Spatial navigation: Cognitive variables involved in route retracing among an elderly population

The cognitive processes involved in route retracing are not well known. This study aims to highlight them in an elderly population in which contradictory results have been obtained, certain studies showing specific difficulties for route retracing, others not. Thirty-nine elderly subjects performed a route-learning task (forward-backward) in a garden, then completed spatial knowledge tasks and standardised cognitive tests. Results show four factors that were predictive of route retracing performance: route repetition, the pointing task, and two standardised cognitive tests, one assessing spatial working memory, and another global cognitive efficiency. According to these results, route retracing involves route and survey knowledge (i.e., egocentric and allocentric strategy), and the integration of forward-backward perspectives is underpinned by the spatial working memory. Moreover, the subjects did not make more errors in route retracing than in the route repetition task, suggesting that a real environment could compensate for a failing allocentric strategy.     

Thinking about the past,present, and future: Time perspective and self-esteem in adolescents,young adults,middle-aged adults,and older adults

We examined time perspective and self-esteem in adolescents, young adults, middle-aged adults, and older adults. Time perspective was measured with scales that assess relative orientations and relationships among the past, present, and future. Age effects were examined with standard analytic strategies to determine categorical differences between age groups and with new statistical techniques designed to show continuous age patterns. Findings indicated that (1) thinking about the future was greatest for adolescents and young adults and lowest for middle-aged and older adults, and thinking about the present increased across ages; (2) fewer adolescents and middle-aged participants perceived that the time periods were interrelated compared to younger and older adults; and (3) across ages, a greater emphasis towards the past compared to other time periods was associated with lower self-esteem, whereas emphasizing the present and the future jointly was associated with higher self-esteem.     

Reproducibility and a unifying explanation: Lessons from the shape bias

The goal of science is to advance our understanding of particular phenomena. However, in the field of development, the phenomena of interest are complex, multifaceted, and change over time. Here, we use three decades of research on the shape bias to argue that while replication is clearly an important part of the scientific process, integration across the findings of many studies that include variations in procedure is also critical to create a coherent understanding of the thoughts and behaviors of young children. The “shape bias,” or the tendency to generalize a novel label to novel objects of the same shape, is a reliable and robust behavioral finding and has been shown to predict future vocabulary growth and possible language disorders. Despite the robustness of the phenomenon, the way in which the shape bias is defined and tested has varied across studies and laboratories. The current review argues that differences in performance that come from even seemingly minor changes to the participants or task can offer critical insight to underlying mechanisms, and that working to incorporate data from multiple labs is an important way to reveal how task variation and a child’s individual pathway creates behavior—a key issue for understanding developmental phenomena.     

What Determines Visual Statistical Learning Performance? Insights From Information Theory

In order to extract the regularities underlying a continuous sensory input, the individual elements constituting the stream have to be encoded and their transitional probabilities (TPs) should be learned. This suggests that variance in statistical learning (SL) performance reflects efficiency in encoding representations as well as efficiency in detecting their statistical properties. These processes have been taken to be independent and temporally modular, where first, elements in the stream are encoded into internal representations, and then the co‐occurrences between them are computed and registered. Here, we entertain a novel hypothesis that one unifying construct—the rate of information in the sensory input—explains learning performance. This theoretical approach merges processes related to encoding of events and those related to learning their regularities into a single computational principle. We present data from two large‐scale experiments with over 800 participants tested in support for this hypothesis, showing that rate of information in a visual stream clearly predicts SL performance, and that similar rate of information values leads to similar SL performance. We discuss the implications for SL theory and its relation to regularity learning.     

Learning How to Generalize

Generalization is a fundamental problem solved by every cognitive system in essentially every domain. Although it is known that how people generalize varies in complex ways depending on the context or domain, it is an open question how people learn the appropriate way to generalize for a new context. To understand this capability, we cast the problem of learning how to generalize as a problem of learning the appropriate hypothesis space for generalization. We propose a normative mathematical framework for learning how to generalize by learning inductive biases for which properties are relevant for generalization in a domain from the statistical structure of features and concepts observed in that domain. More formally, the framework predicts that an ideal learner should learn to generalize by either taking the weighted average of the results of generalizing according to each hypothesis space, with weights given by how well each hypothesis space fits the previously observed concepts, or by using the most likely hypothesis space. We compare the predictions of this framework to human generalization behavior with three experiments in one perceptual (rectangles) and two conceptual (animals and numbers) domains. Across all three studies we find support for the framework's predictions, including individual‐level support for averaging in the third study.     

Do Additional Features Help or Hurt Category Learning? The Curse of Dimensionality in Human Learners

The curse of dimensionality, which has been widely studied in statistics and machine learning, occurs when additional features cause the size of the feature space to grow so quickly that learning classification rules becomes increasingly difficult. How do people overcome the curse of dimensionality when acquiring real‐world categories that have many different features? Here we investigate the possibility that the structure of categories can help. We show that when categories follow a family resemblance structure, people are unaffected by the presence of additional features in learning. However, when categories are based on a single feature, they fall prey to the curse, and having additional irrelevant features hurts performance. We compare and contrast these results to three different computational models to show that a model with limited computational capacity best captures human performance across almost all of the conditions in both experiments.