In grammaticalization studies, reanalysis is understood as the assignment of new meaning to formally unchanged elements, supported by bridging contexts compatible with the old and the reanalyzed meaning. The source determination hypothesis (SDH) predicts that parallel grammaticalization trajectories occur crosslinguistically, as similar source meanings give rise to similar inferences. One such pattern is the development of recent past markers from FINISH constructions. While grammaticalization pathways are well-documented crosslinguistically, the SDH has never been tested experimentally. In this study, we examine whether modern English speakers are sensitive to inferences arising from a bridging context identified as relevant to the grammaticalization of Old Spanish FINISH into a recent past marker. In a temporal distance judgment task, we examined whether the bridging context identified for Old Spanish facilitates an inference of temporal immediacy in contemporary English, where the construction has not been grammaticalized. In line with the SDH, the bridging context enhanced an inference of immediacy in contemporary English (Exp. 1), with specific grammatical features of the source determining its strength (Exp. 2). These results not only demonstrate the viability of testing hypotheses about language change using experimental pragmatics but also call for empirically refining the concept of source determination.
Recent high-precision experimental confirmations of quantum complementarity have revitalized foundational debates about measurement, description, and realism. This article argues that complementarity is most productively interpreted as an epistemic principle--constraining what can be simultaneously accessed and represented--rather than as an ontological claim about quantum reality. Reexamining the Einstein-Bohr debate through this lens reveals a persistent tension between descriptive completeness and contextual meaning, a tension experiments clarify but do not dissolve. Building on this analysis, we introduce cognitive complementarity as a structural principle governing reasoning under non-classical uncertainty, where mutually constraining representations cannot be jointly optimized. Within this framework, we propose quantum intuition as a testable cognitive capacity: the ability to sustain representational plurality, regulate commitment timing, and resolve perspective-incompatibilities in a context-sensitive manner. Formulated as a naturalistic construct grounded in shared informational constraints, quantum intuition offers a principled bridge between quantum measurement theory and cognition. This work reframes the historical debate, extends epistemic lessons from quantum foundations into cognitive science, and outlines empirical pathways for studying decision-making in contexts of irreducible uncertainty.
Alexander J. Hess, Sandra Iglesias, Laura Köchli
et al.
Computational (generative) modelling of behaviour has considerable potential for clinical applications. In order to unlock the potential of generative models, reliable statistical inference is crucial. For this, Bayesian workflow has been suggested which, however, has rarely been applied in Translational Neuromodeling and Computational Psychiatry (TN/CP) so far. Here, we present a worked example of Bayesian workflow in the context of a typical application scenario for TN/CP. This application example uses Hierarchical Gaussian Filter (HGF) models, a family of computational models for hierarchical Bayesian belief updating. When equipped with a suitable response model, HGF models can be fit to behavioural data from cognitive tasks; these data frequently consist of binary responses and are typically univariate. This poses challenges for statistical inference due to the limited information contained in such data. We present a novel set of response models that allow for simultaneous inference from multivariate (here: two) behavioural data types. Using both simulations and empirical data from a speed-incentivised associative reward learning (SPIRL) task, we show that models harnessing information from two different data streams (binary responses and continuous response times) ensure robust inference (specifically, identifiability of parameters and models). Moreover, we find a linear relationship between log-transformed response times in the SPIRL task and participants’ uncertainty about the outcome. Our analysis illustrates the benefits of Bayesian workflow for a typical use case in TN/CP. We argue that adopting Bayesian workflow for generative modelling helps increase the transparency and robustness of results, which in turn is of fundamental importance for the long-term success of TN/CP.
Computer applications to medicine. Medical informatics, Psychiatry
Sydni M. Nadler, Holly A. Taylor, Tad T. Brunyé
et al.
Abstract Effective multitasking in high-stakes military environments is critical yet often compromised by cognitive overload, leading to operational errors. This scoping review explores the potential of transcranial direct current stimulation (tDCS) as a cognitive enhancement tool for improving multitasking performance, with a focus on task-switching and dual-task paradigms. Evidence suggests that tDCS targeting the dorsolateral prefrontal cortex (DLPFC) shows promise in mitigating task-switching deficits and reducing dual-task interference, particularly under unpredictable or high-demand conditions. However, variability in outcomes, influenced by stimulation parameters, task characteristics, and individual differences, highlights the need for further refinement of this approach. The limited but emerging evidence on high-definition tDCS (HD-tDCS) is also discussed, emphasizing its potential for more precise targeting, though current findings show mixed efficacy for multitasking enhancement. Practical applications of tDCS for military training and operations are examined, including skill acquisition, analyst performance, and drone piloting, where optimized multitasking capabilities could alleviate cognitive overload and enhance operational efficiency. While the findings are encouraging, additional research is essential to establish standardized protocols and assess the real-world utility of tDCS in complex military scenarios. This review highlights the importance of advancing neuromodulation techniques to address the increasing cognitive demands of modern military operations.
