Abstract
Flexible, adaptive behavior is thought to rely on abstract rule representations within lateral prefrontal cortex (LPFC), yet it remains unclear how these representations provide such flexibility. We recently demonstrated that humans can learn complex novel tasks in seconds. Here we hypothesized that this impressive mental flexibility may be possible due to rapid transfer of practiced rule representations within LPFC to novel task contexts. We tested this hypothesis using functional MRI and multivariate pattern analysis, classifying LPFC activity patterns across 64 tasks. Classifiers trained to identify abstract rules based on practiced task activity patterns successfully generalized to novel tasks. This suggests humans can transfer practiced rule representations within LPFC to rapidly learn new tasks, facilitating cognitive performance in novel circumstances.
Highlights
The ability to flexibly adapt to novel circumstances is a fundamental aspect of human intelligence (McClelland, 2009; Cole et al, 2010b)
The present results suggest this is possible because the human brain can retain the benefits of practice even during novel task performance, through the transfer of practiced rule representations within lateral prefrontal cortex (LPFC) into novel contexts
These results suggest that individuals use the same abstract decision rule representations within LPFC even after extensive practice with a given complex task
Summary
The ability to flexibly adapt to novel circumstances is a fundamental aspect of human intelligence (McClelland, 2009; Cole et al, 2010b). Measures of fluid intelligence – associated with LPFC (Duncan, 2000; Burgess et al, 2011) – test for the ability to solve complex novel puzzles, and are able to predict important life outcomes such as academic and job performance (Blair, 2006; Gottfredson and Saklofske, 2009). A compositional scheme of rule representation – in which new task representations can be constructed from different combinations of familiar rule representations – would allow for rapid representation of a wide variety of novel task states within LPFC. Rather than having to learn each complex set of task rules from scratch, a compositional coding scheme could allow LPFC to transfer skills and knowledge tied to constituent familiar rules into new task contexts (i.e., unique combinations of constituent rules) to vastly improve task learning. Rather than having to learn the task “If the answer to ‘is it sweet?’ is the same for both words, press your left index finger” all at once, compositional representation could allow recent practice assessing sameness of decision outcomes (the SAME rule) and, separately, practice with judging the sweetness of objects from memory (the SWEET rule) to facilitate learning this novel task
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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.
