Selection enables enhancement: An integrated model of object tracking.

The diversity of research on visual attention and multiple-object tracking presents challenges for anyone hoping to develop a unified account. One key challenge is identifying the attentional limitations that give rise to competition among targets during tracking. To address this challenge, we present a computational model of object tracking that relies on two attentional mechanisms: serial selection and parallel enhancement. Selection picks out an object for further processing, whereas enhancement increases sensitivity to stimuli in regions where objects have been selected previously. In this model, multiple target locations can be tracked in parallel via enhancement, whereas a single target can be selected so that additional information beyond its location can be processed. In simulations of two psychological experiments, we demonstrate that spatial competition during enhancement and temporal competition for selection can explain a range of findings on multiple-object tracking, and we argue that the interaction between selection and enhancement captured in the model is critical to understanding attention more broadly.

Attentional Strategies and the Transition From Subitizing to Estimation in Numerosity Perception.

The common view of the transition between subitizing and numerosity estimation regimes is that there is a hard bound on the subitizing range, and beyond this range, people estimate. However, this view does not adequately address the behavioral signatures of enumeration under conditions of attentional load or in the immediate post-subitizing range. The possibility that there might exist a numerosity range where both processes of subitizing and estimation operate in conjunction has so far been ignored. Here, we investigate this new proposal, that people strategically combine the processes of subitizing and estimation to maximize accuracy and precision, given time or attentional constraints. We present a process-level account of how subitizing and estimation can be combined through strategic deployment of attention to maximize the precision of perceived numerosity given time constraints. We then describe a computational model of this account and apply it in two experimental simulations to demonstrate how it can explain key findings in prior enumeration research. While recent modeling work has argued that the behavioral signatures of enumeration can best be explained through a single numerosity system with a single form of representation, we argue that our model demonstrates how the traditional two-systems view of numerical representation accounts for behavioral data through coordination with a unified attentional mechanism, rather than a unified representation.

Apophatic science: how computational modeling can explain consciousness.

This study introduces a novel methodology for consciousness science. Consciousness as we understand it pretheoretically is inherently subjective, yet the data available to science are irreducibly intersubjective. This poses a unique challenge for attempts to investigate consciousness empirically. We meet this challenge by combining two insights. First, we emphasize the role that computational models play in integrating results relevant to consciousness from across the cognitive sciences. This move echoes Alan Newell's call that the language and concepts of computer science serve as a lingua franca for integrative cognitive science. Second, our central contribution is a new method for validating computational models that treats them as providing negative data on consciousness: data about what consciousness is not. This method is designed to support a quantitative science of consciousness while avoiding metaphysical commitments. We discuss how this methodology applies to current and future research and address questions that others have raised.

Social network analysis of physician interactions: the effect of institutional boundaries on breast cancer care.

Although comparative effectiveness trials and nationally recognized clinical guidelines offer substantial guidance about ideal patient treatment, we remain largely uninformed about the patterns of care seen in everyday clinical practice. To address this gap in knowledge, we looked at registry-based data on breast cancer care at two neighboring healthcare institutions with a specific focus on whether organizational boundaries determine the physicians that a patient will see. From an initial patient-oriented data set, we developed a social network of physicians, modeling their interactions over the course of the provided treatments. Applying a mixture of visual and quantitative analyses to this network, we found evidence for strong intra-institutional ties and poignantly weak connections to physicians operating out of other healthcare centers.

Alignment and clustering of breast cancer patients by longitudinal treatment history.

Longitudinal treatment histories may offer valuable information about clinical practice patterns to the clinical researcher as part of data exploration, cohort identification, or discovery of potentially beneficial or harmful practices in the health care community. We present a novel approach to temporal clustering of patient treatment information based on the semantic similarity of longitudinal histories. Using combined breast cancer registry data from two neighboring health care institutions, we constructed a database of longitudinal treatment histories that included surgical procedures, radiation therapy, chemotherapy, and hormone replacement therapy. We then did pair-wise similarity comparisons of treatment histories, and used the similarity measures to cluster patients with machine learning methods. An evaluation of our results found that patients clustered on stage of breast cancer and type of treatment provided. We propose that this approach can be applied towards identification of similar cohorts, and for discovery of novel or anomalous clinical practice patterns.

Two kinds of knowledge in scientific discovery.

Research on computational models of scientific discovery investigates both the induction of descriptive laws and the construction of explanatory models. Although the work in law discovery centers on knowledge-lean approaches to searching a problem space, research on deeper modeling tasks emphasizes the pivotal role of domain knowledge. As an example, our own research on inductive process modeling uses information about candidate processes to explain why variables change over time. However, our experience with IPM, an artificial intelligence system that implements this approach, suggests that process knowledge is insufficient to avoid consideration of implausible models. To this end, the discovery system needs additional knowledge that constrains the model structures. We report on an extended system, SC-IPM, that uses such information to reduce its search through the space of candidates and to produce models that human scientists find more plausible. We also argue that although people carry out less extensive search than SC-IPM, they rely on the same forms of knowledge--processes and constraints--when constructing explanatory models.