William T. Swaney, Amy Jose, Chelsie Hirons-Major
et al.
Abstract Social groups vary in the benefits that they offer to individuals through characteristics such as group size and composition, and consequently individual animals often exhibit preferences for groups with properties indicating greater benefits for members. Animals choosing between social groups may have to balance different preferences and integrate information about multiple group features to make optimal decisions and select the group that offers the greatest net benefit. We investigated how preferences for familiar individuals and for larger groups interact in the decision-making of zebrafish (Danio rerio) given a choice between two conspecific shoals. Adult subjects were given a series of choice tests with a shoal of four familiar fish, and a shoal of between four to eight unfamiliar fish. Subjects were tested on their preferences to spend time in proximity to the two shoals, and to interact with them. Zebrafish preferred to interact with the familiar shoal when both shoals comprised four individuals, however they did not show a preference for either shoal when choosing between four familiar fish and either five or six unfamiliar fish. When choosing between four familiar fish and either seven or eight unfamiliar fish, zebrafish showed clear preferences for the larger unfamiliar shoals over the familiar shoals. Our findings establish that zebrafish evaluate both the size and familiarity status of conspecific shoals, and integrate these multiple sources of information into social decision-making.
SFUSED English (Simon Fraser University Speech Error Database – English) is the first large scale database of speech errors developed from audio recordings of spontaneous speech. This article describes the structure of the database and the standards used to construct it, including collection and classification methods, record mark-up, data quality measures, and adherence to standard practices in psycholinguistics and English linguistics. Additional information on these methods and the entire database are available on the OSF repository: https://osf.io/8c9rg/.
Abstract Cognition, how individuals perceive, store, process and respond to information, influences decision-making. However, biases in cognitive processes can distort behavioural outcomes. Judgement bias occurs when individuals infer information to be more positive, or negative than it is. For captive animals, judgement bias is typically measured by testing behavioural responses to ambiguous cues that are intermediate to cues with learned valence (positive and negative). However, these tests have rarely been used in the wild. We therefore examined if a behavioural test of judgement bias commonly used in captive animals could be adapted to test a wild bird, the toutouwai (North Island robin, Petroica longipes). Toutouwai were faster to approach ambiguous cues that were more similar to a learned positive cue, compared to a learned negative cue. This positive bias mirrors behavioural response patterns observed across a range of species in captivity, including other birds. Responses toward ambiguous cues were repeatable over a short timespan, indicating there was consistent individual variation among our test birds in their degree of bias when judging ambiguous cues. Males tended to approach cues more quickly than females. Our results demonstrate that judgement bias can be tested for in situ in wild birds. However, as is typical of many island species, toutouwai are bold and fearless, which facilitated their participation in our experiment. Therefore, to enable research examining the ecological correlates of judgement biases in nature, we must develop tests of cognitive biases that can be used with a wider range of species in the wild.
Cognitive flexibility has been extensively studied in human cognition but remains relatively unexplored in the context of Visual Large Language Models (VLLMs). This study assesses the cognitive flexibility of state-of-the-art VLLMs (GPT-4o, Gemini-1.5 Pro, and Claude-3.5 Sonnet) using the Wisconsin Card Sorting Test (WCST), a classic measure of set-shifting ability. Our results reveal that VLLMs achieve or surpass human-level set-shifting capabilities under chain-of-thought prompting with text-based inputs. However, their abilities are highly influenced by both input modality and prompting strategy. In addition, we find that through role-playing, VLLMs can simulate various functional deficits aligned with patients having impairments in cognitive flexibility, suggesting that VLLMs may possess a cognitive architecture, at least regarding the ability of set-shifting, similar to the brain. This study reveals the fact that VLLMs have already approached the human level on a key component underlying our higher cognition, and highlights the potential to use them to emulate complex brain processes.
In value-based decision-making there is wide behavioural variability in how individuals respond to uncertainty. Maladaptive responses to uncertainty have been linked to a vulnerability to mental illness, for example, between risk aversion and affective disorders. Here, we examine individual differences in risk sensitivity when subjects confront options drawn from different value distributions, where these embody the same or different means and variances. In simulations, we show that a model that learns a distribution using Bayes’ rule and reads out different parts of the distribution under the influence of a risk-sensitive parameter (Conditional Value at Risk, CVaR) predicts how likely an agent is to prefer a broader over a narrow distribution (pro-variance bias/risk-seeking) under the same overall means. Using empirical data, we show that CVaR estimates correlate with participants’ pro-variance biases better than a range of alternative parameters derived from other models. Importantly, across two independent samples, CVaR estimates and participants’ pro-variance bias negatively correlated with trait rumination, a common trait in depression and anxiety. We conclude that a Bayesian-CVaR model captures individual differences in sensitivity to variance in value distributions and task-independent trait dispositions linked to affective disorders.
