Thanks to co-authors @morenobote.bsky.social and @benhayden.bsky.social for such a great opportunity, to @upf.edu, @crmatematica.bsky.social, and all supporters. 🧠🧪 #neurojc #neuroscience #neuropapers #compneuro #neuroeconomics #neurosky #academicsky #neuroskynce #decisionmaking #BehavioralEcology

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Using a reference-dependent model based on Prospect Theory (PT), we reveal a functional asymmetry: More neurons encoded potential gains than losses, and gain-related signals were more tightly aligned with choices. This highlights a bias toward positive, goal-relevant outcomes in dACC value coding.

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The core novelty of our model stands in the definition of a Prospect Theory (Kahneman & Tversky 1979) inspired reference r(ATC) that dynamically models the utility u(v,ATC) as the comparison of alternative offers for ATC<3 🤑 (r(ATC)→0) and of offer value to missing tokens (r(ATC)→6-ATC) for ATC≥3 😬.

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Even when explicitly modeling interactions between value and token accumulation, neural encoding remained primarily driven by value signals. However, these signals were subtly reshaped across the task, suggesting that token accumulation modulates, but does not simply scale, value representations.

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At the neural level we found that dorsal anterior cingulate cortex (dACC) encodes subjective value (SV) and ATC. SV-related signals peak during offer presentation, while ATC signals are sustained across the task, indicating progress monitoring. SV- but not ATC-related signals correlate with choices.

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