Related Experiment Video
Updated: Aug 14, 2026
06:15
Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism
Published on: October 3, 2018
Multiple-spell, discrete-time survival analysis of developmental data: object concept in pigtailed macaques
J C Ha1, C L Kimpo, G P Sackett
1Regional Primate Research Center, University of Washington, Seattle 98195-7330, USA. jcha@u.washington.edu
Developmental Psychology
|December 31, 1997
Summary
Cognitive development in pigtailed macaques was assessed using Piagetian object concept milestones. Birth weight significantly influenced early cognitive development across different task types.
Area of Science:
- Primatology
- Cognitive Science
- Developmental Psychology
Background:
- Understanding cognitive development in non-human primates provides insights into evolutionary psychology.
- The Piagetian object concept framework is a standard measure for cognitive milestones.
Purpose of the Study:
- To measure cognitive development in pigtailed macaques using classical object concept milestones.
- To analyze the influence of birth weight, sex, age, and task type on cognitive milestone attainment.
Main Methods:
- 123 pigtailed macaques were studied for object concept development (plain reach, partial-hide, full-hide, A-not-B).
- Two-dimensional (screen) and three-dimensional (well) tasks were utilized.
- Multiple-spell, discrete-time survival analysis examined predictors of milestone completion.
Main Results:
- Birth weight and task type were significant predictors for the 3D (well) task sequence.
- Birth weight, number of test sessions, and their interaction predicted the 2D (screen) task sequence.
- Birth weight showed a stronger influence on earlier developmental stages.
Conclusions:
- Survival analysis offers unique insights into primate cognitive development beyond traditional methods.
- Birth weight is a critical factor in early cognitive development and task performance in pigtailed macaques.
Related Concept Videos
01:29Life Histories
Constrained by limited energy and resources, organisms must compromise between offspring quantity and parental investment. This trade-off is represented by two primary reproductive strategies; K-strategists produce few offspring but provide substantial parental support, whereas r-strategists produce much progeny that receives little care. These strategies are related to an organism’s survival likelihood across its lifespan, which is represented by a survivorship curve. Three general types of...
01:18Introduction To Survival Analysis
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
The primary goal of survival analysis is to estimate survival time—the time until a...
01:15Assumptions of Survival Analysis
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
01:20Comparing the Survival Analysis of Two or More Groups
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
01:09Truncation in Survival Analysis
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
01:19Survival Tree
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a survival tree begins...
Building a Survival Tree
Constructing a survival tree begins...