Emotional facial expressions affect visual rule learning in 7- to 8-month-old infants

Rule learning (RL) is an implicit learning mechanism that allows infants to detect and generalize rule-like repetition-based patterns (such as ABB and ABA) from a sequence of elements. Increasing evidence shows that RL operates both in the auditory and the visual domain and is modulated by the perceptual expertise with the to-be-learned stimuli. Yet, whether infants’ ability to detect a high-order rule from a sequence of stimuli is affected by affective information remains a largely unexplored issue. Using a visual habituation paradigm, we investigated whether the presence of emotional expressions with a positive and a negative value (i.e., happiness and anger) modulates 7- to 8-month-old infants’ ability to learn a rule-like pattern from a sequence of faces of different identities. Results demonstrate that emotional facial expressions (either positive and negative) modulate infants’ visual RL mechanism, even though positive and negative facial expressions affect infants’ RL in a different manner: while anger disrupts infants’ ability to learn the rule-like pattern from a face sequence, in the presence of a happy face infants show a familiarity preference, thus maintaining their learning ability. These findings show that emotional expressions exert an influence on infants’ RL abilities, contributing to the investigation on how emotion and cognition interact in face processing during infancy.     

Modeling the Influence of Language Input Statistics on Children's Speech Production

We trained a computational model (the Chunk-Based Learner; CBL) on a longitudinal corpus of child–caregiver interactions in English to test whether one proposed statistical learning mechanism—backward transitional probability—is able to predict children's speech productions with stable accuracy throughout the first few years of development. We predicted that the model less accurately reconstructs children's speech productions as they grow older because children gradually begin to generate speech using abstracted forms rather than specific “chunks” from their speech environment. To test this idea, we trained the model on both recently encountered and cumulative speech input from a longitudinal child language corpus. We then assessed whether the model could accurately reconstruct children's speech. Controlling for utterance length and the presence of duplicate chunks, we found no evidence that the CBL becomes less accurate in its ability to reconstruct children's speech with age.     

Examples,practice problems,or both? Effects on motivation and learning in shorter and longer sequences

Research suggests some sequences of examples and problems (i.e., EE, EP) are more effective (higher test performance) and efficient (attained with equal/less mental effort) than others (PP, sometimes also PE). Recent findings suggest this is due to motivational variables (i.e., self-efficacy), but did not test this during the training phase. Moreover, prior research used only short task sequences. Therefore, we investigated effects on motivational variables, effectiveness, and efficiency in a short (Experiment 1; four learning tasks; n = 157) and longer task sequence (Experiment 2; eight learning tasks; n = 105). With short sequences, all example conditions were more effective, efficient, and motivating than PP. With longer sequences, all example conditions were more motivating and efficient than PP, but only EE was more effective than PP. Moreover, EE was most efficient during training, regardless of sequence length. These results suggest that example study (only) is more effective, efficient, and more motivating than PP.     

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     

Dormio: A targeted dream incubation device

Information processing during sleep is active, ongoing and accessible to engineering. Protocols such as targeted memory reactivation use sensory stimuli during sleep to reactivate memories and demonstrate subsequent, specific enhancement of their consolidation. These protocols rely on physiological, as opposed to phenomenological, evidence of their reactivation. While dream content can predict post-sleep memory enhancement, dreaming itself remains a black box. Here, we present a novel protocol using a new wearable electronic device, Dormio, to automatically generate serial auditory dream incubations at sleep onset, wherein targeted information is repeatedly presented during the hypnagogic period, enabling direct incorporation of this information into dream content, a process we call targeted dream incubation (TDI). Along with validation data, we discuss how Dormio and TDI protocols can serve as tools for controlled experimentation on dream content, shedding light on the role of dreams in the overnight transformation of experiences into memories.     

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.