Dataset Information

Human infants detect other people's interactions based on complex patterns of kinematic information.

ABSTRACT: Do infants perceive other people's interactions by means of a mechanism that integrates biological motion information across the observed individuals? In support of this view, the present study demonstrates that infants (N?=?28, Age ?=?14 months) discriminate between point light displays representing disrupted and non-disrupted interactions between people, even though the two interaction types are identical at the level of individual point light agents. Moreover, a second experiment (sample 2: N?=?28, Age ?=?14 months) indicated that visual preference in this context is influenced by an audiovisual integration processes that takes into account the presence of an interaction between people. All these results were found exclusively for upright displays--when stimuli were shown upside-down (disrupting biological motion processing), performance was random. Collectively, these findings point to an important role for biological motion in social perception in human infants.

SUBMITTER: Galazka MA

PROVIDER: S-EPMC4237354 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Human infants detect other people's interactions based on complex patterns of kinematic information.

Galazka Martyna A MA Roché Laëtitia L Nyström Pär P Falck-Ytter Terje T

PloS one 20141119 11

Do infants perceive other people's interactions by means of a mechanism that integrates biological motion information across the observed individuals? In support of this view, the present study demonstrates that infants (N = 28, Age = 14 months) discriminate between point light displays representing disrupted and non-disrupted interactions between people, even though the two interaction types are identical at the level of individual point light agents. Moreover, a second experiment (sample 2: N ...[more]

PMID: 25409449

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Project description:Uncovering the roles of biotic interactions in assembling and maintaining species-rich communities remains a major challenge in ecology. In plant communities, interactions between individuals of different species are expected to generate positive or negative spatial interspecific associations over short distances. Recent studies using individual-based point pattern datasets have concluded that (a) detectable interspecific interactions are generally rare, but (b) are most common in communities with fewer species; and (c) the most abundant species tend to have the highest frequency of interactions. However, it is unclear how the detection of spatial interactions may change with the abundances of each species, or the scale and intensity of interactions. We ask if statistical power is sufficient to explain all three key results.We use a simple two-species model, assuming no habitat associations, and where the abundances, scale and intensity of interactions are controlled to simulate point pattern data. In combination with an approximation to the variance of the spatial summary statistics that we sample, we investigate the power of current spatial point pattern methods to correctly reject the null model of pairwise species independence.We show the power to detect interactions is positively related to both the abundances of the species tested, and the intensity and scale of interactions, but negatively related to imbalance in abundances. Differences in detection power in combination with the abundance distributions found in natural communities are sufficient to explain all the three key empirical results, even if all pairwise interactions are identical. Critically, many hundreds of individuals of both species may be required to detect even intense interactions, implying current abundance thresholds for including species in the analyses are too low. Sy n thesis. The widespread failure to reject the null model of spatial interspecific independence could be due to low power of the tests rather than any key biological process. Since we do not model habitat associations, our results represent a first step in quantifying sample sizes required to make strong statements about the role of biotic interactions in diverse plant communities. However, power should be factored into analyses and considered when designing empirical studies.

Dataset Information

Human infants detect other people's interactions based on complex patterns of kinematic information.

Publications

Human infants detect other people's interactions based on complex patterns of kinematic information.

Similar Datasets

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