Habiba Azab

This study shows that accumulating tokens toward a jackpot reward improves decision-making accuracy and speed in macaques, with neural activity in dACC reflecting reference-dependent value encoding.

The paper extends the PRO model to explain decision-making under risk in humans and macaques, showing ACC represents outcome variance and links to mean-variance theories.

The paper uses a Bayesian regression approach to show that dACC neurons partially integrate value determinants, indicating they are neither pre- nor post-evaluative.

The paper examines macaque pupil responses to offered and anticipated rewards, finding opposing correlations that challenge the simulation hypothesis and suggest an accept-reject model of comparison.

This study shows that monkeys exhibit flexible risk attitudes, with varying utility and probability weighting functions across tasks, suggesting dynamic construction of reward value rather than fixed traits.