Trends in Cognitive Sciences

Young children's screen time is increasing, raising concerns about its negative impact on language development, particularly vocabulary. However, digital media is used in a variety of ways, which likely differentially impact language development. Instead of asking 'how much' screen time, the focus should be on how digital media is used.

The study of human working memory (WM) holds significant importance in neuroscience; yet, exploring the role of the medial temporal lobe (MTL) in WM has been limited by the technological constraints of noninvasive methods. Recent advancements in human intracranial neural recordings have indicated the involvement of the MTL in WM processes. These recordings show that different regions of the MTL are involved in distinct aspects of WM processing and also dynamically interact with each other and the broader brain network.

Memories are sometimes best forgotten, but how do our brains weaken unwanted details of the past? We propose a theoretical framework in which memory reactivation during sleep supports adaptive forgetting. This mnemonic rebalancing underpins the affective benefits of sleep by ensuring that our memories remain aligned with our emotional goals.

The standard explanation for bilingual effects on cognition is that an aspect of language processing transfers to nonverbal cognitive performance, leading to improvements in executive functioning. However, much evidence is incompatible with that view, and transfer across those domains seems unlikely. The present argument is that bilingual experience modifies cognition through an adaptation to the underlying attention system, making attention more efficient. 'Transfer' focuses on the overlap of specific processes, so task similarity predicts outcomes.

Cognitive scientists often infer multidimensional representations from data. Whether the data involve text, neuroimaging, neural networks, or human judgments, researchers frequently infer and analyze latent representational spaces (i.e., embeddings). However, the properties of a latent representation (e.g., prediction performance, interpretability, compactness) depend on the inference procedure, which can vary widely across endeavors. For example, dimensions are not always globally interpretable and the dimensionality of different embeddings may not be readily comparable.

A robust association exists between math anxiety and math achievement, with higher levels of anxiety correlating with lower achievement. Understanding this relationship is crucial due to the importance of math proficiency at individual and societal levels. In this review, we explore two prominent theories: Reduced Competency Theory, which suggests that initial low math achievement leads to math anxiety, and Processing Efficiency Theory, which suggests that math anxiety impairs performance by diverting cognitive resources.

Decision making is often necessary before performing an action. Traditionally, it has been assumed that decision making and motor control are independent, sequential processes. Ogasa et al. challenge this view, and demonstrate that the decision-making process significantly impacts on the formation and retrieval of motor memory by tagging it with the level of confidence.

Amir Hosein Hadian Rasanan, Nathan J Evans, Laura Fontanesi, Catherine Manning, Cynthia Huang-Pollock, Dora Matzke, Andrew Heathcote, Jörg Rieskamp, Maarten Speekenbrink, Michael J Frank, Stefano Palminteri, Christopher G Lucas, Jerome R Busemeyer, Roger Ratcliff, Jamal Amani Rad

While decision theories have evolved over the past five decades, their focus has largely been on choices among a limited number of discrete options, even though many real-world situations have a continuous-option space. Recently, theories have attempted to address decisions with continuous-option spaces, and several computational models have been proposed within the sequential sampling framework to explain how we make a decision in continuous-option space. This article aims to review the main attempts to understand decisions on continuous-option spaces, give an overview of applications of these types of decisions, and present puzzles to be addressed by future developments.

Cognitive economics is an emerging interdisciplinary field that uses the tools of cognitive science to study economic and social decision-making. Although most strains of cognitive economics share commitments to bridging levels of analysis (cognitive, behavioral, and systems) and embracing interdisciplinary approaches, we review a newer strand of cognitive economic thinking with a further commitment: conceptualizing minds and markets each as complex adaptive systems. We describe three ongoing research programs that strive toward these goals: (i) studying narratives as a cognitive and social representation used to guide decision-making; (ii) building cognitively informed agent-based models; and (iii) understanding markets as an extended mind - the Market Mind Hypothesis - analyzed using the concepts, methods, and tools of Coordination Dynamics.

Tim Bayne, Anil Seth, Marcello Massimini, Joshua Shepherd, Axel Cleeremans, Stephen M Fleming, Rafael Malach, Jason B Mattingley, David K Menon, Adrian M Owen, Megan A K Peters, Adeel Razi, Liad Mudrik

Steven T Piantadosi, Dyana C Y Muller, Joshua S Rule, Karthikeya Kaushik, Mark Gorenstein, Elena R Leib, Emily Sanford

For decades, cognitive scientists have debated what kind of representation might characterize human concepts. Whatever the format of the representation, it must allow for the computation of varied properties, including similarities, features, categories, definitions, and relations. It must also support the development of theories, ad hoc categories, and knowledge of procedures. Here, we discuss why vector-based representations provide a compelling account that can meet all these needs while being plausibly encoded into neural architectures.