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Human behavior is shaped by two opposing motivational drives: accumulation of reward and avoidance of harm. It is still unsettled whether, in the human brain, gains and losses are represented separately, enabling independent pursuit of both goals, or conjointly, by a collective pursuit on a common value scale. Human decision making is characterized by fundamental asymmetries between gain and loss processing, hinting at separate representation. Functional imaging studies nevertheless mostly favor a unified scale interpretation. We hypothesized that the poor spatial resolution of fMRI may account for these discrepancies: a voxel averages hundreds of thousands of neurons, so two intermingled populations with opposite preferences are indistinguishable from one bidirectional population. Here, we show that learning signals for gain and loss contexts are encoded by functionally separate neuronal populations that coexist within the same anatomical structures. We analyzed single-unit activity from 370 single neurons in prefrontal cortex, anterior cingulate cortex, temporal cortex, hippocampus and amygdala in 17 patients implanted with intracranial electrodes for epilepsy monitoring, while they performed a probabilistic choice task with separate gain and loss contexts. Trial-by-trial prediction errors were estimated from a reinforcement learning model fit to each participant’s choices. Gain and loss prediction errors were primarily represented by distinct, intermingled neuronal subpopulations, each selectively encoding context-specific prediction errors. A small subset of neurons encoded prediction errors in both contexts, frequently with opposite codes. Participants with greater segregation between the two codes showed a larger asymmetry in learning, with higher error and exploration rates in loss contexts than in gain contexts. Gain and loss errors therefore appear to be implemented through independent but anatomically interleaved neural channels, allowing independent processing pathways. This separation may offer insights into psychopathologies of biased valuation.