Understanding multitasking through parallelized strategy exploration and individualized cognitive modeling

Human multitasking often involves complex task interactions and subtle tradeoffs which might be best understood through detailed computational cognitive modeling, yet traditional cognitive modeling approaches may not explore a sufficient range of task strategies to reveal the true complexity of multitasking behavior. This study proposes a systematic approach for exploring a large number of strategies using a computer-cluster-based parallelized modeling system. The paper demonstrates the efficacy of the approach for investigating and revealing the effects of different microstrategies on human performance, both within and across individuals, for a time-pressured multimodal dual task. The modeling results suggest that multitasking performance is not simply a matter of interleaving cognitive and sensorimotor processing but is instead heavily influenced by the selection of subtask microstrategies. Author Keywords Cognitive modeling; high performance computing; mode

Shortlinks and tiny keyboards: a systematic exploration of design trade-offs in link shortening services

Link-shortening services save space and make the manual entry of URLs less onerous. Short links are often included on printed materials so that people using mobile devices can quickly enter URLs. Although mobile transcription is a common use-case, link-shortening services generate output that is poorly suited to entry on mobile devices: links often contain numbers and capital letters that require time consuming mode switches on touch screen keyboards. With the aid of computational modeling, we identified problems with the output of a link-shortening service, bit.ly. Based on the results of this modeling, we hypothesized that longer links that are optimized for input on mobile keyboards would improve link entry speeds compared to shorter links that required keyboard mode switches. We conducted a human performance study that confirmed this hypothesis. Finally, we applied our method to a selection of different non-word mobile data-entry tasks. This work illustrates the need for service design to fit the constraints of the devices people use to consume services

The Emergence of Interactive Behaviour: A Model of Rational Menu Search

ABSTRACT One reason that human interaction with technology is difficult to understand is because the way in which people perform interactive tasks is highly adaptive. One such interactive task is menu search. In the current article we test the hypothesis that menu search is rationally adapted to (1) the ecological structure of interaction, (2) cognitive and perceptual limits, and (3) the goal to maximise the trade-off between speed and accuracy. Unlike in previous models, no assumptions are made about the strategies available to or adopted by users, rather the menu search problem is specified as a reinforcement learning problem and behaviour emerges by finding the optimal policy. The model is tested against existing empirical findings concerning the effect of menu organisation and menu length. The model predicts the effect of these variables on task completion time and eye movements. The discussion considers the pros and cons of the modelling approach relative to other well-known modelling approaches

The Emergence of Interactive Behaviour: A Model of Rational Menu Search

ABSTRACT One reason that human interaction with technology is difficult to understand is because the way in which people perform interactive tasks is highly adaptive. One such interactive task is menu search. In the current article we test the hypothesis that menu search is rationally adapted to (1) the ecological structure of interaction, (2) cognitive and perceptual limits, and (3) the goal to maximise the trade-off between speed and accuracy. Unlike in previous models, no assumptions are made about the strategies available to or adopted by users, rather the menu search problem is specified as a reinforcement learning problem and behaviour emerges by finding the optimal policy. The model is tested against existing empirical findings concerning the effect of menu organisation and menu length. The model predicts the effect of these variables on task completion time and eye movements. The discussion considers the pros and cons of the modelling approach relative to other well-known modelling approaches

Amortised experimental design and parameter estimation for user models of pointing

Funding Information: This work was supported by the Academy of Finland (Flagship programme: Finnish Center for Artifcial Intelligence FCAI) and ELISE Networks of Excellence Centres (EU Horizon:2020 grant agreement 951847) and Bitville Oy. The authors want to thank the Probabilistic Machine Learning (PML) and the User Interfaces research groups at Aalto University for fruitful discussions and feedback. Publisher Copyright: © 2023 Owner/Author. | openaire: EC/H2020/951847/EU//ELISEUser models play an important role in interaction design, supporting automation of interaction design choices. In order to do so, model parameters must be estimated from user data. While very large amounts of user data are sometimes required, recent research has shown how experiments can be designed so as to gather data and infer parameters as efficiently as possible, thereby minimising the data requirement. In the current article, we investigate a variant of these methods that amortises the computational cost of designing experiments by training a policy for choosing experimental designs with simulated participants. Our solution learns which experiments provide the most useful data for parameter estimation by interacting with in-silico agents sampled from the model space thereby using synthetic data rather than vast amounts of human data. The approach is demonstrated for three progressively complex models of pointing.Peer reviewe

How does rumination impact cognition? A first mechanistic model.

Rumination is a process of uncontrolled, narrowly-foused neg- ative thinking that is often self-referential, and that is a hall- mark of depression. Despite its importance, little is known about its cognitive mechanisms. Rumination can be thought of as a specific, constrained form of mind-wandering. Here, we introduce a cognitive model of rumination that we devel- oped on the basis of our existing model of mind-wandering. The rumination model implements the hypothesis that rumina- tion is caused by maladaptive habits of thought. These habits of thought are modelled by adjusting the number of memory chunks and their associative structure, which changes the se- quence of memories that are retrieved during mind-wandering, such that during rumination the same set of negative memo- ries is retrieved repeatedly. The implementation of habits of thought was guided by empirical data from an experience sam- pling study in healthy and depressed participants. On the ba- sis of this empirically-derived memory structure, our model naturally predicts the declines in cognitive task performance that are typically observed in depressed patients. This study demonstrates how we can use cognitive models to better un- derstand the cognitive mechanisms underlying rumination and depression