Biology subjects

Knapen, T.

Publications and source records attributed to Knapen, T..

23 records · Page 2Linked to original sources

bioRxiv2018

Pupil responses as indicators of value-based decision-making

Pupil responses have been used to track cognitive processes during decision-making. Studies have shown that in these cases the pupil reflects the joint activation of many cortical and subcortical brain regions, also those traditionally implicated in value-based learning. However, how the pupil tracks value-based decisions and reinforcement learning is unknown. We combined a reinforcement learning task with a computational model to study pupil responses during value-based decisions, and decision evaluations. We found that the pupil closely tracks reinforcement learning both across trials and participants. Prior to choice, the pupil dilated as a function of trial-by-trial fluctuations in value beliefs. After feedback, early dilation scaled with value uncertainty, whereas later constriction scaled with reward prediction errors. Our computational approach systematically implicates the pupil in value-based decisions, and the subsequent processing of violated value beliefs. These dissociable influences provide an exciting possibility to non-invasively study ongoing reinforcement learning in the pupil.

neuroscience↗

bioRxiv2018

Mapping the Dark Side: Visual Selectivity of Default Network Deactivations

The default network (DN) activates for internally referenced mental states, whereas it deactivates in response to sensory stimulation. We show that BOLD signal decreases in the DN are tuned to the spatial location of visual stimuli, allowing us to decode the location of a visual stimulus from DN regions. Our results indicate that the DN represents sensory information, and suggest it may utilize this reference frame for higher-level cognition.

neuroscience↗

bioRxiv2017

Cross-task contributions of fronto-basal ganglia circuitry in response inhibition and conflict-induced slowing

Why are we so slow in choosing the lesser of two evils? We considered whether such slowing relates to uncertainty about the value of these options, which arises from the tendency to avoid them during learning, and whether such slowing relates to fronto-subthalamic inhibitory control mechanisms. 49 participants performed a reinforcement-learning task and a stop-signal task while fMRI was recorded. A reinforcement-learning model was used to quantify learning strategies. Individual differences in lose-lose slowing related to information uncertainty due to sampling, and independently, to less efficient response inhibition in the stop-signal task. Neuroimaging analysis revealed an analogous dissociation: subthalamic nucleus (STN) BOLD activity related to variability in stopping latencies, whereas weaker fronto-subthalamic connectivity related to slowing and information sampling. Across tasks, fast inhibitors increased STN activity for successfully cancelled responses in the stop task, but decreased activity for lose-lose choices. These data support the notion that fronto-STN communication implements a rapid but transient brake on response execution, and that slowing due to decision uncertainty could result from an inefficient release of this \"hold your horses\" mechanism.

neuroscience↗

bioRxiv2017

Porcupine: a visual pipeline tool for neuroimaging analysis

The field of neuroimaging is rapidly adopting a more reproducible approach to data acquisition and analysis. Data structures and formats are being standardised and data analyses are getting more automated. However, as data analysis becomes more complicated, researchers often have to write longer analysis scripts, spanning different tools across multiple programming languages. This makes it more difficult to share or recreate code, reducing the reproducibility of the analysis. We present a tool, Porcupine, that constructs ones analysis visually and automatically produces analysis code. The graphical representation improves understanding of the performed analysis, while retaining the flexibility of modifying the produced code manually to custom needs. Not only does Porcupine produce the analysis code, it also creates a shareable environment for running the code, in the form of a Docker image. Together, this forms a reproducible way of constructing, visualising and sharing ones analysis. Currently, Porcupine links to Nipype functionalities, which in turn accesses most standard neuroimaging analysis tools. With Porcupine, we bridge the gap between a conceptual and an implementational level of analysis and thus create reproducible and shareable science. We give the researcher a better oversight of their pipeline, both while developing and communicating their work. This will reduce the threshold at which less expert users can generate reusable pipelines. We provide a wide range of examples and documentation, as well as installer files for all platforms on our website: https://timvanmourik.github.io/Porcupine. Porcupine is free, open source, andreleased under the GNU General Public License v3.0.\n\nAuthor SummaryThe neuroimaging community is fervently debating that its reproducibility and transparency should be improved, but it is a challenging problem as to how to accomplish this. We here propose a tool, Porcupine, to aid in this process by more easily creating shareable workflows for analysing neuroimaging data. The conceptual understanding of a pipeline is improved by means of the graphical interface, and it automatically produces the code to perform the analysis and to create a sharing environment. This retains full flexibility to modify the script afterwards but in principle produces readily executable code for an end-to-end analysis. Porcupine currently links to all Nipype functionality, but is designed to be extendable to other workflow packages in neuroimaging and beyond. Porcupine is free and is released under the GNU General Public License.

neuroscience↗

bioRxiv2017

Global interactions between feature-based and spatial attention redistribute feature sensitivity across visual space

Spatial attention changes the sampling of visual space. Behavioral studies suggest that feature-based attention modulates this resampling to optimize the attended features sampling. We investigate this hypothesis by estimating spatial sampling in visual cortex while independently varying both feature-based and spatial attention.\n\nOur results show that spatial and feature-based attention interacted: resampling of visual space depended on both the attended location and feature (color vs. temporal frequency). This interaction occurred similarly throughout visual cortex, regardless of an areas overall feature preference. However, the interaction did depend on spatial sampling properties of voxels that prefer the attended feature. These findings are parsimoniously explained by variations in the precision of an attentional gain field.\n\nOur results demonstrate that the deployment of spatial attention is tailored to the spatial sampling properties of units that are sensitive to the attended feature.

neuroscience↗