Prior to submitting, we recommend that you run your manuscript through the Paperpal Preflight screening tool, which instantly checks your manuscript, helps you address common errors and omissions and performs a language quality review.
Paperpal Preflight provides feedback and suggestions for improvements of your manuscript, but does not guarantee acceptance. Some of the checks performed may only apply to specific article types; please check the journal's Instructions for Authors for further detail. Should your manuscript be accepted, journal editors may still require significant changes based on journal and style guidelines.
Use Paperpal Preflight to confirm that your manuscript meets the requirements of Assembly Automation
Once you upload we’ll run the following suite of technical and language checks on your manuscript and highlight the issues we found. This checklist is specific to Assembly Automation and is maintained by Assembly Automation editors.Free
For a one-time purchase we’ll also recommend fixes for the issues we found. You’ll get a Word file with suggestions as tracked changes, so you’ll retain complete control over your text.$29
John Paperpal, Jane Cactus
Corresponding Author: firstname.lastname@example.org
Estimation of learning curves is ubiquitously based on the proportion of correct responses within moving trial windows. TherebyTherefore, it is assumed that the learning performance staysremainsconstant within the moving windows, which may oftennot be the case. In the present study, we demonstrate that this assumption's violations lead to systematic errors in the analysis of learning curves, and. We explore the dependency of these errors on window size, different statistical models, and the learning phase. For reducingTo reduce these errors in the analysis of single subject data, we propose adequate statistical methods for the estimation of learning curves and construction of confidence intervals in a trial by trialtrial-by-trial manner. Applied to data from an avoidance learning experiment with rodents, these methods revealed performance changes happeningoccurring at multiple time scales across training sessions. Our work also shows that the proper assessment of the behavioral dynamics of learning at high temporal resolution can highlight specific learning processes, thus allows to refineallowing the refinement of existing learning concepts. It further disambiguates the interpretation of neurophysiological signal changes recorded during training in relation to learning.
Learning, the acquisition of knowledge through experience, manifests as behavioral changes induring the course of training. Learning behavior does relyrelies on a multitude of neural and cognitive processes that act on different spatial and temporal scales [1–3]; however, many of these processes are not experimentally accessible. Therefore, any particular learning experiment is influenced by numerous uncontrolled variables. This entails a certain degree of unaccountable variability of behavior across time within a subject, as well as between subjects . As a consequence, single behavioral responses of individual subjects are difficult to interpret with respect to learning.
We are the only pre-submission screening tool that looks at both technical and language reporting. With a combined set of 35+ checks we offer a thorough second pair of eyes to not just assess but improve your manuscript.
Preflight is used by more than 20,000 authors submitting to more than 130 journals across academic disciplines and spanning the portfolio of top global publishers.
We use cutting-edge machine learning trained on millions of academic manuscripts to provide suggestions for improvements on par with those provided by a human editor.
We adhere to the highest standards of data security. Uploaded manuscripts are stored on an encrypted server and are automatically deleted after 90 days.
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