Many previous studies suggest that spatially organized priority maps in prefrontal and parietal cortex provide attentional top-down signals in the form of ‘attentional landscapes’. Within the prefrontal cortex (PFC), the frontal eye fields (FEF) and the inferior frontal junction (IFJ) are differentially selective to spatial vs non-spatial information. They are both involved in the control of visual attentio, working memory and pther top-fown processes. Non-spatial control in IFJ provides signals whenever spatial attention cannot be used to solve the task, such as in controlled feature-based or object-based attention paradigms. We performed an activation likelihood estimation (ALE) to accurately infer the localization of FEF and IFJ using GingerALE (voxel-level FEW = 0.01, 5000 permutations). Using the coordinates of the main FEF and IFJ clusters, we ran a meta-analytic connectivity modeling analysis (MACM) by retrieving all relevant experiments in the brainmap database.
Separate pathways for ‘what’ and ‘where’ information for attentional top-down control within prefrontal cortex / Baldauf, Daniel; Bedini, Marco. - (2021). (Intervento presentato al convegno Tagung experimentell arbeitender Psychologen, TEAP 2021 tenutosi a Ulm, Germania nel 1.6.2021).
Separate pathways for ‘what’ and ‘where’ information for attentional top-down control within prefrontal cortex
Baldauf, Daniel
;Bedini, Marco
2021-01-01
Abstract
Many previous studies suggest that spatially organized priority maps in prefrontal and parietal cortex provide attentional top-down signals in the form of ‘attentional landscapes’. Within the prefrontal cortex (PFC), the frontal eye fields (FEF) and the inferior frontal junction (IFJ) are differentially selective to spatial vs non-spatial information. They are both involved in the control of visual attentio, working memory and pther top-fown processes. Non-spatial control in IFJ provides signals whenever spatial attention cannot be used to solve the task, such as in controlled feature-based or object-based attention paradigms. We performed an activation likelihood estimation (ALE) to accurately infer the localization of FEF and IFJ using GingerALE (voxel-level FEW = 0.01, 5000 permutations). Using the coordinates of the main FEF and IFJ clusters, we ran a meta-analytic connectivity modeling analysis (MACM) by retrieving all relevant experiments in the brainmap database.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione