Peter Pirolli

Florida Institute for Human and Machine Cognition | IHMC

We present a computational cognitive model that incorporates and formalizes aspects of theories of individual-level behavior change and present simulations of COVID-19 behavioral response that modulates transmission rates. This formalization includes addressing the psychological constructs of attitudes, self-efficacy, and motivational intensity. Th...

There is little significant work at the intersection of mathematical and computational epidemiology and detailed psychological processes, representations and mechanisms. This is true despite general agreement in the scientific community and the general public that human behavior–in its seemingly infinite variation and heterogeneity, susceptibility...

Recommendation systems play an important role in today’s digital world. They have found applications in various areas such as music platforms, e.g., Spotify, and movie streaming services, e.g., Netflix. Less research effort has been devoted to physical exercise recommendation systems. Sedentary lifestyles have become the major driver of several dis...

Recommendation systems play an important role in today's digital world. They have found applications in various applications such as music platforms, e.g., Spotify, and movie streaming services, e.g., Netflix. Less research effort has been devoted to physical exercise recommendation systems. Sedentary lifestyles have become the major driver of seve...

COGLE (COmmon Ground Learning and Explanation) is an explainable artificial intelligence (XAI) system where autonomous drones deliver supplies to field units in mountainous areas. The mission risks vary with topography, flight decisions, and mission goals. The missions engage a human plus AI team where users determine which of two AI-controlled dro...

We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI). Casting both the human and learner as cognitive models provides common mechanisms to compare and understand their underlying decision-making processes. This com...

Understanding how humans respond to an ongoing pandemic and interventions is crucial to monitoring and forecasting the dynamics of viral transmission. Heterogeneous response over time and geographical regions may depend on the individual beliefs and information consumption patterns of populations. To address the need for more precise and accurate e...

COGLE (COmmon Ground Learning and Explanation) is an explainable artificial intelligence (XAI) system for autonomous drones that deliver supplies in mountainous areas to field units. The drone missions have risks that vary with topography, flight decisions, and mission goals in a simulated environment. Users must determine which AI-controlled drone...

Strategic response options to the COVID-19 pandemic have been greatly influenced by predictive epidemiological models. Effects of non-pharmaceutical interventions (NPIs; such as mask wearing) unfortunately are based on an abundance of very large uncertainties around the extent to which the population adopts risk reducing behaviors. The effects of N...

BACKGROUND Unhealthy behaviors, e.g., physical inactivity and unhealthful food choice, are the primary healthcare cost drivers in developed countries. Pervasive computational, sensing, and communication technology provided by smartphones and smartwatches have made it possible to support individuals in their everyday lives to develop healthier lifes...

We recently put forth a thesis, the Resolution Thesis, that suggests that cognitive science and generative social science are interdependent and should thus be mutually informative. The thesis invokes a paradigm, the reciprocal constraints paradigm, that was designed to leverage the interdependence between the social and cognitive levels of scale f...

Experts from a range of disciplines explore how humans and artificial agents can quickly learn completely new tasks through natural interactions with each other. Humans are not limited to a fixed set of innate or preprogrammed tasks. We learn quickly through language and other forms of natural interaction, and we improve our performance and teach o...

We present a series of mHealth applications and studies pursued as part of the Fittle+ project. This program of research has the dual aims of (1) bringing scalable evidence-based behavior-change interventions to mHealth and evaluating them and (2) developing theoretically based predictive models to better understand the dynamics of the impact of th...

We put forth a thesis, the Resolution Thesis, that suggests that cognitive science and generative social science are interdependent and should thus be mutually informative. The thesis invokes a paradigm, the reciprocal constraints paradigm, that was designed to leverage the interdependence between the social and cognitive levels of scale for the pu...

Sensemaking is a common activity in the analysis of a large or complex amount of information. This active area of HCI research asks how DO people come to understand such difficult sets of information? The information workplace is increasing dominated by high velocity, high volume, complex information streams. At the same time, understanding how sen...

Sensemaking is a common activity in the analysis of a large or complex amount of information. This active area of HCI research asks how DO people come to understand such difficult sets of information? The information workplace is increasing dominated by high velocity, high volume, complex information streams. At the same time, understanding how sen...

Background Implementation intentions are mental representations of simple plans to translate goal intentions into behavior under specific conditions. Studies show implementation intentions can produce moderate to large improvements in behavioral goal achievement. Human associative memory mechanisms have been implicated in the processes by which imp...

