A distinction between the use of computational models in cognitive science and a philosophically inspired reductivist thesis is developed. PF is found questionable for phenomenal states, and, by analogy, dubious for the nonphenomenal introspectible mental states of common sense. PF is also shown to be threatened for the sub-cognitive theoretical states of cognitive science by the work of the so-called New Connectionists. CMM is shown to be less vulnerable to these criticisms.

In this paper, I argue for a modified version of what Devitt calls the Representational Thesis. According to RT, syntactic rules or principles are psychologically real, in the sense that they are represented in the mind/brain of every linguistically competent speaker/hearer. I present a range of behavioral and neurophysiological evidence for the claim that the human sentence processing mechanism constructs mental representations of the syntactic properties of linguistic stimuli. I then survey a range of psychologically plausible computational models (...) of comprehension and show that they are all committed to RT. I go on to sketch a framework for thinking about the nature of the representations involved in sentence processing. My claim is that these are best characterized not as propositional attitudes but, rather, as subpersonal states whose representational properties are determined by their functional role. Finally, I distinguish between explicit and implicit representations and argue that the latter can be drawn on as data by the algorithms that constitute our sentence processing routines. I conclude that skepticism concerning the psychological reality of grammars cannot be sustained. (shrink)

This book argues that Kant's transcendental idealism has been misinterpreted: it denies not simply the super-sensory reality of space, time, and appearances, but their reality outside imagination as well. After adducing extensive and explicit textual evidence in its favor, Waxman shows this interpretation to be essential to the Transcendental Deduction, the affirmation of things in themselves, and the attempt to surmount Hume's scepticism. He further argues that Kant's much-neglected claim that, besides himself, "no psychologist has so much as (...) even thought that the imagination might be a necessary constituent of perception," should be construed so that even our consciousness of sensation itself (visual, tactile, etc.) is impossible without imagination. A compelling and original contribution to Kantian scholarship, Kant's Model of the Mind will also bear close examination by students and scholars of Hume, the philosophy of mind, and the philosophy of science. (shrink)

I present an outline of the Omega Point theory, which is a model for an omnipresent, omniscient, omnipotent, evolving, personal God who is both transcendent to spacetime and immanent in it, and who exists necessarily. The model is a falsifiable physical theory, deriving its key concepts not from any religious tradition but from modern physical cosmology and computer science; from scientific materialism rather than revelation. Four testable predictions of the model are given. The theory assumes that thinking is a (...) purely physical process of the brain, and that personality dies with the brain. Nevertheless, I show that the Omega Point theory suggests a future universal resurrection of the dead very similar to the one predicted in the Judeo‐Christian‐Islamic tradition. The notions of “grace” and the “beatific vision” appear naturally in the model. (shrink)

This is the first work to apply complex systems science to the psychological interplay of order and chaos. The author draws on thought from a wide range of disciplines-both conventional and unorthodox-to address such questions as the nature of consciousness, the relation between mind and reality, and the justification of belief systems. The material should provoke thought among systems scientists, theoretical psychologists, artificial intelligence researchers, and philosophers.

An important, but relatively neglected, aspect of human theory of mind is emotion inference: understanding how and why a person feels a certain why is central to reasoning about their beliefs, desires and plans. The authors review recent work that has begun to unveil the structure and determinants of emotion inference, organizing them within a unified probabilistic framework.

Computational modeling is one of the primary approaches to constructing protein–protein interfaces in the laboratory. The algorithm-driven computational protein design has been successfully applied to the construction of functional proteins with improved binding affinity and increased thermostability. It is intriguing how a computational protein modeling approach can construct and shape the reality of new functional proteins from scratch. I articulate an account of abstraction and exploration-driven strategies in this computational endeavor. I aim to show that how a computational modelling (...) approach, which is laden with mathematics and algorithms, can have a constructive force on the target protein. (shrink)

The purpose of this paper is to describe some limitations on scientific behaviorist and computational models of the mind. These limitations stem from the inability of either model to account for the integration of experience and behavior. Behaviorism fails to give an adequate account of felt experience, whereas the computational model cannot account for the integration of our behavior with the world. Both approaches attempt to deal with their limitations by denying that the domain outside their limits is (...) a part of psychology. These attempts to turn the shortcomings of the two models into virtues would be more convincing if their limitations were not diametrically opposed. I will argue that in each case the limitations are too restrictive unless the theories are augmented by physiology. (shrink)

Computational models of emotions have been thriving and increasingly popular since the 1990s. Such models used to be concerned with the emotions of individual agents when they interact with other agents. Out of the array of models for the emotions, we are going to devote special attention to the approach in Adamatzky’s Dynamics of Crowd-Minds. The reason it stands out, is that it considers the crowd, rather than the individual agent. It fits in computational intelligence. It works (...) by mathematical simulation on a crowd of simple artificial agents: by letting the computer program run, the agents evolve, and crowd behaviour emerges. Adamatzky’s purpose is to give an account of the emergence of allegedly “irrational” behaviour. This is not without problem, as the irrational to one person may seem entirely rational to another, and this in turn is an insight that, in the history of crowd psychology, has affected indeed the competition among theories of crowd dynamics. Quite importantly, Adamatzky’s book argues for the transition from individual agencies to a crowd’s or a mob’s coalesced mind as so, and at any rate for coalesced crowd’s agency. (shrink)

How do we ensure that future generally intelligent AI share our values? This is the value-alignment problem. It is a weighty matter. After all, if AI are neutral with respect to our wellbeing, or worse, actively hostile toward us, then they pose an existential threat to humanity. Some philosophers have argued that one important way in which we can mitigate this threat is to develop only AI that shares our values or that has values that ‘align with’ ours. However, there (...) is nothing to guarantee that this policy will be universally implemented—in particular, ‘bad actors’ are likely to flout it. In this paper, I show how the predictive processing model of the mind, currently ascendant in cognitive science, may ameliorate the value-alignment problem. In essence, I argue that there are a plurality of reasons why any future generally intelligent AI will possess a predictive processing cognitive architecture (e.g. because we decide to build them that way; because it is the only possible cognitive architecture that can underpin general intelligence; because it is the easiest way to create AI.). I also argue that if future generally intelligent AI possess a predictive processing cognitive architecture, then they will come to share our pro-moral motivations (of valuing humanity as an end; avoiding maleficent actions; etc.), regardless of their initial motivation set. Consequently, these AI will pose a minimal threat to humanity. In this way then, I conclude, the value-alignment problem is significantly ameliorated under the assumption that future generally intelligent AI will possess a predictive processing cognitive architecture. (shrink)

