Recent uci_cogsci_oapdeposits items

3.12 Metacognition and consciousness

Fri, 6 Nov 2026 00:00:00 +0000

Metacognition – the capacity to monitor and regulate one's own cognitive states – is often treated as a self-evaluation process that runs alongside conscious experience. This chapter argues for a stronger claim: that hierarchical uncertainty monitoring and the metacognitive construction of higher-order representations is partly causal in creating conscious experience, not merely correlated with it. The chapter establishes how metacognition may be implicit or explicit, traces the history of its measurement, discusses how it empirically dissociates from general task performance capacity, and briefly discusses potential functions of metacognition: consciousness, reality-monitoring, learning, and information-seeking. The chapter concludes by applying the discussed framework to artificial intelligence.

Twelve quick tips for AI-assisted coding in science

Mon, 2 Nov 2026 00:00:00 +0000

While AI coding tools have demonstrated potential to accelerate software development, their use in scientific computing raises critical questions about code quality and scientific validity. In this paper, we provide twelve practical tips for AI-assisted coding that balance the capabilities of AI with the demands of scientific and methodological rigor. We address how AI can be leveraged strategically throughout the development cycle with four key themes: problem preparation and understanding, managing context and interaction, testing and validation, and code quality assurance and iterative improvement. These principles serve to emphasize maintaining human agency in coding decisions, establishing robust validation procedures, and preserving the domain expertise essential for methodologically sound research. These tips are intended to help researchers harness AI's transformative potential for faster software development while ensuring that their code meets the standards of reliability,...

A Reconstruct-then-Bootstrap Test for the Sufficiency of Diffusion Processes

Mon, 2 Nov 2026 00:00:00 +0000

Diffusion processes are commonly used in the modeling of time series that exhibit stationarity and Markovianity. However, those two properties do not guarantee that a diffusive process is sufficient for the time series. In this paper, we develop a test for the sufficiency of a diffusion process for an observed time series. To develop the test we capitalize on the Kramers–Moyal (KM) expansion: a Taylor expansion of the integral form of the master equation that describes Markov continuous-time processes. In the idealized case, if the observed data indeed arise from a true diffusion process, then the KM expansion should truncate naturally after the second term. In theory, this means that any higher-order ($$\varvec{\ge } \varvec{3}$$) KM coefficients should be zero. However, in practice, the discrete nature of measurement introduces artificial higher-order KM coefficients, even when the underlying process is truly diffusive. Nonetheless, for genuinely diffusive systems, it is expected...

Robust Bayesian hypothesis testing with the hierarchical EZ-DDM

Mon, 2 Nov 2026 00:00:00 +0000

The EZ-diffusion model (EZ-DDM) uses a method of moments to provide closed-form estimators for the three-parameter drift-diffusion model from summary statistics. In previous work, we showed that using the sampling distributions of these statistics enables the implementation of hierarchical EZ-DDM extensions, supporting scalable Bayesian inference in cognitive psychometrics applications. However, the summary statistics used in EZ-DDM implementations (the mean and variance of the response time distribution) are sensitive to contaminant data points, limiting its utility in real-world applications. To address this, we propose a variation on the EZ-DDM implementation in which the summary statistics are replaced with robust alternatives, substituting mean RT with median RT and RT variance with an estimate derived from the interquartile range. We explore and evaluate the effectiveness of this substitution through simulation studies using a within-subject t test design across varying...

Comment on Shiffrin et al. (2026): Illusions of Replication, Illusions of Truth

Mon, 2 Nov 2026 00:00:00 +0000

Shiffrin et al. argue that scientific practice often produces illusions of understanding; situations in which familiar inferential tools generate misplaced confidence. We describe a related illusion that arises not from statistical misapplications, but from ordinary theory testing itself. Here we highlight a different source of epistemic uncertainty or overconfidence; one that emerges even when statistical tools are used correctly, effect sizes are precisely estimated, findings replicate without controversy, and heterogeneity is explicitly acknowledged within hierarchical models.

Epistemic Disadvantage in Open Science Practices

Mon, 2 Nov 2026 00:00:00 +0000

Open science advocates aim to address epistemic injustice and global power asymmetries in research infrastructures yet underestimate structural vulnerabilities and risks embedded in data sharing practices. Drawing on an ethnographic study of the GCRF South–South Migration Inequality and Development Hub’s (MIDEQ), we argue that even well-intentioned systems, such as the MIDEQ hub, do not reach these vulnerabilities. To capture these overlooked harms, we employ the concept of epistemic disadvantage, a category coextensive with, but separate from epistemic injustice. Epistemic disadvantage includes ethically justified data withholding and misappropriation within consensual systems, which is not captured by existing notions of extractivism as those focus on the monetisation of epistemic resources without regard for the loss incurred by the originating community. To better account for these harms, we draw on two recent epistemic attitudes: the Book of Truths (BoT), which sees scientific...