Computer applications to medicine. Medical informatics, Psychiatry
Silvia Serino, Daniele Di Lernia, Giulia Magni
et al.
Extensive literature elucidated the mechanisms underlying the ability to memorize the positions of objects in space. However, less is known about the impact that objects' features have on spatial memory. The present study aims to investigate differences in egocentric and allocentric object-location memory between hand stimuli depicted in a first-person perspective (1PP) or in a third-person one (3PP). Fifty-two adults encoded spatial positions within a virtual museum environment featuring four square buildings. Each of these buildings featured eight paintings positioned along the walls, with two pictures displayed on each of the four walls. Thirty-two stimuli were employed, which represented pictures of the right hand performing various types of gestures. Half of the stimuli depicted the hand in the 1PP, while the other half depicted the hand in the 3PP. Both free and guided explorations served as encoding conditions. Immediately after that, participants underwent a two-step object-location memory task. Participants were provided with a map of the museum and asked to identify the correct building where the image was located (allocentric memory). Then, they were presented with a schematic representation of the exhibition room divided into four sections and instructed to select the section where they thought the picture was located (egocentric memory). Our findings indicate a memory performance boost associated with egocentric recall, regardless of the perspective of the bodily stimuli. The results are discussed considering the emerging literature on the mnemonic properties of body-related stimuli for spatial memory.
This paper introduces a unifying framework that links the Context-Content Uncertainty Principle (CCUP) with optimal transport (OT) via primal-dual inference. We propose that cognitive representations are not static encodings but active dual constraints that shape feasible manifolds for learning and inference. Cognition is formalized as the dynamic alignment of high-entropy contexts with low-entropy content, implemented through cycle-consistent inference that minimizes conditional entropy. Central to this framework is the concept of inverted inference: a goal-driven mechanism that reverses the direction of conditioning to simulate latent trajectories consistent with internal goals. This asymmetric inference cycle closes the duality gap in constrained optimization, aligning context (primal variables) with content (dual constraints), and reframing inference as structure-constrained entropy minimization. Temporally, we introduce recursive bootstrapping, where each inference cycle sharpens the structural manifold for the next, forming memory chains that support path-dependent optimization and hierarchical goal decomposition. Spatially, we extend the model via hierarchical spatial bootstrapping, connecting to Hierarchical Navigable Small World (HNSW) graphs to enable sublinear retrieval of goal-consistent latent states. Altogether, this framework provides a computational theory of cognition in which dynamic alignment across time and space supports efficient generalization, abstraction, and adaptive planning. CCUP emerges as a scalable principle for both slow, recursive reasoning and fast, structure-aware recognition through layered primal-dual cycles.
In this paper, we are aiming to propose a novel mathematical model that studies the dynamics of synaptic damage in terms of concentrations of toxic neuropeptides/neurotransmitters during neurotransmission processes. Our primary objective is to employ Wardrop's first and second principles within a neural network of the brain. In order to comprehensively incorporate Wardrop's first and second principles into the neural network of the brain, we introduce two novel concepts: \textit{neuropeptide's (neurotransmitter's) equilibrium} and \textit{synapses optimum}. The \textit{neuropeptide/neurotransmitter equilibrium} refers to \textit{a distribution of toxic neuropeptides/neurotransmitters that leads to uniform damage across all synaptic links}. Meanwhile, \textit{synapses optimum} is \textit{the most desirable distribution of toxic neuropeptides/neurotransmitters that minimizes the cumulative damage experienced by all synapses}. In the context of a neural network within the brain, an analogue of the price of anarchy is \textit{the price of cognition} which is \textit{the most unfavorable ratio between the overall impairment caused by toxic neuropeptide's (neurotransmitter's) equilibrium in comparison to the optimal state of synapses (synapses optimum)}. To put it differently, \textit{the price of cognition} measures \textit{the loss of cognitive ability resulting from increased concentrations of toxic neuropeptides/neurotransmitters}. Additionally, a replicator equation is proposed within this framework that leads to the establishment of the synapses optimum during the neurotransmission process.
