Visual Working Memory Resources Are Best Characterized as Dynamic, Quantifiable Mnemonic Traces
2017, Veksler, Bella Z., Boyd, Rachel, Myers, Christopher W., Gunzelmann, Glenn, Neth, Hansjörg, Gray, Wayne D.
Visual working memory (VWM) is a construct hypothesized to store a small amount of accurate perceptual information that can be brought to bear on a task. Much research concerns the construct's capacity and the precision of the information stored. Two prominent theories of VWM representation have emerged: slot-based and continuous-resource mechanisms. Prior modeling work suggests that a continuous resource that varies over trials with variable capacity and a potential to make localization errors best accounts for the empirical data. Questions remain regarding the variability in VWM capacity and precision. Using a novel eye-tracking paradigm, we demonstrate that VWM facilitates search and exhibits effects of fixation frequency and recency, particularly for prior targets. Whereas slot-based memory models cannot account for the human data, a novel continuous-resource model does capture the behavioral and eye tracking data, and identifies the relevant resource as item activation.
Melioration dominates maximization : stable suboptimal performance despite global feedback
2006, Neth, Hansjörg, Sims, Chris R., Gray, Wayne D.
Situations that present individuals with a conflict between local and global gains often evoke a behavioral pattern known as melioration — a preference for immediate rewards over higher long-term gains. Using a variant of a binary forced- choice paradigm by Tunney & Shanks (2002), we explored the potential role of global feedback as a means to reduce this bias. We hypothesized that frequent explicit feedback about future expected and optimal gains might enable decision makers to overcome the documented tendency to meliorate when choices are rewarded probabilistically. Our results suggest that the human tendency to meliorate is tenacious and even prospective normative feedback is insufficient to reliably overcome inefficient choice allocation. We identify human memory limitations as a potential source of this problem and sketch a reinforcement learning model that mimics the effects of a variable feedback horizon on performance. We conclude that melioration is a powerful explanatory mechanism that can account for a wide range of human behavior.
You can't play straight TRACS and win : memory updates in a dynamic Task environment
2004, Neth, Hansjörg, Sims, Chris R., Veksler, Vladislav D., Gray, Wayne D.
To investigate people's ability to update memory in a dynamic task environment we use the experimental card game TRACSTM (Burns, 2001). In many card games card counting is a component of optimal performance. However, for TRACS, Burns (2002a) reported that players exhibited a base- line bias: rather than basing their choices on the actual num- ber of cards remaining in the deck, they chose cards based on the initial composition of the deck. Both a task analysis and computer simulation show that a perfectly executed memory update strategy has minimal value in the original game, suggesting that a baseline strategy is a rational adaptation to the demands of the original game. We then redesign the game to maximize the difference in performance between baseline and update strategies. An empirical study with the new game shows that players perform much better than could be achieved by a baseline strategy. Hence, we conclude that people will adopt a memory update strategy when the benefits outweigh the costs.
Rational Task Analysis : A Methodology to Benchmark Bounded Rationality
2016-03, Neth, Hansjörg, Sims, Chris R., Gray, Wayne D.
How can we study bounded rationality? We answer this question by proposing rational task analysis (RTA)—a systematic approach that prevents experimental researchers from drawing premature conclusions regarding the (ir-)rationality of agents. RTA is a methodology and perspective that is anchored in the notion of bounded rationality and aids in the unbiased interpretation of results and the design of more conclusive experimental paradigms. RTA focuses on concrete tasks as the primary interface between agents and environments and requires explicating essential task elements, specifying rational norms, and bracketing the range of possible performance, before contrasting various benchmarks with actual performance. After describing RTA’s core components we illustrate its use in three case studies that examine human memory updating, multitasking behavior, and melioration. We discuss RTA’s characteristic elements and limitations by comparing it to related approaches. We conclude that RTA provides a useful tool to render the study of bounded rationality more transparent and less prone to theoretical confusion.
