The effect of task demands on the neural patterns generated by novel instruction encoding.

Verbal instructions allow fast and optimal implementation of novel behaviors. Previous research has shown that different control-related variables structure neural activity in frontoparietal regions during the encoding of novel instructed tasks. However, it is uncertain whether different task goals modulate the organizing effect of these variables. In this study, we investigated whether the neural encoding of three task-relevant variables (dimension integration, response set complexity and target category) is modulated by implementation and memorization demands. To do so, we combined functional Magnetic Resonance Imaging (fMRI), an instruction-following paradigm and multivariate analyses. We addressed how and where distributed activity patterns encoded the instructions' variables and the impact of the implementation and memorization demands on the fidelity of these representations. We further explored the nature of the neural code underpinning this process. Our results reveal, first, that the content of to-be-implemented and to-be-memorized instructions is represented in overlapping brain regions, flexibly using a common neural code across tasks. Importantly, they also suggest that preparing to implement the instructions increases the decodability of task-relevant information in frontoparietal areas, in comparison with memorization demands. Overall, our work emphasizes both similarities and differences in task coding under the two contextual demands. These findings qualify the previous understanding of novel instruction processing, suggesting that representing task attributes in a generalizable code, together with the increase in encoding fidelity induced by the implementation goals, could be key mechanisms for proactive control in novel scenarios.

Induced affective states do not modulate effort avoidance.

Recent research reveals that when faced with alternative lines of action, humans tend to choose the less cognitively demanding one, suggesting that cognitive control is intrinsically registered as costly. This idea is further supported by studies showing that the exertion of cognitive control evokes negative affective states. Despite extensive evidence for mood-induced modulations on control abilities, the impact of affective states on the avoidance of cognitive demand is still unknown. Across two well-powered experiments, we tested the hypothesis that negative affective states would increase the avoidance of cognitively demanding tasks. Contrary to our expectations, induced affective states did not modulate the avoidance of demand, despite having an effect on task performance and subjective experience. Altogether, our results indicate that there are limits to the effect of affective signals on cognitive control and that such interaction might depend on specific affective and control settings.

Multivariate Pattern Analysis Techniques for Electroencephalography Data to Study Flanker Interference Effects.

A central challenge in cognitive neuroscience is to understand the neural mechanisms that underlie the capacity to control our behavior according to internal goals. Flanker tasks, which require responding to stimuli surrounded by distracters that trigger incompatible action tendencies, are frequently used to measure this conflict. Even though the interference generated in these situations has been broadly studied, multivariate analysis techniques can shed new light into the underlying neural mechanisms. The current study is an initial approximation to adapt an interference Flanker paradigm embedded in a Demand-Selection Task (DST) to a format that allows measuring concurrent high-density electroencephalography (EEG). We used multivariate pattern analysis (MVPA) to decode conflict-related electrophysiological markers associated with congruent or incongruent target events in a time-frequency resolved way. Our results replicate findings obtained with other analysis approaches and offer new information regarding the dynamics of the underlying mechanisms, which show signs of reinstantiation. Our findings, some of which could not have been obtained with classic analytical strategies, open novel avenues of research.

Neural representation of current and intended task sets during sequential judgements on human faces.

Engaging in a demanding activity while holding in mind another task to be performed in the near future requires the maintenance of information about both the currently-active task set and the intended one. However, little is known about how the human brain implements such action plans. While some previous studies have examined the neural representation of current task sets and others have investigated delayed intentions, to date none has examined the representation of current and intended task sets within a single experimental paradigm. In this fMRI study, we examined the neural representation of current and intended task sets, employing sequential classification tasks on human faces. Multivariate decoding analyses showed that current task sets were represented in the orbitofrontal cortex (OFC) and fusiform gyrus (FG), while intended tasks could be decoded from lateral prefrontal cortex (lPFC). Importantly, a ventromedial region in PFC/OFC contained information about both current and delayed tasks, although cross-classification between the two types of information was not possible. These results help delineate the neural representations of current and intended task sets, and highlight the importance of ventromedial PFC/OFC for maintaining task-relevant information regardless of when it is needed.