Developmental changes in memory structure and precision alter the use of retrieved episodes during decisions for reward

Wed, 7 Oct 2026 00:00:00 +0000

Most widely studied option evaluation strategies rely on knowledge accumulated across repeated experiences. But how should options be evaluated in unfamiliar environments, in which knowledge is sparse? In these situations, how do decision makers make efficient use of limited past experience to guide their choices? One possible strategy is episodic sampling, in which a decision maker retrieves a small number of past decisions from memory to estimate the value of present options. By virtue of their age, children and adolescents have less experience than adults, making episodic sampling a particularly useful strategy for them. At the same time, the effectiveness of episodic sampling derives from memory's precision and context sensitivity-properties that continue to develop into adolescence and young adulthood. This tension raises a key question: are developmental differences in episodic memory associated with differences in episodic sampling? To address this question, 106 participants,...

Multistep inference across the human lifespan can be improved with individualized memory interventions

Wed, 7 Oct 2026 00:00:00 +0000

OBJECTIVES: Effective goal-directed decision making relies on memory and planning-processes that are known to decline with age. We tested the hypothesis that these declines stem from a common mechanism by focusing on mnemonic discrimination, a measure of memory precision that shows unique vulnerability to age-related decline. METHODS: We used a latent learning task that measures the ability to learn and make judgments about associations among interconnected stimuli, assessing performance across the adult life span. This task allows us to measure multistep inference judgments that reflect how individuals organize relational structure, previously shown to capture the internal model-construction processes that support model-based planning. In Experiment 1, we examined relationships between judgment performance and memory precision. In Experiment 2, we tested whether a "blocked" learning schedule designed to reduce memory interference by separating overlapping objects could improve...

Discrete Autoregressive Switching Processes with Cumulative Shrinkage Priors for Graphical Modeling of Time Series Data

Wed, 7 Oct 2026 00:00:00 +0000

We propose a flexible Bayesian approach for sparse Gaussian graphical modeling of multivariate time series. We account for temporal correlation in the data by assuming that observations are characterized by an underlying and unobserved hidden discrete autoregressive process. We assume multivariate Gaussian emission distributions and capture spatial dependencies by modeling the state-specific precision matrices via graphical horseshoe priors. We characterize the mixing probabilities of the hidden process via a cumulative shrinkage prior that accommodates zero-inflated parameters for non-active components, and further incorporate a sparsity-inducing Dirichlet prior to estimate the effective number of states from the data. For posterior inference, we develop a sampling procedure that allows estimation of the number of discrete autoregressive lags and the number of states, and that cleverly avoids having to deal with the changing dimensions of the parameter space. We thoroughly investigate...

Structure Inference in Complex Environments Improves From Childhood to Adulthood

Wed, 7 Oct 2026 00:00:00 +0000

Early in development, children can infer latent structure in the world from sparse and ambiguous evidence. Through a process known as structure learning, they extract statistical regularities, construct causal models from those regularities, and use those models to arbitrate between exploiting known options and exploring novel alternatives. In turn, each decision and its outcomes refine the model that produced them. Despite the clear reciprocal relationship between structure learning and decision-making in the real world, developmental research has largely examined these processes separately. To address this gap, we compared how children, adolescents, and adults behaved in a patch-foraging task designed to reveal how structure learning shapes exploratory decisions in a richly structured, dynamic environment. We found that younger participants left patches sooner than adults, enabling them to explore the environment more broadly within the fixed time window of the study. Computational...

Sparsity and memory constraints interact with training sequence to bias learning of associative maps

Wed, 7 Oct 2026 00:00:00 +0000

Cognitive maps support inference and planning by representing associations between experiences encoded in memory. These map-like representations are thought to carry information not only about directly observed links but also about longer paths. The ability to make judgments based on multi-step associations varies with one's experience in an environment and with changes in memory abilities across the lifespan. However, it remains unclear exactly how representations of associative structure are influenced by learning curricula and memory constraints. Prior studies have suggested a tradeoff: memory representations can either be more integrated to improve inference, or more separated to recall distinct direct associations. Whether overlapping associations are experienced nearby in time (interleaved) or spaced apart (blocked) can bias memory representations toward integration or separation. However, key recent findings about how blocked versus interleaved experience bias integration...

Blind ignorance or rational inattention? On the history and future of metabolic considerations for cognitive models.

Wed, 7 Oct 2026 00:00:00 +0000

We build upon Haueis and Colaço's account in two ways. First, we argue that the backgrounding of metabolism was a pragmatic response to a data granularity problem faced by traditional cognitive modelers. Then, we suggest that equivalence demonstrations offer a promising pathway for incrementally introducing metabolic considerations into extant modeling practice.

