopaminergic pathway leads to apathetic behavior. Thus, we conceptually unify the exertion of cognitive and physical effort, studied across a variety of literatures (e.g., motivation and cognitive control) and animal species

Computational models of anterior cingulate cortex : at the crossroads between prediction and effort

In the last two decades the anterior cingulate cortex (ACC) has become one of the most investigated areas of the brain. Extensive neuroimaging evidence suggests countless functions for this region, ranging from conflict and error coding, to social cognition, pain and effortful control. In response to this burgeoning amount of data, a proliferation of computational models has tried to characterize the neurocognitive architecture of ACC. Early seminal models provided a computational explanation for a relatively circumscribed set of empirical findings, mainly accounting for EEG and fMRI evidence. More recent models have focused on ACC's contribution to effortful control. In parallel to these developments, several proposals attempted to explain within a single computational framework a wider variety of empirical findings that span different cognitive processes and experimental modalities. Here we critically evaluate these modeling attempts, highlighting the continued need to reconcile the array of disparate ACC observations within a coherent, unifying framework

Overlapping neural systems represent cognitive effort and reward anticipation

Anticipating a potential benefit and how difficult it will be to obtain it are valuable skills in a constantly changing environment. In the human brain, the anticipation of reward is encoded by the Anterior Cingulate Cortex (ACC) and Striatum. Naturally, potential rewards have an incentive quality, resulting in a motivational effect improving performance. Recently it has been proposed that an upcoming task requiring effort induces a similar anticipation mechanism as reward, relying on the same cortico-limbic network. However, this overlapping anticipatory activity for reward and effort has only been investigated in a perceptual task. Whether this generalizes to high-level cognitive tasks remains to be investigated. To this end, an fMRI experiment was designed to investigate anticipation of reward and effort in cognitive tasks. A mental arithmetic task was implemented, manipulating effort (difficulty), reward, and delay in reward delivery to control for temporal confounds. The goal was to test for the motivational effect induced by the expectation of bigger reward and higher effort. The results showed that the activation elicited by an upcoming difficult task overlapped with higher reward prospect in the ACC and in the striatum, thus highlighting a pivotal role of this circuit in sustaining motivated behavior

Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner

Published: August 24, 2018Optimal decision-making is based on integrating information from several dimensions of decisional space (e.g., reward expectation, cost estimation, effort exertion). Despite considerable empirical and theoretical efforts, the computational and neural bases of such multidimensional integration have remained largely elusive. Here we propose that the current theoretical stalemate may be broken by considering the computational properties of a cortical-subcortical circuit involving the dorsal anterior cingulate cortex (dACC) and the brainstem neuromodulatory nuclei: ventral tegmental area (VTA) and locus coeruleus (LC). From this perspective, the dACC optimizes decisions about stimuli and actions, and using the same computational machinery, it also modulates cortical functions (meta-learning), via neuromodulatory control (VTA and LC). We implemented this theory in a novel neuro-computational model–the Reinforcement Meta Learner (RML). We outline how the RML captures critical empirical findings from an unprecedented range of theoretical domains, and parsimoniously integrates various previous proposals on dACC functioning.MS was funded from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 795919. EV was funded from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 705630. EA was supported by Research Foundation Flanders under contract number 12C4715N. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Dissociable effects of reward magnitude on fronto-medial theta and FRN during performance monitoring

Reward processing is influenced by reward magnitude, as previous EEG studies showed changes in amplitude of the feedback-related negativity (FRN) and reward positivity (RewP), or power of fronto-medial theta (FM theta). However, it remains unclear whether these changes are driven by increased reward sensitivity, altered reward predictions, enhanced cognitive control, or a combination of these effects. To address this question, we asked 36 participants to perform a simple gambling task where feedback valence (reward vs. no-reward), its magnitude (small vs. large reward), and expectancy (expected vs. unexpected) were manipulated in a factorial design, while 64-channel EEG was recorded concurrently. We performed standard ERP analyses (FRN and RewP) as well as time-frequency decompositions (FM theta) of feedback-locked EEG data. Subjective reports showed that large rewards were more liked and expected than small ones. At the EEG level, increasing magnitude led to a larger RewP irrespective of expectancy, whereas the FRN was not influenced by this manipulation. In comparison, FM theta power was overall increased when reward magnitude was large, except if it was unexpected. These results show dissociable effects of reward magnitude on the RewP and FM theta power. Further, they suggest, that although large reward magnitude boosts reward processing (RewP), it can nonetheless undermine the need for enhanced cognitive control (FM theta) in case reward is unexpected. We discuss these new results in terms of optimistic bias or positive mood resulting from an increased reward magnitude

Opportunities and Limitations of Mobile Neuroimaging Technologies in Educational Neuroscience.

Funder: European Association for Research on Learning and InstructionFunder: Jacobs Foundation; Id: http://dx.doi.org/10.13039/501100003986As the field of educational neuroscience continues to grow, questions have emerged regarding the ecological validity and applicability of this research to educational practice. Recent advances in mobile neuroimaging technologies have made it possible to conduct neuroscientific studies directly in naturalistic learning environments. We propose that embedding mobile neuroimaging research in a cycle (Matusz, Dikker, Huth, & Perrodin, 2019), involving lab-based, seminaturalistic, and fully naturalistic experiments, is well suited for addressing educational questions. With this review, we take a cautious approach, by discussing the valuable insights that can be gained from mobile neuroimaging technology, including electroencephalography and functional near-infrared spectroscopy, as well as the challenges posed by bringing neuroscientific methods into the classroom. Research paradigms used alongside mobile neuroimaging technology vary considerably. To illustrate this point, studies are discussed with increasingly naturalistic designs. We conclude with several ethical considerations that should be taken into account in this unique area of research

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)