Predictive learning as the basis of the testing effect.

A prominent learning phenomenon is the testing effect, meaning that testing enhances retention more than studying. Emergent frameworks propose fundamental (Hebbian and predictive) learning principles as its basis. Predictive learning posits that learning occurs based on the contrast (error) between a prediction and the feedback on that prediction (prediction error).

Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits.

Adaptive sequential behavior is a hallmark of human cognition. In particular, humans can learn to produce precise spatiotemporal sequences given a certain context. For instance, musicians can not only reproduce learned action sequences in a context-dependent manner, they can also quickly and flexibly reapply them in any desired tempo or rhythm without overwriting previous learning.

Cognitive Effort Modulates Connectivity between Dorsal Anterior Cingulate Cortex and Task-Relevant Cortical Areas.

Investment of cognitive effort is required in everyday life and has received ample attention in recent neurocognitive frameworks. The neural mechanism of effort investment is thought to be structured hierarchically, with dorsal anterior cingulate cortex (dACC) at the highest level, recruiting task-specific upstream areas. In the current fMRI study, we tested whether dACC is generally active when effort demand is high across tasks with different stimuli, and whether connectivity between dACC and task-specific areas is increased depending on the task requirements and effort level at hand.

Context-dependent modulation of cognitive control involves different temporal profiles of fronto-parietal activity.

To efficiently deal with quickly changing task demands, we often need to organize our behaviour on different time scales. For example, to ignore irrelevant and select relevant information, cognitive control might be applied in reactive (short time scale) or proactive (long time scale) mode. These two control modes play a pivotal role in cognitive-neuroscientific theorizing but the temporal dissociation of the underlying neural mechanisms is not well established empirically.

The unfolding action model of initiation times, movement times, and movement paths.

Converging evidence has led to a consensus in favor of computational models of behavior implementing continuous information flow and parallel processing between cognitive processing stages. Yet, such models still typically implement a discrete step between the last cognitive stage and motor implementation. This discrete step is implemented as a fixed decision bound that activation in the last cognitive stage needs to cross before action can be initiated.

Continuous track paths reveal additive evidence integration in multistep decision making.

Multistep decision making pervades daily life, but its underlying mechanisms remain obscure. We distinguish four prominent models of multistep decision making, namely serial stage, hierarchical evidence integration, hierarchical leaky competing accumulation (HLCA), and probabilistic evidence integration (PEI). To empirically disentangle these models, we design a two-step reward-based decision paradigm and implement it in a reaching task experiment.

Avoiding the conflict: Metacognitive awareness drives the selection of low-demand contexts.

Previous research attempted to explain how humans strategically adapt behavior in order to achieve successful task performance. Recently, it has been suggested that 1 potential strategy is to avoid tasks that are too demanding. Here, we report 3 experiments that investigate the empirically neglected role of metacognitive awareness in this process.

Losing the boundary: Cognition biases action well after action selection.

For selecting an action, traditional theories suggest a cognitive architecture made of serial processing units. Others suggested that action selection emerges from the parallel implementation of and competition between multiple action plans. To disentangle these 2 hypotheses, we created a reaching task assessing the temporal dynamics of action selection.

Setting the stage subliminally: unconscious context effects.

An important approach to understand how the brain gives rise to consciousness is to probe the depth of unconscious processing, thus to define the key features that cause conscious awareness. Here, we investigate the possibility for subliminal stimuli to shape the context for unconscious processing. Context effects have generally been assumed to require consciousness.