In a previous paper, we have shown that an ontology of quantum mechanics in terms of states and events with internal phenomenal aspects, that is, a form of panprotopsychism, is well suited to explaining the phenomenal aspects of consciousness. We have proved there that the palette and grain combination problems of panpsychism and panprotopsychism arise from implicit hypotheses based on classical physics about supervenience that are inappropriate at the quantum level, where an exponential number of emergent properties and states arise. In this article, we address what is probably the first and most important combination problem of panpsychism: the subject-summing problem originally posed by William James. We begin by identifying the physical counterparts of the subjects of experience within the quantum panprotopsychic approach presented in that article. To achieve this, we turn to the notion of subject of experience inspired by the idea of prehension proposed by Whitehead and show that this notion can be adapted to the quantum ontology of objects and events. Due to the indeterminacy of quantum mechanics and its causal openness, this ontology also seems to be suitable for the analysis of the remaining aspects of the structure combination problem, which shows how the structuration of consciousness could have evolved from primitive animals to humans. The analysis imposes conditions on possible implementations of quantum cognition mechanisms in the brain and suggests new problems and strategies to address them. In particular, with regard to the structuring of experiences in animals with different degrees of evolutionary development.
Apoorva Safai, Erin Jonaitis, Rebecca E Langhough
et al.
Neighborhood disadvantage is associated with worse health and cognitive outcomes. Morphological similarity network (MSN) is a promising approach to elucidate cortical network patterns underlying complex cognitive functions. We hypothesized that MSNs could capture changes in cortical patterns related to neighborhood disadvantage and cognitive function. This cross-sectional study included cognitively unimpaired participants from two large Alzheimers studies at University of Wisconsin-Madison. Neighborhood disadvantage status was obtained using the Area Deprivation Index (ADI). Cognitive performance was assessed on memory, processing speed and executive function. Morphological Similarity Networks (MSN) were constructed for each participant based on the similarity in distribution of cortical thickness of brain regions, followed by computation of local and global network features. Association of ADI with cognitive scores and MSN features were examined using linear regression and mediation analysis. ADI showed negative association with category fluency,implicit learning speed, story recall and modified pre-clinical Alzheimers cognitive composite scores, indicating worse cognitive function among those living in more disadvantaged neighborhoods. Local network features of frontal and temporal regions differed based on ADI status. Centrality of left lateral orbitofrontal region showed a partial mediating effect between association of neighborhood disadvantage and story recall performance. Our preliminary findings suggest differences in local cortical organization by neighborhood disadvantage, which partially mediated the relationship between ADI and cognitive performance, providing a possible network-based mechanism to, in-part, explain the risk for poor cognitive functioning associated with disadvantaged neighborhoods.
Quang Dang, Murat Kucukosmanoglu, Michael Anoruo
et al.
Assessing cognitive workload is crucial for human performance as it affects information processing, decision making, and task execution. Pupil size is a valuable indicator of cognitive workload, reflecting changes in attention and arousal governed by the autonomic nervous system. Cognitive events are closely linked to cognitive workload as they activate mental processes and trigger cognitive responses. This study explores the potential of using machine learning to automatically detect cognitive events experienced using individuals. We framed the problem as a binary classification task, focusing on detecting stimulus onset across four cognitive tasks using CNN models and 1-second pupillary data. The results, measured by Matthew's correlation coefficient, ranged from 0.47 to 0.80, depending on the cognitive task. This paper discusses the trade-offs between generalization and specialization, model behavior when encountering unseen stimulus onset times, structural variances among cognitive tasks, factors influencing model predictions, and real-time simulation. These findings highlight the potential of machine learning techniques in detecting cognitive events based on pupil and eye movement responses, contributing to advancements in personalized learning and optimizing neurocognitive workload management.
Veronica Muffato, Laura Miola, Marilina Pellegrini
et al.
Abstract When learning an environment from virtual navigation people gain knowledge about landmarks, their locations, and the paths that connect them. The present study newly aimed to investigate all these domains of knowledge and how cognitive factors such as visuospatial abilities and wayfinding inclinations might support virtual passive navigation. A total of 270 participants (145 women) were tested online. They: (i) completed visuospatial tasks and answered questionnaires on their wayfinding inclinations; and (ii) learnt a virtual path. The environmental knowledge they gained was assessed on their free recall of landmarks, their egocentric and allocentric pointing accuracy (location knowledge), and their performance in route direction and landmark location tasks (path knowledge). Visuospatial abilities and wayfinding inclinations emerged as two separate factors, and environmental knowledge as a single factor. The SEM model showed that both visuospatial abilities and wayfinding inclinations support the environmental knowledge factor, with similar pattern of relationships in men and women. Overall, factors related to the individual are relevant to the environmental knowledge gained from an online virtual passive navigation.