chethanparameshwara / MindReading

Python/MATLAB Scripts to analyze Magnetoencephalography (MEG) data for decoding working memory

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Decoding Phonological Working Memory

Reading relies on phonological and semantic working memory (Caplan & Waters, 1999). Separating the effects of load in either system would be important for understanding how we read. For example, when reading becomes difficult due to either the text or the reader, what systems must the reader rely upon to persevere?

Visual words get stored in terms of phonological, semantic, and orthographic representations (Tousman & Inhoff, 1992; Besner, 1987; Waters et al., 1992; Perfetti & Hart, 2002; Plaut, McClelland, Seidenberg, & Patterson, 1996.)These separate representations are activated to various degrees depending on the task. For example, during rhyme judgment tasks phonological representations increase (Besner, 1987). These representations remain active during the delay. Phonological representations require brain areas which plan articulation (Waters et al., 1992). These areas are Broca’s area and Precentral Gyrus (Poeppel et al., 2012; Guenther et al., 2009)

Hypotheses

If phonological loop works as Waters et al. (1992) predict, what will the articulatory rehearsal look like over the delay?

Which frequencies will correspond with what areas of the cortex? High freqs (Gamma, Alpha): relative local areas of cortex (see Poeppel et al, 2012). Lower freqs (Theta): involvement of subcortical areas/dispersed areas of cortex

Will there be differences based on load? In our case, one vs three-syllable lists

Will there be differences for words and non words? If words are stored differently than nonwords, we expect different decoding results.

For experiment design and results, refer presentation slides.

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Python/MATLAB Scripts to analyze Magnetoencephalography (MEG) data for decoding working memory


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