New Meta-Analysis Reveals Brain Activity Patterns in Emotional Image Processing

Відредаговано: Anna 🌎 Krasko

In the quest for understanding consciousness, researchers have conducted unique experiments using emotional images and functional magnetic resonance imaging (fMRI). A new meta-analysis has emerged from 97 studies.

One of the key tools in emotion research is the International Affective Picture System (IAPS), a collection of images with precisely studied emotional values. Each image has been rated on two dimensions: valence (from pleasant to unpleasant) and arousal (from calm to exciting).

The recent analysis encompassed 97 experiments that examined brain activity while participants viewed images of varying emotional content: from pleasant to neutral to unpleasant. A distinguishing feature of these studies was the focus on cognitive tasks performed during image viewing. Some participants simply viewed the images (known as load-free tasks), while others were asked to evaluate the emotionality of the images, and some had to memorize details while ignoring emotional content.

It was found that the difficulty of the task played a significant role. During simple viewing, areas associated with emotions, such as the amygdala (the center for managing fear and joy) and the prefrontal cortex, were activated. However, as tasks diverted attention from the emotional content of the images, the activity in these structures diminished.

Surprisingly, negative images—regardless of task difficulty—often triggered asymmetrical hemispheric activity, with a greater response in the right part of the frontal cortex. This explains why negative stimuli can be more engaging. Interestingly, during tasks requiring full attention, even emotional titans like sadness or fear were 'silenced' by other brain areas responsible for information processing.

The methodology of the study deserves special recognition. A meta-analysis was employed, combining results from multiple earlier experiments, allowing for the precise identification of common patterns of brain activity. This approach minimizes the risk of errors arising from small sample sizes and differences between studies, offering an exceptionally reliable picture of mind function.

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