Adaptive Learning needs Attention, Meta-learning and Working Memory

We tested which model mechanisms best explain how six animals learn attention sets and found a common set of most-important behavioral mechanisms that account for learning success.
When learning attention sets is easy value based reinforcement learning and working memory are powerful, but when learning problems are more complex learning is more efficient with attention and a meta-learning process that help speeding up learning when errors accumulate. (See our paper Womelsdorf at al. (2022) Learning at variable attentional load requires cooperation between working memory, meta-learning and attention-augmented reinforcement learning. Journal of Cognitive Neuroscience 34(1) 79-107.)

Related News

ACC causally supports learning -difficult- attention sets

We used focused ultrasound (FUS) sonication of the anterior cingualte and striatum to disrupt local processing during learning. FUS in ACC slowed down learning of atetntion sets – but only when the attentional demands were high and the task included the risk of loosing already attaiuned reward tokens. Under these cognitive and motivaitonally challenging conditions […]

Interneuron-specific gamma synchrony indexes uncertainty resolution

Our new paper in eLife shows that a subclass of fast spiking interneurons in prefrontal and anterior cingulate cortex gamma synchronizes when uncertainty about cues and outcomes is resolved. This finding was possible by classifying narrow spiking neurons into fast and non-fast spiking classes and correlating their firing and spike-LFP synchrony during processing of attention […]

M1-selective allosteric modulation enhances cognitive flexibility

We have new research out at PNAS about enhancing cognitive flexibility with highly selective allosteric modulation of the M1 muscarinic receptor (pdf: here)! Muscarinic receptors are known to mediate pro-cognitive effects of acetylcholine, but it has remained unclear whether they differentially affect the cognitive subfunctions of attentional filtering, set shifting, and learning. To clarify the […]