Shared memories of event details in the human brain are altered by misinformation and test expectations

Wed, 12 Aug 2026 00:00:00 +0000

Shared memories of event details are crucial to eyewitness testimony. When different people encode or recall the same event, similar scene-specific neural activity patterns emerge across individual brains. However, it remains unclear whether these patterns are specific to event details and how test expectancy (i.e., expecting free recall or general memory tests) and misinformation affect them. In this study, 100 participants were randomly assigned to view one of two versions of each event. Both versions featured identical scenarios, but with different details. About half of the participants were informed about the upcoming free recall before viewing events, while the others were told to expect a general memory test. Functional magnetic resonance imaging was used to record their brain activity during four stages: viewing original events, initial free recall, reading misinformation, and final free recall of original events. The neuroimaging data were analyzed based on the similarity...

Conspiracy thinking in American politics.

Wed, 12 Aug 2026 00:00:00 +0000

Conspiratorial thinking is an indelible part of American politics; indeed, conspiracy theories proliferated in North America even before the founding of the United States. A current headwind of trends appears to facilitate a surge in conspiratorial thinking, including the increased spread and accessibility of misinformation, steady declines in public trust in authority figures, and an increasingly polarized electorate marked by mutual partisan animosity. The annual symposium of the UC Irvine Center for Neuropolitics brought together experts in law, political science, neuroscience, philosophy, and psychology to discuss why and how conspiracy thought develops and persists. This paper synthesizes the insights from that symposium, addressing the foundations of conspiracy thinking in both individuals and society as a whole, and its place in the current American political landscape. Through integrating various disciplinary perspectives, the symposium aimed to identify possible pathways...

Reward and Imitation in Social Conformity

Wed, 12 Aug 2026 00:00:00 +0000

Purpose

Normative social conformity has been proposed to elicit a hedonic reward signal that is dissociable from informational inferences about decision outcomes. If present, such a signal should reinforce not just the decision that preceded it but also incidental, contextual, and stimulus features. Here, we pit this account against a non-hedonic imitation algorithm.

Method

Across two studies (n = 359), we used a nondeceptive three-participant gambling task in which trial-by-trial information was provided about the selections and monetary payoffs of two other participants facing the same, recurring, options in real time. We use computational cognitive modeling to assess the relative contributions of monetary reward, social reward, and non-hedonic imitation to norm alignment.

Findings

Consistent with both social reward and imitation accounts, but contrary to mere monetary maximization, the probability of staying with a losing option increased with...

Physiological ripples during sleep in scalp electroencephalogram of healthy infants

Wed, 15 Jul 2026 00:00:00 +0000

Physiological ripples during sleep in scalp electroencephalogram of healthy infants

Context Memory Discrimination and Recognition in Humans: Development of a Novel Cognitive Task.

Mon, 29 Jun 2026 00:00:00 +0000

Background

When confronted with ambiguous stimuli, the ability to utilize contextual information is crucial for survival. Context processing involves the ability to discriminate new information from previously encountered information and to recognize something as previously encountered, even briefly or partially. Deficits in context processing are a key feature across a number of psychiatric conditions. Existing tasks only examine the discrimination and recognition of cues as opposed to contextual information. Thus, new tasks using complex scenes are urgently needed.

Objective

We developed the Context Discrimination and Recognition Task (CDRT) and established baseline performance in healthy adults.

Methods

Final analyses included 44 participants (mean age 30.27, SD 11.78) who completed the CDRT to characterize memory performance for (1) discrimination between previously viewed and never viewed complex scenes (Discrete Discrimination Score [DDS]), (2) recognition...

Locus Coeruleus Engagement Drives Network Connectivity Dynamics In Humans And Rats

Thu, 11 Jun 2026 00:00:00 +0000

Locus Coeruleus Engagement Drives Network Connectivity Dynamics In Humans And Rats

Evaluating synthesized speech intelligibility in noise

Fri, 24 Apr 2026 00:00:00 +0000

Humans can modify their speech to improve intelligibility in noisy environments. With the advancement of speech synthesis technology, machines may also synthesize voices that remain highly intelligible in noise condition. This study evaluates both the subjective and objective intelligibility of synthesized speech in speech-shaped noise from three major speech synthesis platforms. It was found that synthesized voices have a similar intelligibility range to human voices, and some synthesized voices were more intelligible than human voices. It was also found that two modern automatic speech recognition systems recognized 10% more words than human listeners.

