Chris R. Sims

The soft constraints hypothesis: A rational analysis approach to resource allocation for interactive behavior

Soft constraints hypothesis (SCH) is a rational analysis approach that holds that the mixture of perceptual-motor and cognitive resources allocated for interactive behavior is adjusted based on temporal cost-benefit tradeoffs. Alternative approaches maintain that cognitive resources are in some sense protected or conserved in that greater amounts of perceptual-motor effort will be expended to conserve lesser amounts of cognitive effort. One alternative, the minimum memory hypothesis (MMH), holds that people favor strategies that minimize the use of memory. SCH is compared with MMH across 3 experiments and with predictions of an Ideal Performer Model that uses ACT-Rs memory system in a reinforcement learning approach that maximizes expected utility by minimizing time. Model and data support the SCH view of resource allocation; at the under 1000-ms level of analysis, mixtures of cognitive and perceptual-motor resources are adjusted based on their cost-benefit tradeoffs for interactive behavior.

An Ideal Observer Analysis of Visual Working Memory

Limits in visual working memory (VWM) strongly constrain human performance across many tasks. However, the nature of these limits is not well understood. In this article we develop an ideal observer analysis of human VWM by deriving the expected behavior of an optimally performing but limited-capacity memory system. This analysis is framed around rate-distortion theory, a branch of information theory that provides optimal bounds on the accuracy of information transmission subject to a fixed information capacity. The result of the ideal observer analysis is a theoretical framework that provides a task-independent and quantitative definition of visual memory capacity and yields novel predictions regarding human performance. These predictions are subsequently evaluated and confirmed in 2 empirical studies. Further, the framework is general enough to allow the specification and testing of alternative models of visual memory (e.g., how capacity is distributed across multiple items). We demonstrate that a simple model developed on the basis of the ideal observer analysis-one that allows variability in the number of stored memory representations but does not assume the presence of a fixed item limit-provides an excellent account of the empirical data and further offers a principled reinterpretation of existing models of VWM.

The Adaptive Nature of Visual Working Memory

A growing body of scientific evidence suggests that visual working memory and statistical learning are intrinsically linked. Although visual working memory is severely resource limited, in many cases, it makes efficient use of its available resources by adapting to statistical regularities in the visual environment. However, experimental evidence also suggests that there are clear limits and biases in statistical learning. This raises the intriguing possibility that performance limitations observed in visual working memory tasks can to some degree be explained in terms of limits and biases in statistical-learning ability, rather than limits in memory capacity.

The cost of misremembering: Inferring the loss function in visual working memory

Visual working memory (VWM) is a highly limited storage system. A basic consequence of this fact is that visual memories cannot perfectly encode or represent the veridical structure of the world. However, in natural tasks, some memory errors might be more costly than others. This raises the intriguing possibility that the nature of memory error reflects the costs of committing different kinds of errors. Many existing theories assume that visual memories are noise-corrupted versions of afferent perceptual signals. However, this additive noise assumption oversimplifies the problem. Implicit in the behavioral phenomena of visual working memory is the concept of a loss function: a mathematical entity that describes the relative cost to the organism of making different types of memory errors. An optimally efficient memory system is one that minimizes the expected loss according to a particular loss function, while subject to a constraint on memory capacity. This paper describes a novel theoretical framework for characterizing visual working memory in terms of its implicit loss function. Using inverse decision theory, the empirical loss function is estimated from the results of a standard delayed recall visual memory experiment. These results are compared to the predicted behavior of a visual working memory system that is optimally efficient for a previously identified natural task, gaze correction following saccadic error. Finally, the approach is compared to alternative models of visual working memory, and shown to offer a superior account of the empirical data across a range of experimental datasets.

Melioration as Rational Choice: Sequential Decision Making in Uncertain Environments

Melioration-defined as choosing a lesser, local gain over a greater longer term gain-is a behavioral tendency that people and pigeons share. As such, the empirical occurrence of meliorating behavior has frequently been interpreted as evidence that the mechanisms of human choice violate the norms of economic rationality. In some environments, the relationship between actions and outcomes is known. In this case, the rationality of choice behavior can be evaluated in terms of how successfully it maximizes utility given knowledge of the environmental contingencies. In most complex environments, however, the relationship between actions and future outcomes is uncertain and must be learned from experience. When the difficulty of this learning challenge is taken into account, it is not evident that melioration represents suboptimal choice behavior. In the present article, we examine human performance in a sequential decision-making experiment that is known to induce meliorating behavior. In keeping with previous results using this paradigm, we find that the majority of participants in the experiment fail to adopt the optimal decision strategy and instead demonstrate a significant bias toward melioration. To explore the origins of this behavior, we develop a rational analysis (Anderson, 1990) of the learning problem facing individuals in uncertain decision environments. Our analysis demonstrates that an unbiased learner would adopt melioration as the optimal response strategy for maximizing long-term gain. We suggest that many documented cases of melioration can be reinterpreted not as irrational choice but rather as globally optimal choice under uncertainty.

