Changing counterproductive beliefs about attention,memory, and multitasking: Impacts of a brief,fully online module

Ubiquitous mobile technology is part of contemporary life, bringing with it the potential for distraction and reduction in performance associated with multitasking. The predisposition toward dysfunctional multitasking may be shaped in part by beliefs that individuals hold about memory and attention. The issue is particularly pressing for college students, given established links between distraction, multitasking, and learning. This project assessed the impact of an online learning module on beliefs about attention, memory, and learning in college students. It also contrasted these beliefs in a college and non-college community sample. Significant reductions in counterproductive beliefs were associated with completing the module; counterproductive beliefs were also no more prevalent in the college vs. the non-college sample. Our findings suggest that brief online modules are a practical way to address counterproductive beliefs related to multitasking with technology, and add to the literature on metacognition, attention, and multitasking in college and non-college populations.     

Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining

In the age of big data, substantial research is now moving toward using digital footprints like social media text data to assess personality. Nevertheless, there are concerns and questions regarding the psychometric and validity evidence of such approaches. We seek to address this issue by focusing on social media text data and (i) conducting a review of psychometric validation efforts in social media text mining (SMTM) for personality assessment and discussing additional work that needs to be done; (ii) considering additional validity issues from the standpoint of reference (i.e. ‘ground truth’) and causality (i.e. how personality determines variations in scores derived from SMTM); and (iii) discussing the unique issues of generalizability when validating SMTM for personality assessment across different social media platforms and populations. In doing so, we explicate the key validity and validation issues that need to be considered as a field to advance SMTM for personality assessment, and, more generally, machine learning personality assessment methods. © 2020 European Association of Personality Psychology     

Learning through observing: Effects of modeling truth‐ and lie‐telling on children’s honesty

The current study examined the influence of observing another's lie‐ or truth‐telling – and its consequences – on children's own honesty about a transgression. Children (N = 224, 5–8 years of age) observed an experimenter (E) tell the truth or lie about a minor transgression in one of five conditions: (a) Truth‐Positive Outcome – E told the truth with a positive outcome; (b) Truth‐Negative Outcome – E told the truth with a negative outcome; (c) Lie‐Positive Outcome – E lied with a positive outcome; (d) Lie‐Negative Outcome – E lied with a negative outcome; (e) Control – E did not tell a lie or tell the truth. Later, to examine children's truth‐ or lie‐telling behavior, children participated in a temptation resistance paradigm where they were told not to peek at a trivia question answer. They either peeked or not, and subsequently lied or told the truth about that behavior. Additionally, children were asked to give moral evaluations of different truth‐ and lie‐telling vignettes. Overall, 85% of children lied. Children were less likely to lie about their own transgression in the TRP when they had previously witnessed the experimenter tell the truth with a positive outcome or tell a lie with a negative outcome.     

Mapping differential responses to cognitive training using machine learning

We used two simple unsupervised machine learning techniques to identify differential trajectories of change in children who undergo intensive working memory (WM) training. We used self‐organizing maps (SOMs)—a type of simple artificial neural network—to represent multivariate cognitive training data, and then tested whether the way tasks are represented changed as a result of training. The patterns of change we observed in the SOM weight matrices implied that the processes drawn upon to perform WM tasks changed following training. This was then combined with K‐means clustering to identify distinct groups of children who respond to the training in different ways. Firstly, the K‐means clustering was applied to an independent large sample (N = 616, M_age = 9.16 years, range = 5.16–17.91 years) to identify subgroups. We then allocated children who had been through cognitive training (N = 179, M_age = 9.00 years, range = 7.08–11.50 years) to these same four subgroups, both before and after their training. In doing so, we were able to map their improvement trajectories. Scores on a separate measure of fluid intelligence were predictive of a child's improvement trajectory. This paper provides an alternative approach to analysing cognitive training data that go beyond considering changes in individual tasks. This proof‐of‐principle demonstrates a potentially powerful way of distinguishing task‐specific from domain‐general changes following training and of establishing different profiles of response to training.     

Investigating route learning,metacognition, and beacon-based strategies using virtual environments

IntroductionDominant theories of wayfinding may have underestimated the ease with which adults can learn a route, even with minimal exposure to that route.ObjectivesIn this paper we present three different experiments to provide a comprehensive picture of whether adults can learn complex routes consisting of a number of choicepoints, and if so, what strategies they might be employing to do so.Methodand results We found that adults could learn a 15-junction route after only a single experience of the route (Experiment 1) but that they underestimated how good they would be at learning the 15-junction route (Experiment 2). In Experiment 3, we found that when learning a route made up of “T” shaped junctions, participants relied on a “beacon” strategy based on visual matching.ConclusionsCollectively, these findings suggest that adults can learn complex routes, even with as many as 15 choicepoints, very quickly and without the need for repeated exposure. These findings have implications for theories of wayfinding and call into question the need for repeated exposure.     

