Some forms of associative learning require only a single experience to create a lasting memory [1, 2]. In contrast, perceptual learning often requires extensive practice within a day for performance to improve across days [3, 4]. This suggests that the requisite practice for durable perceptual learning is integrated throughout each day. If the total amount of daily practice is the only important variable, then a practice break within a day should not disrupt across-day improvement. To test this idea, we trained human listeners on an auditory frequency-discrimination task over multiple days and compared the performance of those who engaged in a single continuous practice session each day [4] with those who were given a 30-min break halfway through each practice session. Continuous practice yielded significant perceptual learning [4]. In contrast, practice with a rest break led to no improvement, indicating that the integration process had decayed within 30 min. In a separate experiment, a 30-min practice break also disrupted durable learning on a non-native phonetic classification task. These results suggest that practice trials are integrated up to a learning threshold within a transient memory store before they are sent en masse into a memory that lasts across days. Thus, the oft cited benefits of distributed over massed training [5, 6] may arise from different mechanisms depending on whether the breaks occur before or after a learning threshold has been reached. Trial integration could serve as an early gatekeeper to plasticity, helping to ensure that longer-lasting changes are only made when deemed worthwhile.
Pubmed ID: 29174894 RIS Download
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Network of ftp and web servers around world that store identical, up to date, versions of code and documentation for R. Package archive network for R programming language.
View all literature mentionsProgramming language for all operating systems that lets users work more quickly and integrate their systems more effectively. Often compared to Tcl, Perl, Ruby, Scheme or Java. Some of its key distinguishing features include very clear and readable syntax, strong introspection capabilities, intuitive object orientation, natural expression of procedural code, full modularity, exception-based error handling, high level dynamic data types, extensive standard libraries and third party modules for virtually every task, extensions and modules easily written in C, C (or Java for Python, or .NET languages for IronPython), and embeddable within applications as a scripting interface.
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