Cognitive Models for Machine Theory of Mind.

Some of the required characteristics for a true machine theory of mind (MToM) include the ability to (1) reproduce the full diversity of human thought and behavior, (2) develop a personalized model of an individual with very limited data, and (3) provide an explanation for behavioral predictions grounded in the cognitive processes of the individual. We propose that a certain class of cognitive models provide an approach that is well suited to meeting those requirements. Being grounded in a mechanistic framework like a cognitive architecture such as ACT-R naturally fulfills the third requirement by mapping behavior to cognitive mechanisms.

Transcutaneous cervical vagus nerve stimulation enhances second-language vocabulary acquisition while simultaneously mitigating fatigue and promoting focus.

Transcutaneous vagus nerve stimulation (tVNS) is a promising technique for enhancing cognitive performance and skill acquisition. Yet, its efficacy for enhancing learning rate and long-term retention in an ecologically valid learning environment has not been demonstrated. We conducted two double-blind sham-controlled experiments examining the efficacy of auricular tVNS (taVNS: Experiment (1) and cervical tVNS (tcVNS: Experiment (2), on a 5 day second-language vocabulary acquisition protocol among highly selected career linguists at the US Department of Defense's premier language school.

A computational cognitive model of behaviors and decisions that modulate pandemic transmission: Expectancy-value, attitudes, self-efficacy, and motivational intensity.

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. The model yields signature phenomena that appear in the oscillating dynamics of mask wearing and the effective reproduction number, as well as the overall increase of rates of mask-wearing in response to awareness of an ongoing pandemic.

Resting-state heart rate variability after stressful events as a measure of stress tolerance among elite performers.

A common trait of elite performers is their ability to perform well when stressed by strong emotions such as fear. Developing objective measures of stress response that reliably predict performance under stress could have far-reaching implications in selection and training of elite individuals and teams. Prior data suggests that (i) Heart rate and heart rate variability (HR/HRV) are associated with stress reaction, (ii) Higher basal sympathetic tone prior to stressful events is associated with higher performance, and (iii) Elite performers tend to exhibit greater increase in parasympathetic tone after a stressful event.

Real-Time Learning from an Expert in Deep Recommendation Systems with Application to mHealth for Physical Exercises.

Recommendation systems play an important role in today's digital world. They have found applications in various areas such as music platforms, e.g.

Toward a Psychology of Deep Reinforcement Learning Agents Using a Cognitive Architecture.

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 common grounding allows us to identify divergences and explain the learner's behavior in human understandable terms.

Managing evidence in food safety and nutrition.

Evidence ('data') is at the heart of EFSA's 2020 Strategy and is addressed in three of its operational objectives: (1) adopt an open data approach, (2) improve data interoperability to facilitate data exchange, and (3) migrate towards structured scientific data. As the generation and availability of data have increased exponentially in the last decade, potentially providing a much larger evidence base for risk assessments, it is envisaged that the acquisition and management of evidence to support future food safety risk assessments will be a dominant feature of EFSA's future strategy. During the breakout session on 'Managing evidence' of EFSA's third Scientific Conference 'Science, Food, Society', current challenges and future developments were discussed in evidence management applied to food safety risk assessment, accounting for the increased volume of evidence available as well as the increased IT capabilities to access and analyse it.

Leveraging Self-Affirmation to Improve Behavior Change: A Mobile Health App Experiment.

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.

Implementation Intention and Reminder Effects on Behavior Change in a Mobile Health System: A Predictive Cognitive Model.

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 implementation intentions produce effects.

Acceptability of a team-based mobile health (mHealth) application for lifestyle self-management in individuals with chronic illnesses.

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 habits in small peer teams. Here, we demonstrate an early study of usability and acceptability of this app in patients with type 2 Diabetes Mellitus and Depression.

From good intentions to healthy habits: towards integrated computational models of goal striving and habit formation.

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-to-day goal achievement data available from mobile health systems. The models refine current psychological theories of self-efficacy, intended effort, and habit formation, and provide an account for the mechanisms by which goal personalization, implementation intentions, and remindings work.

A computational cognitive model of self-efficacy and daily adherence in mHealth.

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 model refines the psychological construct of self-efficacy.

A Group-Based Mobile Application to Increase Adherence in Exercise and Nutrition Programs: A Factorial Design Feasibility Study.

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 whether social support improved adherence in wellness programs.

Reinforcement learning and counterfactual reasoning explain adaptive behavior in a changing environment.

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 counterfactual reasoning might be an additional mechanism that facilitates change detection.

A functional model of sensemaking in a neurocognitive architecture.

Sensemaking is the active process of constructing a meaningful representation (i.e., making sense) of some complex aspect of the world.

Soccer science and the Bayes community: exploring the cognitive implications of modern scientific communication.

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 to support them. This paper reports on a series of experiments in this area and a prototype implementation using a research platform called CACHE.

Investigating the Roles of Knowledge and Cognitive Abilities in Older Adult Information Seeking on the Web.

This study investigated the influences of knowledge, particularly Internet, Web browser, and search engine knowledge, as well as cognitive abilities on older adult information seeking on the Internet. The emphasis on aspects of cognition was informed by a modeling framework of search engine information-seeking behavior. Participants from two older age groups were recruited: twenty people in a younger-old group (ages 60-70) and twenty people in an older-old group (ages 71-85).

Rational analyses of information foraging on the web.

This article describes rational analyses and cognitive models of Web users developed within information foraging theory. This is done by following the rational analysis methodology of (a) characterizing the problems posed by the environment, (b) developing rational analyses of behavioral solutions to those problems, and (c) developing cognitive models that approach the realization of those solutions. Navigation choice is modeled as a random utility model that uses spreading activation mechanisms that link proximal cues (information scent) that occur in Web browsers to internal user goals.