Figure - PMC

Skip to main content

An official website of the United States government

Here's how you know

Here's how you know

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

View full-text article in PMC

. 2020 Feb 21;30(6):3573–3589. doi: 10.1093/cercor/bhz327

Search in PMC
Search in PubMed
View in NLM Catalog
Add to search

© The Author(s) 2020. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

PMC Copyright notice

Relationships between D_2/3R availability and RL and AI parameters governing response stochasticity in participants who completed PET (n = 25). (A) There is a linear relationship between irreducible decision noise and limbic striatal D_2/3R availability (measured as [¹¹C]-(+)-PHNO BP_ND) in the participants who completed a PET scan (r = –0.46, 95% CI [–0.72 –0.08], P = 0.020). Here, was logit transformed as it was not normally distributed: . Without the transform, the correlation remained significant (r = –0.45, 95% CI [–0.72 –0.07], P = 0.023). (B) There is a significant linear relationship between the prior on policy precision and D_2/3R availability, β_linear = –2.1, SE = 0.91, t₂₂ = –2.33, P =0.029; there is also evidence of a quadratic relationship but it is not significant, β_quad = 0.49, SE = 0.26, t₂₂ = 1.92, P = 0.068. (C) This plot shows the relationships between D_2/3R availability and the first factor in the factor analysis of the AI and RL parameters (detailed in Fig. S9). Three parameters governing response stochasticity loaded on this factor (loadings in brackets) – (0.69), _win (0.88), and (0.30)—and this factor has robust linear (β_linear = –0.29, SE = 0.07, t₂₂ = –3.97, P = 0.0006) and quadratic (β_quad = 1.8, SE = 0.06, t₂₂ = 3.06, P =0.006) relationships with D_2/3R availability. (D) We interpret the linear relationships in plots A–C as being due to greater tonic D₂R occupancy suppressing the indirect pathway more, hence making participants’ choices more deterministic.

Inline graphic — Relationships between D_2/3R availability and RL and AI parameters governing response stochasticity in participants who completed PET (n = 25). (A) There is a linear relationship between irreducible decision noise and limbic striatal D_2/3R availability (measured as [¹¹C]-(+)-PHNO BP_ND) in the participants who completed a PET scan (r = –0.46, 95% CI [–0.72 –0.08], P = 0.020). Here, was logit transformed as it was not normally distributed: . Without the transform, the correlation remained significant (r = –0.45, 95% CI [–0.72 –0.07], P = 0.023). (B) There is a significant linear relationship between the prior on policy precision and D_2/3R availability, β_linear = –2.1, SE = 0.91, t₂₂ = –2.33, P =0.029; there is also evidence of a quadratic relationship but it is not significant, β_quad = 0.49, SE = 0.26, t₂₂ = 1.92, P = 0.068. (C) This plot shows the relationships between D_2/3R availability and the first factor in the factor analysis of the AI and RL parameters (detailed in Fig. S9). Three parameters governing response stochasticity loaded on this factor (loadings in brackets) – (0.69), _win (0.88), and (0.30)—and this factor has robust linear (β_linear = –0.29, SE = 0.07, t₂₂ = –3.97, P = 0.0006) and quadratic (β_quad = 1.8, SE = 0.06, t₂₂ = 3.06, P =0.006) relationships with D_2/3R availability. (D) We interpret the linear relationships in plots A–C as being due to greater tonic D₂R occupancy suppressing the indirect pathway more, hence making participants’ choices more deterministic.