40
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Keep it real: rethinking the primacy of experimental control in cognitive neuroscience

      research-article

      Read this article at

      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.

          Abstract

          Naturalistic experimental paradigms in neuroimaging arose from a pressure to test the validity of models we derive from highly-controlled experiments in real-world contexts. In many cases, however, such efforts led to the realization that models developed under particular experimental manipulations failed to capture much variance outside the context of that manipulation. The critique of non-naturalistic experiments is not a recent development; it echoes a persistent and subversive thread in the history of modern psychology. The brain has evolved to guide behavior in a multidimensional world with many interacting variables. The assumption that artificially decoupling and manipulating these variables will lead to a satisfactory understanding of the brain may be untenable. We develop an argument for the primacy of naturalistic paradigms, and point to recent developments in machine learning as an example of the transformative power of relinquishing control. Naturalistic paradigms should not be deployed as an afterthought if we hope to build models of brain and behavior that extend beyond the laboratory into the real world.

          Related collections

          Most cited references130

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

          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Toward an experimental ecology of human development.

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

              Mixed-effects modeling with crossed random effects for subjects and items

                Bookmark

                Author and article information

                Journal
                9215515
                20498
                Neuroimage
                Neuroimage
                NeuroImage
                1053-8119
                1095-9572
                31 December 2020
                13 August 2020
                15 November 2020
                07 January 2021
                : 222
                : 117254
                Affiliations
                [a ]Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
                [b ]Department of Psychology, Princeton University, Princeton, NJ, USA
                Author notes

                CRediT authorship contribution statement

                Samuel A. Nastase: Writing - original draft, Conceptualization. Ariel Goldstein: Writing - review & editing, Conceptualization. Uri Hasson: Writing - original draft, Conceptualization.

                [* ]Corresponding author. sam.nastase@ 123456gmail.com (S.A. Nastase).
                Article
                NIHMS1658218
                10.1016/j.neuroimage.2020.117254
                7789034
                32800992
                101bd4a9-5df5-46e0-815f-fc48e48b6814

                This is an open access article under the CC BY license. ( http://creativecommons.org/licenses/by/4.0/)

                History
                Categories
                Article

                Neurosciences
                ecological psychology,ecological validity,experimental design,generalizability,naturalistic stimuli,representative design

                Comments

                Comment on this article