How much we express love predicts how much we feel loved in daily life

Wed, 25 Mar 2026 00:00:00 +0000

Feeling and expressing love in daily life are interconnected and perhaps mutually influential experiences. In this study we examined the reciprocal dynamics of feeling and expressing love and its relation to well-being using an ecological momentary assessment design. Over a four-week period, we asked participants (N = 52; 67% Female; 80% White) to report their levels of feeling loved and expressing love six times a day. Using a continuous-time process model, we explored individual differences in inertia (i.e., persistence of a process over time) and cross-influences of felt and expressed love over time. We found that increases in expressing love led to increased feelings of being loved over time; however, increases in felt love did not lead to increases in expressing love. Notably, participants who experienced more persistent feelings of love (that is, greater inertia over time) indicated higher levels of flourishing. These results suggest new avenues for psychological well-being...

An Expert Guide to Planning Experimental Tasks For Evidence-Accumulation Modeling

Wed, 25 Mar 2026 00:00:00 +0000

Evidence-accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behavior. EAMs have generated significant theoretical advances in psychology, behavioral economics, and cognitive neuroscience and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues and inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, relating experimental manipulations to EAM parameters, planning appropriate sample sizes, and preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the...

Optimizing the Color Shapes Task for Ambulatory Assessment and Drift Diffusion Modeling: A Factorial Experiment

Wed, 25 Mar 2026 00:00:00 +0000

BACKGROUND: Recent advances in cognitive digital assessment methodology, including high-frequency, ambulatory assessments, promise to improve the detection of subtle cognitive changes. Computational modeling approaches may further improve the sensitivity of digital cognitive assessments to detect subtle cognitive changes by capturing features that map onto core cognitive processes. OBJECTIVE: We explored the validity of a brief smartphone-based adaptation of a visual working memory task that has shown sensitivity for detecting preclinical Alzheimer disease risk. We aimed to optimize properties of the task for computational cognitive feature extraction with drift diffusion modeling. METHODS: We analyzed data from 68 participants (n=47, 69% women; n=55, 81% White; mean age 49, SD 14; range 24-80 years) who completed 60 trials for each of 16 variations of a visual working memory binding task (the Color Shapes task) on smartphones, over an 8-day period. A drift diffusion model was...

Partially Observable Predictor Models for Identifying Cognitive Markers

Wed, 25 Mar 2026 00:00:00 +0000

Repeated assessments of cognitive performance yield rich data from which we can extract markers of cognitive performance. Computational cognitive process models are often fit to repeated cognitive assessments to quantify individual differences in terms of substantively meaningful cognitive markers and link them to other person-level variables. Most studies stop at this point and do not test whether these cognitive markers have utility for predicting some meaningful outcomes. Here, we demonstrate a partially observable predictor modeling approach that can fill this gap. Using this approach, we can simultaneously extract cognitive markers from repeated assessment data and use these together with demographic covariates for predictive modeling of a clinically interesting outcome in a Bayesian multilevel modeling framework. We describe this approach by constructing a predictive process model in which features of learning are combined with demographic variables to predict mild cognitive...

An EZ Bayesian hierarchical drift diffusion model for response time and accuracy

Wed, 25 Mar 2026 00:00:00 +0000

The EZ-diffusion model is a simplification of the popular drift diffusion model of choice response times that allows researchers to calculate diffusion model parameters directly from data with no need for expensive computations. The EZ-diffusion model is based on a system of equations in which the diffusion model’s drift rate, boundary separation, and nondecision time parameters are jointly used to predict three summary statistics (the accuracy rate and the mean and variance of the correct response times). These equations can then be inverted to obtain estimators for the three parameters from these summary statistics. Here, we describe a probabilistic formulation of the EZ-diffusion model that can serve as a hyper-efficient proxy model to the drift diffusion model. The new formulation is based on sampling distributions of summary statistics and consists only of normal and binomial distributions. It can easily be implemented in any probabilistic programming language. We demonstrate...

Current status of mathematical foundations of the Gestalt concepts in psychophysics.

Fri, 6 Mar 2026 00:00:00 +0000

Eileen Kowler was a member of the team of cognitive scientists that was assembled over a half a century ago by Robert M. Steinman. This team had grown and spanned several institutions in this country, as well as in Europe. The mechanisms governing the movements of the eyes were always at the center of Eileens attention, but her research, as well as the research of our interdisciplinary group, is best understood by realizing its roots in Gestalt psychology. In the first part, this paper describes perspectives of two of the authors from the point of view of Eileen Kowlers collaborators. The second part provides a new look at the origins of Gestalt psychology, pointing out the scientific context of what has been referred to as the Gestalt revolution. The Gestalt revolution closely followed similar revolutions in mathematics and in physics, and it gave rise to new questions and challenges that have received recognition only recently.