Background: MHealth interventions can help to improve the physical well-being of participants. Unfortunately, mHealth interventions often have low adherence and high attrition. One possible way to increase adherence is instructing participants to complete self-affirmation exercises. Self-affirmation exercises have been effective in increasing many...

Consumers are increasingly using the Internet to find information to support health-related decisions. This study evaluated two software tools, Mr. Taggy and SparTag.us, which are designed to help users filter and identify relevant information and organize and make sense of information. Participants included 80 adults, aged 35 to 82, who performed...

BACKGROUND Implementation intentions are mental representations of simple plans to translate goal intentions into behavior under specific conditions. Studies show implementation intentions can produce moderate to large improvements in behavioral goal achievement. Human associative memory mechanisms have been implicated in the processes by which imp...

Our research aims at developing interactive, social agents that can coach people to learn new tasks, skills, and habits. In this paper, we focus on coaching sedentary, overweight individuals to exercise regularly. We employ adaptive goal setting in which the coach generates, tracks, and revises personalized exercise goals for a trainee. The goals b...

Human-agent interaction has been studied for a while in AI research. However, very little attention has been paid to developing methods that are human-aware-that can model, reason about, and make decisions based on changes in human physiological, cognitive, and affec-tive states. We argue that health behavior coaching is a domain that requires an a...

Computational models were developed in the ACT-R neurocognitive architecture to address some aspects of the dynamics of behavior change. The simulations aim to address the day-today goal achievement data available from mobile health systems. The models refine current psychological theories of self-efficacy, intended effort, and habit formation, and...

With increased incidence of chronic illnesses arising due to unhealthy lifestyle habits, it is increasingly important to leverage technology applications to promote and sustain health behavior change. We developed a smartphone-based application, NutriWalking (NW), which recommends personalized daily exercise goals and promotes healthy nutritional h...

Mobile health (mHealth) applications provide an excellent opportunity for collecting rich, fine-grained data necessary for understanding and predicting day-to-day health behavior change dynamics. A computational predictive model (ACT-R-DStress) is presented and fit to individual daily adherence in 28-day mHealth exercise programs. The ACT-R-DStress...

Background Novel methods of promoting self-monitoring and social support are needed to ensure long-term maintenance of behavior change. In this paper, we directly investigate the effects of group support in an exercise and nutrition program delivered by an mHealth application called Fittle. Objective Our first specific study aim was to explore whet...

We describe the development of computational cognitive models that predict information selection behavior in simulated geospatial intelligence tasks. These map-based tasks require users to select layers that visualize different types of intelligence, and to revise probability estimates of attack by hypothetical insurgent groups. Our first model has...

Procedural memory and episodic memory are known to be distinct and both underlie the performance of many tasks. Reinforcement learning (RL) and instance-based learning (IBL) represent common approaches to modeling procedural and episodic memory in that order. In this work, we present a neural model utilizing RL dynamics and an ACT-R model utilizing...

We developed an ACT-R model of sensemaking in geospatial intelligence tasks based on two widely used learning processes in ACT-R: instance-based learning and reinforcement learning. This map-based task requires users to select (make visible) layers that visualize different types of intelligence, and to revise probability estimates about which group...

Animals routinely adapt to changes in the environment in order to survive. Though reinforcement learning may play a role in such adaptation, it is not clear that it is the only mechanism involved, as it is not well suited to producing rapid, relatively immediate changes in strategies in response to environmental changes. This research proposes that...

Automated coaching systems offer a convenient, cost-effective way to reduce stress, which can be a serious health issue. However, one concern with such systems is compliance; users fail to achieve daily stress reduction goals because goals are too easy or too difficult. To address this, we built DStress (Design for Stress), a theoretically grounded...

Deciding whether a claim is true or false often requires a deeper understanding of the evidence supporting and contradicting the claim. However, when presented with many evidence documents, users do not necessarily read and trust them uniformly. Psychologists and other researchers have shown that users tend to follow and agree with articles and sou...

Change detection, and which information to attend to, are key research problems relevant to understanding adaptive behavior. Rational analyses of change detection have been developed in optimal foraging theory ( and ) and psychology (Gallistel, Krishan, Liu, Miller, & Latham, 2014). Information foraging models (Pirolli, 2007) have been developed to...

It is crucial for animals to detect changes in their surrounding environment, and reinforcement learning is one of the well-known processes to explain the change detection behavior. However, reinforcement learning itself cannot fully explain rapid, relatively immediate changes in strategy in response to abrupt environment changes. A previous model...