ABSTRACT I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments. INTRODUCTION In contrast to the classic model of randomized-control trials, often with a large number (...) of subjects enrolled, precision medicine attempts to optimize therapeutic outcomes by focusing on the individual.[i] A recent publication highlights the strengths and weakness of both traditional evidence-based medicine and precision medicine.[ii] Plus, it outlines a tension in the shift from evidence-based medicine’s inductive reasoning style (the collection of data to postulate general theories) to precision medicine’s abductive reasoning style (the generation of an idea from the limited data available).[iii] The paper’s main example is the application of precision medicine for the treatment of cancer.[iv] I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. As the name suggests, brain-computer interfaces are a significant advancement in neurotechnology that directly connects someone’s brain to external or implanted devices.[v] Among the various kinds of brain-computer interfaces, adaptive deep brain stimulation devices require numerous personalized adjustments to their settings during the implantation and computation stages in order to provide adequate relief to patients with treatment-resistant disorders. What makes these devices unique is how adaptive deep brain stimulation integrates a sensory component to initiate the stimulation. While not commonly at the level of sophistication as self-supervising or generative large language models,[vi] they currently allow for a semi-autonomous form of neuromodulation. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments.[vii] ANALYSIS I. The State of Precision Medicine in Oncology and the Epistemological Shift While a thorough overview of precision medicine for the treatment of cancer is beyond the scope of this article, its practice can be roughly summarized as identifying clinically significant characteristics a patient possesses (e.g., genetic traits) to land on a specialized treatment option that, theoretically, should benefit the patient the most.[viii] However, in such a practice of stratification patients fall into smaller and smaller populations and the quality of evidence that can be applied to anyone outside these decreases in turn.[ix] As inductive logic helps to articulate, the greater the number of patients that respond to a particular therapy the higher the probability of its efficacy. By straying from this logical framework, precision medicine opens the treatment of cancer to more uncertainty about the validity of these approaches to the resulting disease subcategories.[x] Thus, while contemporary medical practices explicitly describe some treatments as “personalized”, they ought not be viewed as inherently better founded than other therapies.[xi] A relevant contemporary case of precision medicine out of Norway focuses on the care of a patient with cancer between the ventricles of the heart and esophagus, which had failed to respond to the standard regimen of therapies over four years.[xii] In a last-ditch effort, the patient elected to pay out-of-pocket for an experimental immunotherapy (nivolumab) at a private hospital. He experienced marked improvements and a reduction in the size of the tumor. Understandably, the patient tried to pursue further rounds of nivolumab at a public hospital. However, the hospital initially declined to pay for it given the “lack of evidence from randomised clinical trials for this drug relating to this [patient’s] condition.”[xiii] In rebuttal to this claim, the patient countered that he was actually similar to a subpopulation of patients who responded in “open‐label, single arm, phase 2 studies on another immune therapy drug” (pembrolizumab).[xiv] Given this interpretation of the prior studies and the patient’s response, further rounds of nivolumab were approved. Had the patient not had improvements in the tumor’s size following a round of nivolumab, then pembrolizumab’s prior empirical evidence in isolation would have been insufficient, inductively speaking, to justify his continued use of nivolumab.[xv] The case demonstrates a shift in reasoning from the traditional induction to abduction. The phenomenon of ‘cancer improvement’ is considered causally linked to nivolumab and its underlying physiological mechanisms.[xvi] However, “the weakness of abductions is that there may always be some other better, unknown explanation for an effect. The patient may for example belong to a special subgroup that spontaneously improves, or the change may be a placebo effect. This does not mean, however, that abductive inferences cannot be strong or reasonable, in the sense that they can make a conclusion probable.”[xvii] To demonstrate the limitations of relying on the abductive standard in isolation, commentators have pointed out that side effects in precision medicine are hard to rule out as being related to the initial intervention itself unless trends from a group of patients are taken into consideration.[xviii] As artificial intelligence (AI) assists the development of precision medicine for oncology, this uncertainty ought to be taken into consideration. The implementation of AI has been crucial to the development of precision medicine by providing a way to combine large patient datasets or a single patient with a large number of unique variables with machine learning to recommend matches based on statistics and probability of success upon which practitioners can base medical recommendations.[xix] The AI is usually not establishing a causal relationship[xx] – it is predicting. So, as AI bleeds into medical devices, like brain-computer interfaces, the same cautions about using abductive reasoning alone should be carried over. II. Responsive Neurostimulation, AI, and Personalized Medicine Like precision medicine in cancer treatment, computer-brain interface technology similarly focuses on the individual patient through personalized settings. In order to properly expose the intersection of AI, precision medicine, abductive reasoning, and implantable neurotechnologies, the descriptions of adaptive deep brain stimulation systems need to deepen.[xxi] As a broad summary of adaptive deep brain stimulation, to provide a patient with the therapeutic stimulation, a neural signal, typically referred to as a local field potential,[xxii] must first be detected and then interpreted by the device. The main adaptive deep brain stimulation device with premarket approval, the NeuroPace Responsive Neurostimulation system, is used to treat epilepsy by detecting and storing “programmer-defined phenomena.”[xxiii] Providers can optimize the detection settings of the device to align with the patient’s unique electrographic seizures as well as personalize the reacting stimulation’s parameters.[xxiv] The provider adjusts the technology based on trial and error. One day machine learning algorithms will be able to regularly aid this process in myriad ways, such as by identifying the specific stimulation settings a patient may respond to ahead of time based on their electrophysiological signatures.