Population-level right-paw preference in rats assessed by a new computerized food-reaching test

Int J Neurosci. 2003 Dec;113(12):1675-89. doi: 10.1080/00207450390249258.

Abstract

We re-studied the distribution of paw preference in rats using a new computerized food-reaching test, which recorded the times and time intervals between the single right- and left-paw entries. Using the traditional food-reaching test, we found that of 144 rats, 72.7% were right-handed, 19.7% left-handed, and 7.6% mixed-handed. This population-level J-shaped right-hand preference did not fit a binomial chance distribution (25:25:50). Of right -handers, 99.5% first used their right paw and 0.5% left paw; of left-handers, 98.6% first used their left paw and 1.4% right paw. Of mixed-handers, 59% first used the right paw and 41% left paw for food reaching. The time interval between putting the rat into the test cage and the first right-paw entry was significantly shorter than the first left-paw entry in total sample. Males were faster than females (shorter time intervals between right- or left-paw entries). The distribution of the time intervals between right- or left-paw entries was inverse J-shaped, which exhibited a normal distribution after taking the logarithms of the time intervals. There was no significant difference between time intervals for the left-paw entries; time intervals for the right-paw entries were significantly shorter in males than females, accentuating the role of the left brain for sex differences in motor control. The results suggested that humans are not unique in population-level right-hand preference; our new method would be suitable for new developments in handedness research.

Publication types

  • Comparative Study

MeSH terms

  • Animals
  • Behavior, Animal
  • Computer-Aided Design
  • Estrous Cycle
  • Feeding Behavior / physiology*
  • Female
  • Food
  • Functional Laterality / physiology*
  • Male
  • Psychomotor Performance / physiology*
  • Rats
  • Rats, Wistar
  • Sex Characteristics
  • Statistics, Nonparametric
  • Time Factors