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To summarize, we found that it was possible to detect representations of autobiographical memories in individual hippocampal subfields.
Using high-resolution functional magnetic resonance imaging (fMRI) combined with multivoxel pattern analysis we found that it was possible to detect representations of specific autobiographical memories in individual hippocampal subfields.
MVPA has recently been applied to detect representations thought to be coded in fine-grained patterns beyond the resolution of standard fMRI (Kamitani and Tong 2005; Haynes and Rees 2006; Shmuel et al. 2010).
Also, our scan parameters were not optimized for the decoding of representations within the MTL (for example, see Hassabis et al., 2009), and the repeated retrieval of schema memories might have decreased our power to detect representations in the MPFC (Woolgar et al., 2011).
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The temporal classifier detected representations of temporal sequences, which did not change across different spatial sequences (orthogonal to the spatial classification analysis).
In a recent high-resolution fMRI study, Bonnici et al. (2012a) availed themselves of the opportunity afforded by multivoxel pattern analysis (MVPA; Haynes and Rees, 2006; Norman et al., 2006; Chadwick et al., 2012) to provide an alternative to conventional neuropsychological and fMRI approaches by detecting representations of individual autobiographical memories in patterns of fMRI activity.
If talk of "belief boxes" and the like is shorthand for talk of functional role (as Nichols and Stich say), then the Monitoring Mechanism must somehow detect the functional role of the detected representation.
To detect sequence representations anywhere in the cortex, we used a surface-based searchlight approach (Oosterhof et al., 2011).
It is therefore plausible that this approach may be able to detect neural representations related to simulation of future goal heading.
Multivoxel pattern analysis can detect such representations by testing for systematic differences in the local activity patterns for different task conditions.
Thus the inability to detect under-representation of chromosome X in that sample was most probably due to insufficient sampling.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com