Examining aging and numerosity using an integrated diffusion model

Integrated diffusion models for distance effects in number memory

I evaluated three models for the representation of numbers in memory. These were integrated with the diffusion decision model to explain accuracy and response time (RT) data from a recognition memory experiment in which the stimuli were two-digit numbers. The integrated models accounted for distance/confusability effects: when a test number was numerically close to a studied number, accuracy was lower and RTs were longer than when a test number was numerically far from a studied number. For two of the models, the representations of numbers are distributed over number (with Gaussian or exponential distributions) and the overlap between the distributions of a studied number and a test number provides the evidence (drift rate) on which a decision is made. For the third, the exponential gradient model, drift rate is an exponential function of the numerical distance between studied and test numbers. The exponential gradient model fit the data slightly better than the two overlap models. Monte Carlo simulations showed that the variability in the important parameter estimates from fitting data collected over 30-40 min is smaller than the variability among individuals, allowing differences among individuals to be studied. A second experiment compared number memory and number discrimination tasks and results showed different distance effects. Number memory had an exponential-like distance-effect and number discrimination had a linear function which shows radically different representations drive the two tasks.

How much time does it take to discriminate two sets by their numbers of elements?

The ability to evaluate the number of elements in a set-numerosity-without symbolic representation is a form of primitive perceptual intelligence. A simple binomial model was proposed to explain how observers discriminate the numerical proportion between two sets of elements distinct in color or orientation (Raidvee et al., 2017, Attention Perception & Psychophysics, 79[1], 267-282). The binomial model's only parameter β is the probability with which each visual element can be noticed and registered by the perceptual system. Here we analyzed the response times (RT) which were ignored in the previous report since there were no instructions concerning response speed. The relationship between the mean RT and the absolute difference |ΔN| between numbers of elements in two sets was described by a linear regression, the slope of which became flatter as the total number of elements N increased. Because the coefficients of regression between the mean RT and |ΔN| were more directly related to the binomial probability β rather than to the standard deviation of the best fitting cumulative normal distribution, it was regarded as evidence that the binomial model with a single parameter - probability β - is a viable alternative to the customary Thurstonian-Gaussian model.