Harmonized Protocol for Subfield Segmentation in the Hippocampal Body on High‐Resolution In Vivo MRI From the Hippocampal Subfields Group (HSG)

Thu, 26 Feb 2026 00:00:00 +0000

Hippocampal subfields differentially develop and age, and they vary in vulnerability to neurodegenerative diseases. Innovation in high-resolution imaging has accelerated clinical research on human hippocampal subfields, but substantial differences in segmentation protocols impede comparisons of results across laboratories. The Hippocampal Subfields Group (HSG) is an international organization seeking to address this issue by developing a histologically valid, reliable, and freely available segmentation protocol for high-resolution T₂-weighted 3 T MRI (http://www.hippocampalsubfields.com). Here, we report the first portion of the protocol focused on subfields in the hippocampal body; protocols for the head and tail are in development. The body protocol includes definitions of the internal boundaries between subiculum, Cornu Ammonis (CA) 1-3 subfields, and dentate gyrus, in addition to the external boundaries of the hippocampus apart from surrounding white matter and...

Episodic-like memory in a simulation of cuttlefish behavior.

Sat, 31 Jan 2026 00:00:00 +0000

Episodic memory involves remembering the what, when, and where components of an event. It has been observed in humans, other vertebrates, and the invertebrate cuttlefish. In clever behavioral experiments, cuttlefish have been shown to have episodic-like memory, where they demonstrate the ability to remember when and where a preferred food source will appear. The present work replicates this behavior with a parsimonious model of episodic memory. To further test this model and explore episodic-like memory, we introduce a predator-prey scenario in which the agent must remember what creatures (e.g. predator, desirable prey, or less desirable prey) appear at a given time and region of the model environment. This simulates similar situations that cuttlefish face in the wild. They will typically hide when predators are in the area, and hunt for prey when available. When the memory model is queried for an action (e.g., hunt or hide), the cuttlefish agent hunts for preferred food, like...

Identifying indicators of consciousness in AI systems

Thu, 15 Jan 2026 00:00:00 +0000

Rapid progress in artificial intelligence (AI) capabilities has drawn fresh attention to the prospect of consciousness in AI. There is an urgent need for rigorous methods to assess AI systems for consciousness, but significant uncertainty about relevant issues in consciousness science. We present a method for assessing AI systems for consciousness that involves exploring what follows from existing or future neuroscientific theories of consciousness. Indicators derived from such theories can be used to inform credences about whether particular AI systems are conscious. This method allows us to make meaningful progress because some influential theories of consciousness, notably including computational functionalist theories, have implications for AI that can be investigated empirically.

The visual system of the longest-living vertebrate, the Greenland shark

Wed, 14 Jan 2026 00:00:00 +0000

The Greenland shark (Somniosus microcephalus) is the longest-living vertebrate and inhabits the exceptionally dim and cold waters of the Arctic deep sea. Due to its extreme lifespan, harsh environmental conditions, and prevalent corneal parasitisation, the Greenland shark has previously been thought to have impaired or degenerated vision. Here, we present genomic, transcriptomic, histological and functional evidence that the Greenland shark retains an intact visual system well-adapted for life in dim light. Histology and in vitro opsin expression revealed visual adaptations typical of deep-sea species, including densely packed, elongated rods and a short-wavelength shift in rod visual pigment sensitivity compared to shallow-water sharks. In situ hybridisation confirmed the presence of essential visual cell types: rods, Müller glia, and bipolar, amacrine, and ganglion cells. Moreover, despite being over a century old, the examined specimens showed no obvious signs of retinal degeneration....

The history of an idea: The misinformation effect

Wed, 14 Jan 2026 00:00:00 +0000

Abstract When people are exposed to misleading information after an event has occurred, they frequently fall sway to its influence and report the misinformation as their own memory. This phenomenon, known as The Misinformation Effect, has been intensively studied for half a century. Here, we report on where the idea came from and what aspects of the phenomenon have been explored by scientists over this period of time. These explorations have addressed many questions, such as (1) What are the conditions under which people are more or less susceptible to misinformation? (2) Are there certain types of people who are especially susceptible to misinformation? (3) Can warnings about misinformation reduce its influence? and (4) Just how far can you go in terms of planting false ideas into people's minds? We also review work that examines the interplay between modern technology and misinformation. The Misinformation Effect teaches us about the malleability of memory, but it also has important...