Action editor: Christian Schunn: Adapting to the task environment: Explorations in expected value

Small variations in how a task is designed can lead humans to trade off one set of strategies for another. In this paper we discuss our failure to model such tradeoffs in the Blocks World task using ACT-Rs default mechanism for selecting the best production among competing productions. ACT-Rs selection mechanism, its expected value equation, has had many successes (see, for example [Anderson, J. R., & Lebiere, C. (Eds.). (1998). Atomic components of thought. Hillsdale, NJ: Lawrence Erlbaum Associates.]) and a recognized strength of this approach is that, across a wide variety of tasks, it tends to produce models that adapt to their task environment about as fast as humans adapt. (This congruence with human behavior is in marked contrast to other popular ways of computing the utility of alternative choices; for example, Reinforcement Learning or most Connectionist learning methods.) We believe that the failure to model the Blocks World task stems from the requirement in ACT-R that all actions must be counted as a binary success or failure. In Blocks World, as well as in many other circumstances, actions can be met with mixed success or partial failure. Working within ACT-Rs expected value equation we replace the binary success/failure judgment with three variations on a scalar one. We then compare the performance of each alternative with ACT-Rs default scheme and with the human data. We conclude by discussing the limits and generality of our attempts to replace ACT-Rs binary scheme with a scalar credit assignment mechanism.

Adaptive Allocation of Vision under Competing Task Demands

Human behavior in natural tasks consists of an intricately coordinated dance of cognitive, perceptual, and motor activities. Although much research has progressed in understanding the nature of cognitive, perceptual, or motor processing in isolation or in highly constrained settings, few studies have sought to examine how these systems are coordinated in the context of executing complex behavior. Previous research has suggested that, in the course of visually guided reaching movements, the eye and hand are yoked, or linked in a nonadaptive manner. In this work, we report an experiment that manipulated the demands that a task placed on the motor and visual systems, and then examined in detail the resulting changes in visuomotor coordination. We develop an ideal actor model that predicts the optimal coordination of vision and motor control in our task. On the basis of the predictions of our model, we demonstrate that human performance in our experiment reflects an adaptive response to the varying costs imposed by our experimental manipulations. Our results stand in contrast to previous theories that have assumed a fixed control mechanism for coordinating vision and motor control in reaching behavior.

Melioration despite more information : The role of feedback frequency in stable suboptimal performance

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.

Cognitive Metrics Profiling

Cognitive Metrics Profiling promises a new approach to minimizing the cognitive workload of interactive systems. By metering high-fidelity computational cognitive models of embodied cognition, Cognitive Metrics Profiles provide a theory-based prediction of the transient changes in workload demanded by dynamic task environments. Although establishing the reliability and validity of this new approach will not be trivial, our profiles stand on the shoulders of the ACT-R architecture of cognition. More than 30-yrs of research have gone into the ACT line of theories. Over the last decade, hundreds of researchers have used ACT-R to build and test models of human cognition. Hence, although many of the details of the architecture are certainly incomplete, much of ACT-R is approximately correct. We expect that the predictions of a Cognitive Metrics Profile based on ACT-R will provide a better estimate of cognitive workload than the estimates used in current human factors practice.

The insistence of vision: Why do people look at a salient stimulus when it signals target absence?

Researchers and practitioners across many fields would benefit from the ability to predict human search time in complex visual displays. However, a missing element in our ability to predict search time is our ability to quantify the exogenous attraction of visual objects in terms of their impact on search time. The current work represents an initial step in this direction. We present two experiments using a quadrant search task to investigate how exogenous and endogenous factors influence human visual search. In Experiment 1, we measure the oculomotor capture—or the tendency of a stimulus to elicit a saccade—of a salient quadrant under conditions in which the salient quadrant does not predict target location. Despite the irrelevance of quadrant salience, we find that subjects persist in making saccades towards the salient quadrant at above-chance levels. We then present a Bayesian-based ideal performer model that predicts search time and oculomotor capture when the salient quadrant never contains the search target. Experiment 2 tested the predictions of the ideal performer model and revealed human performance to be in close correspondence with the model. We conclude that, in our speeded search task, the influence of an exogenous attractor on saccades can be quantified in terms of search time costs and, when these costs are considered, both search time and search behaviour reflect a boundedly optimal adaptation to the cost structure of the environment.