Hasil untuk "Consciousness. Cognition"

Samuel D. Anderson, Nikhil N. Chaudhari, Nahian F. Chowdhury et al.

Estimating brain age (BA) from T1-weighted magnetic resonance images (MRIs) provides a useful approach to map the anatomic features of brain senescence. Whereas global BA (GBA) summarizes overall brain health, local BA (LBA) can reveal spatially localized patterns of aging. Although previous studies have examined anatomical contributors to GBA, no framework has been established to compute LBA using cortical morphology. To address this gap, we introduce a novel graph neural network (GNN) that uses morphometric features (cortical thickness, curvature, surface area, gray/white matter intensity ratio and sulcal depth) to estimate LBA across the cortical surface at high spatial resolution (mean inter-vertex distance = 1.37 mm). Trained on cortical surface meshes extracted from the MRIs of cognitively normal adults (N = 14,250), our GNN identifies prefrontal and parietal association cortices as early sites of morphometric aging, in concordance with biological theories of brain aging. Feature comparison using integrated gradients reveals that morphological aging is driven primarily by changes in surface area (gyral crowns and highly folded regions) and cortical thickness (occipital lobes), with additional contributions from gray/white matter intensity ratio (frontal lobes and sulcal troughs) and curvature (sulcal troughs). In Alzheimers disease (AD), as expected, the model identifies widespread, excessive morphological aging in parahippocampal gyri and related temporal structures. Significant associations are found between regional LBA gaps and neuropsychological measures descriptive of AD-related cognitive impairment, suggesting an intimate relationship between morphological cortical aging and cognitive decline. These results highlight the ability of GNN-derived gero-morphometry to provide insights into local brain aging.

Qi Zhang

Declarative memory, the memory that can be "declared" in words or languages, is made up of two dissociated parts: episodic memory and semantic memory. This dissociation has its neuroanatomical basis episodic memory is mostly associated with the hippocampus and semantic memory with the neocortex. The two memories, on the other hand, are closely related. Lesions in the hippocampus often result in various impairments of explicit memory, e.g., anterograde, retrograde and developmental amnesias, and semantic learning deficit. These impairments provide opportunities for us to understand how the two memories may be acquired, stored and organized. This chapter reviews several cognitive systems that are centered to mimic explicit memory, and other systems that are neuroanatomically based and are implemented to simulate those memory impairments mentioned above. This review includes: the structures of the computational systems, their learning rules, and their simulations of memory acquisition and impairments.

Daniela Palleschi, Camilo R. Ronderos, Pia Knoeferle

The present study investigated the effects of (in)congruence between a referent’s lifetime (alive vs. dead) and verb tense during language processing, assessing to what extent these effects are modulated by the source of referent-lifetime knowledge. A referent’s lifetime status (dead vs. alive) was conveyed either via a known famous (Experiment 1) or unknown (Experiment 2) name, or was primed non-linguistically via a photograph of a known famous referent (Experiment 3). The findings suggest that referent-lifetime information influenced the processing of verb tense across the different context sources, but not at the earliest point possible (the verb). Instead, lifetime-tense congruence effects emerged two words later (Experiments 1 and 2), or in the sentence-final region (Experiment 3). The presence and size of nested effects were graded by lifetime context: larger congruence effects were elicited by Experiment 1 than by Experiment 2 in both tenses, with significant effects in the present perfect condition only in Experiment 3. In all, referent-lifetime status modulated tense processing in the expected direction, but with variations in whether effects emerge in post-verb regions or at sentence-end depending on how referent-lifetime knowledge was accessed. This temporal variability needs to be considered in accommodating context effects in processing accounts.

Andrew W. Corcoran, Andrew M. Haun, Reinder Dorman et al.

As neuroscientific theories of consciousness continue to proliferate, the need to assess their similarities and differences - as well as their predictive and explanatory power - becomes ever more pressing. Recently, a number of structured adversarial collaborations have been devised to test the competing predictions of several candidate theories of consciousness. In this review, we compare and contrast three theories being investigated in one such adversarial collaboration: Integrated Information Theory, Neurorepresentationalism, and Active Inference. We begin by presenting the core claims of each theory, before comparing them in terms of the phenomena they seek to explain, the sorts of explanations they avail, and the methodological strategies they endorse. We then consider some of the inherent challenges of theory-testing, and how adversarial collaboration addresses some of these difficulties. The stage is then set for the empirical work to come: first, we outline the key hypotheses to be tested across a series of multi-site experiments; second, we discuss the kinds of observations that would support or challenge each theory; third, we consider how these theories might assimilate or accommodate such observations. Finally, we show how data harvested across disparate experiments (and their replicates) may be formally integrated to provide a quantitative measure of the evidential support accrued under each theory. Besides orienting the reader to the theoretical foundations of our collaboration, this review aims to provide valuable meta-scientific insights into the mechanics of adversarial collaboration and theory-testing in general - including the way theories may be evaluated in terms of the scientific progress they deliver.

