This paper considers the way mathematical and computational models are used in network neuroscience to deliver mechanistic explanations. Two case studies are considered: Recent work on klinotaxis by Caenorhabditis elegans, and a longstanding research effort on the network basis of schizophrenia in humans. These case studies illustrate the various ways in which network, simulation and dynamical models contribute to the aim of representing and understanding network mechanisms in the brain, and thus, of delivering mechanistic explanations. (...) After outlining this mechanistic construal of network neuroscience, two concerns are addressed. In response to the concern that functional network models are non-explanatory, it is argued that functional network models are in fact explanatory mechanism sketches. In response to the concern that models which emphasize a network’s organization over its composition do not explain mechanistically, it is argued that this emphasis is both appropriate and consistent with the principles of mechanistic explanation. What emerges is an improved understanding of the ways in which mathematical and computational models are deployed in network neuroscience, as well as an improved conception of mechanistic explanation in general. (shrink)

In this paper, I examined whether evidence from the neuroscience of morality supports the standard models of phronesis, i.e., Jubilee and Aretai Centre Models. The standard models explain phronesis as a multifaceted construct based on interaction and coordination among functional components. I reviewed recent neuroscience studies focusing on brain networks associated with morality and their connectivity to examine the validity of the models. Simultaneously, I discussed whether the evidence helps the models address challenges, particularly those from the phronesis (...) eliminativism. Neuroscientific evidence supported the importance of brain networks, i.e., the default mode, salience, and central executive functioning networks, in moral functioning in general. The findings favorably supported the multifaceted and integrative nature of phronesis proposed by the standard models. Finally, I considered how the two models could explain the mechanisms of phronesis more integratively based on neuroscientific findings. At the end of this paper, with the evidence, I proposed several practical ideas to promote the cultivation of phronesis, e.g., the consideration of coordination among components for moral functioning and the use of moral exemplars. (shrink)

A fundamental claim associated with parallel distributed processing theories of cognition is that knowledge is coded in a distributed manner in mind and brain. This approach rejects the claim that knowledge is coded in a localist fashion, with words, objects, and simple concepts, that is, coded with their own dedicated representations. One of the putative advantages of this approach is that the theories are biologically plausible. Indeed, advocates of the PDP approach often highlight the close parallels between distributed representations learned (...) in connectionist models and neural coding in brain and often dismiss localist theories as biologically implausible. The author reviews a range a data that strongly challenge this claim and shows that localist models provide a better account of single-cell recording studies. The author also contrast local and alternative distributed coding schemes and argues that common rejection of grandmother cell theories in neuroscience is due to a misunderstanding about how localist models behave. The author concludes that the localist representations embedded in theories of perception and cognition are consistent with neuroscience; biology only calls into question the distributed representations often learned in PDP models. (shrink)

The Necker cube and the productive class of related stimuli involving multiple depth interpretations driven by corner-like line junctions are often taken to be ambiguous. This idea is normally taken to be as little in need of defense as the claim that the Necker cube gives rise to multiple distinct percepts. In the philosophy of language, it is taken to be a substantive question whether a stimulus that affords multiple interpretations is a case of ambiguity. If we take into account (...) what have been identified as hallmark features of ambiguity and look at the empirical record, it appears that the Necker cube and related stimuli are not ambiguous. I argue that this raises problems for extant models of multistable perception in cognitive neuroscience insofar as they are purported to apply to these stimuli. Helpfully, similar considerations also yield reasons to suggest that the relevant models are well motivated for other instances of multistable perception. However, a different breed of model seems to be required for the Necker cube and related stimuli. I end with a sketch how one may go about designing such a model relying on oscillatory patters in neural firing. I suggest that distinctions normally confined to the philosophy of language are important for the study of perception, a perspective with a growing number of adherents. (shrink)

