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Neural Encoding of Outcome Magnitude: Evidence from fMRI

Authors :

Amin Mohammad Mohammadi¹ Shaghayegh Mahmoudi² Narjes Amin³ Farid Hosseinzadeh⁴ Elias Ebrahimzadeh⁵ Hamid Soltanian-Zadeh⁶

1- دانشگاه تهران 2- دانشگاه علم و فرهنگ تهران 3- دانشگاه تهران 4- دانشگاه تهران 5- دانشگاه تهران 6- دانشگاه تهران

Keywords :

fMRI،decision-making،gambling،monetary incentive،reward magnitude،punishment magnitude،feedback processing،parametric modulation،dorsal anterior cingulate cortex (dACC)

Abstract :

Reward and punishment outcomes play a central role in adaptive decision-making, yet while the effects of outcome valence have been extensively studied, the neural encoding of outcome magnitude remains less well understood. Previous neuroimaging research has typically examined reward magnitude in isolation, often with only two outcome levels, limiting the precision of magnitude-related findings. To address this gap, we conducted a functional MRI (fMRI) study using a modified monetary gambling task designed to parametrically vary both gain and loss magnitudes. Twenty-four healthy participants performed 100 randomized trials while undergoing 3 Tesla fMRI scanning. Outcome magnitudes were modeled as parametric modulators within the general linear model framework, allowing us to identify brain regions whose blood-oxygen-level-dependent (BOLD) responses scaled with feedback size. Group-level analyses revealed significant magnitude-related activations in a distributed network including the dorsal anterior cingulate cortex (dACC), Rolandic Operculum extending into the insula, bilateral inferior frontal gyrus (IFG), precuneus, and right cerebellum. Notably, the dACC showed robust bilateral engagement, consistent with its role in evaluating outcome salience and encoding motivationally relevant information. These findings demonstrate that outcome magnitude, across both rewards and punishments, recruits neural circuits beyond classical reward areas, engaging regions involved in cognitive control, interoceptive processing, and adaptive behavioral regulation. Our results advance the understanding of how the human brain encodes the quantitative features of feedback and highlight the importance of considering magnitude alongside valence in models of decision-making and reinforcement learning.

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