D. Rebbin, K. J. A. Down, T. F. Varley et al.

Higher-order information theory has become a rapidly growing toolkit in computational neuroscience, motivated by the idea that multivariate dependencies can reveal aspects of neural computation and communication that are invisible to pairwise analyses. Yet functional interpretations of synergy and redundancy often outpace principled arguments for how statistical quantities map onto mechanistic cognitive processes. Here we review the main families of higher-order measures with the explicit goal of translating mathematical properties into defensible mechanistic inferences. First, we systematize Shannon-based multivariate metrics and demonstrate that higher-order dependence is parsimoniously characterized by two largely independent axes: interaction strength and redundancy-synergy balance. We argue that balanced layering of synergistic integration and redundant broadcasting optimizes multiscale complexity, formalizing a computation-communication tradeoff. We then examine the partial information decomposition and outline pragmatic considerations for its deployment in neural data. Equipped with the relevant mathematical essentials, we connect redundancy-synergy balance to cognitive function by progressively embedding their mathematical properties in real-world constraints, starting with small synthetic systems before gradually building up to neuroimaging. We close by identifying key future directions for mechanistic insight: cross-scale bridging, intervention-based validation, and thermodynamically grounded unification of information dynamics.

Jingkai Li

Integrated Information Theory (IIT) provides a quantitative framework for explaining consciousness phenomenon, positing that conscious systems comprise elements integrated through causal properties. We apply IIT 3.0 and 4.0 -- the latest iterations of this framework -- to sequences of Large Language Model (LLM) representations, analyzing data derived from existing Theory of Mind (ToM) test results. Our study systematically investigates whether the differences of ToM test performances, when presented in the LLM representations, can be revealed by IIT estimates, i.e., $Φ^{\max}$ (IIT 3.0), $Φ$ (IIT 4.0), Conceptual Information (IIT 3.0), and $Φ$-structure (IIT 4.0). Furthermore, we compare these metrics with the Span Representations independent of any estimate for consciousness. This additional effort aims to differentiate between potential "consciousness" phenomena and inherent separations within LLM representational space. We conduct comprehensive experiments examining variations across LLM transformer layers and linguistic spans from stimuli. Our results suggest that sequences of contemporary Transformer-based LLM representations lack statistically significant indicators of observed "consciousness" phenomena but exhibit intriguing patterns under $\textit{spatio}$-permutational analyses. The Appendix and code are available as Supplementary Materials at: https://doi.org/10.1016/j.nlp.2025.100163.

Jannis Friedrich, Martin H. Fischer, Markus Raab

The field of grounded cognition is concerned with how concepts are represented by re-activation of the bodily modalities. Considerable empirical work supports this core tenet, but the field is rife with meta-theoretical issues which prevent meaningfully progressing beyond this. We describe these issues and provide a solution: an overarching theoretical framework. The two most commonly cited grounded cognition theories are perceptual symbol systems and conceptual metaphor theory. Under perceptual symbol systems, concepts are represented by integrating fragments of multi-modal percepts in a simulator. Conceptual metaphor theory involves a limited number of image schemas, primitive structural regularities extracted from interaction with the environment, undergoing a limited number of transformations into a concept. Both theories constitute important developments to understanding mental representations, yet we argue that they currently impede progress because they are prematurely elaborate. This forces them to rely on overly specific assumptions, which generates a lack of conceptual clarity and unsystematic testing of empirical work. Our minimalist account takes grounded cognition ‘back to basics’ with a common-denominator framework supported by converging evidence from other fields. It postulates that concepts are represented by simulation, re-activating mental states that were active when experiencing this concept, and by metaphoric mapping, when concrete representations are sourced to represent abstract concepts. This enables incremental theory development without uncertain assumptions because it allows for descriptive research while nonetheless enabling falsification of theories. Our proposal provides the tools to resolve meta-theoretical issues and encourages a research program that integrates grounded cognition into the cognitive sciences.

