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.