Abstract
Beyond static scenes, our visual experience is populated by dynamic visual events — and these frequently overlap in time, as events are continuously and asynchronously starting, unfolding, and ending all around us. How do we represent and remember events amidst this rush of things that are always happening? The mind could simply accumulate information largely regardless of how that information is bound into particular discrete events. Or information could be prioritized when it marks the onset of a new event. Or information may only be stored for as long as its 'parent' event is ongoing (as in models that posit 'memory flushing' at event boundaries). We explored such possibilities using maximally simple visual events. Observers viewed animations with a number of initially-static dots. A subset of dots then moved in random directions and speeds, and eventually (e.g. after 1s) were again static — but in some conditions these motions could occur asynchronously, with each dot potentially beginning and ending its motion at a different moment. On each trial, observers simply had to estimate the number of dots that had moved. An animation's event structure — i.e. just when the motions started and stopped — had a strong impact on performance: asynchronous motions were consistently underestimated relative to synchronous motions. Further comparisons revealed that the (a)synchrony of motion onsets had little effect, whereas asynchrony of motion offsets always led to underestimation (regardless of whether onsets were simultaneously synchronous or not). Thus, when attempting to answer questions of "how many moved?", the visual system seems to instead deliver information about "how many just stopped moving?". In other words, the ends of events seem to have an outsize influence on working memory: once a motion ends, it seems more difficult to recall that particular motion as having occurred as a distinct event.
Meeting abstract presented at VSS 2018