Matlab code for the Bidimensional regression to calculate estimated position and heading from replaced locations of objects, implementing (Friedman & Kohler, 2003)
| dc.contributor.author | Weimin Mou | |
| dc.date.accessioned | 2025-05-01T20:53:37Z | |
| dc.date.available | 2025-05-01T20:53:37Z | |
| dc.date.issued | 2019-04-23 | |
| dc.description | A participants learns objects in five original locations (e.g. O). After navigation, the participant replaces objects from the testing position P and heading h. Conceptually, the bidimensional regression produces a prediction function, which is the transformation matrix that converts the replaced locations to the predicted locations so that the predicted locations are the closest to the original locations overall (Friedman & Kohler, 2003). The prediction function then calculates h’ and P’ using h and P as the values of the predictor respectively. The locations are connected by lines only to highlight the configuration of them. This method produced similar results based on the methods proposed by Mou & Zhang (2014, see https://doi.org/10.7939/R3FT8F06G) for heading errors and position angular errors. | |
| dc.identifier.doi | https://doi.org/10.7939/r3-2tj7-xq22 | |
| dc.language.iso | en | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | |
| dc.subject | spatial cognition | |
| dc.title | Matlab code for the Bidimensional regression to calculate estimated position and heading from replaced locations of objects, implementing (Friedman & Kohler, 2003) | |
| dc.type | http://purl.org/coar/resource_type/c_1843 | |
| ual.jupiterAccess | http://terms.library.ualberta.ca/public |