Irrelevant‐action imitation is short‐term and contextual: Evidence from two under‐studied populations

Across the lifespan and across populations, humans ‘overimitate’ causally unnecessary behaviors. Such irrelevant‐action imitation facilitates faithful cultural transmission, but its immediate benefits to the imitator are controversial. Over short time scales, irrelevant‐action imitation may bootstrap artifact exploration or interpersonal affiliation, and over longer time scales it may facilitate acquisition of either causal models or social conventions. To investigate these putative functions, we recruited community samples from two under‐studied populations: Yasawa, Fiji, and Huatasani, Peru. We use a two‐action puzzle box: first after a video demonstration, and again one month later. Treating age as a continuous variable, we reveal divergent developmental trajectories across sites. Yasawans (44 adults, M = 39.9 years, 23 women; 42 children, M = 9.8 years, 26 girls) resemble documented patterns, with irrelevant‐action imitation increasing across childhood and plateauing in adulthood. In contrast, Huatasaneños (48 adults, M = 37.6 years, 33 women; 47 children, M = 9.3 years, 13 girls) evince a parabolic trajectory: adults at the site show the lowest irrelevant‐action imitation of any demographic set in our sample. In addition, all age sets in both populations reduce their irrelevant actions at Time 2, but do not reduce their relevant‐action imitation or goal attainment. Taken together, and considering the local cultural contexts, our results suggest that irrelevant‐action imitation serves a short‐term function and is sensitive to the social context of the demonstration.     

Statistical learning of multiple speech streams: A challenge for monolingual infants

Language acquisition depends on the ability to detect and track the distributional properties of speech. Successful acquisition also necessitates detecting changes in those properties, which can occur when the learner encounters different speakers, topics, dialects, or languages. When encountering multiple speech streams with different underlying statistics but overlapping features, how do infants keep track of the properties of each speech stream separately? In four experiments, we tested whether 8‐month‐old monolingual infants (N = 144) can track the underlying statistics of two artificial speech streams that share a portion of their syllables. We first presented each stream individually. We then presented the two speech streams in sequence, without contextual cues signaling the different speech streams, and subsequently added pitch and accent cues to help learners track each stream separately. The results reveal that monolingual infants experience difficulty tracking the statistical regularities in two speech streams presented sequentially, even when provided with contextual cues intended to facilitate separation of the speech streams. We discuss the implications of our findings for understanding how infants learn and separate the input when confronted with multiple statistical structures.     

ELM-HTM guided bio-inspired unsupervised learning for anomalous trajectory classification

Artificial intelligent systems often model the solutions of typical machine learning problems, inspired by biological processes, because of the biological system is faster and much adaptive than deep learning. The utility of bio-inspired learning methods lie in its ability to discover unknown patterns, and its less dependence on mathematical modeling or exhaustive training. In this paper, we propose a new bio-inspired learning model for a single-class classifier to detect abnormality in video object trajectories. The method uses a simple but dynamic extreme learning machine (ELM) and hierarchical temporal memory (HTM) together referred to as ELM-HTM in an unsupervised way to learn and classify time series patterns. The method has been tested on trajectory sequences in traffic surveillance to find abnormal behaviors such as high-speed, unusual stops, driving in wrong directions, loitering, etc. Experiments have also been performed with 3D air signatures captured using sensors and used for biometric authentication(forged/genuine). The results indicate a significant gain over training time and classification accuracy. The proposed method outperforms in predicting long-time patterns by observing small steps with an average accuracy gain of 15% as compared to the state-of-the-art HTM. The method has applications in detecting abnormal activities in videos by learning the movement patterns as well as in biometric authentication.     

Socially emotional brain-inspired cognitive architecture framework for artificial intelligence

Artificial Intelligence of the next generation needs to interact with users socially, convincing them in its ability to understand human minds, including emotions. For this to happen, an artificial emotional intelligence is needed, capable of adequate, believable behavior in social emotional interactions. Building on previous developments, the present work extends the general framework of emotional Biologically Inspired Cognitive Architecture (eBICA: Samsonovich, 2013, 2018), endowing it with fluents describing, in addition to appraisals, somatic markers, feelings, emotions, moods, emotional reactions and biases. Key building blocks that integrate them are moral schemas and semantic maps. The model describes interaction of three factors: plans and commitments, moral and ethical values, and somatic comfort. Learning in this framework includes self-organization of semantic maps that in turn may provide guidance for active humanlike learning. Implications for empirical studies and practical applications are discussed together with the expected impact.