Markovian models for the developmental study of social behavior.

Abstract

Behavioral development involves changes in the probabilities of both social and nonsocial activities and the sequential pattern of activities over time. A number of methods have been offered for the analysis of these patterns of behavioral sequences. However, there continue to be problematic issues, including the analysis of nonstationary data; accommodation of changes in patterns within an observation period, or over repeated observations or age; and identification of differences in pattern changes between individuals or groups, and the factors responsible for these differences. In this work, we analyze data from 15 young monkeys (Macaca nemestrina) using classification and Markovian methods, including a new approach to nonstationary data called the double-chain Markov model (DCCM). These methods allowed us to identify differences in behavior patterns that differentiate between normal subjects and those presenting developmental anomalies.