Large language models surpass human experts in predicting neuroscience results.

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts.

The Dimensions of dimensionality.

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.

Disrupting dorsal hippocampus impairs category learning in rats.

Categorization requires a balance of mechanisms that can generalize across common features and discriminate against specific details. A growing literature suggests that the hippocampus may accomplish these mechanisms by using fundamental mechanisms like pattern separation, pattern completion, and memory integration. Here, we assessed the role of the rodent dorsal hippocampus (HPC) in category learning by combining inhibitory DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) and simulations using a neural network model.

Distinct hippocampal mechanisms support concept formation and updating.

Unlabelled: Learning systems must constantly decide whether to create new representations or update existing ones. For example, a child learning that a bat is a mammal and not a bird would be best served by creating a new representation, whereas updating may be best when encountering a second similar bat. Characterizing the neural dynamics that underlie these complementary memory operations requires identifying the exact moments when each operation occurs.

You can't play 20 questions with nature and win redux.

An incomplete science begets imperfect models. Nevertheless, the target article advocates for jettisoning deep-learning models with some competency in object recognition for toy models evaluated against a checklist of laboratory findings; an approach which evokes Alan Newell's 20 questions critique. We believe their approach risks incoherency and neglects the most basic test; can the model perform its intended task.

A too-good-to-be-true prior to reduce shortcut reliance.

Despite their impressive performance in object recognition and other tasks under standard testing conditions, deep networks often fail to generalize to out-of-distribution (o.o.d.

Signatures of cross-modal alignment in children's early concepts.

Whether supervised or unsupervised, human and machine learning is usually characterized as event-based. However, learning may also proceed by systems alignment in which mappings are inferred between entire systems, such as visual and linguistic systems. Systems alignment is possible because items that share similar visual contexts, such as a car and a truck, will also tend to share similar linguistic contexts.

Confound-leakage: confound removal in machine learning leads to leakage.

Background: Machine learning (ML) approaches are a crucial component of modern data analysis in many fields, including epidemiology and medicine. Nonlinear ML methods often achieve accurate predictions, for instance, in personalized medicine, as they are capable of modeling complex relationships between features and the target. Problematically, ML models and their predictions can be biased by confounding information present in the features.

Modeling Similarity and Psychological Space.

Similarity and categorization are fundamental processes in human cognition that help complex organisms make sense of the cacophony of information in their environment. These processes are critical for tasks such as recognizing objects, making decisions, and forming memories. In this review, we provide an overview of the current state of knowledge on similarity and psychological spaces, discussing the theories, methods, and empirical findings that have been generated over the years.

A multilevel account of hippocampal function in spatial and concept learning: Bridging models of behavior and neural assemblies.

A complete neuroscience requires multilevel theories that address phenomena ranging from higher-level cognitive behaviors to activities within a cell. We propose an extension to the level of mechanism approach where a computational model of cognition sits in between behavior and brain: It explains the higher-level behavior and can be decomposed into lower-level component mechanisms to provide a richer understanding of the system than any level alone. Toward this end, we decomposed a cognitive model into neuron-like units using a neural flocking approach that parallels recurrent hippocampal activity.

Dorsomedial striatum, but not dorsolateral striatum, is necessary for rat category learning.

Categorization is an adaptive cognitive function that allows us to generalize knowledge to novel situations. Converging evidence from neuropsychological, neuroimaging, and neurophysiological studies suggest that categorization is mediated by the basal ganglia; however, there is debate regarding the necessity of each subregion of the basal ganglia and their respective functions. The current experiment examined the roles of the dorsomedial striatum (DMS; homologous to the head of the caudate nucleus) and dorsolateral striatum (DLS; homologous to the body and tail of the caudate nucleus) in category learning by combining selective lesions with computational modeling.

Reassessing hierarchical correspondences between brain and deep networks through direct interface.

