Cognitive Science Research Articles

Ideological Mind-Shaping or Brain-Shaping: Fusing Empirical Biopolitics and Political Philosophy of Mind

There are two primary philosophical approaches to examining the relationship between human bodies and political bodies. The first, reflected in traditional political theory on the body politic, is concerned with the question of how individuals aggregate into functioning or malfunctioning collectives—how singular citizen bodies come to constitute wider political entities. The second approach, maturing later in 20th century social and political philosophy, considers the opposite relation: instead of evaluating how the body politic emerges from the bottom-up, it focuses on how bodies become politicized from the top-down. This, more sociological, perspective explores how human bodies are disciplined and learn to self-discipline into particular physical forms and functions. Dominant ideologies can infiltrate the bodies of adherents, and the task of the social critic or political philosopher is to delineate how, why, where, and when political structures shape the minds and bodies of citizens, as well as whether the effects are coercive, destructive, or liberatory. I will argue that a political philosophy of mind cannot flourish without attention to the cognitive science of ideologies. Until recently there has been little serious dialogue between biology and political philosophy, partly due to disciplinary balkanization and mostly because of the infancy of empirical biopolitics, the science of how cognitive and neurobiological processes are molded by political ideologies. I will suggest that attending to the emerging science of ideological cognition can allow us to build a stronger, more compelling, and provocative thesis on the mind-shaping consequences of immersion in ideologies than is possible with conceptual tools alone

Современная парадигма пенсионного обеспечения в Европе

Introduction. Pension systems are a key element of social protection for the elderly. They are designed to provide senior citizens with stable income not from work. However, pension systems currently being developed in Europe are increasingly exposing the working-age population to the risk of poverty in old age. The European pension paradigm strives for an adequate level of pension payments and builds financial protection against poverty, while the future evolution of wages and prices remains unknown for Europe as a whole.Methods. Scientific research is based on general and special methods of scientific cognition. In the process of forming the concept of the scientific article, modern scientific methods of studying economic and social phenomena were used, including methods of system-structural, functional, factorial and comparative analysis, as well as categorical, logical and structural analysis and synthesis, detailing and generalization, grouping and comparison, identification of cause-and-effect relationships. The authors proceed from an understanding of the scientific hypothesis of reforming the pension system, which ensures long-term investment by the working-age population with income in the future. The empirical base of the study was made up of current data from cross-country comparative studies of Europe in terms of typology of state pension systems.Results. Based on the functional method of cognition, the authors of the scientific article systematize the main types of state pension systems of the European Union, representing the complex content of the indicator value of pension payments. It is noted that in the context of reforming the pension systems of the European Union countries, the reorganization of the functional and ideological components of pension provision, aimed at protecting consumers of pension savings, makes it possible to increase the socio-economic standard of living. Although EU pension systems differ in many respects, they all have one common problem - finding ways to maintain the financial sustainability of the budgetary system while paying pensions in the long term against the backdrop of an aging population and the problem of increasing the ratio of pensioners to the working-age population.

Cognitive microfoundations and social interaction dynamics. The implications of complexity for institutional theory

AbstractThis paper investigates the intersection of cognitive sciences and social network theory and its counterpart, the complexity sciences, aiming to shed light on the compatibility and potential integration of these frameworks into institutional theory. Institutional scholars have for long selectively adopted notions linked with the cognitive sciences and complexity sciences, such as the notion of path dependence, without exploring the broader implications of systematically integrating such perspectives into institutionalism. This paper aims to advance such a comprehensive theoretical integration, by investigating the effective combination of these approaches and their significant implications. It shows how the complexity sciences contribute to dissolving the barriers between the cognitive and social realms and illustrates how this impacts notions of human agency and reflexivity. Theoretical integration also involves acknowledging considerable diversity in individual human agency, which in turn prompts a reconsideration of how notions of institutional stability, change, diffusion and adaptation are understood. Furthermore, the paper addresses the epistemological challenge presented by the complexity sciences, before it highlights the general relevance of institutional theory in analyzing complex social phenomena. Finally, the paper explores implications for research methodology, proposing that a fusion of institutional theory and the complexity sciences provides a metatheoretical framework for assessing the contextual suitability of different theoretical and methodological approaches.

