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The Impact of a Human Figure in a Scene on Spatial Descriptions in Speech, Gesture, and Gesture Alone

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Abstract

The presence of a human figure in a scene appears to change how people describe it. About 20% of participants take the human figure’s viewpoint (Tversky and Hard in Cognition 110:124–129, 2009. http://doi.org/10.1016/j.cognition.2008.10.008). Five exploratory studies compare descriptions of a scene with no person to descriptions of a scene with a person. About 20% of participants are predicted to use the person’s point of view in the “person” conditions. Study 1 replicates the original pattern. Study 2 shows that the pattern holds when object/scene are changed, and that the figure’s gaze towards/away from the object does not change the pattern. Studies 3 and 4 show the pattern holds when the object has different positions and when it is moving. Study 5 shows the pattern holds when the describer is talking to an interlocutor, in both speech and co-speech gesture, and when the person is using gesture alone. The presence of a human figure in a scene appears to be a robust variable in shaping spatial descriptions.

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Authors and Affiliations

  1. Department of Cognitive Science, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, 41106, USA

    Fey Parrill, Alexsis Blocton, Paige Veta, Mary Lowery & Ava Schneider

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  1. Fey Parrill
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Correspondence to Fey Parrill.

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Appendix

Appendix

Bayes factor provides readers with a “likelihood” interpretation of the extent to which the evidence favors the null, thus addressing some of the pitfalls of Null Hypothesis Significance Testing. Glossing over the details (though see Jarosz and Wiley 2014 for explanation), Bayes factor can be understood as the likelihood of seeing the pattern you see if the null is true, divided by likelihood of seeing the pattern you see if the alternative is true. A larger Bayes factor can be interpreted as stronger evidence in favor of the null. For example, a Bayes factor of 9 indicates a 9 to 1 likelihood of seeing the data you see if the null is true, which could be considered strong evidence. One can also take the inverse of Bayes factor in order to obtain a more easily understood measure: the likelihood of seeing the data you see if the alternative is true, usually what researchers are actually interested in. This would result in 1/9 = .11, or a .11 to 1 odds in favor of the alternative, which could be considered very weak evidence. For all results, we present the JZS Bayes factor [see Jarosz and Wiley (2014), Rouder et al. (2009) for details on the calculation of this number] in terms of evidence in favor of either the null or alternative, and specify which. We believe this is the easiest way to interpret the information provided by Bayes factor.

Studies 4 and 5: Coding When the Cup was Not the Landmark

In about 10% of trials, participants described the motion of the train using a landmark other than the cup. These landmarks were: viewer, camera, and in the person condition, model. The coding of these trials depends on the spatial term used and the condition, and is shown in Table 8.

Table 8 Coding of path in studies 4 and 5 when cup was not the landmark
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Parrill, F., Blocton, A., Veta, P. et al. The Impact of a Human Figure in a Scene on Spatial Descriptions in Speech, Gesture, and Gesture Alone. J Psycholinguist Res 49, 73–97 (2020). https://doi.org/10.1007/s10936-019-09672-9

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