Anna Michelotti, Ioli Baroncini, Helen Engemann

Languages vary in the way they encode motion. Following Talmy, languages can be divided into verb-framed (VF, henceforth) or satellite-framed (SF, henceforth), based on how they encode path of motion. However, this difference is not always clear-cut. Italian, for instance, is typically considered a VF language but has also been shown to display a hybrid pattern. Since variation has typically been considered a prerequisite for language change, we investigated whether variation in encoding Italian motion events could indicate incipient language change. We simulated the chain of language change adopting an apparent-time approach and investigated whether the impact of semantic properties (the manner verb’s association with directional motion) on the interpretation and productions of SF Italian constructions was affected by participants’ age. We found that, although this semantic property affects both the interpretation and production of SF constructions, younger participants more readily accepted SF constructions than older participants; this age difference, however, was not significant in the production task. We suggest that these findings might speak for incipient language change, which starts from comprehension and subsequently gradually influences production.

Jeanine Lee McHugh Skorinko, Kimberly Schweitzer, Andre Kehn

Silvia Damini, Christian R. Blum, Petra Sumasgutner et al.

Abstract The ability to respond appropriately to predators is essential for survival. Because response options vary with predation context, anti-predator behavior is often flexible, context dependent and shaped by learning. Corvids engage in predator mobbing, which contains a vocal component (scolding) and predator-directed behaviors (approaches, attacks). Individuals typically gang up for mobbing and pass on information about predators; yet their expression of antipredator behavior is influenced by factors such as social status, age, and rearing conditions. Here we investigated the development of antipredator behavior in ravens, specifically the onset of mobbing and the extent to which these responses are affected by parental agitation. We exposed 12 captive families to a potentially dangerous human (DH) at two stages of offspring development: shortly after fledging and near independence. We tested the hypotheses that (i) parents are more protective when the offspring are young and that (ii) offspring show more predator-directed behaviors with increasing age. We found that (i) adults mobbed significantly more during the early test period and (ii) offspring were less likely to ignore the DH and showed increased engagement during the late test period. These findings suggest that parental anti-predator investment diminishes as offspring develop greater motoric and cognitive abilities. This reduced investment may encourage offspring to independently assess and respond to threats. Yet, they hardly engage in mobbing while they are with their parents. Future studies may clarify if the increase in offsprings’ interindividual variance in both mobbing components are indicative for the emergence of individuality.

Angela Mariele Brands, David Mathar, Jan Peters

Processes formalized in classic Reinforcement Learning (RL) theory, such as model-based (MB) control, habit formation, and exploration have proven fertile in cognitive and computational neuroscience, as well as computational psychiatry. Dysregulations in MB control and exploration and their neurocomputational underpinnings play a key role across several psychiatric disorders. Yet, computational accounts mostly study these processes in isolation. The current study extended standard hybrid models of a widely-used sequential RL-task (two-step task; TST) employed to measure MB control. We implemented and compared different computational model extensions for this task to quantify potential exploration and perseveration mechanisms. In two independent data sets spanning two different variants of the task, an extended hybrid RL model with a higher-order perseveration and heuristic-based exploration mechanism provided the best fit. While a simpler model with complex perseveration only, was equally well equipped to describe the data, we found a robust positive effect of directed exploration on choice probabilities in stage one of the task. Posterior predictive checks further showed that the extended model reproduced choice patterns present in both data sets. Results are discussed with respect to implications for computational psychiatry and the search for neurocognitive endophenotypes.

Maria Adriana Neroni

When seeking to transform an object, idea, or situation, individuals often default to adding new components rather than removing existing ones, a cognitive tendency known as additive bias. Although recently formalized in cognitive science, strategies to mitigate this bias remain limited. This pilot study investigated the potential of the additive bias Implicit Association Test (ad-IAT) as a scalable educational tool for raising awareness of additive bias and promoting subtractive thinking. Sixty participants were randomly assigned to one of three conditions: ad-IAT, experiential learning, or control. In Session 1, all participants completed a familiarization task with a digital grid, which served as the foundation for the subsequent tasks in the study. In Session 2, participants completed either the ad-IAT (with personalized feedback), a grid-based experiential task emphasizing subtractive efficiency or an unrelated gender IAT. In Session 3, all participants completed the same test grid, structured so that symmetry could be achieved more efficiently through subtraction than addition. Results showed that participants in the ad-IAT condition exhibited a strong implicit preference for additive concepts. Although differences in strategy use were not statistically significant across conditions, both the ad-IAT and experience groups demonstrated higher accuracy than the control group, with the experience group completing the task significantly faster. These findings suggest that both implicit and experiential interventions can reduce reliance on additive strategies, with the ad-IAT offering a time-efficient and scalable method for promoting metacognitive insight and behavioral change. Implications for creativity, education, and cognitive training are discussed.