Psychomatics-A Multidisciplinary Framework for Understanding Artificial Minds.

Although large language models (LLMs) and other artificial intelligence systems demonstrate cognitive skills similar to humans, such as concept learning and language acquisition, the way they process information fundamentally differs from biological cognition. To better understand these differences, this article introduces Psychomatics, a multidisciplinary framework bridging cognitive science, linguistics, and computer science. It aims to delve deeper into the high-level functioning of LLMs, focusing specifically on how LLMs acquire, learn, remember, and use information to produce their outputs. To achieve this goal, Psychomatics will rely on a comparative methodology, starting from a theory-driven research question-is the process of language development and use different in humans and LLMs?-drawing parallels between LLMs and biological systems. Our analysis shows how LLMs can map and manipulate complex linguistic patterns in their training data. Moreover, LLMs can follow Grice's Cooperative principle to provide relevant and informative responses. However, human cognition draws from multiple sources of meaning, including experiential, emotional, and imaginative facets, which transcend mere language processing and are rooted in our social and developmental trajectories. Moreover, current LLMs lack physical embodiment, reducing their ability to make sense of the intricate interplay between perception, action, and cognition that shapes human understanding and expression. Ultimately, Psychomatics holds the potential to yield transformative insights into the nature of language, cognition, and intelligence, both artificial and biological. Moreover, by drawing parallels between LLMs and human cognitive processes, Psychomatics can inform the development of more robust and human-like artificial intelligence systems.

Gods of Participation

This paper is a theoretical discussion of how the relation between religion and morality – one of the main research areas in the cognitive and evolutionary sciences of religion (CESR) – may be rephrased in terms of 4E cognition. This aim is realized through redefining morality and religion in terms of participatory sense-making – a model of social cognition situated within the 4E framework. This paper explains the basic tenets of the 4E (embodied, embedded, extended, and enactive) approach to cognition. It clarifies how the notion of mental representations – pivotal for any theorizing within CESR – can be fitted within this model by adopting a radical enactive cognition view. It then proceeds to define participatory sense-making and demonstrate how morality as a system for regulating cooperative behavior fits this concept. Finally, it discusses the role of representations of gods’ moral concerns in reinforcing morality. It argues that focusing on the meaning-making potential of social interactions can elucidate where these representations come from and how they unfold.

New Developments in the Cognitive Science of Religion: The Rationality of Religious Belief, by Hans van Eyghen, Rik Peels, and Gijsbert van den Brink

New Developments in the Cognitive Science of Religion: The Rationality of Religious Belief, by Hans van Eyghen, Rik Peels, and Gijsbert van den Brink. Springer. 2018. New Approaches to the Scientific Study of Religion 4. i–vii + 220pp. $139.99. ISBN: 978-3-319-90238-8.

Imagining the Cognitive Science of Religion: Magic Bullets, Complex Theories, Experimental Adventures, by E. Thomas Lawson

Imagining the Cognitive Science of Religion: Magic Bullets, Complex Theories, Experimental Adventures, by E. Thomas Lawson. London: Bloomsbury Publishing, 2023. Scientific Studies of Religion Inquiry and Explanation. 184 pp. $21.56. ISBN: 9781350355880

Toward a "third culture": MIT SPR as an outlet for 21st-century scientific storytelling

In an era marked by the persistent onslaught of global disturbances, MIT SPR is poised to continue to serve as a bastion of accessible and conscientious communication between scientists, the general public, and policymakers. Drawing on a heritage of scientific thinkers from the past half-century, I argue for a reorientation of our self-image at the journal from communicators to storytellers to move toward a culture that best unites all stakeholders needed to safeguard our collective future.

