The diversity of possible constitutive components in cognitive neurosciences.

I aim to discuss which constitutive components are essential for explaining how the mind works. Rather than focusing on some specific components, I emphasize their diversity. Thus, I seek to complement the recent mechanistic proposal by underscoring that researchers should remain open-minded about which constitutive components should be investigated.

Embodied (4EA) cognitive computational neuroscience.

I argue that ideas and models about the mechanisms of neural computation and representation - including computational architecture, representational format, encoding schemes, learning methods, computation-representation coordination, and substrate-dependent aspects - must be tested by studying embodied neural systems. Thus, cognitive computational neuroscience - the study of neural computations over neural representations - must be an embodied research program.

Incorporating individual differences into a mechanistic embodied cognitive neuroscience.

The review article Theoretical strategies for an embodied cognitive neuroscience proposes that the embodied cognition framework can be applied to develop mechanistic explanations for cognitive neuroscience phenomena. In our commentary we argue that any mechanistic explanation of such phenomena must be able to account for individual differences in cognition that are an inevitable consequence of the varied brain-body-environment experiences that comprise embodied cognition. We propose that, while mechanistic accounts may be able to model individual differences, the definition of mechanistic models may limit their application to the study of individual differences.

Beyond embodiment: Rethinking the integration of cognitive neuroscience and mechanistic explanations.

This commentary critiques Mougenot and Matheson's proposal to integrate embodied cognition with mechanistic explanations in cognitive neuroscience. We suggest more promising directions for embodied cognitive neuroscience, focusing on neuroethological research and evolutionary studies of nervous systems. These approaches, compatible with wide mechanistic explanations, offer a robust path forward by examining central nervous system function within whole organisms in their environments.

How to build a better 4E cognition.

Mougenot and Matheson outline a theoretical approach to cognitive neuroscience that combines the commitments of embodied cognition with a mechanistic approach to scientific explanation. They argue that this theoretical approach provides several general benefits, including enabling researchers to develop more robust theories and ontologies that do not require either neuroscientific reductionism or the complete autonomy of psychology from neuroscience. In this commentary, I argue that the sort of embodied cognitive neuroscience that they envision has a more specific benefit: it has the potential to help resolve internal tensions within 4E cognition.

Mechanisms after the end of New Mechanism.

Mougenot and Matheson provide an interesting analysis on how some core ideas of the 'New Mechanists' - the proponents of a normative framework for scientific explanations based on the identification and description of mechanisms - might be relevant for the development of an embodied approach to cognitive neuroscience. Although we are highly sympathetic to such an approach, we struggle to identify the benefits of adopting the notion of mechanism for such enterprise.

Imposing vs finding unity.

The target article argues that embodied cognitive neuroscience converges on a mechanistic approach to explanation. We argue that it does not. Even some of the article's paradigms for embodied cognitive neuroscience are explicitly non- or anti-mechanistic.

Separating minimal from radical embodied cognitive neuroscience.

Mougenot and Matheson (2024) make a compelling case for the development of a mechanistic cognitive neuroscience that is embodied. However, their analysis of extant work under this header plays down important distinctions between 'minimal' and 'radical' embodiment. The former remains firmly neurocentric and therefore has limited potential to move the needle in understanding the functional contributions of neural dynamics to cognition in the context of wider organism-environment dynamics.

Inference to the best neuroscientific explanation.

Neuroscientists routinely use reverse inference (RI) to draw conclusions about cognitive processes from neural activation data. However, despite its widespread use, the methodological status of RI is a matter of ongoing controversy, with some critics arguing that it should be rejected wholesale on the grounds that it instantiates a deductively invalid argument form. In response to these critiques, some have proposed to conceive of RI as a form of abduction or inference to the best explanation (IBE). We side with this response but at the same time argue that a defense of RI requires more than identifying it as a form of IBE. In this paper, we give an analysis of what determines the quality of an RI conceived as an IBE and on that basis argue that whether an RI is warranted needs to be decided on a case-by-case basis. Support for our argument will come from a detailed methodological discussion of RI in cognitive neuroscience in light of what the recent literature on IBE has identified as the main quality indicators for IBEs.

Evaluating the explanatory power of the Conscious Turing Machine.

