The reinforcement metalearner as a biologically plausible meta-learning framework.

We argue that the type of meta-learning proposed by Binz et al. generates models with low interpretability and falsifiability that have limited usefulness for neuroscience research. An alternative approach to meta-learning based on hyperparameter optimization obviates these concerns and can generate empirically testable hypotheses of biological computations.

Temperament and probabilistic predictive coding in visual-spatial attention.

Cholinergic (Ach), Noradrenergic (NE), and Dopaminergic (DA) pathways play an important role in the regulation of spatial attention. The same neurotransmitters are also responsible for inter-individual differences in temperamental traits. Here we explored whether biologically defined temperamental traits determine differences in the ability to orient spatial attention as a function of the probabilistic association between cues and targets. To this aim, we administered the Structure of Temperament Questionnaire (STQ-77) to a sample of 151 participants who also performed a Posner task with central endogenous predictive (80 % valid/20 % invalid) or non-predictive cues (50 % valid/50 % invalid). We found that only participants with high scores in Plasticity and Intellectual Endurance showed a selective abatement of attentional costs with non-predictive cues. In addition, stepwise regression showed that costs in the non-predictive condition were negatively predicted by scores in Plasticity and positively predicted by scores in Probabilistic Thinking. These results show that stable temperamental characteristics play an important role in defining the inter-individual differences in attentional behaviour, especially in the presence of different probabilistic organisations of the sensory environment. These findings emphasize the importance of considering temperamental and personality traits in social and professional environments where the ability to control one's attention is a crucial functional skill.