Radicalizing enactivism Hutto, Daniel D; Myin, Erik
2013, 20121214, 2012, 2013-06-26
eBook, Book
A book that promotes the thesis that basic forms of mentality—intentionally directed cognition and perceptual experience—are best understood as embodied yet contentless.
Most of what humans do and ...experience is best understood in terms of dynamically unfolding interactions with the environment. Many philosophers and cognitive scientists now acknowledge the critical importance of situated, environment-involving embodied engagements as a means of understanding basic minds—including basic forms of human mentality. Yet many of these same theorists hold fast to the view that basic minds are necessarily or essentially contentful—that they represent conditions the world might be in. In this book, Daniel Hutto and Erik Myin promote the cause of a radically enactive, embodied approach to cognition that holds that some kinds of minds—basic minds—are neither best explained by processes involving the manipulation of contents nor inherently contentful. Hutto and Myin oppose the widely endorsed thesis that cognition always and everywhere involves content. They defend the counter-thesis that there can be intentionality and phenomenal experience without content, and demonstrate the advantages of their approach for thinking about scaffolded minds and consciousness.
The computer analogy of the mind has been as widely adopted in contemporary cognitive neuroscience as was the analogy of the brain as a collection of organs in phrenology. Just as the phrenologist ...would insist that each organ must have its particular function, so contemporary cognitive neuroscience is committed to the notion that each brain region must have its fundamental computation. InAfter Phrenology, Michael Anderson argues that to achieve a fully post-phrenological science of the brain, we need to reassess this commitment and devise an alternate, neuroscientifically grounded taxonomy of mental function. Anderson contends that the cognitive roles played by each region of the brain are highly various, reflecting different neural partnerships established under different circumstances. He proposes quantifying the functional properties of neural assemblies in terms of their dispositional tendencies rather than their computational or information-processing operations. Exploring larger-scale issues, and drawing on evidence from embodied cognition, Anderson develops a picture of thinking rooted in the exploitation and extension of our early-evolving capacity for iterated interaction with the world. He argues that the multidimensional approach to the brain he describes offers a much better fit for these findings, and a more promising road toward a unified science of minded organisms.
Dreams, conceived as conscious experience or phenomenal states during sleep, offer an important contrast condition for theories of consciousness and the self. Yet, although there is a wealth of ...empirical research on sleep and dreaming, its potential contribution to consciousness research and philosophy of mind is largely overlooked. This might be due, in part, to a lack of conceptual clarity and an underlying disagreement about the nature of the phenomenon of dreaming itself. InDreaming, Jennifer Windt lays the groundwork for solving this problem. She develops a conceptual framework describing not only what it means to say that dreams are conscious experiences but also how to locate dreams relative to such concepts as perception, hallucination, and imagination, as well as thinking, knowledge, belief, deception, and self-consciousness.Arguing that a conceptual framework must be not only conceptually sound but also phenomenologically plausible and carefully informed by neuroscientific research, Windt integrates her review of philosophical work on dreaming, both historical and contemporary, with a survey of the most important empirical findings. This allows her to work toward a systematic and comprehensive new theoretical understanding of dreaming informed by a critical reading of contemporary research findings. Windt's account demonstrates that a philosophical analysis of the concept of dreaming can provide an important enrichment and extension to the conceptual repertoire of discussions of consciousness and the self and raises new questions for future research.
What is consciousness, and could machines have it? Dehaene, Stanislas; Lau, Hakwan; Kouider, Sid
Science (American Association for the Advancement of Science),
10/2017, Letnik:
358, Številka:
6362
Journal Article
Recenzirano
Odprti dostop
The controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses it: ...the human brain. We suggest that the word “consciousness” conflates two different types of information-processing computations in the brain: the selection of information for global broadcasting, thus making it flexibly available for computation and report (C1, consciousness in the first sense), and the self-monitoring of those computations, leading to a subjective sense of certainty or error (C2, consciousness in the second sense). We argue that despite their recent successes, current machines are still mostly implementing computations that reflect unconscious processing (C0) in the human brain. We review the psychological and neural science of unconscious (C0) and conscious computations (C1 and C2) and outline how they may inspire novel machine architectures.
Human spatial ability is modulated by a number of factors, including age 1–3 and gender 4, 5. Although a few studies showed that culture influences cognitive strategies 6–13, the interaction between ...these factors has never been globally assessed as this requires testing millions of people of all ages across many different countries in the world. Since countries vary in their geographical and cultural properties, we predicted that these variations give rise to an organized spatial distribution of cognition at a planetary-wide scale. To test this hypothesis, we developed a mobile-app-based cognitive task, measuring non-verbal spatial navigation ability in more than 2.5 million people and sampling populations in every nation state. We focused on spatial navigation due to its universal requirement across cultures. Using a clustering approach, we find that navigation ability is clustered into five distinct, yet geographically related, groups of countries. Specifically, the economic wealth of a nation was predictive of the average navigation ability of its inhabitants, and gender inequality was predictive of the size of performance difference between males and females. Thus, cognitive abilities, at least for spatial navigation, are clustered according to economic wealth and gender inequalities globally, which has significant implications for cross-cultural studies and multi-center clinical trials using cognitive testing.
•We designed a mobile video game to test spatial ability in humans•We tested more than 2.5 million people from every country in the world•Spatial ability of the population of a country is correlated with economic wealth•Gender inequality of a country is predictive of gender differences in navigation ability
Coutrot et al. used a mobile app to test the spatial ability of more than 2.5 million people around the world. Spatial ability declines across the adult lifespan and at the population level is strongly correlated with a country’s economic wealth, with gender differences being reflective of the gender inequality in a country.
See Sokoliuk and Cruse (doi:10.1093/brain/awy267) for a scientific commentary on this article.
Detecting awareness in patients who recover from coma but remain behaviourally unresponsive is a major ...challenge. Engemann, Raimondo et al. demonstrate that machine learning on EEG signals enables robust classification of state-of-consciousness in data from brain-injured patients in multiple hospitals.
Abstract
Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice. However, it is unknown how dependable these EEG markers are in the face of signal variability because of different EEG configurations, EEG protocols and subpopulations from different centres encountered in practice. In this study we analysed 327 recordings of patients with disorders of consciousness (148 unresponsive wakefulness syndrome and 179 minimally conscious state) and 66 healthy controls obtained in two independent research centres (Paris Pitié-Salpêtrière and Liège). We first show that a non-parametric classifier based on ensembles of decision trees provides robust out-of-sample performance on unseen data with a predictive area under the curve (AUC) of ~0.77 that was only marginally affected when using alternative EEG configurations (different numbers and positions of sensors, numbers of epochs, average AUC = 0.750 ± 0.014). In a second step, we observed that classifiers based on multiple as well as single EEG features generalize to recordings obtained from different patient cohorts, EEG protocols and different centres. However, the multivariate model always performed best with a predictive AUC of 0.73 for generalization from Paris 1 to Paris 2 datasets, and an AUC of 0.78 from Paris to Liège datasets. Using simulations, we subsequently demonstrate that multivariate pattern classification has a decisive performance advantage over univariate classification as the stability of EEG features decreases, as different EEG configurations are used for feature-extraction or as noise is added. Moreover, we show that the generalization performance from Paris to Liège remains stable even if up to 20% of the diagnostic labels are randomly flipped. Finally, consistent with recent literature, analysis of the learned decision rules of our classifier suggested that markers related to dynamic fluctuations in theta and alpha frequency bands carried independent information and were most influential. Our findings demonstrate that EEG markers of consciousness can be reliably, economically and automatically identified with machine learning in various clinical and acquisition contexts.
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