The recent "Conscious Turing Machine" (CTM) proposal offered by Manuel and Lenore Blum aims to define and explore consciousness, contribute to the solution of the hard problem, and demonstrate the value of theoretical computer science with respect to the study of consciousness. Surprisingly, given the ambitiousness and novelty of the proposal (and the prominence of its creators), CTM has received relatively little attention. We here seek to remedy this by offering an exhaustive evaluation of CTM. Our evaluation considers the explanatory power of CTM in three different domains of interdisciplinary consciousness studies: the philosophy of mind, cognitive neuroscience, and computation. Based on our evaluation in each of the target domains, at present, any claim that CTM constitutes progress is premature. Nevertheless, the model has potential, and we highlight several possible avenues of future research which proponents of the model may pursue in its development.

Towards an ecologically valid naturalistic cognitive neuroscience of memory and event cognition.

The landscape of human memory and event cognition research has witnessed a transformative journey toward the use of naturalistic contexts and tasks. In this review, we track this progression from abrupt, artificial stimuli used in extensively controlled laboratory experiments to more naturalistic tasks and stimuli that present a more faithful representation of the real world. We argue that in order to improve ecological validity, naturalistic study designs must consider the complexity of the cognitive phenomenon being studied. Then, we review the current state of "naturalistic" event segmentation studies and critically assess frequently employed movie stimuli. We evaluate recently developed tools like lifelogging and other extended reality technologies to help address the challenges we identified with existing naturalistic approaches. We conclude by offering some guidelines that can be used to design ecologically valid cognitive neuroscience studies of memory and event cognition.

Centering cognitive neuroscience on task demands and generalization.

Cognitive neuroscience seeks generalizable theories explaining the relationship between behavioral, physiological and mental states. In pursuit of such theories, we propose a theoretical and empirical framework that centers on understanding task demands and the mutual constraints they impose on behavior and neural activity. Task demands emerge from the interaction between an agent's sensory impressions, goals and behavior, which jointly shape the activity and structure of the nervous system on multiple spatiotemporal scales. Understanding this interaction requires multitask studies that vary more than one experimental component (for example, stimuli and instructions) combined with dense behavioral and neural sampling and explicit testing for generalization across tasks and data modalities. By centering task demands rather than mental processes that tasks are assumed to engage, this framework paves the way for the discovery of new generalizable concepts unconstrained by existing taxonomies, and moves cognitive neuroscience toward an action-oriented, dynamic and integrated view of the brain.

Cognition of Time and Thinking Beyond.

A common research protocol in cognitive neuroscience is to train subjects to perform deliberately designed experiments while recording brain activity, with the aim of understanding the brain mechanisms underlying cognition. However, how the results of this protocol of research can be applied in technology is seldom discussed. Here, I review the studies on time processing of the brain as examples of this research protocol, as well as two main application areas of neuroscience (neuroengineering and brain-inspired artificial intelligence). Time processing is a fundamental dimension of cognition, and time is also an indispensable dimension of any real-world signal to be processed in technology. Therefore, one may expect that the studies of time processing in cognition profoundly influence brain-related technology. Surprisingly, I found that the results from cognitive studies on timing processing are hardly helpful in solving practical problems. This awkward situation may be due to the lack of generalizability of the results of cognitive studies, which are under well-controlled laboratory conditions, to real-life situations. This lack of generalizability may be rooted in the fundamental unknowability of the world (including cognition). Overall, this paper questions and criticizes the usefulness and prospect of the abovementioned research protocol of cognitive neuroscience. I then give three suggestions for future research. First, to improve the generalizability of research, it is better to study brain activity under real-life conditions instead of in well-controlled laboratory experiments. Second, to overcome the unknowability of the world, we can engineer an easily accessible surrogate of the object under investigation, so that we can predict the behavior of the object under investigation by experimenting on the surrogate. Third, the paper calls for technology-oriented research, with the aim of technology creation instead of knowledge discovery.

Differently different?: A commentary on the emerging social cognitive neuroscience of female autism.

