1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      The role of visual feedback in detecting and correcting typing errors: A signal detection approach

      ,
      Journal of Memory and Language
      Elsevier BV

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references51

          • Record: found
          • Abstract: not found
          • Article: not found

          lmerTest Package: Tests in Linear Mixed Effects Models

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            jsPsych: a JavaScript library for creating behavioral experiments in a Web browser.

            Online experiments are growing in popularity, and the increasing sophistication of Web technology has made it possible to run complex behavioral experiments online using only a Web browser. Unlike with offline laboratory experiments, however, few tools exist to aid in the development of browser-based experiments. This makes the process of creating an experiment slow and challenging, particularly for researchers who lack a Web development background. This article introduces jsPsych, a JavaScript library for the development of Web-based experiments. jsPsych formalizes a way of describing experiments that is much simpler than writing the entire experiment from scratch. jsPsych then executes these descriptions automatically, handling the flow from one task to another. The jsPsych library is open-source and designed to be expanded by the research community. The project is available online at www.jspsych.org .
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              An integrated theory of language production and comprehension.

              Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume that actors construct forward models of their actions before they execute those actions, and that perceivers of others' actions covertly imitate those actions, then construct forward models of those actions. We use these accounts of action, action perception, and joint action to develop accounts of production, comprehension, and interactive language. Importantly, they incorporate well-defined levels of linguistic representation (such as semantics, syntax, and phonology). We show (a) how speakers and comprehenders use covert imitation and forward modeling to make predictions at these levels of representation, (b) how they interweave production and comprehension processes, and (c) how they use these predictions to monitor the upcoming utterances. We show how these accounts explain a range of behavioral and neuroscientific data on language processing and discuss some of the implications of our proposal.
                Bookmark

                Author and article information

                Journal
                Journal of Memory and Language
                Journal of Memory and Language
                Elsevier BV
                0749596X
                April 2021
                April 2021
                : 117
                : 104193
                Article
                10.1016/j.jml.2020.104193
                81a58acc-036a-4bd1-b311-ffdf6a986829
                © 2021

                https://www.elsevier.com/tdm/userlicense/1.0/

                History

                Comments

                Comment on this article