[xxv] Either way, with AI or programmers, adaptive neurostimulation technologies are individualized and therefore operate in line with precision medicine rather than standard treatments based on large clinical trials. Contemporary neurostimulation devices are not usually sophisticated enough to be prominent in AI discussions where the topics of neural networks, deep learning, generative models, and self-attention dominate the conversation. However, implantable high-density electrocorticography arrays (a much more sensitive version than adaptive deep brain stimulation systems use) have been used in combination with neural networks to help patients with neurologic deficits from a prior stroke “speak” through a virtual avatar.[xxvi] In some experimental situations, algorithms are optimizing stimulation parameters with increasing levels of independence.[xxvii] An example of neurostimulation that is analogous to the use of nivolumab in Norway surrounds a patient in the United States who was experiencing both treatment-resistant OCD and temporal lobe epilepsy.[xxviii]Given the refractory nature of her epilepsy, implantation of an adaptive deep brain stimulation system was indicated. As a form of experimental therapy, her treatment-resistant OCD was also indicated for the off-label use of an adaptive deep brain stimulation set-up. Another deep brain stimulation lead, other than the one implanted for epilepsy, was placed in the patient’s right nucleus accumbens and ventral pallidum region given the correlation these nuclei had with OCD symptoms in prior research. Following this, the patient underwent “1) ambulatory, patient-initiated magnet-swipe storage of data during moments of obsessive thoughts; (2) lab-based, naturalistic provocation of OCD-related distress (naturalistic provocation task); and (3) lab-based, VR [virtual reality] provocation of OCD-related distress (VR provocation task).”[xxix] Such signals were used to identify when to deliver the therapeutic stimulation in order to counter the OCD symptoms. Thankfully, following the procedure and calibration the patient exhibited marked improvements in their OCD symptoms and recently shared her results publicly.[xxx] In both cases, there is a similar level of abductive justification for the efficacy of the delivered therapy. In the case study in which the patient was treated with adaptive deep brain stimulation, they at least had their neural activity tested in various settings to determine the optimum parameters for treatment to avoid them being based on guesswork. Additionally, the adaptive deep brain stimulation lead was already placed before the calibration trials were conducted, meaning that the patient had already taken on the bulk of the procedural risk before the efficacy could be determined. Such an efficacy test could have been replicated in the first patient’s cancer treatment, had it been biopsied and tested against the remaining immunotherapies in vitro. Yet, in the case of cancer with few options, one previous dose of a drug that appeared to work on the patient may justify further doses. However, as the Norwegian case presents, corroboration with known responses to a similar drug (from a clinical trial) could be helpful to validate the treatment strategy. (It should be noted that both patients were resigned to these last resort options regardless of the efficacy of treatment.) There are some elements of inductive logic seen with adaptive deep brain stimulation research in general. For example, abductively the focus could be that patient X’s stimulation parameters are different from patient Y’s and patient Z’s. In contrast, when grouped as subjects who obtained personalized stimulation, patients X, Y, and Z demonstrate an inductive aspect to this approach’s safety and/or efficacy. The OCD case holds plenty of abductive characteristics in line with precision medicine’s approach to treating cancer and as more individuals try the method, there will be additional data. With the gradual integration of AI into brain-computer interfaces in the name of efficacy, this reliance on abduction will continue, if not grow, over time. Moving forward, if a responsive deep brain stimulation treatment is novel and individualized (like the dose of nivolumab) and there is some other suggestion of efficacy (like clinical similarities to other patients in the literature), then it may justify insurance coverage for the investigative intervention, absent other unrelated reasons to deny it. III. Ethical Implications and Next Steps While AI’s use in oncology and neurology is not yet as prominent as its use in other fields (e.g., radiology), it appears to be on the horizon for both.[xxxi] AI can be found in both the functioning of the neurotechnologies as well as the implementation of precision medicine. The increasing use of AI may serve to further individualize both oncologic and neurological therapies. Given these implications and the handful of publications cited in this article, it is important to have a nuanced evaluation of how these treatments, which heavily rely on abductive justification, ought to be managed. The just use an abductive approach may be difficult as AI infused precision medicine is further pursued. At baseline, such technology relies on a level of advanced technology literacy among the general public and could exclude populations who lack access to basic technological infrastructure or know-how from participation.[xxxii] Even among nations with adequate infrastructure, as more patients seek out implantable neurotechnologies, which require robust healthcare resources, the market will favor patient populations that can afford this complex care.[xxxiii] If patients already have the means to pay for an initial dose/use of a precision medicine product out of pocket, should insurance providers be required to cover subsequent treatments?[xxxiv] That is, if a first dose of a cancer drug or a deep brain stimulator over its initial battery life is successful, patients may feel justified in having the costs of further treatments covered. The Norwegian patient’s experience implies there is a precedent for the idea that some public insurance companies ought to cover successful cancer therapies, however, insurance companies may not all see themselves as obligated to cover neurotechnologies that rely on personalized settings or that are based on precision/abductive research more than on clinical trials. CONCLUSION The fact that the cases outlined above rely on abductive style of reasoning implies that there may not be as strong a justification for coverage by insurance, as they are both experimental and individualized, when compared to the more traditional large clinical trials in which groups have the same or a standardized protocol (settings/doses). If a study is examining the efficacy of a treatment with a large cohort of patients or with different experimental groups/phases, insurance companies may conclude that the resulting symptom improvements are more likely to be coming from the devices themselves. A preference for inductive justification may take priority when ruling in favor of funding someone’s continued use of an implantable neurostimulator. There are further nuances to this discussion surrounding the classifications of these interventions as research versus clinical care that warrant future exploration, since such a distinction is more of a scale[xxxv] than binary and could have significant impacts on the “right-to-try” approach to experimental therapies in the United States.