Valence and Imageability in Word Processing UCL: Eleni Orfanidou, Dafydd Waters, Davidf Vinson Joe Devlin, Gabriella Vigliocco San Raffaele: Pasquale della.

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Presentation transcript:

Valence and Imageability in Word Processing UCL: Eleni Orfanidou, Dafydd Waters, Davidf Vinson Joe Devlin, Gabriella Vigliocco San Raffaele: Pasquale della Rosa, Stefano Cappa

Background & Question(s) Abstract > Concrete in rACC (Kousta et al., in preparation) Abstract > more valenced (and arousing) than concrete thus abstract words grounded in affective associations (evidence that rACC linked to emotion: (Beckmann et al., IoN, 2009; Bush et al., 2000, TICs). However, valence and arousal were not explicitly manipulated. No greater rACC for valenced than neutral words in other studies (eg. Lewis et al., 2007) Combine a tightly controlled set embedded in a larger set of words sampled across the range of valence and imageability ratings, which resembles the natural distribution of both these variables > rACC to valenced words when other variables are controlled? Modulation of rACC activation for parametric manipulation of valence (see Lewis et al., 2007)? M odulation of bilateral/left fusyform activation for imageability (see Hauk et al., 2008; Wise et al., 2000) ?

Stimuli and Design SET 1: 4 Conditions (Positive, Negative, Neutral and Pseudowords, 111 words) SET 2: 369 additional words chosen to span across entire range of valence and imageability ratings and pseudowords. 160 (25%) pseudowords + Baseline … t + sum ? + qlv + edy ? + rod *** ~60s 15s Mixed (block and event-related design): Lexical Decision task

Study Details Participants: 24 adult right-handed native English speakers Scanning Details:whole brain fMRI (3x3x3mm voxel; TE = 50ms; TR = 3s) jittered ISI (1-4s) Test Session:~72min (7/8 EPI runs) 54 mini-blocks Analysis 1 (Parametric): 1 st LEVEL - GLM with multiple linear regression (with user-specified parametric regressors). 2 nd LEVEL – Random Effects analysis (Group). One sample T-tests on single regressors and ANOVAs for F-tests of differences between the main trial effects and the parametric regressors Analysis 2 (Categorical): 1 st LEVEL - GLM modeling 6 conditions (Pos, Neg. Neut, pseudowords (special), words (controlled), pseudowords (other). 2 nd LEVEL – Random Effects analysis (Group). One sample T-tests on contrast images from each single subject on simple main effects and direct comparisons ANOVAs to test differences between the contrast of interests