Will Bridewell

United States Naval Research Laboratory | NRL · The Navy Center for Applied Research in Artificial Intelligence

Artificial intelligence and machine learning are increasingly used to offload decision making from people. In the past, one of the rationales for this replacement was that machines, unlike people, can be fair and unbiased. Evidence suggests otherwise. We begin by entertaining the ideas that algorithms can replace people and that algorithms cannot b...

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....

Finding claims that researchers have made considerable progress in artificial intelligence over the last several decades is easy. However, our everyday interactions with cognitive systems quickly move from intriguing to frustrating. The root of those frustrations rests in a mismatch between the expectations we have due to our inherent, folk-psychol...

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. Fi...

If artificial agents are to be created such that they occupy space in our social and cultural milieu, then we should expect them to be targets of folk psychological explanation. That is to say, their behavior ought to be explicable in terms of beliefs, desires, obligations, and especially intentions. Herein, we focus on the concept of intentional a...

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 r...

For decades AI researchers have built agents that are capable of carrying out tasks that require human-level or human-like intelligence. During this time, questions of how these programs compared in kind to humans have surfaced and led to beneficial interdisciplinary discussions, but conceptual progress has been slower than technological progress....

It is easy to see that social robots will need the ability to detect and evaluate deceptive speech; otherwise they will be vulnerable to manipulation by malevolent humans. More surprisingly, we argue that effective social robots must also be able to produce deceptive speech. Many forms of technically deceptive speech perform a positive pro-social f...

Research on multiple-object tracking suggests that the visual system can track targets through occlusions by extrapolating future positions from past motion. Evidence for such extrapolation is clearer with a smaller number of targets. Specifically, Luu and Howe (2015) showed that participants were able to track two targets better with predictable m...

We present a computational model exploring goal-directed deployment of attention during object tracking. Once selected, objects are tracked in parallel, but serial attention can be directed to an object that is visually crowded and in danger of being lost. An attended object's future position can be extrapolated from its past motion trajectory, all...

Recent studies in the perception of numerosity have indicated that subitizing (the rapid and accurate enumeration of small quantities) requires attention. We present a novel computational model of enumeration in which attention unifies distinct processes of numerosity approximation, subitizing, and explicit counting. We demonstrate how this model a...

Attention provides a way to direct limited cognitive resources to a subset of available information. In the human case, this capacity enables selective mental processing, thereby shaping what we see, think, and do. Importantly, attention has the uncommon characteristic that people can direct it not only outward at objects, agents, and events in the...

Attention is thought to be a part of a larger cluster of mechanisms that serve to orient a cognitive system, to filter contents with respect to their task relevance, and to devote more computation to certain options than to others. All these activities proceed under the plausible assumption that not all information can be or ought to be processed f...

The rich literature on multiple object tracking (MOT) conclusively demonstrates that humans are able to visually track a small number of objects. There is considerably less agreement on what perceptual and cognitive processes are involved. While it is clear that MOT is attentionally demanding, various accounts of MOT performance centrally involve p...

With few exceptions, architectural approaches to modeling cognition have historically emphasized what happens in the mind following the transduction of environmental signals into percepts. To our knowledge, none of these architectures implements a sophisticated, general theory of human attention. In this paper we summarize progress to date on a new...

With few exceptions, architectural approaches to modeling cognition have historically emphasized what happens in the mind following the transduction of environmental signals into percepts. To our knowledge, none of these architectures implements a sophisticated, general theory of human attention. In this paper we summarize progress to date on a new...

This paper considers the problem of detecting deceptive agents in a conversational context. We argue that distinguishing between types of deception is required to generate successful action. This consideration motivates a novel taxonomy of deceptive and ignorant mental states, emphasizing the importance of an ulterior motive when classifying decept...

In previous publications, we have reported a computational approach to constructing quantitative process models of dynamic systems from time-series data and background knowledge. However, our experience with these systems suggests that process knowledge is insufficient to avoid the consideration of implausible models. To this end, we have identifie...

In this paper, we discuss a mechanism for transfer learn-ing in the context of inductive process modeling. We be-gin by describing the dual role of knowledge as a source of model components and structural constraints. Next, we review the task of inductive process modeling and emphasize the effect of domain knowledge on the learn-ing component. We t...

Scientists use two forms of knowledge in the construc-tion of explanatory models: generalized entities and processes that relate them; and constraints that spec-ify acceptable combinations of these components. Pre-vious research on inductive process modeling, which constructs models from knowledge and time-series data, has relied on handcrafted con...

In this paper, we review the paradigm of inductive process modeling and examine its application to human physiology. This framework represents models as a set of interacting processes, each with associated differential or algebraic equations that express causal relations among variables. Simulating such a quantitative process model produces traject...

Social cognition is a key feature of human-level intelligence. However, social reasoning faculties are rarely included in cognitive systems. To encourage research in this direction, we introduce a practical, computational framework that enables socially aware inference. We demonstrate the framework's ability to model a common, complex, and under-in...

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 heal...

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...

This paper focuses on three emerging techniques for improving the process of automating analysis and retrieval of electronically stored information in discovery proceedings: (1) machine learning to extend and apply users' hypotheses (theories) of document relevance; (2) a hypothesis ontology to generalize user modeling regarding relevance theories;...

Quantitative modeling plays a key role in the natural sciences, and systems that address the task of inductive process modeling can assist researchers in explaining their data. In the past, such systems have been limited to data sets that recorded change over time, but many interesting problems involve both spatial and temporal dynamics. To meet th...

People constantly apply acquired knowledge to new learning tasks, but machines almost never do. Research on transfer learning attempts to address this dissimilarity. Working within this area, we report on a procedure that learns and transfers constraints in the context of inductive process modeling, which we review. After discussing the role of con...