Autism is a neurodevelopmental condition, behaviourally identified, which is generally characterised by social communication differences, and restrictive and repetitive patterns of behaviour and interests. It has long been claimed that it is more common in males. This observed preponderance of males in autistic populations has served as a focussing framework in all spheres of autism-related issues, from recognition and diagnosis through to theoretical models and research agendas. One related issue is the near total absence of females in key research areas. For example, this paper reports a review of over 120 brain-imaging studies of social brain processes in autism that reveals that nearly 70% only included male participants or minimal numbers (just one or two) of females. Authors of such studies very rarely report that their cohorts are virtually female-free and discuss their findings as though applicable to all autistic individuals. The absence of females can be linked to exclusionary consequences of autism diagnostic procedures, which have mainly been developed on male-only cohorts. There is clear evidence that disproportionately large numbers of females do not meet diagnostic criteria and are then excluded from ongoing autism research. Another issue is a long-standing assumption that the female autism phenotype is broadly equivalent to that of the male autism phenotype. Thus, models derived from male-based studies could be applicable to females. However, it is now emerging that certain patterns of social behaviour may be very different in females. This includes a specific type of social behaviour called camouflaging or masking, linked to attempts to disguise autistic characteristics. With respect to research in the field of sex/gender cognitive neuroscience, there is emerging evidence of female differences in patterns of connectivity and/or activation in the social brain that are at odds with those reported in previous, male-only studies. Decades of research have excluded or overlooked females on the autistic spectrum, resulting in the construction of inaccurate and misleading cognitive neuroscience models, and missed opportunities to explore the brain bases of this highly complex condition. A note of warning needs to be sounded about inferences drawn from past research, but if future research addresses this problem of male bias, then a deeper understanding of autism as a whole, as well as in previously overlooked females, will start to emerge.

Autism is a neurodevelopmental condition, behaviourally identified, which is generally characterised by social communication differences, and restrictive and repetitive patterns of behaviour and interests. It has long been claimed that it is more common in males, with oft-quoted ratios of 4M: 1F. This has been reflected in the development of diagnostic criteria for autism and, consequently, of measures of eligibility for autism research programmes, with females being (as is now emerging) disproportionately excluded.As outlined in this review, this issue has been particularly problematic in brain-based studies of autism. Many studies have only tested male autistic participants, or minimal numbers of autistic females. By default, sex differences were not examined. But the impression given by such research reports has commonly been that the findings would be applicable to all autistic individuals.Recent psychological and clinical research has shown that there are a significant number of autistic females who have been missed by traditional diagnostic practices. Their inclusion has increased their eligibility for autism research studies. With respect to brain research, it has become possible to devise studies with matched numbers of autistic females and males, and to replicate studies that have previously only tested males. Newly emerging findings from such studies are demonstrating that the 'robust' autism-related differences previously observed in autistic male-only cohorts do not fully generalise to autistic females.It will be necessary to exercise caution in drawing inferences from previous male-biased studies of the autistic brain. However, the identification and inclusion of previously excluded female autistic participants hopefully offers more accurate insights into this highly complex and heterogeneous condition.

Bridging cognitive neuroscience and education: Insights from EEG recording during mathematical proof evaluation.

BACKGROUND: Much of modern mathematics education prioritizes symbolic formalism even at the expense of non-symbolic intuition, we contextualize our study in the ongoing debates on the balance between symbolic and non-symbolic reasoning. We explore the dissociation of oscillatory dynamics between algebraic (symbolic) and geometric (non-symbolic) processing in advanced mathematical reasoning during a naturalistic design. METHOD: Employing mobile EEG technology, we investigated students' beta and gamma wave patterns over frontal and parietal regions while they engaged with mathematical demonstrations in symbolic and non-symbolic formats within a tutor-student framework. We used extended, naturalistic stimuli to approximate an authentic educational setting. CONCLUSION: Our findings reveal nuanced distinctions in neural processing, particularly in terms of gamma waves and activity in parietal regions. Furthermore, no clear overall format preference emerged from the neuroscientific perspective despite students rating symbolic demonstrations higher for understanding and familiarity.

The motifs of radical embodied neuroscience.

