Training-based Executive Functions Plasticity And Its Transfer Effect

Executive functions(EF),also termed as cognitive control,are a set of cognitive functions comprising of working memory(WM),inhibition and cognitive flexibility.According to the literature,EF reflect the ability to modulate individuals’ goal-directed behaviors through top-down control signals,in case to adapte to changing environment.This ability is a core component of human cognition mediating a variety of high-level cognitive processes and is essential for lifelong achievement and mental health.The importance of EF has attracted attention from numerous researchers to focus on improving it,where cognitve training is proposed as the most effective and reliable way.However,most of previous studies have focused on WM training,but not other subcomponents of EF.Therefore,it is still unclear whether different components of EF would show different patterns of training and transfer effects.In fact,there has been a great controversy over whether there is a steady transfer effect in cognitive training.In addition,the neural associates of EF training are still understudied.In current study,we sought to explore those issues by focusing on two key components of EF: conflict control(CC)and executive attention(EA).CC refers the ability to solve interference between stimuli or between stimulus and response through top-down modulation,while EA reflects the ability to control attention based on task goals.In general,CC is regarded as a domain-specfic component of EF,and EA as a domain-general component.Therefore,CC and EA are different by definition and reflect different aspects of EF,investigating the training and transfer effects of those two components could deepen our understading of EF plasticity.In current study,we hypothesized that cognitive training could yield different training and transfer effects concerning CC and EA.In Study 1,we aimed to explore the plasticity of CC and its transfer effect based on five experiments.In Experiment 1,we employed the Stroop task as training task to conduct a 5-day CC training.The results showed that training significantly reduced the conflict effect in Stroop task,but not in the Simon task,indicating the absence of near transfer.In Experiment 2,we conducted a further 5-day cognitive training using the multi-source interference task(MSIT).As revealed in the results,training improved the performance in MSIT,but not in attention network test or 2-back task,indicating the absence of far transfer effect.Experiment 3 examined the neural bases of CC training on regional activity level.We found that reduced conflict-related activity in the posterior parietal cortex(PPC),but not other regions in a distributed fronto-parietal network,was correlated with improved performance,which was best explained by a quadratic model.Meanwhile,changes in the coupling between the PPC and the occipital cortex did not correlated to the behavioral gains,although connectivity strength of the pathway associated with behavioral scores in the pretest stage.The findings suggested that reduced conflict effect after training was subserved by increased neural efficiency in the region responsible for resolving conflict.In Experiment 4,we further examined the neural bases of CC training on the level of neural interaction.The results showed that increase in the connectivity strength of several network interactions,such as the connectivity from the CON to the cerebellum and to the primary visual network,was associated with behavioral gains.Meanwhile,there were also nonlinear correlations between behavioral and neural changes.These findings therefore highlighted a critical role of the modulation of CON on other networks in mediating CC plasticity.Results from all those four experiments indicated that CC training could only improve the performance on the training task,which was mediated by improved neural efficiency in task-specific pathways.Absence of transfer effect might be due to the domain-specific nature of CC.To test whether CC could also manifested domain-specific processing in different task backgrounds,we manipulated background colors to explore its influence on conflict adaptation effect.We found that the processing pattern of conflict adaptation was altered in blue background,but not in grey and erd backgrounds,which confirmed the domain-specific processing of CC.In Study 2,we aimed to examine the plasticity of EA and its transfer effect through four experiment.In Experiemnt 1,we trained the paticipants on an adaptive antisaccade task for 7 days,and the results showed that training significantly improved the performance on antisaccade task.In addition,performance on different types of conflict task was also improved,along with the performance on N-Back task.Those results indicated that EA training could yield significant transfer effects on CC and WM.To support the findings,we conducted a second experiment with an integrated MSIT—N-Back task to mearsure transfer effect.However,results of the Experiment 2 did not replicated the findings mentioned above,which suggested that transfer of EA on CC and EA could only be achieved when conditions are satisfied.In Experiment 3,we investigated the influence of EA training on fluild intelligence with the employment of two independent samples.We found that training significantly improved the performance on Raven’s advanced progressive matrice.In addition,training altered the way of EA processing,and increased correlation between control process and fluid intelligence after training was the key factor contributing to such transfer effect.In Experiment 4,we examined the neural associates of such training and transfer effect.As revealed by the results,the intrinsic neural activity associated with EA processing was altered after training,and there was a significant correlation between improved behavioral performances and the neural activity of fronto-parietal attention control network.Training-related neural changes showed both linear and non-linear correlations with behavior gains.Therefore,results from Study 2 suggested that training on EA could improve the performance of the target task,and such effect could transfer to the behavioral performances of CC,WM and fluid intelligence.When combining results from Study 1 and Study 2,we found that training targeting on different components of EF yield distinct effects: training on domain-specific CC component could only improve performance on the training task,while training on domain-general EA component could transfer to other cognitive functions.Meanwhile,there were also different neural patterns concerning the training effects: those neural changes relating to CC training could only be observed in taskspecific neural pathways,while EA training altered the engagement of fronto-parietal attention control network in task processing,which mediated the transfer effect.Results from the CC training study support the modular view of EF construct,according to which,different components of EF are relatively independent,and training on one component of EF may not benefit performances on other components.In contrast,findings from the EA training study support the unitary view of EF construct,which argues that EF is a unitary system and training on one component of EF could transfer to other components.Nonetheless,the current study proposes that EF is a complex construct that includes both modular components and general components,training on different types of components could result in different patterns that are associated with distinct neural pathways.Taken together,there are several key findings:(1)training effects on EF relie on the type of EF components,while training targeting domain-specific components could only improve performances on the training task,training on domain-general components could transfer to other cognitive functions.(2)Gains after training on domain-specific components are associated with improved efficiency in task-specific neural pathways;in contrast,gains and transfer after training on domain-general components are subserved by altered neural engagement of the fronto-parietal attention control network.(3)Training-related behavioral and neural associations manifest several kinds of patterns,as both linear and non-linear correlations were detected in current study.Therefore,the current study unravels the behavioral and neural bases of EF training based on different subcomponents,and we suggest that there is the essential to distinguish between different kinds of EF components when conducting EF training in laboratory and practice.