Deciding how to decide: A conceptual model for consensually fostering urban infrastructure maintenance

Infrastructure owners face challenges in effective maintenance decision-making due to the process's multidisciplinary nature, spanning mathematics to cognitive science. This study delves into enhancing maintenance for complex infrastructure systems, specifically in scenarios where a single primary owner must consider the preferences and requisites of multiple stakeholders. In such unstructured problems characterised by diverse perspectives and potentially conflicting interests in uncertain environments, systematic decision analysis is paramount. In response, this paper proposes a generic conceptual model to outline the primary critical consideration of decision-making through a holistic scheme of the decision problem; it facilitates discovering shared views when defining the decision problem, eliciting objectives early in the decision-making process, and ensuring that selected agendas align with desired outcomes and core values. It provides valuable guidance for decision-modellers in infrastructure rehabilitation. These claims are reinforced with the application of the proposed model to case studies, illustrating its practicality in real-world settings.

EEG-CogNet: A deep learning framework for cognitive state assessment using EEG brain connectivity

The assessment of cognitive states such as workload, attention, and fatigue is crucial in cognitive science and human performance fields due to its significant impact on work efficiency and decision-making. This paper introduces a deep learning framework for cognitive state assessment using Electroencephalogram (EEG) brain connectivity. The framework was evaluated using EEG data from 26 participants through three computer-based tasks: the Dual N-Back Task, Visual Search Task, and Continuous Performance Task, each designed to induce varying levels of cognitive workload, attention, and fatigue, respectively. A comprehensive dataset was meticulously compiled, including physiological, behavioral, and subjective data to ensure robust analyses. The EEG data underwent rigorous pre-processing and feature extraction processes, focusing on brain connectivity metrics such as Coherence and Phase-Locking Value across multiple frequency bands and employing three neural network architectures: Convolutional Neural Networks (CNN), Graph Convolutional Networks (GCN), and Convolutional Long Short-Term Memory networks (ConvLSTM) for classification. The proposed work achieved classification accuracies of 96.53% for workload, 98.40% for attention, and 97.86% for fatigue in the combined frequency bands. The study’s findings underscore the potential of EEG-based methods for non-invasive cognitive state monitoring. By addressing existing limitations such as small sample sizes and task-specific models, this research enhances the generalizability and applicability of EEG-based cognitive assessments. The implications of these advancements are significant for fields requiring continuous cognitive monitoring, such as defense, healthcare, and high-risk operational environments.

The Meandering Course from Psycholinguistics to Cognitive Science and Neuroimaging: The Organization of (Inter-) Disciplinary Research in the History of the Max Planck Institute for Psycholinguistics

The Meandering Course from Psycholinguistics to Cognitive Science and Neuroimaging: The Organization of (Inter-) Disciplinary Research in the History of the Max Planck Institute for Psycholinguistics

Metacognition bridges experiences and beliefs in sense of agency

Cognitive scientists differentiate the “minimal self” – subjective experiences of agency and ownership in our sensorimotor interactions with the world – from declarative beliefs about the self that are sustained over time. However, it remains an open question how individual sensory experiences of agency are integrated into the belief ofbeing an agent.We administered a sensorimotor task to measure subjects’ (n = 195) propensity to classify stimuli as self-caused and metacognitive monitoring of such judgements, and we compared these behavioral metrics to declarative beliefs about their agency. Subjects who were less sensitive to control cues also reported more negative agency beliefs, though positive beliefs were not clearly correlated with any sensorimotor measure. Importantly, this relationship between first-order sensitivity and declarative beliefs essentially disappears when controlling for metacognitive sensitivity. Results suggest agency beliefs are not related directly to the propensity to make positive agency judgements but are connected through introspective access.

Synergy Makes Direct Perception Inefficient.