Neuroscience can relate to ethics and normative issues via the brain’s cognitive control network. This network accomplishes several executive processes, such as planning, task-switching, monitoring, and inhibiting. These processes allow us to increase the accuracy of our perceptions and our memory recall. They also allow us to plan much farther into the future, and with much more detail than any of our fellow mammals. These abilities also make us fitting subjects for responsibility claims. Their activity, or lack (...) thereof, is at the heart of culpability. For instance, planning to kill someone is strong evidence of what the law calls mens rea—a guilty mind. Claims about norms, or ethical “should” claims, express two-level propositions, directed at the behaving person at one level, and at that person’s mind and cognitive control network at another level. Thus, “People should stop themselves from hurting others,” is a claim about how people should behave and also a claim about how their cognitive control networks should behave—i.e., they should inhibit harmful behavior, or the intentions leading up to it. Planning is both an ability of the full person, and of that person’s mind. Neuroscience affirms the common notion, seen both in law and folk psychology, that what makes us guilty or culpable are certain events and states that exist in our minds. Overt behavior, including speech, is fallible evidence of these states and processes. Cases of negligence still involve the executive processes, but “negatively,” in that negligence results when certain types of executive activity fail to take place. (shrink)

Network neuroscience provides a systems approach to the study of the brain and enables the examination of interactions measured at different temporal and spatial scales. We review current methods to quantify the structure of brain networks and compare that structure across different clinical cohorts, cognitive states, and subjects. We further introduce the emerging mathematical concept of multilayer networks and describe the advantages of this approach to model changing brain dynamics over time. We conclude by offering several concrete examples (...) of how multilayer network approaches to neuroimaging data provide novel insights into brain structure and evolving function. (shrink)

Network neuroscience represents the brain as a collection of regions and inter-regional connections. Given its ability to formalize systems-level models, network neuroscience has generated unique explanations of neural function and behavior. The mechanistic status of these explanations and how they can contribute to and fit within the field of neuroscience as a whole has received careful treatment from philosophers. However, these philosophical contributions have not yet reached many neuroscientists. Here we complement formal philosophical efforts by (...) providing an applied perspective from and for neuroscientists. We discuss the mechanistic status of the explanations offered by network neuroscience and how they contribute to, enhance, and interdigitate with other types of explanations in neuroscience. In doing so, we rely on philosophical work concerning the role of causality, scale, and mechanisms in scientific explanations. In particular, we make the distinction between an explanation and the evidence supporting that explanation, and we argue for a scale-free nature of mechanistic explanations. In the course of these discussions, we hope to provide a useful applied framework in which network neuroscience explanations can be exercised across scales and combined with other fields of neuroscience to gain deeper insights into the brain and behavior. (shrink)

Neuroscience can relate to ethics and normative issues via the brain’s cognitive control network. This network accomplishes several executive processes, such as planning, task-switching, monitoring, and inhibiting. These processes allow us to increase the accuracy of our perceptions and our memory recall. They also allow us to plan much farther into the future, and with much more detail than any of our fellow mammals. These abilities also make us fitting subjects for responsibility claims. Their activity, or lack (...) thereof, is at the heart of culpability. For instance, planning to kill someone is strong evidence of what the law calls mens rea—a guilty mind. Claims about norms, or ethical “should” claims, express two-level propositions, directed at the behaving person at one level, and at that person’s mind and cognitive control network at another level. Thus, “People should stop themselves from hurting others,” is a claim about how people should behave and also a claim about how their cognitive control networks should behave—i.e., they should inhibit harmful behavior, or the intentions leading up to it. Planning is both an ability of the full person, and of that person’s mind. Neuroscience affirms the common notion, seen both in law and folk psychology, that what makes us guilty or culpable are certain events and states that exist in our minds. Overt behavior, including speech, is fallible evidence of these states and processes. Cases of negligence still involve the executive processes, but “negatively,” in that negligence results when certain types of executive activity fail to take place. (shrink)

The neural reuse framework developed primarily by Michael Anderson proposes that brain regions are involved in multiple and diverse cognitive tasks and that brain regions flexibly and dynamically interact in different combinations to carry out cognitive functioning. We argue that the evidence cited by Anderson and others falls short of supporting the fundamental principles of neural reuse. We map out this problem and provide solutions by drawing on recent advances in network neuroscience, and we argue that methods employed (...) in network neuroscience provide the means to fully engage in a research program operating under the principles of neural reuse. (shrink)