Corrigendum to “Do total hippocampus and hippocampal subfield volumes relate to navigation ability? A call towards methodological consistency” [Cortex 181 (2024) 233–257]

Thu, 4 Dec 2025 00:00:00 +0000

Corrigendum to “Do total hippocampus and hippocampal subfield volumes relate to navigation ability? A call towards methodological consistency” [Cortex 181 (2024) 233–257]

Do total hippocampus and hippocampal subfield volumes relate to navigation ability? A call towards methodological consistency

Thu, 4 Dec 2025 00:00:00 +0000

Despite the need for successful navigation, humans vary greatly in their ability to navigate, and these individual differences may relate to variation in brain structure. While prior research provides support for a correlation between hippocampal volume and navigation ability in both navigation experts and in older individuals, this relationship is under scrutiny for healthy, young adults. We assessed 99 healthy young adults' ability to navigate in a virtual, desktop maze and correlated their performance with total hippocampal gray matter volume. For a subset of these individuals, we further segmented the medial temporal lobe-including regions of the hippocampus-into anatomically-distinct subregions to uniquely examine the association between volumes of hippocampal subfields and navigation. Given the need to distinguish between similar-looking maze hallways and partially overlapping routes, young adults with stronger pattern separation ability may perform better in this task....

Late‐life emergence of neuropsychiatric symptoms and risk of cognitive impairment in cognitively unimpaired individuals

Thu, 2 Oct 2025 00:00:00 +0000

INTRODUCTION: Neuropsychiatric symptoms (NPS) often precede cognitive impairment. We investigated the longitudinal relationship between emergent, significant NPS and cognitive decline in cognitively unimpaired older adults. We also assessed the combined effect of NPS and Alzheimer's disease (AD) biomarkers on subsequent cognitive impairment. METHODS: We included 190 cognitively unimpaired participants without NPS at baseline and tracked the emergence of significant NPS and conversion to mild cognitive impairment (MCI). RESULTS: The average follow-up was 8.1 years, with a maximum of 19 years. Participants with emergent, significant NPS had a 3.92-fold higher risk of cognitive impairment than those without (95% confidence interval 1.51-10.17, P = 0.005). Individuals with NPS and AD biomarkers showed a markedly higher conversion rate to MCI than those with neither risk factor. DISCUSSION: Our findings emphasize the role of emergent, significant NPS as early clinical indicators...

Reasoning Goals and Representational Decisions in Computational Cognitive Neuroscience: Lessons From the Drift Diffusion Model

Thu, 3 Jul 2025 00:00:00 +0000

Computational cognitive models are powerful tools for enhancing the quantitative and theoretical rigor of cognitive neuroscience. It is thus imperative that model users-researchers who develop models, use existing models, or integrate model-based findings into their own research-understand how these tools work and what factors need to be considered when engaging with them. To this end, we developed a philosophical toolkit that addresses core questions about computational cognitive models in the brain and behavioral sciences. Drawing on recent advances in the philosophy of modeling, we highlight the central role of model users' reasoning goals in the application and interpretation of formal models. We demonstrate the utility of this perspective by first offering a philosophical introduction to the highly popular drift diffusion model (DDM) and then providing a novel conceptual analysis of a long-standing debate about decision thresholds in the DDM. Contrary to most existing work,...

A Bayesian Time-Varying Psychophysiological Interaction Model

Thu, 3 Jul 2025 00:00:00 +0000

A Bayesian Time-Varying Psychophysiological Interaction Model

Cognitive Graphs: Representational Substrates for Planning

Thu, 3 Jul 2025 00:00:00 +0000

Making plans for upcoming actions is a computationally demanding process. To mitigate these demands, individuals can build extensive internal models of their environment-states, actions, and their sequential relationships-that allow for plans to be developed with minimal computational costs. Initially, these models reflect elaborate networks of learned associative relationships, which can be used to generate plans for reward through more iterative computations such as trajectory sampling. After sufficient experience, compressed forms of these models can efficiently capture long-range sequential structure, allowing them to be used for rapid planning even in pursuit of novel or changing rewards. Here, we review recent work on the multitude of representations that can support different forms of planning. We discuss how cognitive graphs, a framework with roots in both cognitive psychology and computer science, can provide a unifying view of these representations and their relationships...

Who benefits from mobile health interventions? A dynamical systems analysis of psychological well‐being in early adults

Fri, 20 Jun 2025 00:00:00 +0000

Research shows that skills for improving Psychological Well-Being (PWB) may be learned through PWB interventions; however, the dynamic mechanisms underlying this learning process are not well understood. Using an Ecological Momentary Intervention (EMI) design, we conducted an 8-week Randomized Controlled Trial (N = 160; aged 18-22 years), implemented in a mobile Health (mHealth) platform to characterize these dynamical mechanisms. College-attending early adults were randomized to three groups: an active control group (N = 55); an intervention group (N = 51) with positive practices intervention; and a second intervention group (N = 54) with positive practices and meditation intervention. The mHealth implementation allowed us to introduce the interventions in participants' daily lives while also assessing their PWB (in terms of positive emotions and relationship quality) several times a day. We used a Bayesian process model to analyze changes in PWB in terms of the underlying dynamical...