Neuromodulatory systems and prefrontal cortex are involved in a number of decision-making contexts. In this work, we adapt a recent neural network model that simulates interactions between neuromodulatory and prefrontal areas to the problem of prognostic foraging—that is choosing information to update or form a hypothesis. In the context of a simul...

Smartphone platforms provide an excellent opportunity for projecting existing or new behavior-change methods into everyday life at great economies of scale. In this paper we present an experimental test of a new behavior-change smartphone platform and application called Fittle, which delivers ecological momentary interventions and group support to...

This presentation will discuss complex human-information interaction problems involving information foraging, sensemaking, and lifestyle change (behavior change), predictive models of human cognition in these contexts, as well as novel interaction techniques inspired by these models.

This presentation will discuss complex human-information interaction problems involving information foraging, sensemaking, and lifestyle change (behavior change), predictive models of human cognition in these contexts, as well as novel interaction techniques inspired by these models.

Over the past 30 years science has played a key role in shaping and advancing research in Human-Computer Interaction. Informed in part by methods, theories and findings from the behavioral sciences and from computer science, scientific contributions to HCI have provided explanations of how and why people interact through and with technology. We arg...

Cognitive engineering is an interdisciplinary approach to the analysis, modeling, and design of engineered systems or workplaces in which humans and technologies jointly operate to achieve system goals. As individuals, teams, and organizations become increasingly reliant on information technology and automation, it is more important than ever for s...

We adapt methods from the stochastic theory of invasions – for which a key question is whether a propagule will grow to an established population or fail – to show how monitoring early participation in a social collaboration network allows prediction of success. Social collaboration networks have become ubiquitous and can now be found in widely div...

This handbook collects and organizes contemporary cognitive engineering research, drawing on the original research of more than 60 contributing experts. Coverage of human factors, human-computer interaction, and the conceptual foundations of cognitive engineering is extensive, addressing not only cognitive engineering in broader organizations and c...

Sensemaking is the active process of constructing a meaningful representation (i.e., making sense) of some complex aspect of the world. In relation to intelligence analysis, sensemaking is the act of finding and interpreting relevant facts amongst the sea of incoming reports, images, and intelligence. We present a cognitive model of core informatio...

Deciding whether a claim is true or false often requires understanding the evidence supporting and contradicting the claim. However, when learning about a controversial claim, human biases and viewpoints may affect which evidence documents are considered "trustworthy" or credible. It is important to overcome this bias and know both viewpoints to ge...

One of the key challenges for users of social media is judging the topical expertise of other users in order to select trustful information sources about specific topics and to judge credibility of content produced by others. In this paper, we explore the usefulness of different types of user-related data for making sense about the topical expertis...

An ACT-R model of sensemaking in a geospatial intelligence task was developed based on Instance-Based Learning Theory (IBLT). The model (a) maintains hypotheses about the probability of attacks by insurgent groups, (b) seeks new information based on those hypotheses, and (c) updates hypotheses based on new evidence. The model provides a functional...

Judging topical expertise of micro-blogger is one of the key challenges for information seekers when deciding which information sources to follow. However, it is unclear how useful different types of information are for people to make expertise judgments and to what extent their background knowledge influences their judgments. This study explored d...

A thermodynamic framework is presented to characterize the evolution of efficiency, order, and quality in social content production systems, and this framework is applied to the analysis of Wikipedia. Contributing editors are characterized by their (creative) energy levels in terms of number of edits. We develop a definition of entropy that can be...

We propose a user model to support personalized learning paths through online material. Our approach is a variant of student modeling using the computer tutoring concept of knowledge tracing. Knowledge tracing involves representing the knowledge required to master a domain, and, from traces of online user behavior, diagnosing user knowledge states...

Twitter has been used for engaging with audiences online in several popular political movements. In this paper we explore factors that influence the engagement of Twitter users during the recent Occupy Wall Street movement, where engagement is measured by retweets and hashtag usage related to the movement. Through analyzing Twitter activities of mo...

When presented with many relevant documents about a controversial topic, humans do not always read and trust them uniformly. Instead, they tend to follow and agree with articles and sources that hold similar viewpoints as theirs, a phenomenon known as confirmation bias. This suggests that when acquiring additional knowledge about a controversial to...

In this paper, we propose an ACT-R cognitive model for making credibility judgments about the credibility of Twitter authors. We abstracted the cognitive processes involved in three levels: attending to information on Web page, comprehending information to identify credibility cues, and integrating credibility cues to make a judgment. We represent...

