Gregory J. Koop

The Role of Mnemonic Processes in Pure-Target and Pure-Foil Recognition Memory.

Surprisingly, response patterns in a recognition memory test are very similar regardless of whether the test list contains both targets and foils or just one class of items. To better understand these effects, we evaluate performance over the course of testing. Output interference (OI) is the decrease in performance across test trials due to an increase in noise caused by encoded test items. Critically, OI is predicted on pure lists if the mnemonic evidence for each test item is evaluated. In two experiments, participants received accurate feedback, no feedback, or random feedback that was unrelated to the response on each test trial and pure or standard test lists. When no feedback was provided, performance was nearly identical for standard and pure test lists, replicating previous findings. Only in the presence of accurate feedback were participants able to successfully adapt to pure list environments and improve their accuracy. Critically, OI was observed, demonstrating that participants continued to evaluate mnemonic evidence even in pure list conditions. We discuss the implication of these data for models of memory.

The Response Dynamics of Recognition Memory: Sensitivity and Bias.

Advances in theories of memory are hampered by insufficient metrics for measuring memory. The goal of this paper is to further the development of model-independent, sensitive empirical measures of the recognition decision process. We evaluate whether metrics from continuous mouse tracking, or response dynamics, uniquely identify response bias and mnemonic evidence, and demonstrate 1 application of these metrics to the strength-based mirror-effect paradigm. In 4 studies, we show that response dynamics can augment our current analytic repertoire in a way that speaks to the psychological mechanisms underlying recognition memory. We manipulated familiarity and response bias via encoding strength and the proportion of targets at test (Experiment 1) and found that the initial degree of deviation of the mouse movement toward a response is a robust indicator of response bias. In order to better isolate measures of memory strength, we next minimized response bias through the use of 2-alternative forced-choice tests (Experiments 2 and 3). Changes in the direction of movement along the x-axis provided an indication of encoding strength. We conclude by applying these metrics to the typical strength-based mirror effect design (Experiment 4) in an attempt to further discriminate between differentiation and criterion-shift accounts. (PsycINFO Database Record

Decision fusion by people: Experiments, models, and sociotechnical system design

People and machines perform tasks differently. Building optimal systems that include people and machines, requires understanding their respective behavioral properties. The task of decision fusion is considered and the performance of people is compared to the optimal fusion rule. Our behavioral experiments demonstrate that people perform decision fusion in a stochastic manner dependent on various factors, whereas optimal rule is deterministic. A Bayesian hierarchical model is developed to characterize the observed human behavior. This model captures the differences observed in people at individual level, crowd level, and population level. The implications of such a model on developing large-scale human-machine systems are presented by developing optimal decision fusion trees with both human and machine agents.

Experiments and Models for Decision Fusion by Humans in Inference Networks

With the advent of the Internet of Things (IoT) and a rapid deployment of smart devices and wireless sensor networks (WSNs), humans interact extensively with machine data. These human decision makers use sensors that provide information through a sociotechnical network. The sensors can be other human users or they can be IoT devices. The decision makers themselves are also part of the network, and there is a need to understand how they will behave. In this paper, the decision fusion behavior of humans is analyzed on the basis of behavioral experiments. The data collected from these experiments demonstrate that people perform decision fusion in a stochastic manner dependent on various factors, unlike machines that perform this task in a deterministic manner. A Bayesian hierarchical model is developed to characterize the observed stochastic human behavior. This hierarchical model captures the differences observed in people at individual, crowd, and population levels. The implications of such a model on designing large-scale inference systems are presented by developing optimal decision fusion trees with both human and machine agents.