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting (...) brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data. (shrink)

The relation between mental states and brain states is important in computational neuroscience, and in psychiatry in which interventions with medication are made on brain states to alter mental states. The relation between the brain and the mind has puzzled philosophers for centuries. Here a neuroscience approach is proposed in which events at the sub-neuronal, neuronal, and neuronal network levels take place simultaneously to perform a computation that can be described at a high level as a mental (...) state, with content about the world. It is argued that as the processes at the different levels of explanation take place at the same time, they are linked by a non-causal supervenient relationship: causality can best be described in brains as operating within but not between levels. This allows the supervenient (e.g., mental) properties to be emergent, though once understood at the mechanistic levels they may seem less emergent, and expected. This mind-brain theory allows mental events to be different in kind from the mechanistic events that underlie them; but does not lead one to argue that mental events cause brain events, or vice versa: they are different levels of explanation of the operation of the computational system. This approach may provide a way of thinking about brains and minds that is different from dualism and from reductive physicalism, and which is rooted in the computational processes that are fundamental to understanding brain and mental events, and that mean that the mental and mechanistic levels are linked by the computational process being performed. Explanations at the different levels of operation may be useful in different ways. For example, if we wish to understand how arithmetic is performed in the brain, description at the mental level of the algorithm being computed will be useful. But if the brain operates to result in mental disorders, then understanding the mechanism at the neural processing level may be more useful, in for example, the treatment of psychiatric disorders. (shrink)

The relation between mental states and brain states is important in computational neuroscience, and in psychiatry in which interventions with medication are made on brain states to alter mental states. The relation between the brain and the mind has puzzled philosophers for centuries. Here a neuroscience approach is proposed in which events at the sub-neuronal, neuronal, and neuronal network levels take place simultaneously to perform a computation that can be described at a high level as a mental (...) state, with content about the world. It is argued that as the processes at the different levels of explanation take place at the same time, they are linked by a non-causal supervenient relationship: causality can best be described in brains as operating within but not between levels. This allows the supervenient properties to be emergent, though once understood at the mechanistic levels they may seem less emergent, and expected. This mind-brain theory allows mental events to be different in kind from the mechanistic events that underlie them; but does not lead one to argue that mental events cause brain events, or vice versa: they are different levels of explanation of the operation of the computational system. This approach may provide a way of thinking about brains and minds that is different from dualism and from reductive physicalism, and which is rooted in the computational processes that are fundamental to understanding brain and mental events, and that mean that the mental and mechanistic levels are linked by the computational process being performed. Explanations at the different levels of operation may be useful in different ways. For example, if we wish to understand how arithmetic is performed in the brain, description at the mental level of the algorithm being computed will be useful. But if the brain operates to result in mental disorders, then understanding the mechanism at the neural processing level may be more useful, in for example, the treatment of psychiatric disorders. (shrink)

This introductory chapter attempts to clarify the philosophical, empirical, and theoretical bases on which a cognitive neuroscience approach to consciousness can be founded. We isolate three major empirical observations that any theory of consciousness should incorporate, namely (1) a considerable amount of processing is possible without consciousness, (2) attention is a prerequisite of consciousness, and (3) consciousness is required for some specific cognitive tasks, including those that require durable information maintenance, novel combinations of operations, or the spontaneous generation of (...) intentional behavior. We then propose a theoretical framework that synthesizes those facts: the hypothesis of a global neuronal workspace. This framework postulates that, at any given time, many modular cerebral networks are active in parallel and process information in an unconscious manner. An information becomes conscious, however, if the neural population that represents it is mobilized by top-down attentional amplification into a brain-scale state of coherent activity that involves many neurons distributed throughout the brain. The long-distance connectivity of these `workspace neurons' can, when they are active for a minimal duration, make the information available to a variety of processes including perceptual categorization, long-term memorization, evaluation, and intentional action. We postulate that this global availability of information through the workspace is what we subjectively experience as a conscious state. A complete theory of consciousness should explain why some cognitive and cerebral representations can be permanently or temporarily inaccessible to consciousness, what is the range of possible conscious contents, how they map onto specific cerebral circuits, and whether a generic neuronal mechanism underlies all of them. We confront the workspace model with those issues and identify novel experimental predictions. Neurophysiological, anatomical, and brain-imaging data strongly argue for a major role of prefrontal cortex, anterior cingulate, and the areas that connect to them, in creating the postulated brain-scale workspace. (shrink)