A task of primary importance for social network users is to decide whose updates to subscribe to in order to maximize the relevance, credibility, and quality of the information received. To address this problem, we conducted an experiment designed to measure the extent to which different factors in online social networks affect both explicit and im...

How social media are expanding traditional research and development topics for computer and information scientists.

Social networking sites (e.g. Facebook), microblogging services (e.g. Twitter), and content-sharing sites (e.g. YouTube and Flickr) have introduced the opportunity for wide-scale, online social participation. Visibility of national and international priorities such as public health, political unrest, disaster relief, and climate change has increase...

vSked is an interactive and collaborative assistive technology for students with autism, combining visual schedules, choice boards, and a token-based reward system into an integrated classroom system. In this paper, we present the results of a study ...

As more and more information becomes available in larger, ever more rapid flows, the skills of sensemaking are no longer required just of specialists in intelligence analysis, but increasingly of everyone. We all live in a data world with continually flowing streams of information. These articles are the beginning of a coordinated effort to build b...

Extensive research is needed to build upon currently used media and tools to foster wider participation, address national priorities, and deal with potential dangers associated with technology-mediated social participation.

Technology-mediated social-participation systems, such as Wikipedia and TopCoder, allow a vast user base to collaborate to solve difficult problems. TMSP could be applied to many current social issues, but doing so requires new theory and infrastructure for social design.

Prior research in the social search space has focused on the informational benefits of collaborating with others during web and workplace information seeking. However, social interactions, especially during complex tasks, can have cognitive benefits as well. Our goal in this paper is to document the methods and outcomes of using social resources to...

Retweeting is the key mechanism for information diffusion in Twitter. It emerged as a simple yet powerful way of disseminating information in the Twitter social network. Even though a lot of information is shared in Twitter, little is known yet about how and why certain information spreads more widely than others. In this paper, we examine a number...

Science is a form of distributed analysis involving both individual work that produces new knowledge and collaborative work to exchange information with the larger community. There are many particular ways in which individual and community can interact in science, and it is difficult to assess how efficient these are, and what the best way might be...

As an increasingly large amount of knowledge is shared between users in Twitter, it is becoming a popular source of relevant information to many people. In Twit-ter, information is transferred primarily via a social relationship called following. Identifying users to follow who are highly relevant to a particular topic of interest can be a difficul...

Prior research on Wikipedia has characterized the growth in content and editors as being fundamentally exponential in nature, extrapolating current trends into the future. We show that recent editing activity suggests that Wikipedia growth has slowed, and perhaps plateaued, indicating that it may have come against its limits to growth. We measure g...

In this keynote presentation I discuss some of the exciting phenomena and challenges that are emerging as the digital universe evolves to become a more social medium that supports more complex information-seeking and learning activities. This discussion emerges from attempts to extend previous work on Information Foraging Theory [1] to address thes...

Social annotation systems such as SparTag.us and del.icio.us have been designed to encourage individual reading and marking behaviors that, when shared, accumulate to build collective knowledge spaces. Prior work reported on the experimental design and performance effects observed in a con- trolled study of SparTag.us. Study participants working in...

Task forces of expert knowledge workers would benefit from more advanced web tools supporting activity awareness and social sensemaking. This paper proposes the design of a task force workspace, which is under develop- ment. It introduces the problem through a scenario, specifies requirements, il- lustrates a modeling approach and the mockups of th...

In prior work we reported on the design of a social annotation system, SparTag.us, for use in sensemaking activities such as work-group reading and report writing. Previous studies of note-taking systems have demonstrated behavioral differences in social annotation practices, but are not clear in the actual performance gains provided by social feat...

An experiment was conducted to study how credibility judgments about Wikipedia are affected by providing users with an interactive visualization (WikiDashboard) of article and author editing history. Overall, users who self-reported higher use of Internet information and higher rates of Wikipedia usage tended to produce lower credibility judgments...

How does one make sense of a large or complex task? By the term "sensemaking" we mean the processes people go through to frame, collect, organize and structure information to help understand a problem. Sensemaking is what people do to get from the earliest phases of an information collecting and organizing task to the conclusion. Sensemaking tasks...

User interfaces and information systems have become increasingly social in recent years, aimed at supporting the decentralized, cooperative production and use of content. A theory that predicts the impact of interface and interaction designs on such factors as participation rates and knowledge discovery is likely to be useful. This paper reviews a...

To what extent can social interactions augment people's natural search experiences? What factors influence the decision to turn to a friend for help? Our paper presents the preliminary results of a social sensemaking task that begin to address such questions by examining the cognitive consequences of social search.