Neural representation of newly instructed rule identities during early implementation trials

Abstract

By following explicit instructions, humans instantaneously get the hang of tasks they have never performed before. We used a specially calibrated multivariate analysis technique to uncover the elusive representational states during the first few implementations of arbitrary rules such as ‘for coffee, press red button’ following their first-time instruction. Distributed activity patterns within the ventrolateral prefrontal cortex (VLPFC) indicated the presence of neural representations specific of individual stimulus-response (S-R) rule identities, preferentially for conditions requiring the memorization of instructed S-R rules for correct performance. Identity-specific representations were detectable starting from the first implementation trial and continued to be present across early implementation trials. The increasingly fluent application of novel rule representations was channelled through increasing cooperation between VLPFC and anterior striatum. These findings inform representational theories on how the prefrontal cortex supports behavioral flexibility specifically by enabling the ad-hoc coding of newly instructed individual rule identities during their first-time implementation.

Fronto-parietal, cingulo-opercular and striatal contributions to learning and implementing control policies

“…As such, RT dynamics may not reflect processes within fronto-parietal cortex, as has been suggested (Ruge and Wolfensteller, 2010). However, Ruge et al (2019) recently showed that newly instructed rules are persistently coded in lateral PFC, even though univariate activity falls to baseline levels, suggesting that these activity dynamics possibly index a different process.…”

“…In particular, we focused on 'multipledemand' regions in the cingulo-opercular network (CON), the fronto-parietal network (FPN) as well as the striatum (STR). These regions have been implicated in task control (Dosenbach et al, 2008) and also overlap with regions implicated in rule learning and implementation (Hartstra et al, 2011;Stocco et al, 2012;Muhle-Karbe et al, 2017;Ruge et al, 2019), including of hierarchical rules ((Badre et al, 2010).…”

“…Previous studies of task automatization have primarily focused on learning and implementation of task rules (stimulus-response contingencies) through trial-and-error (Cao et al, 2016) or instruction (Brass and Von Cramon, 2002;Brass and Cramon, 2004;Cole et al, 2010;Ruge and Wolfensteller, 2010;Dumontheil et al, 2011;Hartstra et al, 2011;Stocco et al, 2012;Mohr et al, 2016;Muhle-Karbe et al, 2017;Bourguignon et al, 2018;Hampshire et al, 2019;Ruge et al, 2019). These studies have implicated the fronto-parietal networks and striatum in automatization.…”

Optimisation of functional network resources when learning behavioural strategies for performing complex tasks

“…Some studies have attempted to address this by developing paradigms where individuals randomly volunteer when to task-switch 10,[13][14][15] . However, these paradigms fail to capture the natural inclination to apply structure to behaviour 16,17 and to optimise behavioural strategies through learning [18][19][20][21][22][23] .…”

“…More broadly, network neuroscience studies of cognitive control demonstrate that when engaging with a task there is a substantial reorganization of the brain's functional connectome 49,60,61 , characterised by the transition from a state of high-activation to a low-activation highconnectivity state [18][19][20][21][22][23]62 , and this transition rate may depend on learning. Critically, the established task-state expresses a stable 44,62 connectivity pattern that is specific enough to the task 22,47,50,[63][64][65] , cognitive load 22 , performance 66 and clinical abnormalities [67][68][69] to be decoded with machine learning.…”