A typical claim in anti-representationalist approaches to cognition such as ecological psychology or radical embodied cognitive science is that ecological information is sufficient for guiding behavior. According to this view, affordances are immediately perceptually available to the agent (in the so-called "ambient energy array"), so sensory data does not require much further inner processing. As a consequence, mental representations are explanatorily idle: perception is immediate and direct. Here we offer one way to formalize this direct-perception claim and identify some important limits to it. We argue that the claim should be read as saying that successful behavior just implies picking out affordance-related information from the ambient energy array. By relying on the Partial Information Decomposition framework, and more concretely on its development of the notion of synergy, we show that in multimodal perception, where various energy arrays carry affordance-related information, the "just pick out affordance-related information" approach is very inefficient, as it is bound to miss all synergistic components. Efficient multimodal information combination requires transmitting sensory-specific (and not affordance-specific) information to wherever it is that the various information streams are combined. The upshot is that some amount of computation is necessary for efficient affordance reconstruction.

Large models of what? Mistaking engineering achievements for human linguistic agency

In this paper we argue that key, often sensational and misleading, claims regarding linguistic capabilities of Large Language Models (LLMs) are based on at least two unfounded assumptions: the assumption of language completeness and the assumption of data completeness. Language completeness assumes that a distinct and complete thing such as “a natural language” exists, the essential characteristics of which can be effectively and comprehensively modelled by an LLM. The assumption of data completeness relies on the belief that a language can be quantified and wholly captured by data. Work within the enactive approach to cognitive science makes clear that, rather than a distinct and complete thing, language is a means or way of acting. Languaging is not the kind of thing that can admit of a complete or comprehensive modelling. From an enactive perspective we identify three key characteristics of enacted language; embodiment, participation, and precariousness, that are absent in LLMs, and likely incompatible in principle with current architectures. We argue that these absences imply that LLMs are not now and cannot in their present form be linguistic agents the way humans are. We illustrate the point in particular through the phenomenon of “algospeak”, a recently described pattern of high-stakes human language activity in heavily controlled online environments. On the basis of these points, we conclude that sensational and misleading claims about LLM agency and capabilities emerge from a deep misconception of both what human language is and what LLMs are.

Manipulating and measuring variation in deep neural network (DNN) representations of objects

We explore how DNNs can be used to develop a computational understanding of individual differences in high-level visual cognition given their ability to generate rich meaningful object representations informed by their architecture, experience, and training protocols. As a first step to quantifying individual differences in DNN representations, we systematically explored the robustness of a variety of representational similarity measures: Representational Similarity Analysis (RSA), Centered Kernel Alignment (CKA), and Projection-Weighted Canonical Correlation Analysis (PWCCA), with an eye to how these measures are used in cognitive science, cognitive neuroscience, and vision science. To manipulate object representations, we next created a large set of models varying in random initial weights and random training image order, training image frequencies, training category frequencies, and model size and architecture and measured the representational variation caused by each manipulation. We examined both small (All-CNN-C) and commonly-used large (VGG and ResNet) DNN architectures. To provide a comparison for the magnitude of representational differences, we established a baseline based on the representational variation caused by image-augmentation techniques used to train those DNNs. We found that variation in model randomization and model size never exceeded baseline. By contrast, differences in training image frequency and training category frequencies caused representational variation that exceeded baseline, with training category frequency manipulations exceeding baseline earlier in the networks. These findings provide insights into the magnitude of representational variations that can be expected with a range of manipulations and provide a springboard for further exploration of systematic model variations aimed at modeling individual differences in high-level visual cognition.

Book Review: New Directions (and Insights) in Braitenberg Vehicles and Cognitive Science

Book Review: New Directions (and Insights) in Braitenberg Vehicles and Cognitive Science

Subsequent Memory Effects in Cortical Pattern Similarity Differ by Semantic Class.

