When on the move, cognitive resources are reserved partly for passively monitoring and reacting to contexts and events, and partly for actively constructing them. The Resource Competition Framework (RCF), building on the Multiple Resources Theory, explains how psychosocial tasks typical of mobile situations compete for cognitive resources and then suggests that this leads to the depletion of resources for task interaction and eventually results in the breakdown of fluent interaction. RCF predictions were tested in a semi-naturalistic field study measuring attention during the performance of assigned Web search tasks on mobile phone while moving through nine varied but typical urban situations. Notably, we discovered up to eight-fold differentials between micro-level measurements of attentional resource fragmentation, for example from spans of over 16 seconds in a laboratory condition dropping to bursts of just a few seconds in difficult mobile situations. By calibrating perceptual sampling, reducing resource usage for tasks of secondary importance, and resisting the impulse to switch tasks before finalization, participants compensated for the resource depletion. The findings are compared to previous studies in office contexts. The work is valuable in many areas of HCI dealing with mobility. ACM Classification Keywords: H5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous

We present a detailed process theory of the moment-by-moment working-memory retrievals and associated control structure that subserve sentence comprehension. The theory is derived from the application of independently motivated principles of memory and cognitive skill to the specialized task of sentence parsing. The resulting theory construes sentence processing as a series of skilled associative memory retrievals modulated by similarity-based interference and fluctuating activation. The cognitive principles are formalized in computational form in the Adaptive Control of Thought–Rational (ACT–R) architecture, and our process model is realized in ACT–R. We present the results of 6 sets of simulations: 5 simulation sets provide quantitative accounts of the effects of length and structural interference on both unambiguous and garden-path structures. A final simulation set provides a graded taxonomy of double center embeddings ranging from relatively easy to extremely difficult. The explanation of center-embedding difficulty is a novel one that derives from the model’s complete reliance on discriminating retrieval cues in the absence of an explicit representation of serial order information. All fits were obtained with only 1 free scaling parameter fixed across the simulations; all other parameters were ACT–R defaults. The modeling results support the hypothesis that fluctuating activation and similarity-based interference are the key factors shaping working memory in sentence processing. We contrast the theory and empirical predictions with several related accounts of sentence-processing complexity.

What is the computational goal of auditory scene analysis? This is a key issue to address in the Marrian information-processing framework. It is also an important question for researchers in computational auditory scene analysis (CASA) because it bears directly on how a CASA system should be evaluated. In this chapter I discuss different objectives used in CASA. I suggest as a main CASA goal the use of the ideal time-frequency (T-F) binary mask whose value is one for a T-F unit where the target energy is greater than the interference energy and is zero otherwise. The notion of the ideal binary mask is motivated by the auditory masking phenomenon. Properties of the ideal binary mask are discussed, including their relationship to automatic speech recognition and human speech intelligibility. This CASA goal has led to algorithms that directly estimate the ideal binary mask in monaural and binaural conditions, and these algorithms have substantially advanced the state-of-the-art performance in speech separation. 1.

The authors present a theory of how relational inference and generalization can be accomplished within a cognitive architecture that is psychologically and neurally realistic. Their proposal is a form of symbolic connectionism: a connectionist system based on distributed representations of concept meanings, using temporal synchrony to bind fillers and roles into relational structures. The authors present a specific instantiation of their theory in the form of a computer simulation model, Learning and Inference with Schemas and Analogies (LISA). By using a kind of self-supervised learning, LISA can make specific inferences and form new relational generalizations and can hence acquire new schemas by induction from examples. The authors demonstrate the sufficiency of the model by using it to simulate a body of empirical phenomena concerning analogical inference and relational generalization. A fundamental aspect of human intelligence is the ability to form and manipulate relational representations. Examples of relational thinking include the ability to appreciate analogies between seemingly different objects or events (Gentner, 1983; Holyoak & Thagard, 1995), the ability to apply abstract rules in novel situations (e.g., Smith, Langston, & Nisbett, 1992), the ability to understand and learn language (e.g., Kim, Pinker, Prince, & Prasada, 1991), and even the ability to appreciate perceptual similarities

This article investigates the cognitive strategies that people use to search computer displays. Several different visual layouts are examined: unlabeled layouts that contain multiple groups of items but no group headings, labeled layouts in which items are grouped and each group has a useful heading, and a target-only layout that contains just one item. A number of plausible strategies were proposed for each layout. Each strategy was programmed into the EPIC cognitive architecture, producing models that simulate the human visual-perceptual, oculomotor, and cognitive processing required for the task. The models generate search time predictions. For unlabeled layouts, the mean layout search times are predicted by a purely random search strategy, and the more detailed positional search times are predicted by a noisy systematic strategy. The labeled layout search times are predicted by a hierarchical strategy in which first the group labels are systematically searched, and then the contents of the target group. The target-only layout search times are predicted by a strategy in which the eyes move directly to the