We examine recent work in cognitive neuroscience that investigates brain networks. Brain networks are characterized by the ways in which brain regions are functionally and anatomically connected to one another. Cognitive neuroscientists use various noninvasive techniques (e.g., fMRI) to investigate these networks. They represent them formally as graphs. And they use various graph theoretic techniques to analyze them further. We distinguish between knowledge of the graph theoretic structure of such networks (structural knowledge) and knowledge of what instantiates that structure (...) (nonstructural knowledge). And we argue that this work provides structural knowledge of brain networks. We explore the significance of this conclusion for the scientific realism debate. We argue that our conclusion should not be understood as an instance of a global structural realist claim regarding the structure of the unobservable part of the world, but instead, as a local structural realist attitude towards brain networks in particular. And we argue that various local approaches to the realism debate, i.e., approaches that restrict realist commitments to particular theories and/or entities, are problematic insofar as they don't allow for the possibility of such a local structural realist attitude. (shrink)

In spite of the remarkable progress made in the burgeoning field of social neuroscience, the neural mechanisms that underlie social encounters are only beginning to be studied and could —paradoxically— be seen as representing the ‘dark matter’ of social neuroscience. Recent conceptual and empirical developments consistently indicate the need for investigations, which allow the study of real-time social encounters in a truly interactive manner. This suggestion is based on the premise that social cognition is fundamentally different when we (...) are in interaction with others rather than merely observing them. In this article, we outline the theoretical conception of a second-person approach to other minds and review evidence from neuroimaging, psychophysiological studies and related fields to argue for the development of a second-person neuroscience, which will help neuroscience to really go social; this may also be relevant for our understanding of psychiatric disorders construed as disorders of social cognition. (shrink)

In 1895 in the Project for a Scientific Psychology Freud tried to integrate psychology and neurology in order to develop a neuroscientific psychology. Since 1880 Freud made no distinction between psychology and physiology. His papers from the end of the 1880s to1890 were very clear on this scientific overlap: as with many of its contemporaries, Freud thought about psychology essentially as the physiology of the brain. Years later he had to surrender, realizing a technological delay, not capable to pursue its (...) ambitious aim, and until that moment psychoanalysis would have to use its more suitable clinical method. Also, he seemed skeptical about the phrenology drift, typical of that time, in which any psychological function needed to be located in its neuroanatomical area. He could not see the progresses of neurosciences and its fruitful dialogue with psychoanalysis, occurred also thanks to the improvements in the field of neuroimaging, which has made possible a remarkable advance in the knowledge of the mind-brain system and a better observation of the psychoanalytical theories. After years of investigations, deriving from research and clinical work of the last century, the discovery of neural networks, together with the free energy principle, we are observing under a new light the psychodynamic neuroscience in its exploration of the mind-brain system. In this narrative review we summarize the important developments of the psychodynamic neuroscience, with particular regard to the free energy principle, the resting state networks, especially the Default Mode Network in its link with the Self; finally we suggest a discussion by approaching the concept of Alpha Function, proposed by the psychoanalyst Wilfred Ruprecht Bion, trying to speculate about the connection with neuroscience. (shrink)

The broad issue in this paper is the relationship between cognitive psychology and neuroscience. That issue arises particularly sharply for cognitive neurospsychology, some of whose practitioners claim a methodological autonomy for their discipline. They hold that behavioural data from neuropsychological impairments are sufficient to justify assumptions about the underlying modular structure of human cognitive architecture, as well as to make inferences about its various components. But this claim to methodological autonomy can be challenged on both philosophical and empirical grounds. (...) A priori considerations about (cognitive) multiple realisability challenge the thesis on philosophical grounds, and neuroscientific findings from developmental disorders substantiate that challenge empirically. The conclusion is that behavioural evidence alone is inadequate for scientific progress since appearances of modularity can be thoroughly deceptive, obscuring both the dynamic processes of neural development and the endstate network architecture of real cognitive systems. (shrink)