Although living and nonliving stimuli are known to rely on distinct brain regions during perception, it is largely unknown if their episodic memory encoding mechanisms differ as well. To investigate this issue, we asked participants to encode object pictures (e.g., a picture of a tiger) and to retrieve them later in response to their names (e.g., word "tiger"). For each of four semantic classes (living-animate, living-inanimate, nonliving-large, and nonliving-small), we examined differences in the similarity in activation patterns (neural pattern similarity [NPS]) for subsequently remembered versus forgotten items. Higher NPS for remembered items suggests an advantage of within-class item similarity, whereas lower NPS for remembered items indicates an advantage for item distinctiveness. We expect NPS within class-specific regions to be higher for remembered than for forgotten items. For example, the parahippocampal cortex has a well-known role in scene processing [Aminoff, E. M., Kveraga, K., & Bar, M. The role of the parahippocampal cortex in cognition. Trends in Cognitive Sciences, 17, 379-390, 2013], and the anterior temporal and inferior frontal gyrus have well-known roles in object processing [Clarke, A., & Tyler, L. K. Object-specific semantic coding in human perirhinal cortex. Journal of Neuroscience, 34, 4766-4775, 2014]. As such, we expect to see higher NPS for remembered items in these regions pertaining to scenes and objects, respectively. Consistent with this hypothesis, in fusiform, parahippocampal, and retrosplenial regions, higher NPS predicted memory for subclasses of nonliving objects, whereas in the left inferior frontal and left retrosplenial regions, lower NPS predicted memory for subclasses of living objects. Taken together, the results support the idea that subsequent memory depends on a balance of similarity and distinctiveness and demonstrate that the neural mechanisms of episodic encoding differ across semantic categories.

A Workflow for Building Computationally Rational Models of Human Behavior

Computational rationality explains human behavior as arising due to the maximization of expected utility under the constraints imposed by the environment and limited cognitive resources. This simple assumption, when instantiated via partially observable Markov decision processes (POMDPs), gives rise to a powerful approach for modeling human adaptive behavior, within which a variety of internal models of cognition can be embedded. In particular, such an instantiation enables the use of methods from reinforcement learning (RL) to approximate the optimal policy solution to the sequential decision-making problems posed to the cognitive system in any given setting; this stands in contrast to requiring ad hoc hand-crafted rules for capturing adaptive behavior in more traditional cognitive architectures. However, despite their successes and promise for modeling human adaptive behavior across everyday tasks, computationally rational models that use RL are not easy to build. Being a hybrid of theoretical cognitive models and machine learning (ML) necessitates that model building take into account appropriate practices from both cognitive science and ML. The design of psychological assumptions and machine learning decisions concerning reward specification, policy optimization, parameter inference, and model selection are all tangled processes rife with pitfalls that can hinder the development of valid and effective models. Drawing from a decade of work on this approach, a workflow is outlined for tackling this challenge and is accompanied by a detailed discussion of the pros and cons at key decision points.

Dimensions of disagreement: Divergence and misalignment in cognitive science and artificial intelligence.

Dimensions of disagreement: Divergence and misalignment in cognitive science and artificial intelligence.

Judges and lawyers’ beliefs in repression and dissociative amnesia may imperil justice: further guidance required

ABSTRACT This article examines continuing misunderstanding about memory function especially for trauma, across three UK samples (N = 717). Delayed allegations of child sexual and physical abuse are prevalent in Western legal systems and often rely upon uncorroborated memory testimony to prove guilt. U.K. legal professionals and jurors typically assess the reliability of such memory recall via common sense, yet decades of scientific research show common sense beliefs often conflict with science. Recent international surveys show controversial notions of repression and accurate memory recovery remain strongly endorsed. In historical cases, these notions may lead to wrongful convictions. The current study surveyed the U.K. public, lawyers, and mental health professionals’ beliefs about repression, dissociative amnesia and false memories. Study findings give unique data on judges’ and barristers’ beliefs. Overall, the study findings reinforce international scientists’ concerns of a science – knowledge-gap. Repression was strongly endorsed by lay, legal and clinical participants (> 78%) as was dissociative amnesia (> 87%). Moreover, suboptimal professional legal education and juror guidance may increase misunderstanding. Correcting beliefs about memory function, and extending the contribution of memory science in the courtroom remains an important quest for cognitive scientists.