3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Both the domain-general and the mentalising processes affect visual perspective taking

      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

          People’s attention cannot help being affected by what others are looking at. The dot-perspective task has been often employed to investigate this visual attentional shift. In this task, participants are presented with virtual scenes with a cue facing some targets and must judge how many targets are visible from their own or the cue perspective. Typically, this task shows an interference pattern: Participants record slower reaction times (RTs) and more errors when the cue is facing away from the targets. Interestingly, this occurs also when participants take their own perspective. Two accounts contend the explanation of this interference. The mentalising account focuses on the social relevance of the cue, while the domain-general account focuses on the directional features of the cue. To investigate the relative contribution of the two accounts, we developed a Social_Only cue, a cue having only social features and compared its effects with a Social+Directional cue, which had both social and directional features. Results show that while the Social+Directional cue generates the typical interference pattern, the Social_Only cue does not generate interference in the RTs, only in the error rate. We advance an integration between the mentalising and the domain-general accounts. We suggest that the dot-perspective task requires two processes: an orienting process, elicited by the directional features of the cue and measured by the RTs, and a decisional process elicited by the social features of the cue and measured also by the error rate.

          Related collections

          Most cited references56

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

          Inference from Iterative Simulation Using Multiple Sequences

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

            brms: An R Package for Bayesian Multilevel Models Using Stan

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

              Stan: A Probabilistic Programming Language

              Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
                Bookmark

                Author and article information

                Journal
                Q J Exp Psychol (Hove)
                Q J Exp Psychol (Hove)
                QJP
                spqjp
                Quarterly Journal of Experimental Psychology (2006)
                SAGE Publications (Sage UK: London, England )
                1747-0218
                1747-0226
                2 June 2022
                March 2023
                : 76
                : 3
                : 469-484
                Affiliations
                [1 ]Centre for Behavioural Science and Applied Psychology, Sheffield Hallam University, Sheffield, UK
                [2 ]School of Psychological Sciences, University of Bristol, Bristol, UK
                Author notes
                [*]Gabriele Pesimena, Centre for Behavioural Science & Applied Psychology, Sheffield Hallam University, Heart of the Campus, Collegiate Crescent, Broomhall, Sheffield S10 2BQ, UK. Email: g.pesimena@ 123456shu.ac.uk
                Author information
                https://orcid.org/0000-0001-6457-2532
                Article
                10.1177_17470218221094310
                10.1177/17470218221094310
                9936435
                35360994
                25f6032c-c3f6-4db3-a7e6-0532ad13385b
                © Experimental Psychology Society 2022

                This article is distributed under the terms of the Creative Commons Attribution 4.0 License ( https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 28 July 2021
                : 2 March 2022
                : 7 March 2022
                Categories
                Original Articles
                Custom metadata
                open-data
                open-materials
                ts1

                Clinical Psychology & Psychiatry
                visual perspective taking,attention,dot-perspective task,spatial cueing,mentalising,bayesian statistics

                Comments

                Comment on this article