Today’s wayfinding assistance systems provide route directions that are significantly different to those generated by humans, resulting in a gap between what users expect and what the system delivers. This dissertation contributes to closing this gap by presenting a process that adapts instructions to environmental characteristics and a route’s properties, thereby implementing principles of human direction giving. The process generates an abstract, relational specification of route directions, which can, for example, be externalized verbally. 1

Without well-provisioned dedicated servers, modern fast-paced action games limit the number of players who can interact simultaneously to 16–32. This is because interacting players must frequently exchange state updates, and high player counts would exceed the bandwidth available to participating machines. In this paper, we describe Donnybrook, a system that enables epicscale battles without dedicated server resources, even in a fastpaced game with tight latency bounds. It achieves this scalability through two novel components. First, it reduces bandwidth demand by estimating what players are paying attention to, thereby enabling it to reduce the frequency of sending less important state updates. Second, it overcomes resource and interest heterogeneity by disseminating updates via a multicast system designed for the special requirements of games: that they have multiple sources, are latency-sensitive, and have frequent group membership changes. We present user study results using a prototype implementation based on Quake III that show our approach provides a desirable user experience. We also present simulation results that demonstrate Donnybrook’s efficacy in enabling battles of up to 900 players.

Participants memorized briefly presented sets of digits, a subset of which had to be accessed as input for arithmetic tasks (the active set), whereas another subset had to be remembered independently of the concurrent task (the passive set). Latencies for arithmetic operations were a function of the setsize of active but not passive sets. Object-switch costs were observed when successive operations were applied to different digits within an active set. Participants took2stoencode a passive set so that it did not affect processing latencies (Experiment 2). The results support a model distinguishing 3 states of representations in working memory: the activated part of long-term memory, a capacity limited region of direct access, and a focus of attention. Working memory is commonly described as a system for simultaneous storage and processing of information. The relation between “storage ” and “processing, ” however, is rarely specified. Resource models generally posit a common resource (e.g., activation) that must be shared between the two functions (Just & Carpenter, 1992). Evidence from dual task studies, however, casts doubt on the resource-sharing hypothesis: There are numerous examples in the literature of processing that is largely unimpaired by a concurrent short-term memory demand, even when the memory demand is close to the maximum span (e.g., Foos & Wright,

The extent to which memory for information content is reliable, trustworthy, and accurate is crucial in the information age. Being forced to divert attention to interrupting messages is common, however, and can cause memory loss. The memory e#ects of interrupting messages were investigated in three experiments. In Experiment 1, attending to an interrupting message decreased memory accuracy. Experiment 2, where four interrupting messages were used, replicated this result. In Experiment 3, an interrupting message was shown to be most disturbing when it was semantically very close to the main message. Drawing from a theory of long-term working memory it is argued that interrupting messages can both disrupt the active semantic elaboration of content during encoding and cause semantic interference upon retrieval. Properties of the interrupting message a#ect the extent and type of errors in remembering. Design implications are discussed.

The current state of A.D. Baddeley and G.J. Hitch’s (1974) multicomponent working memory model is reviewed. The phonological and visuospatial subsystems have been extensively investigated, leading both to challenges over interpretation of individual phenomena and to more detailed attempts to model the processes underlying the subsystems. Analysis of the controlling central executive has proved more challenging, leading to a proposed clarification in which the executive is assumed to be a limited The term working memory appears to have been first proposed by Miller, Galanter, and Pribram (1960) in their classic book Plans and the Structure of Behavior. The term has subsequently been used in computational modeling approaches (Newell & Simon, 1972) and in animal learning studies, in which the participant animals are required to hold information across a number of trials within the same day (Olton, 1979). Finally, within cognitive psychology, the term has been adopted to cover the system or systems involved in the temporary maintenance and manipulation of information. Atkinson and Shiffrin (1968) applied the term to a unitary short-term store, in contrast to the proposal of Baddeley and Hitch (1974), who used it to refer to a system comprising multiple components. They emphasized the functional importance of this system, as opposed to its simple storage capacity. It is this latter concept of a multicomponent working memory that forms the focus of the discussion that follows. I myself have been using the concept for over 25 years; does it still work? Before addressing this issue, it is perhaps appropriate to consider what are the criteria for working. The multicomponent model of working memory was proposed as a theoretical framework whose function was to give