A cognitive model of online event segmentation

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

A computational theory of temporal inference

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

ALICE: The Ultraviolet Imaging Spectrograph aboard the New Horizons Pluto-Kuiper Belt Mission

The New Horizons ALICE instrument is a lightweight (4.4 kg), low-power (4.4 Watt) imaging spectrograph aboard the New Horizons mission to Pluto/Charon and the Kuiper Belt. Its primary job is to determine the relative abundances of various species in Pluto's atmosphere. ALICE will also be used to search for an atmosphere around Pluto's moon, Charon, as well as the Kuiper Belt Objects (KBOs) that New Horizons hopes to fly by after Pluto-Charon, and it will make UV surface reflectivity measurements of all of these bodies as well. The instrument incorporates an off-axis telescope feeding a Rowland-circle spectrograph with a 520-1870 angstroms spectral passband, a spectral point spread function of 3-6 angstroms FWHM, and an instantaneous spatial field-of-view that is 6 degrees long. Different input apertures that feed the telescope allow for both airglow and solar occultation observations during the mission. The focal plane detector is an imaging microchannel plate (MCP) double delay-line detector with dual solar-blind opaque photocathodes (KBr and CsI) and a focal surface that matches the instrument's 15-cm diameter Rowland-circle. In what follows, we describe the instrument in greater detail, including descriptions of its ground calibration and initial in flight performance.Comment: 24 pages, 29 figures, 2 tables; To appear in a special volume of Space Science Reviews on the New Horizons missio

Developing a Predictive Model of Postcompletion Errors

A postcompletion error is a type of procedural error that occurs after the main goal of a task has been accomplished. There is a strong theoretical foundation accounting for postcompletion errors (Altmann & Trafton, 2002; Byrne & Bovair, 1997). This theoretical foundation has been leveraged to develop a logistic regression model of postcompletion errors based on reaction time and eye movement measures (Ratwani, McCurry, & Trafton, 2008). The work presented here further develops and extends this predictive model by (1) validating the model and the general set of predictors on a new task to test the robustness of the model, and (2) determining which specific theoretical components are most important to postcompletion error prediction