Functional correspondences between deep convolutional neural networks (DCNNs) and the mammalian visual system support a hierarchical account in which successive stages of processing contain ever higher-level information. However, these correspondences between brain and model activity involve shared, not task-relevant, variance. We propose a stricter account of correspondence: If a DCNN layer corresponds to a brain region, then replacing model activity with brain activity should successfully drive the DCNN's object recognition decision.

System alignment supports cross-domain learning and zero-shot generalisation.

Recent findings suggest conceptual relationships hold across modalities. For instance, if two concepts occur in similar linguistic contexts, they also likely occur in similar visual contexts. These similarity structures may provide a valuable signal for alignment when learning to map between domains, such as when learning the names of objects.

A neural network account of memory replay and knowledge consolidation.

Replay can consolidate memories through offline neural reactivation related to past experiences. Category knowledge is learned across multiple experiences, and its subsequent generalization is promoted by consolidation and replay during rest and sleep. However, aspects of replay are difficult to determine from neuroimaging studies.

Sequential consumer choice as multi-cued retrieval.

Whether adding songs to a playlist or groceries during an online shop, how do we decide what to choose next? We develop a model that predicts such open-ended, sequential choices using a process of cued retrieval from long-term memory. Using the past choice to cue subsequent retrievals, this model predicts the sequential purchases and response times of nearly 5 million grocery purchases made by more than 100,000 online shoppers. Products can be associated in different ways, such as by their episodic association or semantic overlap, and we find that consumers query multiple forms of associative knowledge when retrieving options.

When unsupervised training benefits category learning.

Humans continuously categorise inputs, but only rarely receive explicit feedback as to whether or not they are correct. This implies that they may be integrating unsupervised information together with their sparse supervised data - a form of semi-supervised learning. However, experiments testing semi-supervised learning are rare, and are bedevilled with conflicting results about whether the unsupervised information affords any benefit.

Robust priors for regularized regression.

Induction benefits from useful priors. Penalized regression approaches, like ridge regression, shrink weights toward zero but zero association is usually not a sensible prior. Inspired by simple and robust decision heuristics humans use, we constructed non-zero priors for penalized regression models that provide robust and interpretable solutions across several tasks.

The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks.

People deploy top-down, goal-directed attention to accomplish tasks, such as finding lost keys. By tuning the visual system to relevant information sources, object recognition can become more efficient (a benefit) and more biased toward the target (a potential cost). Motivated by selective attention in categorisation models, we developed a goal-directed attention mechanism that can process naturalistic (photographic) stimuli.

Prelimbic cortex maintains attention to category-relevant information and flexibly updates category representations.

Category learning groups stimuli according to similarity or function. This involves finding and attending to stimulus features that reliably inform category membership. Although many of the neural mechanisms underlying categorization remain elusive, models of human category learning posit that prefrontal cortex plays a substantial role.

Bidirectional influences of information sampling and concept learning.

Contemporary models of categorization typically tend to sidestep the problem of how information is initially encoded during decision making. Instead, a focus of this work has been to investigate how, through selective attention, stimulus representations are "contorted" such that behaviorally relevant dimensions are accentuated (or "stretched"), and the representations of irrelevant dimensions are ignored (or "compressed"). In high-dimensional real-world environments, it is computationally infeasible to sample all available information, and human decision makers selectively sample information from sources expected to provide relevant information.

Similarity as a Window on the Dimensions of Object Representation.

Hebart et al. recently analysed 1.5 million human similarity judgments and found that natural objects are described by a small set of interpretable dimensions.

Abstract Neural Representations of Category Membership beyond Information Coding Stimulus or Response.

For decades, researchers have debated whether mental representations are symbolic or grounded in sensory inputs and motor programs. Certainly, aspects of mental representations are grounded. However, does the brain also contain abstract concept representations that mediate between perception and action in a flexible manner not tied to the details of sensory inputs and motor programs? Such conceptual pointers would be useful when concepts remain constant despite changes in appearance and associated actions.