Humans can flexibly pursue goal-oriented behavior in the face of changes in the environment. Cognitive control refers to this set of processes allowing such adjustments, and is thought to rely on neural oscillations in the theta band (4-8Hz). First, theta amplitude increases when control is needed, and second, shifts of peak frequency in the theta band have been suggested to reliably balance task representation and gating of task-relevant sensory and action information. However, it remains unknown how these two characteristics of the control signal interact and how optimal configuration for task performance is achieved. To tackle this question, we developed a computational model that relies on reinforcement learning principles to find optimal control settings for task performance. Our simulations show that these different oscillatory characteristics play distinct roles in the flexible adjustment to task demands. This work opens new avenues for research on the mechanisms allowing cognitive flexibility.

Research on human reasoning has both popularized and struggled with the idea that intuitive and deliberate thoughts stem from two different systems, raising the question how people switch between them. Inspired by research on cognitive control and conflict monitoring, we argue that detecting the need for further thought relies on an intuitive, context-sensitive process that is learned in itself.

Recent studies and models have been successful in describing human search in choice-rich environments in terms of different generalization and exploration strategies. However, it remains an open question how and to what extent people adapt these strategies to different environments. Therefore, we used a within-subject manipulation to study human search in two classes of environments, where the reward distributions differed in the strength of spatial correlations. These environments were chosen through simulations designed to maximize the need for deploying different generalization strategies. We describe participant behavior with the parameters of a model using Gaussian Process function learning to generalize and an Upper Confidence Bound to sample from unexplored options. Our results show that while participants adapted their exploration strategies, their generalization remained unchanged across environments. Together, these findings offer important first insights into how people adapt decision-making strategies depending on environmental demands in vast decision spaces.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses¹. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset^2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

A combined search for the Standard Model Higgs boson with the ATLAS experiment at the LHC using datasets corresponding to integrated luminosities from 1.04 fb-1 to 4.9 fb-1 of pp collisions collected at s=7TeV is presented. The Higgs boson mass ranges 112.9-115.5 GeV, 131-238 GeV and 251-466 GeV are excluded at the 95% confidence level (CL), while the range 124-519 GeV is expected to be excluded in the absence of a signal. An excess of events is observed around mH~126GeV with a local significance of 3.5 standard deviations (σ). The local significances of H→γγ, H→ZZ(z.ast;)→ℓ+ℓ-ℓ'+ℓ'- and H→WW(z.ast;)→ℓ+νℓ'-ν- the three most sensitive channels in this mass range, are 2.8σ, 2.1σ and 1.4σ, respectively. The global probability for the background to produce such a fluctuation anywhere in the explored Higgs boson mass range 110-600 GeV is estimated to be ~1.4% or, equivalently, 2.2σ. © 2012 CERN.