In this study, we merged methods from machine learning and human neuroimaging to causally test the role of self-induced affect states in biasing the affective perception of subsequent image stimuli. To test this causal relationship, we developed a novel paradigm in which (n=40) healthy adult participants observed multivariate neural decodings of their real-time functional magnetic resonance image (rtfMRI) responses as feedback to guide explicit regulation of their brain (and corollary affect processing) state towards a positive valence goal state. By this method, individual differences in affect regulation ability were controlled. Attaining this brain-affect goal state triggered the presentation of pseudo-randomly selected affectively congruent (positive valence) or incongruent (negative valence) image stimuli drawn from the International Affective Picture Set. Separately, subjects passively viewed randomly triggered positively and negatively valenced image stimuli during fMRI acquisition. Multivariate neural decodings of the affect processing induced by these stimuli were modeled using the task trial type (state- versus randomly-triggered) as the fixed-effect of a general linear mixed effects model. Random effects were modeled subject-wise. We found that self-induction of a positive affective valence state significantly positively biased the perceived valence of subsequent stimuli. As a manipulation check, we validated affective state induction achieved by the image stimuli using independent psychophysiological response measures of hedonic valence and autonomic arousal. We also validated the predictive fidelity of the trained neural decoding models for brain states induced by an out-of-sample set of image stimuli. Beyond its contribution to our understanding of the neural mechanisms that bias affect processing, this work demonstrated the viability of novel experimental paradigms triggered by pre-defined affective cognitive states. This line of individual differences experimentation potentially provides scientists with a valuable tool for causal exploration of the roles and identities of intrinsic cognitive processing mechanisms that shape our perceptual processing of sensory stimuli.
bioRxiv Subject Collection: Neuroscience