Beta and theta oscillations differentially support free versus forced control over multiple-target search

Abstract

Many important situations require human observers to simultaneously search for more than one object.Despite a long history of research into visual search, the behavioral and neural mechanisms associated with multiple-target search are poorly understood. Here we test the novel theory that the efficiency of looking for multiple targets critically depends on the mode of cognitive control the environment affords to the observer. We used an innovative combination of EEG and eye tracking while participants searched for two targets, within two different contexts: Either both targets were present in the search display and observers were free to prioritize either one of them, thus enabling proactive control over selection; or only one of the two targets would be present in each search display, which requires reactive control to reconfigure selection when the wrong target is prioritized. During proactive control, both univariate and multivariate signals of beta-band (15-35 Hz) power suppression prior to display onset predicted switches between target selections. This signal originated over midfrontal and sensorimotor regions and has previously been associated with endogenous state changes. In contrast, imposed target selections requiring reactive control elicited prefrontal power enhancements in the delta/theta-band (2-8 Hz), but only after display onset. This signal predicted individual differences in associated oculomotor switch costs, reflecting reactive reconfiguration of target selection. The results provide compelling evidence that multiple target representations are differentially prioritized during visual search, and for the first time reveal distinct neural mechanisms underlying proactive and reactive control over multiple-target search.Keywords: visual search | priority states | cognitive control | medial frontal cortex | beta band | theta band

Decoding Object-Based Auditory Attention from Source-Reconstructed MEG Alpha Oscillations

“…We used a linear classifier as implemented in the Amsterdam Decoding and Modeling toolbox (ADAM; Fahrenfort et al, 2018), an open source, script-based toolbox in MATLAB for backward-decoding and forward-encoding modeling of EEG/MEG data. Note that we replaced the standard time-frequency decomposition in the toolbox with the custom-written Morlet wavelet convolution described above (de Vries et al, 2019;van Driel et al, 2019), and based on Cohen (2014), as this arguably provides a better trade-off between temporal and spectral precision. We applied the following 10fold cross-validation procedure: first, the trial order was randomized, and trials were partitioned in 10 equal-sized folds; next, a leave-one-out procedure was used in which the classifier was trained on 9 folds and tested on the remaining fold.…”