Three-systems for visual numerosity: A single case study

MOT TASK Each file refers to a session with a particular condition. It is spelt out in the file name the number of objects to track as well as the total amount of objects on the screen Within each file it is found a variable called “MatriceRisultati”. Which contains: Number of targets to follow Number of correct answers Number of trials at the condition Percent correct responses (i.e. value_2 / value_3) NECKLACE – DISTANCE TASK Each file contains raw data for each session. All the data are store in a variable called RESP, which contains parameters for each trial. The crucial columns are Inter dot distance in the reference (in pixels – typically 1 pixel =~0.03 cm) Interdot distance in the test (in pixels) Subject choice to the question “which contains closer dots” For analysis one has to draw a psychometric curve (i.e. a cumulative gaussian) that fits the data of column 3, as a function of interdot distance (column 2). Varinat may include dividing column 2 by column 1 (so to have the ratio between test and reference) and run the psychometric curve on such normalized dimension Further explanation of the other columns (seeds for generating the stimuli) can be obtained from the authors. Numerosity discrimination The relevant columns in the matrix ‘a’ contain the following information: 1st: Numerosity 2nd: Log10 Numerosity 3rd: Response Further explanation of the other columns (seeds for generating the stimuli) can be obtained from the authors. Numerosity estimation The relevant columns in the matrix ‘ContengoRisultati’ contain the following information: 1st: Numerosity 2nd: Response Further explanation of the other columns (seeds for generating the stimuli) can be obtained from the authors.