[xxxvi] Namely, given the inherent limitations of conducting large cohort trials for deep brain stimulation interventions on patients with neuropsychiatric disorders, surgically innovative frameworks that blend abductive and inductive methodologies, like with sham stimulation phases, have traditionally been used.[xxxvii] Similarly, for adaptive brain-computer interface systems, if there are no large clinical trials and instead only publications that demonstrate that something similar worked for someone else, then, in addition to the evidence that the first treatment/dose worked for the patient in question, the balance of reasoning would be valid and arguably justify insurance coverage. As precision approaches to neurotechnology become more common, frameworks for evaluating efficacy will be crucial both for insurance coverage and for clinical decision making. ACKNOWLEDGEMENT This article was originally written as an assignment for Dr. Francis Shen’s “Bioethics & AI” course at Harvard’s Center for Bioethics. I would like to thank Dr. Shen for his comments as well as my colleagues in the Lázaro-Muñoz Lab for their feedback. - [i] Jonathan Kimmelman and Ian Tannock, “The Paradox of Precision Medicine,” Nature Reviews. Clinical Oncology 15, no. 6 (June 2018): 341–42, https://doi.org/10.1038/s41571-018-0016-0. [ii] Henrik Vogt and Bjørn Hofmann, “How Precision Medicine Changes Medical Epistemology: A Formative Case from Norway,” Journal of Evaluation in Clinical Practice 28, no. 6 (December 2022): 1205–12, https://doi.org/10.1111/jep.13649. [iii] David Barrett and Ahtisham Younas, “Induction, Deduction and Abduction,” Evidence-Based Nursing 27, no. 1 (January 1, 2024): 6–7, https://doi.org/10.1136/ebnurs-2023-103873. [iv] Vogt and Hofmann, “How Precision Medicine Changes Medical Epistemology,” 1208. [v] Wireko Andrew Awuah et al., “Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications,” World Neurosurgery, May 22, 2024, S1878-8750(24)00867-2, https://doi.org/10.1016/j.wneu.2024.05.104. [vi] Mark Riedl, “A Very Gentle Introduction to Large Language Models without the Hype,” Medium (blog), May 25, 2023, https://mark-riedl.medium.com/a-very-gentle-introduction-to-large-language-models-without-the-hype-5f67941fa59e. [vii] David E. Burdette and Barbara E. Swartz, “Chapter 4 - Responsive Neurostimulation,” in Neurostimulation for Epilepsy, ed. Vikram R. Rao (Academic Press, 2023), 97–132, https://doi.org/10.1016/B978-0-323-91702-5.00002-5. [viii] Kimmelman and Tannock, 2018. [ix] Kimmelman and Tannock, 2018. [x] Simon Lohse, “Mapping Uncertainty in Precision Medicine: A Systematic Scoping Review,” Journal of Evaluation in Clinical Practice 29, no. 3 (April 2023): 554–64, https://doi.org/10.1111/jep.13789. [xi] Kimmelman and Tannock, “The Paradox of Precision Medicine.” [xii] Vogt and Hofmann, 1206. [xiii] Vogt and Hofmann, 1206. [xiv] Vogt and Hofmann, 1206. [xv] Vogt and Hofmann, 1207. [xvi] Vogt and Hofmann, 1207. [xvii] Vogt and Hofmann, 1207. [xviii] Vogt and Hofmann, 1210. [xix] Mehar Sahu et al., “Chapter Three - Artificial Intelligence and Machine Learning in Precision Medicine: A Paradigm Shift in Big Data Analysis,” in Progress in Molecular Biology and Translational Science, ed. David B. Teplow, vol. 190, 1 vols., Precision Medicine (Academic Press, 2022), 57–100, https://doi.org/10.1016/bs.pmbts.2022.03.002. [xx] Stefan Feuerriegel et al., “Causal Machine Learning for Predicting Treatment Outcomes,” Nature Medicine 30, no. 4 (April 2024): 958–68, https://doi.org/10.1038/s41591-024-02902-1. [xxi] Sunderland Baker et al., “Ethical Considerations in Closed Loop Deep Brain Stimulation,” Deep Brain Stimulation 3 (October 1, 2023): 8–15, https://doi.org/10.1016/j.jdbs.2023.11.001. [xxii] David Haslacher et al., “AI for Brain-Computer Interfaces,” 2024, 7, https://doi.org/10.1016/bs.dnb.2024.02.003. [xxiii] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 103–4; “Premarket Approval (PMA),” https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P100026. [xxiv] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 104. [xxv] Burdette and Swartz, 126. [xxvi] Sean L. Metzger et al., “A High-Performance Neuroprosthesis for Speech Decoding and Avatar Control,” Nature 620, no. 7976 (August 2023): 1037–46, https://doi.org/10.1038/s41586-023-06443-4. [xxvii] Hao Fang and Yuxiao Yang, “Predictive Neuromodulation of Cingulo-Frontal Neural Dynamics in Major Depressive Disorder Using a Brain-Computer Interface System: A Simulation Study,” Frontiers in Computational Neuroscience 17 (March 6, 2023), https://doi.org/10.3389/fncom.2023.1119685; Mahsa Malekmohammadi et al., “Kinematic Adaptive Deep Brain Stimulation for Resting Tremor in Parkinson’s Disease,” Movement Disorders 31, no. 3 (2016): 426–28, https://doi.org/10.1002/mds.26482. [xxviii] Young-Hoon Nho et al., “Responsive Deep Brain Stimulation Guided by Ventral Striatal Electrophysiology of Obsession Durably Ameliorates Compulsion,” Neuron 0, no. 0 (October 20, 2023), https://doi.org/10.1016/j.neuron.2023.09.034. [xxix] Nho et al. [xxx] Nho et al.; Erik Robinson, “Brain Implant at OHSU Successfully Controls Both Seizures and OCD,” OHSU News, accessed March 3, 2024, https://news.ohsu.edu/2023/10/25/brain-implant-at-ohsu-successfully-controls-both-seizures-and-ocd. [xxxi] Awuah et al., “Bridging Minds and Machines”; Haslacher et al., “AI for Brain-Computer Interfaces.” [xxxii] Awuah et al., “Bridging Minds and Machines.” [xxxiii] Sara Green, Barbara Prainsack, and Maya Sabatello, “The Roots of (in)Equity in Precision Medicine: Gaps in the Discourse,” Personalized Medicine 21, no. 1 (January 2024): 5–9, https://doi.org/10.2217/pme-2023-0097. [xxxiv] Green, Prainsack, and Sabatello, 7. [xxxv] Robyn Bluhm and Kirstin Borgerson, “An Epistemic Argument for Research-Practice Integration in Medicine,” The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine 43, no. 4 (July 9, 2018): 469–84, https://doi.org/10.1093/jmp/jhy009. [xxxvi] Vijay Mahant, “‘Right-to-Try’ Experimental Drugs: An Overview,” Journal of Translational Medicine 18 (June 23, 2020): 253, https://doi.org/10.1186/s12967-020-02427-4. [xxxvii] Michael S. Okun et al., “Deep Brain Stimulation in the Internal Capsule and Nucleus Accumbens Region: Responses Observed during Active and Sham Programming,” Journal of Neurology, Neurosurgery & Psychiatry 78, no. 3 (March 1, 2007): 310–14, https://doi.org/10.1136/jnnp.2006.095315. (shrink)

