The Neural Formula Behind Wildlife Conservation Donations: Faces and Evolutionary Kinship

Conservation organizations spend millions of dollars selecting images for fundraising campaigns, social media posts, and awareness drives. Most of that selection process relies on intuition, past experience, or focus group feedback. A Stanford University study published in February 2026 in PNAS Nexus offers a different approach: measuring which neural signals drive individual engagement decisions, then testing whether those signals predict what happens at scale on actual social media platforms.

The answer is yes - and the predictive variables point toward specific, actionable image features that conservation communicators can use.

From Brain to Instagram Feed

The study combined three methodological layers. Thirty-four adults underwent functional MRI scanning while viewing 56 wildlife images drawn from National Geographic's Instagram feed. During scanning, participants made rapid decisions about whether to "like" each image and whether to donate money to protect the depicted species. The researchers then compared brain activity patterns with actual engagement metrics from the same Instagram account - the ratio of likes to follower count for each image.

Two brain regions showed consistent predictive power. The nucleus accumbens, associated with reward anticipation, and the medial prefrontal cortex, which plays a central role in social cognition and theory of mind, both predicted individual choices to like and donate. When the team averaged medial prefrontal cortex activity across all participants for each image, the resulting signal forecast actual Instagram engagement for the same images.

The medial prefrontal cortex finding proved particularly valuable because it connected two levels of analysis: individual brain responses in a laboratory and collective behavior on a global social media platform. The fact that a small, scanned sample's averaged neural response could track real-world engagement suggests the underlying cognitive mechanism is shared broadly across people, not idiosyncratic to the study participants.

Animal Faces as the Key Variable

Further analysis revealed that medial prefrontal cortex activity correlated with brain regions specialized for face processing. The researchers coded all 56 images for two variables: whether an animal face was clearly visible, and the phylogenetic distance of the depicted species from humans. Both variables predicted neural engagement and, by extension, actual social media response.

Images of mammals - species more closely related to humans on the evolutionary tree - generated stronger engagement than images of reptiles, fish, or invertebrates. Images showing an animal's face generated stronger responses than images showing its body or habitat. The researchers applied a model built on these neural-predictive features to 276 additional wildlife images from the same account that participants had never seen. The model successfully forecasted engagement for this broader set.

"If you want to encourage people to protect an animal, you might depict it in a way that evokes a social or emotional connection," said Brian Knutson, professor of psychology at Stanford and co-author of the study.

A Tension the Data Cannot Resolve

The practical implication is specific: conservation organizations may benefit from prioritizing images of mammalian species and clear facial expressions over ecologically important but visually less compelling organisms - even when the latter are more critical to the ecosystem being protected. This creates a tension the researchers do not fully resolve. Charismatic megafauna already dominate conservation imagery. The neural data confirms this is not irrational, but it also means that the species generating the strongest neural responses may not be the ones most in need of conservation attention.

The 34-person sample limits the generalizability of the findings; participants were likely already interested in wildlife, and whether the same brain patterns would appear in different demographic groups is unknown. Future work using generative AI to systematically modify image features could allow testing of whether the neural-predicted variables can be deliberately optimized to increase engagement and charitable giving at scale.