Validation of spectral sleep scoring with polysomnography using forehead EEG device

Thu, 5 Jun 2025 00:00:00 +0000

Introduction: Visual scoring of sleep electroencephalography (EEG) has long been considered the gold standard for sleep staging. However, it has several drawbacks, including high cost, time-intensiveness, vulnerability to human variability, discomfort to patients, lack of visualization to validate the hypnogram, and no acknowledgment of differences between delta and slow oscillation deep sleep. This report highlights a spectral scoring approach that addresses all these shortcomings of visual scoring. Past algorithms have used spectral information to help classify traditional visual stages. The current method used the clearly visible spectral patterns to develop new spectral stages, which are similar to but not the same as visual stages. Importantly, spectral scoring delivers both a hypnogram and a whole-night spectrogram, which can be visually inspected to ensure accurate scoring. Methods: This study compared traditional visual scoring of 32-channel polysomnography with forehead-only...

Uncovering Hidden Hearing Loss

Wed, 4 Jun 2025 00:00:00 +0000

Uncovering Hidden Hearing Loss

Customized strategies for managing cochlear implant stimulation side effects

Wed, 7 May 2025 00:00:00 +0000

OBJECTIVES: Cochlear implants restore functional hearing but may cause side effects like facial nerve stimulation, sound sensitivity or reactive tinnitus. The present study aimed to establish a general framework for optimizing stimulation parameters to manage these side effects while maximizing speech perception performance. A second objective was to understand how side effect origins impact treatment outcomes. METHODS: Eight adult cochlear implant subjects had intolerable side effects that rendered device usage difficult or even impossible. New maps were created by reducing stimulation levels, increasing pulse duration, reducing stimulation rate, altering channel gains and frequency maps, deactivating problematic electrodes, or a combination of the above. Outcomes were measured in terms of side effect reduction and changes in speech performance. RESULTS: Facial nerve stimulation was reduced or eliminated in five of five subjects. Sound hypersensitivity was eliminated in two of...

Industrial Distraction

Tue, 22 Apr 2025 00:00:00 +0000

Abstract: There are myriad techniques industry actors use to shape the public understanding of science. While a naive view might assume these techniques typically involve fraud or outright deception, the truth is more nuanced. This paper analyzes industrial distraction, a common technique where industry actors fund and share research that is accurate, often high-quality, but nonetheless misleading on important matters of fact. This involves reshaping causal understanding of phenomena with distracting information. Using case studies and causal models, we illustrate how this impacts belief and decision making even for rational learners, informing science policy and debates about misleading content.

REM refines and rescues memory representations: a new theory

Mon, 14 Apr 2025 00:00:00 +0000

Despite extensive evidence on the roles of nonrapid eye movement (NREM) and REM sleep in memory processing, a comprehensive model that integrates their complementary functions remains elusive due to a lack of mechanistic understanding of REM's role in offline memory processing. We present the REM Refining and Rescuing (RnR) Hypothesis, which posits that the principal function of REM sleep is to increase the signal-to-noise ratio within and across memory representations. As such, REM sleep selectively enhances essential nodes within a memory representation while inhibiting the majority (Refine). Additionally, REM sleep modulates weak and strong memory representations so they fall within a similar range of recallability (Rescue). Across multiple NREM-REM cycles, tuning functions of individual memory traces get sharpened, allowing for integration of shared features across representations. We hypothesize that REM sleep's unique cellular, neuromodulatory, and electrophysiological...

Whole-brain causal discovery using fMRI

Fri, 11 Apr 2025 00:00:00 +0000

Despite significant research, discovering causal relationships from fMRI remains a challenge. Popular methods such as Granger causality and dynamic causal modeling fall short in handling contemporaneous effects and latent common causes. Methods from causal structure learning literature can address these limitations but often scale poorly with network size and need acyclicity. In this study, we first provide a taxonomy of existing methods and compare their accuracy and efficiency on simulated fMRI from simple topologies. This analysis demonstrates a pressing need for more accurate and scalable methods, motivating the design of Causal discovery for Large-scale Low-resolution Time-series with Feedback (CaLLTiF). CaLLTiF is a constraint-based method that uses conditional independence between contemporaneous and lagged variables to extract causal relationships. On simulated fMRI from the macaque connectome, CaLLTiF achieves significantly higher accuracy and scalability than all tested...