I give a defense of the Massive Modularity hypothesis: the view that the mind is composed of discrete, encapsulated, informationally isolated computational structures dedicated to particular problem domains. This view contrasts with Psychological Rationalism: the view that mental structures take the form of unencapsulated representational items, all available as inputs to one domain-general computational processor. I argue that although Psychological Rationalism is in principle able to overcome the `intractability objection', the view must borrow many features of a massively modular (...) architecture in order to do so, that although it can, in principle, overcome the `optimality objection', the way it does so does not correlate with the way we think, and that although it can, in principle, respond to the `argument from biology', it cannot do so without advancing an unrealistic and unsupported account of cognitive evolution. (shrink)

Any analysis of the concept of computation as it occurs in the context of a discussion of the computational model of the mind must be consonant with the philosophic burden traditionally carried by that concept as providing a bridge between a physical and a psychological description of an agent. With this analysis in hand, one may ask the question: are connectionist-based systems consistent with the computational model of the mind? The answer depends upon which of several versions of (...) connectionism one presupposes: non-learning connectionist-based systems as simulated on digital computers are consistent with the computational model of the mind, whereas connectionist-based systems (/dynamical systems) qua analog systems are not. (shrink)

Physical reality as an explanatory model is an abstraction of the mind. Every perceptual system is a user interface, like the dashboard of an aeroplane or the desktop of a computer. We do not see or otherwise perceive reality but only interface with reality. The user interface concept is a starting point for a critical dialogue with those epistemic theories that present themselves as veridical and take explanatory abstractions as ontological primitives. At the heart of (...) any scientific model are assumptions about which things exist, how they are related, and how we can know them. Scientific models take our knowledge beyond ordinary experience toward explanatory abstractions. The main problem with veridical models lies in why we cannot express our theories and the explanatory abstractions associated with them other than through classical perceptual interface symbols. This study analyses the limits, possibilities and constraints of explanatory abstractions. (shrink)

This author explores the intersection between cognitive science, as exemplified by the computational model of mind, and epistemologyó specifically, epistemic justification theory. Her analysis leads to the conclusion that some very specific and somewhat technical issues in epistemic justification theory can be at least partially resolved, if not entirely cleared up, by the use of the computational model. The third and fourth chapters of this work are devoted directly to that effort. Chapter one examines in detail epistemology and cognitive (...) sciences, while chapters two and three offer a thorough introduction to standard epistemic justification theory. Finally, chapter five is a critique of the computational model. (shrink)

Groups are only real, and only serve as a basis for collective action, when their members perceive them to be real. For a computational model to have analytic fidelity and predictive validity it, therefore, needs to engage with the psychological reality of groups, their internal structure, and their structuring by the social context in which they function.

Is your mind the same thing as your brain, or are there aspects of mind beyond the brain's biology? This is the mind-body problem, and it has captivated curious minds since the dawn of human contemplation. Today many insist that the mind is completely reducible to the brain. But is that claim justified? In this stimulating anthology, twenty-five philosophers and scientists offer fresh insights into the mind-brain debate, drawing on psychology, neurology, philosophy, computer science, (...) and neurosurgery. Their provocative conclusion? The mind is indeed more than the brain. (shrink)

This volume examines two main questions: What is linguistics about? And how do the results of linguistic theorizing bear on inquiry in related fields, particularly in psychology? The book develops views that depart from received wisdom in both philosophy and linguistics. With regard to questions concerning the subject matter, methodological goals, and ontological commitments of formal syntactic theorizing, it argues that the cognitive conception adopted by most linguists and philosophers is not the only acceptable view, and that the arguments in (...) its favor collapse under scrutiny. Nevertheless, as the book shows, a detailed examination of the relevant psycholinguistic results and computational models does support the claim that the theoretical constructs of formal linguistics are operative in real-time language comprehension. These constructs fall into two categories: mental phrase markers and mental syntactic principles. Both are indeed psychologically real, but in importantly different ways. The book concludes by drawing attention to the importance of the often-elided distinction between personal and subpersonal psychological states and processes, as well as the logical character of dispositional and occurrent states. By clarifying these concepts, particularly by reference to up-and-running psychological and computational models, the book yields a richer and more satisfying perspective on the psychological reality of language. (shrink)

The frame problem is a fundamental challenge in AI, and the Lucas-Penrose argument is supposed to show a limitation of AI if it is successful at all. Here we discuss both of them from a unified Gödelian point of view. We give an informational reformulation of the frame problem, which turns out to be tightly intertwined with the nature of Gödelian incompleteness in the sense that they both hinge upon the finitarity condition of agents or systems, without which their alleged (...) limitations can readily be overcome, and that they can both be seen as instances of the fundamental discrepancy between finitary beings and infinitary reality. We then revisit the Lucas-Penrose argument, elaborating a version of it which indicates the impossibility of information physics or the computational theory of the universe. It turns out through a finer analysis that if the Lucas-Penrose argument is accepted then information physics is impossible too; the possibility of AI or the computational theory of the mind is thus linked with the possibility of information physics or the computational theory of the universe. We finally reconsider the Penrose’s Quantum Mind Thesis in light of recent advances in quantum modelling of cognition, giving a structural reformulation of it and thereby shedding new light on what is problematic in the Quantum Mind Thesis. Overall, we consider it promising to link the computational theory of the mind with the computational theory of the universe; their integration would allow us to go beyond the Cartesian dualism, giving, in particular, an incarnation of Chalmers’ double-aspect theory of information. (shrink)

Hilary Putnam, who may have been the first philosopher to advance the notion that the computer is an apt model for the mind, takes a radically new view of his...

Computational models of memory presented in this issue reflect varied empirical data and levels of representation. From mathematical models to neural and cognitive architectures, all aim to converge on a unified theory of the mind.

A book review on Keith A. Chandler’s The mind paradigm: A Unified Model of Mental and Physical Reality.