Steps towards integrated models of cognitive systems : a levels-of-analysis approach to comparing human performance to model predictions in a complex task environment
2006, Schoelles, Michael J., Neth, Hansjörg, Myers, Christopher W., Gray, Wayne D.
Attempts to model complex task environments can serve as benchmarks that enable us to assess the state of cognitive theory and to identify productive topics for future research. Such models must be accompanied by a thorough examination of their fit to overall performance as well as their detailed fit to the microstructure of performance. We provide an example of this approach in our Argus Prime Model of a complex simulated radar operator task that combines real- time demands on human cognitive, perceptual, and action with a dynamic decision-making task. The generally good fit of the model to overall performance is a mark of the power of contemporary cognitive theory and architectures of cognition. The multiple failures of the model to capture fine-grained details of performance mark the limits of contemporary theory and signal productive areas for future research.
Feedback design for the control of a dynamic multitasking system : dissociating outcome feedback from control feedback
2008, Neth, Hansjörg, Khemlani, Sangeet S., Gray, Wayne D.
We distinguish outcome feedback from control feedback to show that sub- optimal performance in a dynamic multitasking system may be caused by limits inher- ent to the information provided rather than human resource limits.
Tardast is a paradigm for investigating human multitasking behavior, complex system management, and supervisory control. Prior research attributed the suboptimal perfor- mance of Tardast operators to poor strategic task management.
We varied the nature of performance feedback in the Tardast paradigm to compare continuous, cumulative feedback (global feedback) on performance outcome with feedback lim- ited to the most recent system state (local feedback).
Participants in both con- ditions improved with practice, but those with local feedback performed better than those with global feedback. An eye gaze analysis showed increased visual attention directed toward the feedback display in the local feedback condition.
Pre- dicting performance in the control of a dynamic multitasking system requires under- standing the interactions between embodied cognition, the task being performed, and characteristics of performance feedback. In the current case, at least part of what had been diagnosed as a deficit caused by limited cognitive resources has been shown to be data limited.
Perfect outcome feedback can provide inadequate control feedback. Instances of suboptimal performance can be alleviated by better feedback de- sign that takes into account the temporal dynamics of the human-system interaction.
Juggling multiple tasks : a rational analysis of multitasking in a synthetic task environment
2006, Neth, Hansjörg, Khemlani, Sangeet S., Oppermann, Brittney, Gray, Wayne D.
Tardast (Shakeri 2003; Shakeri & Funk, in press) is a new and intriguing paradigm to investigate human multitasking behavior, complex system management, and supervisory control. We present a replication and extension of the original Tardast study that assesses operators’ learning curve and explains gains in performance in terms of increased sensitivity to task parameters and a superior ability of better operators to prioritize tasks. We then compare human performance profiles to various artificial software agents that provide benchmarks of optimal and baseline performance and illustrate the outcomes of simple heuristic strategies. Whereas it is not surprising that human operators fail to achieve an ideal criterion of performance, we demonstrate that humans also fall short of a principally achievable standard. Despite significant improvements with practice, Tardast operators exhibit stable sub-optimal performance in their time-to-task allocations.
Melioration despite more information : The role of feedback frequency in stable suboptimal performance
2005, Neth, Hansjörg, Gray, Wayne D., Sims, Chris R.
Situations that present individuals with a conflict between local and global gains often result in a behavioral pattern known as melioration — a preference for immediate rewards over higher long-term gains. Using a variant of a paradigm by Tunney & Shanks (2002), we explored the potential role of feedback as a means to reduce this bias. We hypothesized that frequent and informative feedback about optimal performance might be the key to enable people to overcome the documented tendency to meliorate when choices are rewarded probabilistically. Much to our surprise, this intuition turned out to be mistaken. Instead of maximizing, 19 out of 22 participants demonstrated a clear bias towards melioration, regardless of feedback condition. From a human factors perspective, our results suggest that even frequent normative feedback may be insufficient to overcome inefficient choice allocation. We discuss implications for the theoretical notion of rationality and provide suggestions for future research that might promote melioration as an explanatory mechanism in applied contexts.