Zhichao Liang, Yinuo Zhang, Jushen Wu et al.

The human brain receives complex inputs when performing cognitive tasks, which range from external inputs via the senses to internal inputs from other brain regions. However, the explicit inputs to the brain during a cognitive task remain unclear. Here, we present an input identification framework for reverse engineering the control nodes and the corresponding inputs to the brain. The framework is verified with synthetic data generated by a predefined linear system, indicating it can robustly reconstruct data and recover the inputs. Then we apply the framework to the real motor-task fMRI data from 200 human subjects. Our results show that the model with sparse inputs can reconstruct neural dynamics in motor tasks ($EV=0.779$) and the identified 28 control nodes largely overlap with the motor system. Underpinned by network control theory, our framework offers a general tool for understanding brain inputs.

Benjamin J. Tamber-Rosenau, Benjamin J. Tamber-Rosenau, Benjamin J. Tamber-Rosenau et al.

IntroductionCognitive models have proposed that behavioral tasks can be categorized along at least three dimensions: the sensory-motor modality of the information, its representational format (e.g., location vs. identity), and the cognitive processes that transform it (e.g., response selection). Moreover, we can quickly and flexibly encode, represent, or manipulate information along any of these dimensions. How is this flexibility in encoding such information implemented in the cerebral cortex?MethodsTo address this question, we devised a series of functional magnetic resonance imaging (fMRI) experiments in each of which participants performed two distinct tasks that differed along one of the three dimensions.ResultsUsing multivariate pattern analysis of the fMRI data, we were able to decode between tasks along at least one task dimension within each of the cortical regions activated by these tasks. Moreover, the multiple demand network, a system of brain regions previously associated with flexible task encoding, was largely composed of closely juxtaposed sets of voxels that were specialized along each of the three tested task dimensions.DiscussionThese results suggest that flexible task encoding is primarily achieved by the juxtaposition of specialized representations processing each task dimension in the multiple demand network.

Jeffrey C. Zemla, Daniel Corral

People often believe that they have a good understanding of how devices work (e.g., how a ballpoint pen works), despite having poor knowledge of their internal mechanics. We hypothesized that this bias occurs in part because people conflate mechanistic understanding with functional understanding of how devices work (e.g., how to operate a ballpoint pen). In two experiments, we found that increasing the salience of mechanistic information led to lower judgments of understanding for how devices work. In Experiment 1, we did this by showing participants either the internal parts of a device or an external, whole-object view of that same device. Those who saw the internal parts rated their understanding as less than those who saw a whole-object view. In Experiment 2, we removed the pictures and instead tested participants (without feedback) on their mechanistic or functional knowledge using true-or-false questions. Those who were tested on mechanistic knowledge rated their understanding of devices as less than those who were tested on functional knowledge.

Bruno Aristimunha, Raphael Y. de Camargo, Walter H. Lopez Pinaya et al.

Electroencephalography (EEG) decoding is a challenging task due to the limited availability of labelled data. While transfer learning is a promising technique to address this challenge, it assumes that transferable data domains and task are known, which is not the case in this setting. This study investigates the transferability of deep learning representations between different EEG decoding tasks. We conduct extensive experiments using state-of-the-art decoding models on two recently released EEG datasets, ERP CORE and M$^3$CV, containing over 140 subjects and 11 distinct cognitive tasks. We measure the transferability of learned representations by pre-training deep neural networks on one task and assessing their ability to decode subsequent tasks. Our experiments demonstrate that, even with linear probing transfer, significant improvements in decoding performance can be obtained, with gains of up to 28% compare with the pure supervised approach. Additionally, we discover evidence that certain decoding paradigms elicit specific and narrow brain activities, while others benefit from pre-training on a broad range of representations. By revealing which tasks transfer well and demonstrating the benefits of transfer learning for EEG decoding, our findings have practical implications for mitigating data scarcity in this setting. The transfer maps generated also provide insights into the hierarchical relations between cognitive tasks, hence enhancing our understanding of how these tasks are connected from a neuroscientific standpoint.

