Scalar effects in the visual discrimination of numerosity by pigeons

Abstract

Pigeons trained in a conditional discrimination procedure to respond to a visual array made a left or right choice, depending on which of two numbers of elements (i.e., anchor numerosities) the array contained. They were then tested with novel arrays at these anchor numerosities, as well as at interpolated and extrapolated numerosities. Various control conditions showed that the birds' discrimination p performance was primarily based on stimulus numerosity, and not on other factors, such as brightness or area. Results from a series of tests, spanning a wide range of numerosities, conformed to scalar p principles. Psychometric functions showed superposition, indicating that Weber's law applies to numerosity discrimination. The subjective midpoint between anchor values was at the geometric mean. V Variability about this bisection point increased in proportion to the numerical value of the mean.

Discrimination and representation of relative numerosity in a bisection task by pigeons

“…Research has studied how nonhumans bisect both visual and auditory numerical stimuli (e.g., Alsop & Honig, 1991;Fernandes & Church, 1982;Honig & Stewart, 1989;Meck & Church, 1983;Roberts & Mitchell, 1994), as well as response number (Emmerton, 1998;Emmerton & Renner, 2006;Fetterman, 1993;Rilling & McDiarmid, 1965). These have found largely positive results, showing that animals are able to respond differentially to stimuli that differ in relative numerosity, successfully categorizing them as large or small, and are able to transfer this performance to novel numerical values both within and outside the training range.…”

“…In addition, transfer tests included values two units outside of the upper and lower limits of the training range. Whereas previous experiments have generally examined transfer to numbers within the baseline values (with the exception of Emmerton & Renner, 2006), the use of transfer test values outside of the training range yields a stronger test of numerical competence.…”

Quantity representation in children and rhesus monkeys: Linear versus logarithmic scales

“…This task also sometimes is used with human children (e.g., Brannon & Van de Walle, 2001; Feigenson, Carey, & Hauser, 2003). The second task is the bisection task that has been used extensively with rats and pigeons (e.g., Emmerton & Renner, 2006;Fetterman, 1993;Meck & Church, 1983;Roberts, 2005Roberts, , 2006 and occasionally with nonhuman primates and human children (e.g., Beran, Smith, Redford, & Washburn, 2006;Droit-Volet, Clement, & Fayol, 2003;Jordan & Brannon, 2006a, 2006b). In the bisection task, two anchor values (e.g., smallest set size and largest set size) are established through training, and then a larger range of values, including intermediate values, are presented.…”

“…In addition, there are very few direct comparisons of the performance of children and nonhuman animals on the identical tasks designed to assess numerical or quantitative skills (but see Jordan & Brannon, 2006a). Typically, data from nonhuman animals indicate that the PSE falls nearer to the geometric mean (e.g., Emmerton & Renner, 2006), suggesting that representations of quantity are scalar in nature or that there is logarithmic compression of those representations.We presented rhesus monkeys and human children with the identical computerized bisection task. We tested children who were slightly younger than those in previous studies, we used more anchor values with each participant, and our bisection task differed from previous studies as the response choices were symbolic stimuli rather than analog stimuli matching the sample array.…”

“…In addition, there are very few direct comparisons of the performance of children and nonhuman animals on the identical tasks designed to assess numerical or quantitative skills (but see Jordan & Brannon, 2006a). Typically, data from nonhuman animals indicate that the PSE falls nearer to the geometric mean (e.g., Emmerton & Renner, 2006), suggesting that representations of quantity are scalar in nature or that there is logarithmic compression of those representations.…”