Presentation on theme: "Neural markers of error correction and stimulus valence using a social group belief paradigm Spiers/Murphy (UCL) LePelley (Cardiff)"— Presentation transcript:
Neural markers of error correction and stimulus valence using a social group belief paradigm Spiers/Murphy (UCL) LePelley (Cardiff)
Background Questions: Do experimentally induced stereotypes show error correction and summary affect reflecting correlational learning? Can we find neural markers for this learning? Experimentally induced negative minority group stereotypes suggest that people acquire an illusory correlation between group membership and valence (Hamilton & Gifford, 1976; McGarty et al., 1997). Previous theories have suggested that negative valence is a consequence of low frequency distinctiveness. Alternatively we propose that this reflects an error correction algorithm and pre-asymptotic learning of valence. Hamilton &Gifford 76 PosNeg Maj2010 Min105
Design ParticipantsProcedure 18 (9/9)ISI - 2 seconds (Jitter?) Right-Handed180 trials (42min.) Normal10 blocks of 18, pseudo random order Scanning parameters will be optimized for Amygdala & Prefrontal (Orbital + Dorsolateral) Participants provide evaluative ratings of the four groups after first 3 blocks of trials, then every subsequent block(7) Post-scan ratings of all experienced sentences for valence, novelty and imagery. A 40 neg/20posB 20 neg/10pos C 60 neutralD 30 neutral A helped an old lady X B took their shy friend out for a drink X D 2 222 2 10 Time line Frequency Valence
Analysis and Predictions SPM5 with standard preprocessing and a GLM model. A mixed event/block analysis, with events consisting of the onset of the group label (e.g., A) and blocks: the sentences. Learning will be modeled with a parametric modulation Main analyses of interest: Do group labels (A and B) come to be associated with increased amygdala activity (and other affective regions) relative to C and D? Main effect of valence for group labels (A+B>C+D) also examine 2 nd half compared with 1 st half Interaction between Valence and Frequency. We predict there may be regions within affective brain network that are modulated by frequency A B C D This may take the form: (A >B) > (C + D), where group A are more disliked than group B which are in tern more disliked relative to C and D Also we will: Model the acquisition using a learning algorithm with subjects post-scan ratings of valence of the sentences to set the learning rate parameter We predict a dorsolateral prefrontal region will track this learning
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