2018
Just above Chance: Is It Harder to Decode Information from Prefrontal Cortex Hemodynamic Activity Patterns?
Abstract: The prefrontal cortex (PFC) is central to flexible, goal-directed cognition, and understanding its representational code is an important problem in cognitive neuroscience. In humans, multivariate pattern analysis (MVPA) of fMRI blood oxygenation level-dependent (BOLD) measurements has emerged as an important approach for studying neural representations. Many previous studies have implicitly assumed that MVPA of fMRI BOLD is just as effective in decoding information encoded in PFC neural activity as it is in vi…
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Cited by 90 publications
(82 citation statements)
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“…4d). These findings are consistent with previous studies that have found that higher-order association areas typically have relatively low decoding accuracies, even for tasks that heavily involve those regions 38 .…”
Section: Network Topography Of Ra Relates To the Brain's Intrinsic Organizationsupporting
confidence: 93%
“…4d). These findings are consistent with previous studies that have found that higher-order association areas typically have relatively low decoding accuracies, even for tasks that heavily involve those regions 38 .…”
Section: Network Topography Of Ra Relates To the Brain's Intrinsic Organizationsupporting
confidence: 93%
“…The use of catch trials ensured that the cue and stimulus GLM regressors were appropriately decorrelated. The overall classification accuracies in our data were low, similarly to previous studies that used MVPA for fMRI data across the frontoparietal cortex (Erez & Duncan, 2015;Nelissen, Stokes, Nobre, & Rushworth, 2013;Soon, Brass, Heinze, & Haynes, 2008;, and this may be possibly related to the functional organization at the neuronal population level (Bhandari, Gagne, & Badre, 2018;Dubois, de Berker, & Tsao, 2015).…”
Section: Discussionsupporting
confidence: 80%
“…While our classification accuracies were relatively low, similar levels of performance have been commonly reported when examining goal representation across MDN (Waskom et al, 2014;Erez and Duncan, 2015;Bhandari et al, 2018).…”
Section: Discussionsupporting
confidence: 78%

