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Schinazi VR, Meloni D, Grübel J, Angus DJ, Baumann O, Weibel RP, Jeszenszky P, Hölscher C, Thrash T. Motivation moderates gender differences in navigation performance. Sci Rep 2023; 13:15995. [PMID: 37749312 PMCID: PMC10520045 DOI: 10.1038/s41598-023-43241-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 09/21/2023] [Indexed: 09/27/2023] Open
Abstract
Gender differences in navigation performance are a recurrent and controversial topic. Previous research suggests that men outperform women in navigation tasks and that men and women exhibit different navigation strategies. Here, we investigate whether motivation to complete the task moderates the relationship between navigation performance and gender. Participants learned the locations of landmarks in a novel virtual city. During learning, participants could trigger a top-down map that depicted their current position and the locations of the landmarks. During testing, participants were divided into control and treatment groups and were not allowed to consult the map. All participants were given 16 minutes to navigate to the landmarks, but those in the treatment group were monetarily penalized for every second they spent completing the task. Results revealed a negative relationship between physiological arousal and the time required to locate the landmarks. In addition, gender differences in strategy were found during learning, with women spending more time with the map and taking 40% longer than men to locate the landmarks. Interestingly, an interaction between gender and treatment group revealed that women in the control group required more time than men and women in the treatment group to retrieve the landmarks. During testing, women in the control group also took more circuitous routes compared to men in the control group and women in the treatment group. These results suggest that a concurrent and relevant stressor can motivate women to perform similarly to men, helping to diminish pervasive gender differences found in the navigation literature.
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Affiliation(s)
- Victor R Schinazi
- Chair of Cognitive Science, Department of Humanities, Social and Political Sciences, ETH Zurich, Zurich, Switzerland.
- Department of Psychology, Bond University, Robina, Australia.
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.
| | - Dario Meloni
- Chair of Cognitive Science, Department of Humanities, Social and Political Sciences, ETH Zurich, Zurich, Switzerland
| | - Jascha Grübel
- Chair of Cognitive Science, Department of Humanities, Social and Political Sciences, ETH Zurich, Zurich, Switzerland
- Chair of Geoinformation Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zurich, Zurich, Switzerland
| | - Douglas J Angus
- Department of Psychology, Bond University, Robina, Australia
| | - Oliver Baumann
- Department of Psychology, Bond University, Robina, Australia
| | - Raphael P Weibel
- Chair of Cognitive Science, Department of Humanities, Social and Political Sciences, ETH Zurich, Zurich, Switzerland
- Chair of Technology Marketing, Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland
| | - Péter Jeszenszky
- Chair of Cognitive Science, Department of Humanities, Social and Political Sciences, ETH Zurich, Zurich, Switzerland
- Centre for the Study of Language and Society, University of Bern, Bern, Switzerland
| | - Christoph Hölscher
- Chair of Cognitive Science, Department of Humanities, Social and Political Sciences, ETH Zurich, Zurich, Switzerland
| | - Tyler Thrash
- Chair of Cognitive Science, Department of Humanities, Social and Political Sciences, ETH Zurich, Zurich, Switzerland
- Department of Biology, Saint Louis University, St. Louis, MO, USA
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Delaux A, de Saint Aubert JB, Ramanoël S, Bécu M, Gehrke L, Klug M, Chavarriaga R, Sahel JA, Gramann K, Arleo A. Mobile brain/body imaging of landmark-based navigation with high-density EEG. Eur J Neurosci 2021; 54:8256-8282. [PMID: 33738880 PMCID: PMC9291975 DOI: 10.1111/ejn.15190] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 03/05/2021] [Accepted: 03/14/2021] [Indexed: 01/07/2023]
Abstract
Coupling behavioral measures and brain imaging in naturalistic, ecological conditions is key to comprehend the neural bases of spatial navigation. This highly integrative function encompasses sensorimotor, cognitive, and executive processes that jointly mediate active exploration and spatial learning. However, most neuroimaging approaches in humans are based on static, motion‐constrained paradigms and they do not account for all these processes, in particular multisensory integration. Following the Mobile Brain/Body Imaging approach, we aimed to explore the cortical correlates of landmark‐based navigation in actively behaving young adults, solving a Y‐maze task in immersive virtual reality. EEG analysis identified a set of brain areas matching state‐of‐the‐art brain imaging literature of landmark‐based navigation. Spatial behavior in mobile conditions additionally involved sensorimotor areas related to motor execution and proprioception usually overlooked in static fMRI paradigms. Expectedly, we located a cortical source in or near the posterior cingulate, in line with the engagement of the retrosplenial complex in spatial reorientation. Consistent with its role in visuo‐spatial processing and coding, we observed an alpha‐power desynchronization while participants gathered visual information. We also hypothesized behavior‐dependent modulations of the cortical signal during navigation. Despite finding few differences between the encoding and retrieval phases of the task, we identified transient time–frequency patterns attributed, for instance, to attentional demand, as reflected in the alpha/gamma range, or memory workload in the delta/theta range. We confirmed that combining mobile high‐density EEG and biometric measures can help unravel the brain structures and the neural modulations subtending ecological landmark‐based navigation.
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Affiliation(s)
- Alexandre Delaux
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | | | - Stephen Ramanoël
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Marcia Bécu
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Lukas Gehrke
- Institute of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany
| | - Marius Klug
- Institute of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany
| | - Ricardo Chavarriaga
- Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Zurich University of Applied Sciences, ZHAW Datalab, Winterthur, Switzerland
| | - José-Alain Sahel
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France.,CHNO des Quinze-Vingts, INSERM-DGOS CIC 1423, Paris, France.,Fondation Ophtalmologique Rothschild, Paris, France.,Department of Ophthalmology, The University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Klaus Gramann
- Institute of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany
| | - Angelo Arleo
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
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Jensen G, Terrace HS, Ferrera VP. Discovering Implied Serial Order Through Model-Free and Model-Based Learning. Front Neurosci 2019; 13:878. [PMID: 31481871 PMCID: PMC6710392 DOI: 10.3389/fnins.2019.00878] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Accepted: 08/05/2019] [Indexed: 12/24/2022] Open
Abstract
Humans and animals can learn to order a list of items without relying on explicit spatial or temporal cues. To do so, they appear to make use of transitivity, a property of all ordered sets. Here, we summarize relevant research on the transitive inference (TI) paradigm and its relationship to learning the underlying order of an arbitrary set of items. We compare six computational models of TI performance, three of which are model-free (Q-learning, Value Transfer, and REMERGE) and three of which are model-based (RL-Elo, Sequential Monte Carlo, and Betasort). Our goal is to assess the ability of these models to produce empirically observed features of TI behavior. Model-based approaches perform better under a wider range of scenarios, but no single model explains the full scope of behaviors reported in the TI literature.
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Affiliation(s)
- Greg Jensen
- Department of Psychology, Columbia University, New York, NY, United States
- Department of Neuroscience, Columbia University, New York, NY, United States
| | - Herbert S. Terrace
- Department of Psychology, Columbia University, New York, NY, United States
- Department of Psychiatry, Columbia University, New York, NY, United States
| | - Vincent P. Ferrera
- Department of Neuroscience, Columbia University, New York, NY, United States
- Department of Psychiatry, Columbia University, New York, NY, United States
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