In lieu of an abstract, here is a brief excerpt of the content:Emergent Spacetime, the Megastructure Problem, and the Metaphysics of the SelfSusan Schneider (bio)The aim of this article is to introduce new thoughts on some pressing topics relating to my book, Artificial You, ranging from the fundamental nature of reality to quantum theory and emergence in large language models (LLM) like GPT-4. Since Artificial You was published, the innovations in the domain of AI chatbots like GPT-4 have (...) been rapid-fire, and at times dizzying. The thoughtful discussion in this symposium offers an important opportunity for cross-cultural engagement on these issues and more. I am grateful for the opportunity to learn from the insights of these authors and editors. [End Page 314]The order of my comments will be as follows: in the context of considering Christian Coseru's intriguing response (section 1), I begin by discussing the nature of the self, which I use as a springboard into larger, vexing issues about the nature of fundamental reality, given work on emergent spacetime (ES) (section 2). I offer a "Prototime Interpretation" of quantum mechanics in which spacetime emerges from a prototemporal arena, and use this idea to explore the nature of the self and mind.Using the assumption of ES, I devise an approach to metaphysically fundamental objects (which I call "quantum objects") based on quantum entanglement and the bundle theory of substance. I illustrate how the self or mind, insofar as it is a substance that supervenes on quantum objects, could be partly or fully determined by the properties of the quantum objects on which it supervenes. Intriguingly, this would mean that the self or mind is not located in any singular point in space at a given time. I also explain how the Prototime Interpretation of quantum mechanics is compatible with change and causation at the fundamental level.In section 3 I turn to Inês Hipólito's interesting article, discussing the import of not underestimating AI autonomy in light of the increasingly sophisticated large-language models (LLMs).1 I raise the "AI Megastructure Problem," a new problem of AI control arising in the context of the incipient AGI systems that are now populating the AI ecosystem. The problem, which arose from a collaboration with Kyle Kilian, considers the possibility of emergent properties from the interaction of the LLMs and other AI's in the AI ecosystem (Schneider and Kilian 2023). Then, turning to Noam Cook's intriguing discussion of Japanese philosophy, I consider the pressing issue of testing for machine consciousness, devising a modified version of my AI consciousness test (ACT), originally developed with astrophysicist Edwin Turner. I add an "interpretability component" to the ACT to deal with the case of LLMs (section 4).I. Patternism and the Biological Process ViewChristian Coseru's intriguing piece focuses on the chapters on personal identity in Artificial You, proposing that when it comes to the nature of the self, one should adopt "a biologically informed process-ontology perspective on personal identity," which "means recognizing that organic identity is different in kind: it is an identity of form rather than of matter."His position is akin to the patternist views that I argue against in Artificial You in the sense that it embraces the idea that form, rather than the particular matter that comprises one's brain and body, is key to one's survival over time. But there are important differences as well. For transhumanists tend to believe that the self can survive radical changes in one's substrate, transitioning from thinking with a biological brain to computing with a network of microchips, for instance. Coseru departs from this, writing: [End Page 315]As organisms, persons endure by undergoing constant change on a fundamentally cellular level, including the change in the neural pathways that realize the cognitive processes of thinking and expressing these very thoughts right now.Furthermore:[I]f persons are processes rather than things, then they cannot be cognitively enhanced by augmenting, replacing or transferring their functioning parts (e.g., brain cells).I will call this position the "Biological Process View" (BPV). I have several questions about it. First, how does this position solve one of the key problems I raised for... (shrink)

Molecular models are typical topics of chemical research depending on the technical standards of observation, computation, and representation. Mathematically, molecular structures have been represented by means of graph theory, topology, differential equations, and numerical procedures. With the increasing capabilities of computer networks, computational models and computer-assisted visualization become an essential part of chemical research. Object-oriented programming languages create a virtual reality of chemical structures opening new avenues of exploration and collaboration in chemistry. From an epistemic (...) point of view, virtual reality is a new computer-assisted tool of human imagination and recognition. (shrink)

Artificial intelligence has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised learning (...) methods—namely lasso penalties, bagging, and boosting—offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies. (shrink)

The paper describes a computational model that we have implemented in an experimental dialogue system. Communication in a natural language between two participants A and B is considered, where A has a communicative goal that his/her partner B will make a decision to perform an action D. A argues the usefulness, pleasantness, etc. of D, in order to guide B's reasoning in a desirable direction. A computational model of argumentation is developed, which includes reasoning. Our model is based on the (...) studies in the common-sense conception of how the human mind works in such situations. Theoretical considerations are followed by an analysis of Estonian spoken human–human dialogues. First, calls of clients to travel agencies are studied where a travel agent could use various arguments in order to persuade a client to book a trip. The analysis demonstrates that clients are primarily looking for information; argumentation occurs in a small number of dialogues. Secondly, calls of s... (shrink)

In his famous 1982 paper, Allen Newell [22, 23] introduced the notion of knowledge level to indicate a level of analysis, and prediction, of the rational behavior of a cognitive arti cial agent. This analysis concerns the investigation about the availability of the agent knowledge, in order to pursue its own goals, and is based on the so-called Rationality Principle (an assumption according to which "an agent will use the knowledge it has of its environment to achieve its goals" [22, (...) p. 17]. By using the Newell's own words: "To treat a system at the knowledge level is to treat it as having some knowledge, some goals, and believing it will do whatever is within its power to attain its goals, in so far as its knowledge indicates" [22, p. 13]. In the last decades, the importance of the knowledge level has been historically and system- atically downsized by the research area in cognitive architectures (CAs), whose interests have been mainly focused on the analysis and the development of mechanisms and the processes governing human and (arti cial) cognition. The knowledge level in CAs, however, represents a crucial level of analysis for the development of such arti cial general systems and therefore deserves greater research attention [17]. In the following, we will discuss areas of broad agree- ment and outline the main problematic aspects that should be faced within a Common Model of Cognition [12]. Such aspects, departing from an analysis at the knowledge level, also clearly impact both lower (e.g. representational) and higher (e.g. social) levels. (shrink)

A review of Manfred Spitzer's The mind within the net: Models of learning, thinking, and acting.

Through the concept of self-organizing consciousness (SOC), we posit that the dynamic of the mind stems from the recurrent interplay between the properties of conscious experiences and the properties of the world, hence making it unnecessary to postulate the existence of an unconscious mental level. In contrast, arguments are provided by commentators for the need for a functional level of organization located between the neural and the conscious. Other commentaries challenge us concerning the ability of our model to account (...) for specific phenomena in the domains of language, reasoning, incubation, and creativity. The possibility of unconscious semantic access and other alleged instances of adapted performance in the absence of any conscious counterpart are also put forth as evidence against our view. Our response emphasizes the fact that opponents to our model often present as factual, theory-free evidence which is in fact nothing more than the postulates underlying the classical computational framework. (shrink)

Much of computational cognitive science construes human cognitive capacities as representational capacities, or as involving representation in some way. Computational theories of vision, for example, typically posit structures that represent edges in the distal scene. Neurons are often said to represent elements of their receptive fields. Despite the ubiquity of representational talk in computational theorizing there is surprisingly little consensus about how such claims are to be understood. The point of this chapter is to sketch an account of the nature (...) and function of representation in computational cognitive models. (shrink)

The scope of Platonism is extended by introducing the concept of a “Platonic computer” which is incorporated in metacomputics. The theoretical framework of metacomputics postulates that a Platonic computer exists in the realm of Forms and is made by, of, with, and from metaconsciousness. Metaconsciousness is defined as the “power to conceive, to perceive, and to be self-aware” and is the formless, con-tentless infinite potentiality. Metacomputics models how metaconsciousness generates the perceived actualities including abstract entities and physical (...) and nonphysical realities. It is postulated that this is achieved via digital computation using the Platonic computer. The introduction of a Platonic computer into the realm of Forms thus bridges the “inverse explanatory gap” and therefore solves the “inverse hard problem of consciousness” in the philosophy of mind. (shrink)

