Conditions under which college students cease learning.

Introduction: Effective learning involves the acquisition of information toward a goal and cessation upon reaching that goal. Whereas the process of learning acquisition is well understood, comparatively little is known about how or when learning ceases under naturalistic, open-ended learning conditions in which the criterion for performance is not specified. Ideally, learning should cease once there is no progress toward the goal, although this has never been directly tested in human learners. The present set of experiments explored the conditions under which college students stopped attempting to learn a series of inductive perceptual discrimination problems. Methods: Each problem varied by whether it was solvable and had a criterion for success. The first problem was solvable and involved a pre-determined criterion. The second problem was solvable, but with no criterion for ending the problem so that learners eventually achieved a highly accurate level of performance (overlearning). The third problem was unsolvable as the correct answer varied randomly across features. Measures included the number of trials attempted and the outcome of each problem. Results and Discussion: Results revealed that college students rarely ceased learning in the overlearning or unsolvable problems even though there was no possibility for further progress. Learning cessation increased only by manipulating time demands for completion or reducing the opportunity for reinforcement. These results suggest that human learners show laudable, but inefficient and unproductive, attempts to master problems they should cease.

Information and Agreement in the Reputation Game Simulation.

Modern communication habits are largely shaped by the extensive use of social media and other online communication platforms. The enormous amount of available data and speed with which new information arises, however, often suffices to cause misunderstandings, false conclusions, or otherwise disturbed opinion formation processes. To investigate some of these effects we use an agent-based model on gossip and reputation dynamics with 50 agents, including Bayesian knowledge updates under bounded rationality and up to the second-order theory of mind effects. Thereby, we observe the occurrence of reputation boosts from fake images, as well as the advantage of hiding one's opinion in order to become a strong information trader. In addition, the simulations show fundamentally different mechanisms for reaching high agreement with others and becoming well-informed. Additionally, we investigate the robustness of our results with respect to different knowledge-update mechanisms and argue why it makes sense to especially emphasize the margins of distribution when judging a bounded quantity such as honesty in a reputation game simulation.

Neurofeedback through the lens of reinforcement learning.

Despite decades of experimental and clinical practice, the neuropsychological mechanisms underlying neurofeedback (NF) training remain obscure. NF is a unique form of reinforcement learning (RL) task, during which participants are provided with rewarding feedback regarding desired changes in neural patterns. However, key RL considerations - including choices during practice, prediction errors, credit-assignment problems, or the exploration-exploitation tradeoff - have infrequently been considered in the context of NF. We offer an RL-based framework for NF, describing different internal states, actions, and rewards in common NF protocols, thus fashioning new proposals for characterizing, predicting, and hastening the course of learning. In this way we hope to advance current understanding of neural regulation via NF, and ultimately to promote its effectiveness, personalization, and clinical utility.

Using Methods From Computational Decision-making to Predict Nonadherence to Fitness Goals: Protocol for an Observational Study.

BACKGROUND: Can methods from computational models of decision-making be used to build a predictive model to identify individuals most likely to be nonadherent to personal fitness goals? Such a model may have significant value in the global battle against obesity. Despite growing awareness of the impact of physical inactivity on human health, sedentary behavior is increasingly linked to premature death in the developed world. The annual impact of sedentary behavior is significant, causing an estimated 2 million deaths. From a global perspective, sedentary behavior is one of the 10 leading causes of mortality and morbidity. Annually, considerable funding and countless public health initiatives are applied to promote physical fitness, with little impact on sustained behavioral change. Predictive models developed from multimodal methodologies combining data from decision-making tasks with contextual insights and objective physical activity data could be used to identify those most likely to abandon their fitness goals. This has the potential to enable development of more targeted support to ensure that those who embark on fitness programs are successful. OBJECTIVE: The aim of this study is to determine whether it is possible to use decision-making tasks such as the Iowa Gambling Task to help determine those most likely to abandon their fitness goals. Predictive models built using methods from computational models of decision-making, combining objective data from a fitness tracker with personality traits and modeling from decision-making games delivered via a mobile app, will be used to ascertain whether a predictive algorithm can identify digital personae most likely to be nonadherent to self-determined exercise goals. If it is possible to phenotype these individuals, it may be possible to tailor initiatives to support these individuals to continue exercising. METHODS: This is a siteless study design based on a bring your own device model. A total of 200 healthy adults who are novice exercisers and own a Fitbit (Fitbit Inc) physical activity tracker will be recruited via social media for this study. Participants will provide consent via the study app, which they will download from the Google Play store (Alphabet Inc) or Apple App Store (Apple Inc). They will also provide consent to share their Fitbit data. Necessary demographic information concerning age and sex will be collected as part of the recruitment process. Over 12 months, the scheduled study assessments will be pushed to the subjects to complete. The Iowa Gambling Task will be administered via a web app shared via a URL. RESULTS: Ethics approval was received from Dublin City University in December 2020. At manuscript submission, study recruitment was pending. The expected results will be published in 2022. CONCLUSIONS: It is hoped that the study results will support the development of a predictive model and the study design will inform future research approaches. TRIAL REGISTRATION: ClinicalTrials.gov NCT04783298; https://clinicaltrials.gov/ct2/show/NCT04783298.

