Before 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. This screening is optional and free of charge. If you would like to proceed with submitting your manuscript without the pre-check, please click “Go to submission” below
How Paperpal Preflight works
Paperpal Preflight provides feedback and suggestions to improve your manuscript
Use Paperpal Preflight to check that your manuscript meets the requirements of American Journal of Clinical Oncology and decrease the risk of desk rejection
Review key errors for free and get edited file with all errors marked up at just $29
Upload your manuscript and preview a detailed summary of your paper's performance against the checklist below, which is specific to American Journal of Clinical Oncology and is maintained by American Journal of Clinical Oncology editors.
For a one-time fee, we also suggest fixes for the issues we found. Download the word file with suggestions as tracked changes so that you retain complete control over your text (see sample below).
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. ThereforeThereby, it is assumed that the learning performance remainsstaysconstant 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. To reduceFor reducing 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 occurringhappening 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 allowing the refinement ofallows to refine 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 duringin the course of training. Learning behavior reliesdoes rely 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.
Around 33% of manuscripts fail initial screening due to technical issues. Getting a journal review can take days or weeks. Make sure you get it right the first time by using Preflight.
Preflight is used by more than 20,000 authors submitting to more than 400 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. Learn more about our data security measures
Services Pte Ltd
20 McCallum Street, #19-01,
Tokio Marine Centre,