The aim of this paper is to address the question: Can an artificial neural network (ANN) model be used as a possible characterization of the power of the human mind? We will discuss what might be the relationship between such a model and its natural counterpart. A possible characterization of the different power capabilities of the mind is suggested in terms of the information contained (in its computational complexity) or achievable by it. Such characterization takes advantage of recent (...) results based on natural neural networks (NNN) and the computational power of arbitrary artificial neural networks (ANN). The possible acceptance of neural networks as the model of the human mind's operation makes the aforementioned quite relevant. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Journal of Aesthetic Education 40.1 (2006) 35-53 [Access article in PDF] "Truth Is What Works": Francisco J. Varela on Cognitive Science, Buddhism, the Inseparability of Subject and Object, and the Exaggerations of Constructivism—A Conversation Francisco J. Varela Bernhard Poerksen Institut für Journalistik und Kommunikationswissenschaft Universität Hamburg Francisco J. Varela (1946-2001) studied biology in Santiago de Chile, obtained his doctorate 1970 at Harvard University with a dissertation on the (...) insect eye, and worked there for some time in the laboratory of Torsten Wiesel, the later Nobel Laureate for medicine. From his scientific beginnings as a researcher in biology, he not only studied and practiced biology but, resisting the dominating mainstream, pursued a research program that ignored and broke down traditional disciplinary boundaries. This research program is best characterized as experimental epistemology, a concept introduced by the neuropsychiatrist and cybernetician Warren S. McCulloch. Francisco J. Varela's great aspiration was to examine and answer the philosophical ur-question of cognition with scientific precision and with the help of the best possible theoretical framework. [End Page 35]Having obtained his doctorate, he went back to Chile to work as a professor of biology together with Humberto R. Maturana. He contributed to the writing of the theory of autopoiesis, which was to cause a furor in the world of science as a universally applicable explanatory model. After the overthrow of Allende and the installation of the dictatorship by the putsch general Pinochet, Francisco J. Varela first escaped to Costa Rica, then became a professor at the American University of Colorado and University of New York, and finally returned in 1980 to the University of Chile in Santiago for five years. Temporary positions as guest professor for neurobiology, philosophy, and cognitive science in Germany, Switzerland, and France led him to Paris in the end, where he worked as research director of the Centre National de Recherche Scientifique until his death on May 28, 2001.In his research work embracing cognitive science, evolutionary theory, and immunology, Francisco J. Varela, constantly inspired by his fundamental interest in the key questions of epistemology, gave the epistemological debate a new orientation. In his thinking he refuses to accept the strict separation of subject and object, of knower and known, which as a rule unites realists and constructivists alike. Francisco J. Varela rejects the fundamental dualism dividing mind and world, which had shaped Western philosophy from its earliest beginnings. He does not subscribe to the idea that human individuals can invent their own realities—blindly and arbitrarily, and without experiencing any resistance from the external world and all other things given. He equally distances himself, however, from the diametrically opposite position that overstates the eigenpower of the world of objects. The external world and all other things given cannot determine what happens in an organism. Varela's claim is that individual and world create each other. The Computational Model of the Mind Poerksen: The ancient key questions of philosophy are at the center of modern cognitive science. What is the essence of the mind? Do our conceptions represent a given world, which is independent from our minds? What is the formative power of external objects over our perceptions? How does cognition function? The search for an adequate answer and an improved understanding of the human mind has led many cognitive scientists to entertain the assumption that the brain is actually a kind of computer. Memory is taken to be a store. Thinking and perceiving are understood as data processing in the sense that an independent external world is computationally transformed into symbols and represented in the organism in this manner. You are very critical of this view. Why?Varela: If the brain is considered as a kind of computer, then cognitive research is limited to discovering certain self-sufficient shapes—the symbols—together with the rules [End Page 36] governing them—the programs. But this search for symbols and programs will never be profitable because it... (shrink)

Penrose proved that a computational or formalizable theory of the brainís cognitive functioning is impossible, but suggested that a physical non-computational and non-formalizable one may be viable. Arguments as to why Penroseís program is unrealizable are presented. The main argument is that a non-formalizable theory should be verbal. However, verbal paradoxes based on Cantorís diagonal processes show the impossibility of a consistent verbal theory of the brain comprising its arithmetical cognition. It is suggested that comprehensive theories of the human brain (...) and of physical experience are Kantian rather than Platonic ideas. This suggestion is likewise based on arguments related to diagonal processes. (shrink)

In his book “Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence”, Matt Carter presents a comprehensive exploration of the philosophical questions surrounding artificial intelligence (AI). Carter argues that the development of AI is not merely a technological challenge but fundamentally a philosophical one. He delves into key issues like the nature of mental states, the limits of introspection, the implications of memory decay, and the functionalist framework that allows for the possibility of AI. Carter contrasts functionalism with (...) reductive materialism, highlighting how the former accommodates the concept of artificial minds. He also emphasizes the significance of computationalism, which combines functionalism with computational theory to provide a robust explanation for mental processes. The book further discusses formal systems, the role of register machines in computation, and the inherent challenges in AI research, particularly in developing natural language processing systems. Carter’s work underscores the limitations of current computational models of cognition while remaining hopeful that advances in neuroscience may lead to stronger AI systems in the future. (shrink)

Science fiction is fast becoming reality as scientists and engineers seek to develop new ways of directly accessing and controlling our brains through brain-computer and even brain-to-brain interfaces. If such research is to receive continuing public approval and support—and not invite opposition—it must anticipate the special ethical challenges it creates. By pointing to some of the acute concerns raised by neural engineering technologies—around issues of identity, normality, authority, responsibility, privacy, and justice—Eran Klein and colleagues model and stimulate the (...) kind of reflection that will be needed as new brain technologies are developed. And yet, as I read their informative discussion, I find myself asking even more questions. One concerns the ethical novelty of these technologies. For example, in terms of questions of identity, normality, responsibility, and authority, how different is deep brain stimulation from the potent psychiatric drugs that have been administered for decades? How different in terms of issues of liability associated with malfunction or misuse are brain-computer interfaces from other complex devices now routinely in use? Neither ethics nor law will have to rethink things from scratch as new neural devices and capabilities are introduced: the groundwork for answering some of these questions has already been laid in a variety of fields from bioethics, to health and product liability law. Nevertheless, I conclude that there are new questions here. It is not simply the fact that neural technologies pose questions of identity, privacy, and the like but that they do so with a degree of intensity that creates qualitatively new challenges. (shrink)

