We present a novel model of prospective memory and fit it to data from a classic experimental paradigm (Einstein & Mc- Daniel, 1990). Our model uses memory for goals (Altmann & Trafton, 2002) and elaboration with spreading activation to show how prospective intentions can be cued by perceptual cues. Our model also suggests how to resolve some of the con- troversies concerning prospective memory and aging.

We describe a novel model-based theory of how individuals reason deductively about temporal relations. It positsthat temporal assertions refer to mental models – iconic representations of possibilities – of events (Khemlani, Harrison, &Trafton, 2015; Schaeken, Johnson-Laird, & d’Ydewalle, 1996). In line with recent accounts of spatial reasoning (Ragni &Knauff, 2013), the theory posits that individuals tend to build a single preferred model of a temporal description. The moremodels necessary to yield a correct answer, the harder that problem is. The theory is implemented in a computer program,mReasoner, which draws temporal deductions by building models. It varies four separate factors in the process: the size of amodel, its contents, the propensity to consider alternative models, and the propensity to revise initial conclusions. Two studiescorroborated the predictions of the theory and its computational implementation. We conclude by discussing temporal andrelational inference more broadly.

People automatically segment online perceptual and conceptual experiences into events (Newston, 1973). A newmodel-based theory explains how people construct temporal markers and prioritize those changes to build representations ofevents (Khemlani et al., 2015). The theory is implemented within an embodied extension of the ACT-R cognitive architecture(Anderson, 2007) called ACT-R/E (Trafton et al., 2013). Its principal parameter is the prioritization scheme by which certaindetectable changes (e.g., in a perceived location) are preferred over others (e.g., in perceived states of an object). We tested thepredictions of the theory and its computational model against an experiment on narrative event segmentation. Participants in thestudy read an excerpt of text and were asked to assess whether certain lines marked the start of a new event. The computationalmodel readily accounted for their segmentation behavior. We conclude by discussing event segmentation and its relation toembodied cognition and cognitive robotics.