No abstract

Recent findings suggest that communicative context affects the timing and magnitude of emotion effects in word processing. In particular, social attributions seem to be one important source of plasticity for the processing of affectively charged language. Here, we investigate the timing and magnitude of ERP responses toward positive, neutral, and negative trait adjectives during the anticipation of putative socio-evaluative feedback from different senders (human and computer) varying in predictability. In the first experiment, during word presentation participants could not anticipate whether a human or a randomly acting computer sender was about to give feedback. Here, a main effect of emotion was observed only on the late positive potential (LPP), showing larger amplitudes for positive compared to neutral adjectives. In the second study the same stimuli and set-up were used, but a block-wise presentation was realized, resulting in fixed and fully predictable sender identity. Feedback was supposedly given by an expert (psychotherapist), a layperson (unknown human), and again by a randomly acting computer. Main effects of emotion started with an increased P1 for negative adjectives, followed by effects at the N1 and early posterior negativity (EPN), showing both largest amplitudes for positive words, as well as for the LPP, where positive and negative words elicited larger amplitudes than neutral words. An interaction revealed that emotional LPP modulations occurred only for a human sender. Finally, regardless of content, anticipating human feedback led to larger P1 and P3 components, being highest for the putative expert. These findings demonstrate the malleability of emotional language processing by social contexts. When clear predictions can be made, our brains rapidly differentiate between emotional and neutral information, as well as between different senders. Attributed human presence affects emotional language processing already during feedback anticipation, in line with a selective gating of attentional resources via anticipatory social significance attributions. By contrast, emotion effects occur much later, when crucial social context information is still missing. These findings demonstrate the context-dependence of emotion effects in word processing and are particularly relevant since virtual communication with unknown senders, whose identity is inferred rather than perceived, has become reality for millions of people.

Cognitive processes, such as the generation of language, can be mapped onto the brain using fMRI. These maps can in turn be used for decoding the respective processes from the brain activation patterns. Given individual variations in brain anatomy and organization, analyzes on the level of the single person are important to improve our understanding of how cognitive processes correspond to patterns of brain activity. They also allow to advance clinical applications of fMRI, because in the clinical setting making diagnoses for single cases is imperative. In the present study, we used mental imagery tasks to investigate language production, motor functions, visuo-spatial memory, face processing, and resting-state activity in a single person. Analysis methods were based on similarity metrics, including correlations between training and test data, as well as correlations with maps from the NeuroSynth meta-analysis. The goal was to make accurate predictions regarding the cognitive domain (e.g. language) and the specific content (e.g. animal names) of single 30-second blocks. Four teams used the dataset, each blinded regarding the true labels of the test data. Results showed that the similarity metrics allowed to reach the highest degrees of accuracy when predicting the cognitive domain of a block. Overall, 23 of the 25 test blocks could be correctly predicted by three of the four teams. Excluding the unspecific rest condition, up to 10 out of 20 blocks could be successfully decoded regarding their specific content. The study shows how the information contained in a single fMRI session and in each of its single blocks can allow to draw inferences about the cognitive processes an individual engaged in. Simple methods like correlations between blocks of fMRI data can serve as highly reliable approaches for cognitive decoding. We discuss the implications of our results in the context of clinical fMRI applications, with a focus on how decoding can support functional localization.

No abstract

Cognitive processes, such as the generation of language, can be mapped onto the brain using fMRI. These maps can in turn be used for decoding the respective processes from the brain activation patterns. Given individual variations in brain anatomy and organization, analyzes on the level of the single person are important to improve our understanding of how cognitive processes correspond to patterns of brain activity. They also allow to advance clinical applications of fMRI, because in the clinical setting making diagnoses for single cases is imperative. In the present study, we used mental imagery tasks to investigate language production, motor functions, visuo-spatial memory, face processing, and resting-state activity in a single person. Analysis methods were based on similarity metrics, including correlations between training and test data, as well as correlations with maps from the NeuroSynth meta-analysis. The goal was to make accurate predictions regarding the cognitive domain (e.g. language) and the specific content (e.g. animal names) of single 30-second blocks. Four teams used the dataset, each blinded regarding the true labels of the test data. Results showed that the similarity metrics allowed to reach the highest degrees of accuracy when predicting the cognitive domain of a block. Overall, 23 of the 25 test blocks could be correctly predicted by three of the four teams. Excluding the unspecific rest condition, up to 10 out of 20 blocks could be successfully decoded regarding their specific content. The study shows how the information contained in a single fMRI session and in each of its single blocks can allow to draw inferences about the cognitive processes an individual engaged in. Simple methods like correlations between blocks of fMRI data can serve as highly reliable approaches for cognitive decoding. We discuss the implications of our results in the context of clinical fMRI applications, with a focus on how decoding can support functional localization.