Behavioral Signal Processing: Deriving Human Behavioral Informatics From Speech and Language: Computational techniques are presented to analyze and model expressed and perceived human behavior-variedly characterized as typical, atypical, distressed, and disordered-from speech and language cues and their applications in health, commerce, education, and beyond.

The expression and experience of human behavior are complex and multimodal and characterized by individual and contextual heterogeneity and variability. Speech and spoken language communication cues offer an important means for measuring and modeling human behavior. Observational research and practice across a variety of domains from commerce to healthcare rely on speech- and language-based informatics for crucial assessment and diagnostic information and for planning and tracking response to an intervention. In this paper, we describe some of the opportunities as well as emerging methodologies and applications of human behavioral signal processing (BSP) technology and algorithms for quantitatively understanding and modeling typical, atypical, and distressed human behavior with a specific focus on speech- and language-based communicative, affective, and social behavior. We describe the three important BSP components of acquiring behavioral data in an ecologically valid manner across laboratory to real-world settings, extracting and analyzing behavioral cues from measured data, and developing models offering predictive and decision-making support. We highlight both the foundational speech and language processing building blocks as well as the novel processing and modeling opportunities. Using examples drawn from specific real-world applications ranging from literacy assessment and autism diagnostics to psychotherapy for addiction and marital well being, we illustrate behavioral informatics applications of these signal processing techniques that contribute to quantifying higher level, often subjectively described, human behavior in a domain-sensitive fashion.

Un modelo lógico-formal para el estudio de los argumentos emocionales en los procesos de construcción de acuerdos / Um modelo lógico-formal para o estudo dos argumentos emocionais nos processos de construção de acordos / A logical-formal model for the study of emotional arguments in negotiation processes

Los argumentos son parte de un proceso comunicativo con el cual se trata de incidir en la acción de otros. Gilbert (1994) identifica cuatro modos de argumentación: el modo lógico, el modo emocional, el modo visceral y el modo kisceral. Siguiendo la línea de investigación en psicología computacional marcada por Ortony, Clore y Collins (1988) y el modelo de resolución de conflictos usando negociaciones basadas en argumentos propuesto por Jung y Tambe (2001), este trabajo presenta un modelo lógico-formal para el estudio de un modo concreto de argumentos emocionales dentro del contexto de formación de consensos enmarcado en un proceso de negociación/coordinación. Se discuten sus implicaciones en los modelos cognitivos emocionales basados en el proceso de apreciación/evaluación de la emoción.

Arguments are part of a communicative process through which people try to influence the actions of others. Gilbert (1994) identifies four modes of argumentation: (a) logical, (b) emotional, (c) visceral, and (d) kisceral. Following the line of research in computational psychology proposed by Ortony, Clore and Collins (1988), and the model of conflict resolution using argumentation-based negotiations proposed by Jung and Tambe (2001), this paper presents a logical-formal model for studying of emotional arguments within the context of consensus building framed in negotiation and coordination processes.

Os argumentos são parte de um processo comunicativo com o qual se trata de incidir na ação de outros. Gilbert (1994) identifica quatro modos de argumentação: o modo lógico, o modo emocional, o modo visceral e o modo kisceral. Seguindo a linha de investigação em psicologia computacional definida por Ortony, Clore y Collins (1988) e o modelo de resolução de conflitos usando negociações baseadas nos argumentos propostos por Jung e Tambe (2001), este trabalho apresenta um modelo lógico-formal para o estudo de um modo concreto de argumentos emocionais dentro do contexto de formação de consensos demarcado em um processo de negociação /coordenação. Discutem-se suas implicações nos modelos cognitivos emocionais baseados no processo de apreciação /avaliação da emoção.