2014
DOI: 10.1016/j.neulet.2014.05.008
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Neural correlate of the projection of mental states on the not-structured visual stimuli

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Cited by 21 publications
(24 citation statements)
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“…On the other hand, however, if the input is ambiguous or degraded, the PFC may provide topdown estimations to compensate for less informative sensory inputs and thus facilitating the recognition of objects (Cho-Hisamoto et al, 2015). Interestingly, a very similar interaction between the prefrontal-temporal (top-down) and temporaloccipital (bottom-up) networks has been observed during mental projection (Luciani et al, 2014), i.e., subjective attribution of meaning to ill-structured stimuli or situations, suggesting that predictive coding may underpin this phenomenon. In summary, the observed improvement in ROR response fluency by anodal tDCS may stem from an increased excitability of the PFC, or the prefrontal-temporal brain network, that generates likely estimations of ambiguous stimuli, which in turn enhances the recognition of meaningful patterns.…”
Section: Discussionmentioning
confidence: 98%
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“…On the other hand, however, if the input is ambiguous or degraded, the PFC may provide topdown estimations to compensate for less informative sensory inputs and thus facilitating the recognition of objects (Cho-Hisamoto et al, 2015). Interestingly, a very similar interaction between the prefrontal-temporal (top-down) and temporaloccipital (bottom-up) networks has been observed during mental projection (Luciani et al, 2014), i.e., subjective attribution of meaning to ill-structured stimuli or situations, suggesting that predictive coding may underpin this phenomenon. In summary, the observed improvement in ROR response fluency by anodal tDCS may stem from an increased excitability of the PFC, or the prefrontal-temporal brain network, that generates likely estimations of ambiguous stimuli, which in turn enhances the recognition of meaningful patterns.…”
Section: Discussionmentioning
confidence: 98%
“…Transforming sensory inputs into meaningful patterns is further supported by the semantic system, as indicated by the activation of the left-hemispheric language network during object recognition (Gabrieli et al, 1998;Chouinard et al, 2009;Ralph et al, 2017). Notably, the processing of ill-structured or noisy visual patterns further increases the activation of this network, particularly in the left inferior frontal cortex (Liu et al, 2010(Liu et al, , 2014Luciani et al, 2014). Interestingly, such activity increase in the PFC has also been reported during semantic memory retrieval, especially when the stimuli provide insufficient retrieval cues (Badre and Wagner, 2002) or when multiple instances have to be retrieved in rapid succession (Ghanavati et al, 2019).…”
Section: Introductionmentioning
confidence: 99%
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“…sLORETA (Pascual‐Marqui, ) was used with GeoSource (version 2.0; Electrical Geodesic, Inc., Eugene, OR), allowing us to inferentially determine a virtual map of the brain. Source location was derived from the probabilistic map of the MNI305 according to which gray matter volume was parcellated into 7‐mm voxels; each voxel served as a source location with three orthogonal orientation vectors (Cecchini, Aceto, Altavilla, Palumbo, & Lai ; Lai, Altavilla, Ronconi, & Aceto, ; Lai et al., ; Lancaster et al., ; Luciani et al., ). This resulted in a total of 2,447 source triplets, the anatomical labels for which we estimated using the Talairach Daemon software (Lancaster et al., ).…”
Section: Methodsmentioning
confidence: 99%
“…Based on the probabilistic map, gray matter volume was parcellated into 7mm voxels; each voxel served as a source location with three orthogonal orientation vectors. This resulted in a total of 2,447 source triplets whose anatomical labels were estimated using a Talairach daemon (Massaro, Altavilla, Aceto, Pellicano, Lucarelli, Luciani et al, in press;Lai, Luciani, Di Giorgio, Fiorini, Yaya, Pellicano et al, 2018;Cecchini, Aceto, Altavilla, Palumbo & Lai, 2013;Cecchini, Iannoni, Pandolfo, Aceto & Lai, 2015;Lai, Altavilla, Ronconi & Aceto, 2016;Lancaster, Woldorff, Parsons, Liotti, Freitas, Rainey et al, 2000;Luciani, Cecchini, Altavilla, Palumbo, Aceto, Ruggeri et al, 2014).…”
Section: Source Analysis (S-loreta)mentioning
confidence: 99%