This paper was published in the international journal Cerebral Cortex in March 2020. It was part of a collaboration with researchers from Johns Hopkins University and aims to explore what happens when we learn confliction information.
We aimed to unveil the time course of anterograde interference by tracking its impact on visuomotor adaptation at different intervals throughout a 24-h period.
How do motor memories influence one another? In our new study in Cerebral Cortex, we focus on the origins of anterograde interference (AI).
There is currently no consensus on whether AI is transient or long-lasting, with studies pointing to an effect in the time scale of hs to days. These inconsistencies may obey to the method used to quantify performance, which confounds changes in retention and learning rate.
In our study, we examined the expression of anterograde interference in visuomotor adaptation (VMA) by varying the time elapsed between learning two opposing visual rotations (A and B) through a 24h window.
Our empirical and model-based approaches allowed us to measure the capacity for new learning independently from the influence of a previous memory. We predicted that if AI arises from an impairment in the ability to learn, adaptation in A would reduce the learning rate in B.
In agreement with previous reports, we found that adaptation in A persistently impaired the initial level of performance in B. Although the magnitude of this effect decreased with time, it remained strong (> 50%) at 24 h
Despite this strong initial bias, however, the ability to learn recovered with the passage of time, with release from interference occurring around 6 h post adaptation.
What mechanism could explain the learning impairment observed at short time intervals (5 min, 1 h)? A mathematical (state-space) model attributed this deficit to an impairment in error sensitivity. Trial-to-trial retention, on the other hand, was unaffected by prior learning.
Our work shows that when adapting to conflicting perturbations, impairments in performance are driven by two distinct mechanisms: a long-lasting bias that hinders initial performance, and a short-lasting AI that originates from a reduction in error-sensitivity.