The field of affective neuroscience has emerged from the efforts of Jaak Panksepp in the 1990s and reinforced by the work of, among others, Joseph LeDoux in the 2000s. It is based on the ideas that affective processes are supported by brain structures that appeared earlier in the phylogenetic scale (as the periaqueductal gray area), they run in parallel with cognitive processes, and can influence behaviour independently of cognitive judgements. This kind of approach contrasts with the hegemonic concept of (...) conscious processing in cognitive neurosciences, which is based on the identification of brain circuits responsible for the processing of (cognitive) representations. Within cognitive neurosciences, the frontal lobes are assigned the role of coordinators in maintaining affective states and their emotional expressions under cognitive control. An intermediary view is the Damasio-Bechara Somatic Marker model, which puts cognition under partial somatic-affective control. We present here our efforts to make a synthesis of these views, by proposing the existence of two interacting brain circuits; the first one in charge of cognitive processes and the second mediating feelings about cognitive contents. The coupling of the two circuits promotes an endogenous feedback that supports conscious processes. Within this framework, we present the defence that detailed study of both affective and cognitive processes, their interactions, as well of their respective brain networks, is necessary for a science of consciousness. (shrink)

Recent advances in the neuroscience of episodic memory provide a framework to integrate object relations theory, a psychoanalytic model of mind development, with potential neural mechanisms. Object relations are primordial cognitive-affective units of the mind derived from survival- and safety-level experiences with caretakers during phase-sensitive periods of infancy and toddlerhood. Because these are learning experiences, their neural substrate likely involves memory, here affect-enhanced episodic memory. Inaugural object relations are encoded by the hippocampus-amygdala synaptic plasticity, and systems-consolidated by medial prefrontal (...) cortex (mPFC). Self- and object-mental representations, extracted from these early experiences, are at first dichotomized by contradictory affects evoked by frustrating and rewarding interactions (“partial object relations”). Such affective dichotomization appears to be genetically hardwired the amygdala. Intrinsic propensity of mPFC to form schematic frameworks for episodic memories may pilot non-conscious integration of dichotomized mental representations in neonates and infants. With the emergence of working memory in toddlers, an activated self- and object-representation of a particular valence can be juxtaposed with its memorized opposites creating a balanced cognitive-affective frame (conscious “integration of object relations”). Specific events of object relations are forgotten but nevertheless profoundly influence the mental future of the individual, acting (i) as implicit schema-affect templates that regulate attentional priorities, relevance, and preferential assimilation of new information based on past experience, and (ii) as basic units of experience that are, under normal circumstances, integrated as attractors or “focal points” for interactive self-organization of functional brain networks that underlie the mind. A failure to achieve integrated object relations is predictive of poor adult emotional and social outcomes, including personality disorder. Cognitive, cellular-, and systems-neuroscience of episodic memory appear to support key postulates of object relations theory and help elucidate neural mechanisms of psychodynamic psychotherapy. Derived through the dual prism of psychoanalysis and neuroscience, the gained insights may offer new directions to enhance mental health and improve treatment of multiple forms of psychopathology. (shrink)

Network analysis is increasingly used to discover and represent the organization of complex systems. Focusing on examples from neuroscience in particular, I argue that whether network models explain, how they explain, and how much they explain cannot be answered for network models generally but must be answered by specifying an explanandum, by addressing how the model is applied to the system, and by specifying which kinds of relations count as explanatory.