In sentences such as “Some dogs are mammals,” the literal semantic meaning (“Some and possibly all dogs are mammals”) conflicts with the pragmatic meaning (“Not all dogs are mammals,” known as a scalar implicature). Prior work has shown that adults vary widely in the extent to which they adopt the semantic or pragmatic meaning of such utterances, yet the underlying reason for this variation is unknown. Drawing on theoretical models of scalar implicature derivation, we explore the hypothesis that the (...) cognitive abilities of executive function (EF) and theory of mind (ToM) contribute to this observed variation. In Experiment 1, we show that individuals with better ToM are more likely to compute a scalar implicature and adopt the pragmatic meaning of an utterance; however, EF makes no unique contribution to scalar implicature comprehension after accounting for ToM. In Experiment 2, we replicate this finding and assess whether it generalizes to the comprehension of other pragmatic phenomena such as indirect requests (e.g., “It's hot in here” uttered to ask for something to be done) and metaphor (e.g., “to harvest courage”). This is the first evidence that differences in ToM are associated with pragmatic competence in neurotypical adults across distinct pragmatic phenomena. (shrink)

I argue that there are no plausible non-representational explanations of episodes of hallucination. To make the discussion more specific, I focus on visual hallucinations in Charles Bonnet syndrome. I claim that the character of such hallucinatory experiences cannot be explained away non-representationally, for they cannot be taken as simple failures of cognizing or as failures of contact with external reality—such failures being the only genuinely non-representational explanations of hallucinations and cognitive errors in general. I briefly introduce a recent computational model (...) of hallucination, which relies on generative models in the brain, and argue that the model is a prime example of a representational explanation referring to representational mechanisms. The notion of the representational mechanism is elucidated, and it is argued that hallucinations—and other kinds of representations—cannot be exorcised from the cognitive sciences. (shrink)

The phenomenon of consciousness has always been a central question for philosophers and scientists. Emerging in the past decade are new approaches to the understanding of consciousness in a scientific light. This book presents a series of essays by leading thinkers giving an account of the current ideas prevalent in the scientific study of consciousness. The value of the book lies in the discussion of this interesting though complex subject from different points of view ranging from physics, computer science (...) to the cognitive sciences. Reviews of controversial ideas related to the philosophy of mind from western and eastern sources including classical Indian first person methodologies provide a breadth of coverage that has seldom been attempted in a book before. Additionally, chapters relating to the new approaches in computational modelling of higher order cognitive function and consciousness are included. The book is of great value for established as well as young researchers from a wide cross-section of interdisciplinary scientific backgrounds, aiming to pursue research in this field, as well as an informed public. * Presents the latest developments in the scientific study of consciousness * Critically reviews different theoretical and philosophical explanations related to the subject * An important book for both students and researchers in designing research projects on consciousness. (shrink)

What is the relationship between consciousness and our brain? Are they one and the same? Who are we really? The Consciousness Network presents a novel account of one of the greatest scientific challenges of the 21st century: understanding the connection between brain and mind. The book explores remarkable cases of patients who demonstrate how our impression of reality is created by the brain. Age-old questions about dreams, colour perception, phantom sensations and hallucinations are illuminated by surprising discoveries from (...) the latest brain research. How does consciousness differ from memory, emotions and behaviour? How did it develop during the evolution of life on earth, and does it serve a purpose? Does the brain leave room for free will? In this unique blend of philosophy, history, psychology and neuroscience, Cyriel Pennartz breaks new ground by presenting an original theory of brain and mind, substantiated by brain research in patients and healthy people. This theory, inspired by the 17th century philosopher Spinoza, goes significantly deeper than current thinking based on computer models or artificial intelligence. The Consciousness Network is essential reading for students working at the interface of neuroscience, cognitive psychology, philosophy of mind and cognitive science, as well as anyone interested in consciousness and the brain. (shrink)

Computational modeling plays an increasingly important explanatory role in cases where we investigate systems or problems that exceed our native epistemic capacities. One clear case where technological enhancement is indispensable involves the study of complex systems.1 However, even in contexts where the number of parameters and interactions that define a problem is small, simple systems sometimes exhibit non-linear features which computational models can illustrate and track. In recent decades, computational models have been proposed as a way to assist (...) us in understanding emergent phenomena. (shrink)

Recent years have witnessed rapidly increasing interests in developing quantum theoretical models of human cognition. Quantum mechanisms have been taken seriously to describe how the mind reasons and decides. Papers in this special issue report the newest results in the field. Here we discuss why the two levels of commitment, treating the human brain as a quantum computer and merely adopting abstract quantum probability principles to model human cognition, should be integrated. We speculate that quantum cognition (...) class='Hi'>models gain greater modeling power due to a richer representation scheme. (shrink)

We offer philosophical motivations for a method we call Virtual World Cognitive Science (VW CogSci), in which researchers use virtual embodied agents that are embedded in virtual worlds to explore questions in the field of Cognitive Science. We focus on questions about mental and linguistic representation and the ways that such computational modeling can add rigor to philosophical thought experiments, as well as the terminology used in the scientific study of such representations. We find that this method forces researchers to (...) take a god’s-eye view when describing dynamical relationships between entities in minds and entities in an environment in a way that eliminates the need for problematic talk of belief and concept types, such as the belief that cats are silly, and the concept CAT, while preserving belief and concept tokens in individual cognizers’ minds. We conclude with some further key advantages of VW CogSci for the scientific study of mental and linguistic representation and for Cognitive Science more broadly. (shrink)

theories. What was Freud thinking, when, and why and what were the major influences which shaped his ideas? We follow the inner movement of his theory construction, its meaning and coherence, as well as his conceptual logic and personal directions concerning his evolving views of the reciprocal interactions between mind and body, the motivational force of instinctual drives, and the dominant role of sexuality rooted in evolutionary biology in human development, behaviour, and the creation of neurotic disturbances. We follow (...) Freud's construction and sequential reconstructions of his theoretical models concerning the nature, dynamics, and principles of unconscious mental functioning, including his changing concepts on the nature and purpose of dreams. We trace his changing views on the role of deferred action of early childhood experiences and the determining role of unconscious fantasy, psychic reality, in the formation of adult character structure and neuroses. Through such historical analysis this book provides grounding for a meaningful understanding of Freud's familiar concepts: id, ego, superego, and the Oedipus complex. We explore what these concepts meant to Freud, why he conceived them, and what functions they served in his theory of mind. This is the perfect book for students and trainees wanting to learn more about the development of Freud's ideas, as well as for established psychoanalysts and psychotherapists interested in expanding their knowledge of Freud's theories. (shrink)