| Literature DB >> 26941682 |
Yusuke Moriguchi1, Yasuhiro Kanakogi2, Naoya Todo3, Yuko Okumura4, Ikuko Shinohara5, Shoji Itakura6.
Abstract
It has been shown that there is a significant relationship between children's mentalizing skills and creation of an imaginary companion (IC). Theorists have proposed that interaction with an IC may improve mentalizing skills, but it is also possible that children's mentalizing skills affect their creation of an IC. In this longitudinal study, we examined whether goal attribution in infants younger than 1 years old (Time 1) predicted their creation of ICs at 48 months old (Time 2). At Time 1, infants' goal attribution was measured in an action prediction experiment, where infants anticipated three types of action goals: (1) another person's goal-directed action (GH condition); (2) another person's non-goal-directed (BH condition); and (3) a mechanical claw's goal-directed action (MC condition). At Time 2, parents completed questionnaires assessing whether their children had ICs. The path analyses using Bayesian estimation revealed that infants' anticipation in the MC condition, but not in the GH and BH conditions, predicted their later IC status. These results indicate that infants' goal attributions to non-human agents may be a strong predictor of their later IC creation. Early mentalizing skills toward non-human objects may provide children with a basis for their engagement in imaginative play.Entities:
Keywords: Bayesian estimation; goal-directed actions; imaginary companion; longitudinal study; mentalizing
Year: 2016 PMID: 26941682 PMCID: PMC4763030 DOI: 10.3389/fpsyg.2016.00221
Source DB: PubMed Journal: Front Psychol ISSN: 1664-1078
Figure 1Selected frames from the video stimuli in each condition. The conditions are grasping hand (GH, left panels), back of hand (BH, middle panels), and mechanical claw (MC, right panels). (A) The agents are out of the frame. (B) The agents appear from the bottom of the frame, move upward, and then stop. (C) The agents move toward one of two toys, stop at the target toy, and then make contact by grasping (GH, MC) or touching with the back of the hand (BH). We acknowledge Nature Publishing Group for reuse of figures from Kanakogi and Itakura (2011).
Figure 2Analytical examples of stimulus videos and grasping ability. Example grasping hand condition video. The black rectangles and hexagon represent AOIs within the scene. The upper AOI is the “goal AOI” and encompasses the target object. The lower AOI is the “agent AOI” and encompasses the position where the agent stopped before beginning to move to the target object. The middle AOI is the “trajectory AOI” and encompasses the agent's movement trajectory. We acknowledge Nature Publishing Group for reuse of figures from Kanakogi and Itakura (2011).
Example imaginary companions.
| Hana-chan | An invisible girl who is cute and has long hair |
| Panda-chan | A personified panda who is always hungry and likes walking |
| Me-Me | A personified lamb who is shy |
| Saru-san | A personified monkey who is like a sibling |
Figure 3Path analysis models.
Figure 4An example trace plot of all iterations. This is an example trace plot.
Figure 5An example of every 50th iterations' auto-correlation. This is an example auto-correlation.
Posterior means and 95% highest density intervals of standardized parameters.
| 0.663 (0.123) | 0.415 | 0.855 | 0.395 (0.182) | 0.037 | 0.718 | 0.133 (0.211) | −0.274 | 0.530 | |
| βTO.X | −0.032 (0.260) | −0.531 | 0.463 | 0.053 (0.273) | −‘0.471 | 0.573 | 0.571 (0.201) | 0.167 | 0.894 |
| μX | −2.273 (0.492) | −3.115 | −1.296 | −1.740 (0.708) | −2.954 | −0.290 | −1.383 (0.828) | −2.926 | 0.221 |
| τ1 | −0.824 (0.321) | −1.440 | −0.179 | −0.828 (0.322) | −1.473 | −0.207 | −1.275 (0.316) | −1.901 | −0.643 |
| τ2 | 0.813 (0.320) | 0.194 | 1.442 | 0.801 (0.324) | 0.171 | 1.435 | 0.295 (0.355) | −0.362 | 1.010 |
| 0.545 (0.147) | 0.283 | 0.838 | 0.811 (0.131) | 0.574 | 1.00 | 0.938 (0.073) | 0.782 | 1.00 | |
Lower, lower 2.5%; and Upper, upper 2.5%.
X in the left column is GH in GH path analysis, BH in BH path analysis, and MC in MC path analysis.