Is Ockham’s razor losing its edge? New perspectives on the principle of model parsimony

Thu, 3 Apr 2025 00:00:00 +0000

The preference for simple explanations, known as the parsimony principle, has long guided the development of scientific theories, hypotheses, and models. Yet recent years have seen a number of successes in employing highly complex models for scientific inquiry (e.g., for 3D protein folding or climate forecasting). In this paper, we reexamine the parsimony principle in light of these scientific and technological advancements. We review recent developments, including the surprising benefits of modeling with more parameters than data, the increasing appreciation of the context-sensitivity of data and misspecification of scientific models, and the development of new modeling tools. By integrating these insights, we reassess the utility of parsimony as a proxy for desirable model traits, such as predictive accuracy, interpretability, effectiveness in guiding new research, and resource efficiency. We conclude that more complex models are sometimes essential for scientific progress,...

Vagal heart rate variability during rapid eye movement sleep reduces negative memory bias

Thu, 3 Apr 2025 00:00:00 +0000

Emotional memories change over time, but the mechanisms supporting this change are not well understood. Sleep has been identified as one mechanism that supports memory consolidation, with sleep selectively benefitting negative emotional consolidation at the expense of neutral memories, with specific oscillatory events linked to this process. In contrast, the consolidation of neutral and positive memories, compared to negative memories, has been associated with increased vagally mediated heart rate variability (HRV) during wakefulness. However, how HRV during sleep contributes to emotional memory consolidation remains unexplored. We investigated how sleep oscillations (i.e., sleep spindles) and vagal activity during sleep contribute to the consolidation of neutral and negative memories. Using a double-blind, placebo-controlled, within-subject, cross-over design, we examined the impact of pharmacological vagal suppression using zolpidem on overnight emotional memory consolidation....

The Possibility Space Concept in Neuroscience: Possibilities, Constraints, and Explanations

Thu, 3 Apr 2025 00:00:00 +0000

Although the brain is often characterized as a complex system, theoretical and philosophical frameworks often struggle to capture this. For example, mainstream mechanistic accounts model neural systems as fixed and static in ways that fail to capture their dynamic nature and large set of possible behaviors. In this paper, we provide a framework for capturing a common type of complex system in neuroscience, which involves two main aspects: (i) constraints on the system and (ii) the system's possibility space of available outcomes. Our analysis merges neuroscience examples with recent work in the philosophy of science to suggest that the possibility space concept involves two essential types of constraints, which we call hard and soft constraints. Our analysis focuses on a domain-general notion of possibility space that is present in manifold frameworks and representations, phase space diagrams in dynamical systems theory, and paradigmatic cases, such as Waddington's epigenetic...

Computational Phenotyping of Cognitive Decline With Retest Learning

Wed, 12 Mar 2025 00:00:00 +0000

OBJECTIVES: Cognitive change is a complex phenomenon encompassing both retest-related performance gains and potential cognitive decline. Disentangling these dynamics is necessary for effective tracking of subtle cognitive change and risk factors for Alzheimer's Disease and Related Dementias (ADRD). METHOD: We applied a computational cognitive model of learning and forgetting to data from Einstein Aging Study (EAS; n = 316). EAS participants completed multiple bursts of ultra-brief, high-frequency cognitive assessments on smartphones. Analyzing response time data from a measure of visual short-term working memory, the Color Shapes task, and from a measure of processing speed, the Symbol Search task, we extracted several key cognitive markers: short-term intraindividual variability in performance, within-burst retest learning and asymptotic (peak) performance, across-burst change in asymptote and forgetting of retest gains. RESULTS: Asymptotic performance was related to both mild...

Quantitative Measurement of Cyber Resilience: Modeling and Experimentation

Wed, 12 Mar 2025 00:00:00 +0000

Cyber resilience is the ability of a system to resist and recover from a cyber attack, thereby restoring the system's functionality. Effective design and development of a cyber resilient system requires experimental methods and tools for quantitative measuring of cyber resilience. This article describes an experimental method and test bed for obtaining resilience-relevant data as a system (in our case - a truck) traverses its route, in repeatable, systematic experiments. We model a truck equipped with an autonomous cyber-defense system and which also includes inherent physical resilience features. When attacked by malware, this ensemble of cyber-physical features (i.e., "bonware") strives to resist and recover from the performance degradation caused by the malware's attack. We propose parsimonious mathematical models to aid in quantifying systems' resilience to cyber attacks. Using the models, we identify quantitative characteristics obtainable from experimental data and show...

Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach

Thu, 27 Feb 2025 00:00:00 +0000

BACKGROUND: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems. However, most previous work has focused on healthy subjects in controlled environments, with limited attention to real-world postoperative pain scenarios. This gap necessitates the development of robust, multimodal approaches capable of addressing the unique challenges associated with assessing pain in clinical settings, where factors like motion artifacts, imbalanced label distribution, and sparse data further complicate pain monitoring. OBJECTIVE: This study aimed to develop and evaluate...

Individual differences in cortical processing speed predict cognitive abilities: A model-based cognitive neuroscience account