The debate about how attention is allocated during readinghas been framed in as: Either attention is allocated in a strictlyserial manner, to support the identification of one word at atime, or it is allocated as a gradient, to support the concurrentprocessing of multiple words. The first part of this article re-views reading models to examine the feasibility of both posi-tions. Although word-identification and sentence-processingmodels assume that words are identified serially to incremen-tally build larger units of representation, discourse-processingmodel allow several propositions to be co-active in workingmemory. The remainder of this article then describes an in-stance-based model of word identification, Über-Reader, andsimulations comparing the identification of single words andword pairs. These simulations indicate that, although wordpairs can be identified, accurate identification is restricted toshort high-frequency words due to the computational de-mands of both memory retrieval and limited visual acuity.

Liao et al. (2020) reported an eye-movement experiment in which subtitles were displayed at three different rates, with a key finding being that, with increasing speeds, participants made fewer, shorter fixations and longer saccades. To understand why these eye-movement behaviors might be adaptive, we completed simulations using the E-Z Reader model (Reichle et al., 2012) to examine how subtitle speed might affect word identification and sentence comprehension, as well as the efficacy of six possible compensatory reading strategies. These simulations suggest that the imposition of a lexical-processing deadline and/or strategy of skipping short words may support reading comprehension in impoverished conditions.

The MINERVA 2 (Hintzman, 1984) model of human memory has been used to simulate a variety of cognitive phenomena. These simulations, however, describe cogni-tive phenomena at Marr’s (1982) representation/algorithm level, with little effort to link the core assumptions of the model to an underlying neural implementation (however, see Kelly et al., 2017). This article describes a possible neural implementation of MINERVA 2—one that is sim-ple and arguably biologically plausible. This implementa-tion suggests a novel method for generating response la-tencies and provides a concrete example to support Marr’s claim that the representations and algorithms that mediate human performance in a variety of different cognitive tasks (e.g., decision making; Dougherty, Gettys, & Ogden, 1999) can be investigated and simulated without reference to their underlying neural implementation.

This paper presents simulations of eye movements during read-ing, lexical decision, and naming using Über-Reader, a newcomputational model that aims to provide a complete accountof the perceptual, cognitive, and motor processes involved inreading. The present simulations focused on Über-Reader’sword-identification module—an implementation of the Multi-ple-Trace Memory model (Ans et al., 1998) based on the theo-retical assumptions of the MINERVA 2 model of episodicmemory (Hintzman, 1984)—with a vocabulary comprising thefull corpus of the English Lexicon Project (Balota et al., 2007).The model’s lexicon was probed with words and one-letter-dif-ferent non-words from the Schilling et al. (1998) corpus, andoutputs of the model were scored to evaluate performanceagainst the empirical data. The outcomes of these simulationswill inform further development of Über-Reader by providingthe foundation for our ultimate goal of simulating reading, inits entirety.

In many team-based activities, members search for information to gain situational awareness and thereby structure their own behavior. The extent to which members are coupled and in control of their surrounding environment can be accessed via the fluctuations of their searching behaviors. To facilitate prospective control, assistive technologies such as a head-up display (HUD) can alleviate the demands of search and facilitate team performance. This study investigated how three-person teams divided their labor and structured their search behavior when playing a multiplayer search-and-retrieval task where first-person visibility and access to a HUD were manipulated. Results showed that access to task relevant information facilitated performance and division of labor, and increased prospective control of searching behaviors, as indexed by detrended fluctuation analysis (DFA). Over multiple sessions, teams learned to use the HUD to structure their behavior to achieve the task goal. Results indicate the potential in using DFA for monitoring prospective control in team contexts.