Purchase this article with an account.
Benjamin Balas, Pawan Sinha; A speed-dependent inversion effect in dynamic object matching. Journal of Vision 2008;8(6):837. doi: https://doi.org/10.1167/8.6.837.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
The representations employed by the visual system for dynamic object recognition remain relatively unclear, due in large part to the lack of sufficient data constraining the nature of the underlying encoding processes. In particular, very little is currently known about the extent to which recognition of a moving object is invariant to spatial or spatiotemporal stimulus manipulations. In the current study, our goal was to begin a line of investigation in this vein on the limits of invariant recognition for isolated dynamic objects.
We examined the limits of invariant recognition for unfamiliar moving objects using a simple same/different matching task. Observers were asked to evaluate whether pairs of sequentially presented rigidly-moving objects differed in identity subject to a spatial manipulation (inversion) and a spatiotemporal manipulation (speed change). We find evidence of a speed-dependent inversion effect, such that inversion only incurs a matching cost for objects that move relatively slowly. Furthermore, we observe a deleterious effect of speed change between sample and test stimuli. This indicates that the speed of appearance change is encoded by the visual system for recognition, consistent with previous work regarding “spatiotemporal signatures” as a model for dynamic object recognition. However, we also find that the effect of speed change on matching has an interesting temporal asymmetry: matching a “fast” sample object to a “slow” test object is harder than matching a “slow” sample to a “fast” test stimulus. Taken together, these results suggest distinct modes of processing for fast-moving and slow-moving objects and thus have important consequences for previous proposals regarding the representation of moving objects. We discuss the current data in the context of an emerging model of dynamic object perception.
This PDF is available to Subscribers Only