Abstract
To adjust expectations efficiently, prediction errors need to be associated with the precise features that gave rise to the unexpected outcome, but this credit assignment may be problematic if stimuli differ on multiple dimensions and it is ambiguous which feature dimension caused the outcome. Here, we report a potential solution: neurons in four recorded areas of the anterior fronto-striatal networks encode prediction errors that are specific to feature values of different dimensions of attended multidimensional stimuli. The most ubiquitous prediction error occurred for the reward-relevant dimension. Feature-specific prediction error signals a) emerge on average shortly after non-specific prediction error signals, b) arise earliest in the anterior cingulate cortex and later in dorsolateral prefrontal cortex, caudate and ventral striatum, and c) contribute to feature-based stimulus selection after learning. Thus, a widely-distributed feature-specific eligibility trace may be used to update synaptic weights for improved feature-based attention.
Highlights
To adjust expectations efficiently, prediction errors need to be associated with the precise features that gave rise to the unexpected outcome, but this credit assignment may be problematic if stimuli differ on multiple dimensions and it is ambiguous which feature dimension caused the outcome
We found that 53.1% of neurons (52.7%/53.6% in monkey H/K) across the fronto-striatal areas tested encoded feature-specific positive, negative, and unsigned prediction error signals
The selectively increased firing to color onsets after low reward prediction errors (RPEs) trials was most prominent and statistically significant for ACC neurons encoding color-specific positive RPEs, and for caudate nucleus (CD) neurons encoding colorspecific surprise. These findings suggest that color-specific RPEs during the early reversal learning trials translate into color cue firing rate increases for these same neurons after reversal learning, reminiscent of the temporal transfer of classical dopaminergic prediction error signals
Summary
Prediction errors need to be associated with the precise features that gave rise to the unexpected outcome, but this credit assignment may be problematic if stimuli differ on multiple dimensions and it is ambiguous which feature dimension caused the outcome. Instead of attending all dimensions of a stimulus prioritizing dimensions that are most reward predictive, dramatically enhances learning speed when stimuli are composed of multiple dimensions[1,12] These findings predict that brain circuits combine information about the occurrence of a prediction error with information about the specific stimulus feature of the relevant dimension that should be attended in future trials[13]. We did so using a task that employed stimuli that could be characterized by multiple dimensions (color, location, and motion), of which only one was linked to reward outcome across trials (color) Feature values within this reward-relevant dimension were reversed, akin to intradimensional shifts in the setshifting literature (e.g.,15)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
