Within the vertebrate neocortex and other telencephalic structures, molecularly-defined neurons tend to segregate at first order into GABAergic types and glutamatergic types. Two fundamental questions arise: (1) do non-telencephalic neurons similarly segregate by neurotransmitter status, and (2) do GABAergic (or glutamatergic) types sampled in different structures share many molecular features in common, beyond the few genes directly responsible for neurotransmitter synthesis and release? To address these questions, we used single-nucleus RNA sequencing, analyzing over 2.4 million brain cells sampled from 16 locations in a primate (the common marmoset). Unexpectedly, we find the answer to both is “no”. I will discuss implications for generalizing associations between neurotransmitter utilization and other phenotypes, and share ongoing efforts to map the biodistributions of cell types in the primate brain.

Error monitoring refers to the ability to monitor one's own task performance without explicit feedback. This ability is studied typically in two-alternative forced-choice (2AFC) paradigms. Recent research showed that humans can also keep track of the magnitude and direction of errors in different magnitude domains (e.g., numerosity, duration, length). Based on the evidence that suggests a shared mechanism for magnitude representations, we aimed to investigate whether metric error monitoring ability is commonly governed across different magnitude domains. Participants reproduced/estimated temporal, numerical, and spatial magnitudes after which they rated their confidence regarding first order task performance and judged the direction of their reproduction/estimation errors. Participants were also tested in a 2AFC perceptual decision task and provided confidence ratings regarding their decisions. Results showed that variability in reproductions/estimations and metric error monitoring ability, as measured by combining confidence and error direction judgements, were positively related across temporal, spatial, and numerical domains. Metacognitive sensitivity in these metric domains was also positively associated with each other but not with metacognitive sensitivity in the 2AFC perceptual decision task. In conclusion, the current findings point at a general metric error monitoring ability that is shared across different metric domains with limited generalizability to perceptual decision-making.