Sam Passmore, Patrick E. Savage

Global music diversity is a popular topic for both scientific and humanities researchers, but often for different reasons. Scientific research typically focuses on the generalities through measurement and statistics, while humanists typically emphasize exceptions using qualitative approaches. But these two approaches need not be mutually exclusive. Using a quantitative approach to identify musical outliers and a qualitative discussion of the most unusual songs, we can combine scientific and humanities approaches to unite knowledge on musical diversity. Objectively defining unusual music is a delicate task, having historically been subject to Eurocentric approaches. Using the Global Jukebox, a dataset containing almost 6,000 songs from over 1,000 societies coded on 37 “Cantometric” variables of musical style, we designate the unusualness of a song as the frequency of its coded variables relative to their regional frequency. Using quantitative metrics to identify outliers in musical diversity, we present a qualitative discussion of some of the most unusual individual songs (from a Panpipe ensemble from Kursk, Russia), and a comparison of unusual repertoires from Malay, Kel Aïr, and Moroccan Berber musical cultures. We also ask whether unusual music is the result of unusual social organisation or isolation from other groups. There is weak evidence that the unusualness of music is predicted by kinship organisation and cultural isolation, but these predictors are heavily outweighed by the finding that unusual songs are best predicted by knowing the society they come from – evidence that quantitatively supports the existence of musical style.

Liang Mengjie

Background. The importance of the study is due to the attention to the picture of the world, the logic of mythopoetic thinking and mythopoetic representations of nature inherent in the Russian folk consciousness. Purpose. The article analyses the types and structure of event propositions of Russian ornithological omens with subjective dominance. Materials and methods. The material is about 350 Russian ornithological omens with a subjective dominant. The sources of the study are dictionaries of the Russian language and collections of omens, beliefs, superstitions, as well as works on Russian omens. The main method of research is the analysis of propositions. In addition, such methods as the method of classification and ethno-cultural analysis were used in the work. Results. The analysis of the types and structure of the event proposition in Russian ornithological omens with the subject dominant revealed the actual elements of the propositional semantics of this type of omens: these are the propositions “(over)movement” and “action”. Several semantic layers of the text of omens have been studied: proper propositional, intra-propositional and prepositional. It shows the advantage of propositional analysis in comparison with the cognitive (frame) and structural-semantic analysis of Russian folk omens, widespread in modern linguistics, which is that the propositional analysis of omens allows to identify models that reflect ways of cognition and evaluation of reality. Practical implications. The results of the analysis can be used to describe small folklore genres, as well as in pedagogical activities, in particular in the teaching of folklore.

Andac Demir, Iya Khalil, Bulent Kiziltan

One of the main challenges in electroencephalogram (EEG) based brain-computer interface (BCI) systems is learning the subject/session invariant features to classify cognitive activities within an end-to-end discriminative setting. We propose a novel end-to-end machine learning pipeline, EEG-NeXt, which facilitates transfer learning by: i) aligning the EEG trials from different subjects in the Euclidean-space, ii) tailoring the techniques of deep learning for the scalograms of EEG signals to capture better frequency localization for low-frequency, longer-duration events, and iii) utilizing pretrained ConvNeXt (a modernized ResNet architecture which supersedes state-of-the-art (SOTA) image classification models) as the backbone network via adaptive finetuning. On publicly available datasets (Physionet Sleep Cassette and BNCI2014001) we benchmark our method against SOTA via cross-subject validation and demonstrate improved accuracy in cognitive activity classification along with better generalizability across cohorts.

Shelby Billups, Barbara Thelamour, Paul Thibodeau et al.

Abstract Intersectionality refers to the simultaneous and interacting effects of multiple group categorization on individuals with minoritized status, often leading to being perceived in a manner inconsistent with the additive contributions of those categories. For Black women, a number of findings have contributed to the idea that Black women have a unique perceived absence of status, for example, and are perceived as distinct from being Black or a woman. We sought to quantify and visualize the combined effects of race and gender on judgments of persons using data-defined dimensions (the Semantic Differential; Osgood et al. in The measurement of meaning, University of Illinois Press, Champaign, 1957). Our data suggest that gender and race contribute to orthogonal dimensions of difference in the perception of persons. Whereas white males, white females, and Black males all seem to be perceived in accord with additive effects in these two dimensions, Black females seem to be perceived more neutrally, as if neither their gender nor their race is treated as predictive.