In this paper, I analyse how the emerging scientific framework of radical embodied neuroscience is different from contemporary mainstream cognitive neuroscience. To do so, I propose the notion of motif to enrich the philosophical toolkit of cognitive neuroscience. This notion can be used to characterize the guiding ideas of any given scientific framework in psychology and neuroscience. Motifs are highly unconstrained, open-ended concepts that support equally open-ended families of explanations. Different scientific frameworks-e.g., psychophysics or cognitive neuroscience-provide these motifs to answer the overarching themes of these disciplines, such as the relationship between stimuli and sensations or the proper methods of the sciences of the mind. Some motifs of mainstream cognitive neuroscience are the motif of encoding, the motif of input-output systems, and the motif of algorithms. The two first ones answer the question about the relationship between stimuli, sensations and experience (e.g., stimuli are input and are encoded by brain structures). The latter one answers the question regarding the mechanism of cognition and experience. The three of them are equally unconstrained and open-ended, and they serve as an umbrella for different kinds of explanation-i.e., different positions regarding what counts as a code or as an input. Along with the articulation of the notion of motif, the main aim of this article is to present three motifs for radical embodied neuroscience: the motif of complex stimulation, the motif of organic behaviour and the motif of resonance.

Theoretical strategies for an embodied cognitive neuroscience: Mechanistic explanations of brain-body-environment systems.

Cognitive neuroscience seeks to explain mind, brain, and behavior. But how do we generate explanations? In this integrative theoretical paper, we review the commitments of the 'New Mechanist' movement within the philosophy of science, focusing specifically on the role of mechanistic models in scientific explanation. We highlight how this approach differs from other explanatory approaches within the field, showing its unique contributions to the efforts of scientific explanation. We then argue that the commitments of the Embodied Cognition framework converge with the commitments of the New Mechanist movement in a way that provides a necessary explanatory strategy available to cognitive neuroscience. We then discuss a number of consequences of this convergence, including issues related to the inadequacy of statistical prediction, neuroscientific reduction, the autonomy of psychology from neuroscience, and psychological and neuroscientific ontology. We hope that our integrative thesis provides researchers with a theoretical strategy for an embodied cognitive neuroscience.

Getting closer to social interactions using electroencephalography in developmental cognitive neuroscience.

The field of developmental cognitive neuroscience is advancing rapidly, with large-scale, population-wide, longitudinal studies emerging as a key means of unraveling the complexity of the developing brain and cognitive processes in children. While numerous neuroscientific techniques like functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) have proved advantageous in such investigations, this perspective proposes a renewed focus on electroencephalography (EEG), leveraging underexplored possibilities of EEG. In addition to its temporal precision, low costs, and ease of application, EEG distinguishes itself with its ability to capture neural activity linked to social interactions in increasingly ecologically valid settings. Specifically, EEG can be measured during social interactions in the lab, hyperscanning can be used to study brain activity in two (or more) people simultaneously, and mobile EEG can be used to measure brain activity in real-life settings. This perspective paper summarizes research in these three areas, making a persuasive argument for the renewed inclusion of EEG into the toolkit of developmental cognitive and social neuroscientists.

Overcoming boundaries: Interdisciplinary challenges and opportunities in cognitive neuroscience.

Cognitive neuroscience has considerable untapped potential to translate our understanding of brain function into applications that maintain, restore, or enhance human cognition. Complex, real-world phenomena encountered in daily life, professional contexts, and in the arts, can also be a rich source of information for better understanding cognition, which in turn can lead to advances in knowledge and health outcomes. Interdisciplinary work is needed for these bi-directional benefits to be realized. Our cognitive neuroscience team has been collaborating on several interdisciplinary projects: hardware and software development for brain stimulation, measuring human operator state in safety-critical robotics environments, and exploring emotional regulation in actors who perform traumatic narratives. Our approach is to study research questions of mutual interest in the contexts of domain-specific applications, using (and sometimes improving) the experimental tools and techniques of cognitive neuroscience. These interdisciplinary attempts are described as case studies in the present work to illustrate non-trivial challenges that come from working across traditional disciplinary boundaries. We reflect on how obstacles to interdisciplinary work can be overcome, with the goals of enriching our understanding of human cognition and amplifying the positive effects cognitive neuroscientists have on society and innovation.

Models need mechanisms, but not labels.

The target article proposes a model involving the important but not well-investigated topics of curiosity and creativity. The model, however, falls short of providing convincing explanations of the basic mechanisms underlying these phenomena. We outline the importance of mechanistic thinking in dealing with the concepts outlined in this article specifically and within psychology and cognitive neuroscience in general.