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

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 electroencephalogram (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 has been prioritized. During proactive control, both univariate and multivariate signals of beta-band (15–35 Hz) power suppression before 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

Frontal cortex differentiates between free and imposed target selection in multiple-target search

Cognitive control can involve proactive (preparatory) and reactive (corrective) mechanisms. Using a gaze-contingent eye tracking paradigm combined with fMRI, we investigated the involvement of these different modes of control and their underlying neural networks, when switching between different targets in multiple-target search. Participants simultaneously searched for two possible targets presented among distractors, and selected one of them. In one condition, only one of the targets was available in each display, so that the choice was imposed, and reactive control would be required. In the other condition, both targets were present, giving observers free choice over target selection, and allowing for proactive control. Switch costs emerged only when targets were imposed and not when target selection was free. We found differential levels of activity in the frontoparietal control network depending on whether target switches were free or imposed. Furthermore, we observed core regions of the default mode network to be active during target repetitions, indicating reduced control on these trials. Free and imposed switches jointly activated parietal and posterior frontal cortices, while free switches additionally activated anterior frontal cortices. These findings highlight unique contributions of proactive and reactive control during visual search

Implementing multi-session learning studies out of the lab: Tips and tricks using OpenSesame

Here, we provide tips and tricks for running multisession experiments out of the lab using OpenSesame, a user-friendly experimental tool that is open source and runs on Windows, MacOS, and Linux. We focus on learning experiments that involve the measurement of reaction times. We show how such experiments can be run with traditional desktop-based experiment software on participants’ own notebooks (i.e., out-of-the-lab, but not in a browser). Learning experiments pose specific challenges: accessing individual identifying numbers, accessing session numbers, and counterbalancing conditions across participants. This article includes helpful code and provides hands-on implementation tips that will be useful also beyond the presented use case. The aim of this article is to illustrate how to create multisession learning experiments even with little technical expertise. We conclude that, if done right, out-of-the-lab experiments are a valid alternative to traditional lab testing

The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.Development of the BIDS Standard has been supported by the International Neuroinformatics Coordinating Facility, Laura and John Arnold Foundation, National Institutes of Health (R24MH114705, R24MH117179, R01MH126699, R24MH117295, P41EB019936, ZIAMH002977, R01MH109682, RF1MH126700, R01EB020740), National Science Foundation (OAC-1760950, BCS-1734853, CRCNS-1429999, CRCNS-1912266), Novo Nordisk Fonden (NNF20OC0063277), French National Research Agency (ANR-19-DATA-0023, ANR 19-DATA-0021), Digital Europe TEF-Health (101100700), EU H2020 Virtual Brain Cloud (826421), Human Brain Project (SGA2 785907, SGA3 945539), European Research Council (Consolidator 683049), German Research Foundation (SFB 1436/425899996), SFB 1315/327654276, SFB 936/178316478, SFB-TRR 295/424778381), SPP Computational Connectomics (RI 2073/6-1, RI 2073/10-2, RI 2073/9-1), European Innovation Council PHRASE Horizon (101058240), Berlin Institute of Health & Foundation Charité, Johanna Quandt Excellence Initiative, ERAPerMed Pattern-Cog, and the Virtual Research Environment at the Charité Berlin – a node of EBRAINS Health Data Cloud.N

The past, present, and future of the Brain Imaging Data Structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS

KLI - Opensesame workshop 2021

Materials for the Opensesame workshop, organized by the Kurt Lewin Institute in 202

KLI - Opensesame workshop 2022

Materials for the Opensesame workshop, organized by the Kurt Lewin Institute in 202

Lack of Free Choice Reveals the Cost of Multiple-Target Search Within and Across Feature Dimensions

Data and material of Ort, E., Fahrenfort, J. J., & Olivers, C. N. L. (in press). Lack of Free Choice Reveals the Cost of Multiple-Target Search Within and Across Feature Dimensions. Attention, Perception & Psychophysics

Humans can efficiently look for but not select multiple visual objects

EEG study on the capacity of feature-based attention by Eduard Ort, Johannes Fahrenfort, Tuomas ten Cate, Martin Eimer and Chris Olivers. The manuscript based on these data can be found on BioRxiv: https://www.biorxiv.org/content/10.1101/653030v