Quantitative modeling plays a key role in the natural sciences, and systems that address the task of inductive process modeling can assist researchers in explaining their data. In the past, such systems have been limited to data sets that recorded change over time, but many interesting problems involve both spatial and temporal dynamics. To meet th...

People constantly apply acquired knowledge to new learning tasks, but machines almost never do. Research on transfer learning attempts to address this dissimilarity. Working within this area, we report on a procedure that learns and transfers constraints in the context of inductive process mod-eling, which we review. After discussing the role of co...

Scientific modeling is a creative activity that can benefit from computational support. This chapter reports five challenges that arise in developing such aids, as illustrated by PROMETHEUS, a software environment that supports the construction and revision of explanatory models. These challenges include the paucity of relevant data, the need to in...

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 exampl...

We are developing a curriculum in science informatics that cuts across disciplinary boundaries to ed-ucate students in the computational character of the scientific enterprise. We are designing courses that cover: the storage, retrieval, and analysis of scientific data; the representation of scientific models and their use for prediction and explan...

In this paper, we pose a novel research problem for machine learning that involves constructing a process model from continuous data. We claim that casting learned knowledge in terms of processes with associated equations is desirable for scientific and engineering domains, where such notations are commonly used. We also argue that existing inducti...

In this paper, we introduce an inductive logic programming approach to learning declarative bias. The target learning task is inductive process modeling, which we briefly review. Next we discuss our approach to bias induction while emphasizing predicates that characterize the knowledge and models associated with the HIPM system. We then evaluate ho...

In this paper, we introduce an approach for extracting constraints on process model construction. We begin by clarifying the type of knowledge produced by our method and how one may apply it. Next, we review the task of inductive process modeling, which provides the required data. We then introduce a logical formal- ism and a computational method f...

Existing tools for scientific modeling offer little support for improving models in response to data, whereas computational methods for scientific knowledge discovery provide few opportunities for user input. In this paper, we present a language for stating process models and background knowledge in terms familiar to scientists, along with an inter...

In this paper, we review the task of inductive process mod- eling, which uses domain knowledge to compose explanatory models of continuous dynamic systems. Next we discuss approaches to learning with missing values in time series, noting that these eorts are typically applied for descriptive modeling tasks that use little background knowl- edge. We...

Scientists investigate the dynamics of complex systems with quantitative models, employing them to synthesize knowledge, to explain observations, and to forecast future system behavior. Complete specification of systems is impossible, so models must be simplified abstractions. Thus, the art of modeling involves deciding which system elements to inc...

In this paper, we review the paradigm of in- ductive process modeling, which uses back- ground knowledge about possible component processes to construct quantitative models of dynamical systems. We note that previ- ous methods for this task tend to overt the training data, which suggests ensemble learn- ing as a likely response. However, such tech-...

Research on inductive process modeling combines back- ground knowledge with time-series data to construct explana- tory models, but previous work has placed few constraints on search through the model space. We present an extended formalism that organizes process knowledge in a hierarchi- cal manner, and we describe HIPM, a system that carries out...

Narrative reports in medical records contain a wealth of information that may augment structured data for managing patient information and predicting trends in diseases. Pertinent negatives are evident in text but are not usually indexed in structured databases. The objective of the study reported here was to test a simple algorithm for determining...

Automatically identifying findings or diseases described in clinical textual reports requires determining whether clinical observations are present or absent. We evaluate the use of negation phrases and the frequency of negation in free-text clinical reports. A simple negation algorithm was applied to ten types of clinical reports (n=42,160) dictat...

The present study compared the effects of irrational beliefs measured by the Survey of Personal Beliefs (SPB) and optimism and pessimism as measured by the revised Life Orientation Test (LOT-R) on depressive and anxious symptoms 6 weeks later. Results of analysis of variances for both measures of psychological distress indicated a significant main...

The present study compared the effects of irrational beliefs measured by the Survey of Personal Beliefs (SPB) and optimism and pessimism as measured by the revised Life Orientation Test (LOT-R) on depressive and anxious symptoms 6 weeks later. Results of analysis of variances for both measures of psychological distress indicated a significant main...

The present study examined the role of internalized anger, externalized anger, and anger control (Spielberger, 1991) as predictors of depressive, anxious, and hostile symptoms. Based on regression analyses, internalized anger, followed by lack of anger control, was found to play an important role in predicting both depressive and anxious symptoms....

Building models of a complex system such as an ecosystem or a chemical plant is an arduous task that can take several person months to complete. One rarely knows the scope of the model, its assumptions and claims, at the outset of the task, let alone how to state those in a formal language. To make this task manageable, modelers start at the whiteb...

We define the inductive process model-ing task as the automated construction of quantitative process models from time series and background knowledge. In this task, the background knowledge comprises generic processes that along with a given set of en-tities define the space of candidate model structures. Typically this space grows expo-nentially w...

We present a perspective on theory revision that character- izes the resulting revisions as explanations of anomalous data (i.e., data that contradict a given model). Additionally, we emphasize the plausibility of these explanations as opposed to the performance of a revised model. An explanation gen- erator implementing (part of) John Stuart Mill'...

In this paper, we review the main qualitative charac-teristics of everyday inference in human cognition and present a computational account that is consistent with them. This includes both a representational framework and associated processes that produce abductive expla-nations in a flexible, incremental, and efficient manner. We clarify our appro...

Quantitative modeling plays a key role in the natural sciences, and systems that address the task of inductive process modeling can assist researchers in explaining their data. In the past, such systems have been limited to data sets that recorded change over time, but many in-teresting problems involve both spatial and temporal dy-namics. To meet...