Language processing and comprehension can be understood in terms of both linguistic and non-linguistic processes. To make a decision regarding the meaning of complex linguistic inputs such as idiomatic expressions, one has to perform multiple complex cognitive operations such as prediction, selection and inhibition. In the current study, we used transcranial direct current stimulation (tDCS) to test the hypotheses that (I) a prefrontal cognitive control network is involved in directing decisions required for the comprehension of idioms, and (II) that this prefrontal control may be biased by motivational mechanisms. Participants were randomly allocated to one of two stimulation groups (LH anodal/RH cathodal or RH anodal/LH Cathodal). Over a one-week interval, participants were tested twice, completing a semantic decision task after either receiving active or sham stimulation. The semantic decision task required participants to judge the relatedness of an idiom and a target word, with the idiom being predictable or not. The target word was either figuratively related, literally related, or unrelated to the idiom. Each participant also completed a trait motivation questionnaire and a control task. After DC stimulation, a general deceleration in reaction times to targets was found. In addition, the results indicate that the neural enhancement of a left lateralized prefrontal network improved performance when participants had to make decisions to figurative targets of highly predictable idioms, whereas the neural enhancement of the opposite network improved participants' performance to literal targets of unpredictable idioms. These effects were more pronounced in individuals rated as being most sensitive to reward likelihood. The results are discussed in terms of cognitive control over semantic processing.
|Number of pages||10|
|State||Published - Jul 2012|
Bibliographical noteFunding Information:
This study was supported by a BSF Grant 2007184 awarded to RI and ML, an ERC starting grant (Inspire 200512 ), and an ISF (Grant 100/10 ) awarded to ML.
- Cognitive control
- Semantic processing
- Trait motivation