The growing general interest in understanding how neuroscience can contribute to explanations of our understanding and appreciation of art has been slow to find its way to philosophy of literature. Of course this is not to say that neuroscience has not had any influence on current theories about our engagement, understanding, and appreciation of literary works. Colin Martindale developed a scientific approach to literature in his book The Clockwork Muse (1990). His prototype-preference theory drew heavily on early artificial (...) neural network research. Jenefer Robinson has appealed to results from affective neuroscience in her discussion of our emotional engagement with literary works (see Robinson, 2005). More recently literary critics have tried to extend cognitive literary theory in the direction of neuroscience (Ashton, 2005; Byatt, 2006; Young, 2010). This line of research traces its origin to commentary on a 1995 a paper by Herbert Simon in the Stanford Humanities Review (Franchi and Güzeldere, 1995). So, although there hasn't been much going on that could justifiably be called a neuroscience of literature, discussions of neuroscientific research have woven their way into the fabric of some aspects of philosophy of literature and literary theory. Nonetheless, there has been a great deal of resistance to the creeping insurgence of neuroscience into both fields. I will challenge this skepticism in what follows. In section 1, I will sketch a model for research at the crossroads of philosophy and neuroscience of art and suggest how it can be applied to the philosophy of literature. In Section 2, I will introduce situation models and discuss the role they have been hypothesized play in discourse comprehension and narrative understanding outside of literature. In Section 3, I will explore some ways that situation models can contribute to discussions of narrative understanding, appreciation, and the artistically salient features that differentiate literature as a category of art from non-art literary forms like newspaper columns, magazine articles, or biographies. (shrink)

The field of affective neuroscience has emerged from the efforts of Jaak Panksepp in the 1990s and reinforced by the work of, among others, Joseph LeDoux in the 2000s. It is based on the ideas that affective processes are supported by brain structures that appeared earlier in the phylogenetic scale (as the periaqueductal gray area), they run in parallel with cognitive processes, and can influence behaviour independently of cognitive judgements. This kind of approach contrasts with the hegemonic concept of (...) conscious processing in cognitive neurosciences, which is based on the identification of brain circuits responsible for the processing of (cognitive) representations. Within cognitive neurosciences, the frontal lobes are assigned the role of coordinators in maintaining affective states and their emotional expressions under cognitive control. An intermediary view is the Damasio-Bechara Somatic Marker model, which puts cognition under partial somatic-affective control. We present here our efforts to make a synthesis of these views, by proposing the existence of two interacting brain circuits; the first one in charge of cognitive processes and the second mediating feelings about cognitive contents. The coupling of the two circuits promotes an endogenous feedback that supports conscious processes. Within this framework, we present the defence that detailed study of both affective and cognitive processes, their interactions, as well of their respective brain networks, is necessary for a science of consciousness. (shrink)

It is by now a well-supported hypothesis in cognitive neuroscience that there exists a functional network for the moral appraisal of situations. However, there is a surprising disagreement amongst researchers about the significance of this network for moral actions, decisions, and behavior. Some researchers suggest that we should uncover those ethics [that are built into our brains ], identify them, and live more fully by them, while others claim that we should often do the opposite, viewing the (...) cognitive neuroscience of morality more like a science of pathology. To analyze and evaluate the disagreement, this paper will investigate some of its possible sources. These may include theoretical confusions about levels of explanation in cognitive science, or different senses of ‘morality’ that researchers are looking to explain. Other causes of the debate may come from empirical assumptions about how possible or preferable it is to separate intuitive moral appraisal from moral decisions. Although we will tentatively favor the ‘Set Aside’ approach, the questions outlined here are open areas of ongoing research, and this paper will be confined to outlining the position space of the debate rather than definitively resolving it. (shrink)

In experimental psychology and neuroscience, technological advances and multisensory research have contributed to gradually dismiss a version of reductionism. Empirical results no longer support a brain model in which distinct “modules” perform discrete functions, but rather, a brain of partially overlapping networks. A similarly changed brain model is extending to psychopathology and clinical psychology, and partly accounts for the problems of reductionism.

Commonly, a switch between networks mediating memory encoding and those mediating retrieval is observed. This may not only be due to differential involvement of neural resources due to distinct cognitive processes but could also reflect the formation of new memory traces and their dynamic change during consolidation. We used resting state fMRI to measure functional connectivity (FC) changes during post-encoding rest, hypothesizing that during this phase, new functional connections between encoding- and retrieval-related regions are created. Interfering and reminding tasks served (...) as experimental modulators to corroborate that the observed FC differences indeed reflect changes specific to post-encoding rest. The right inferior occipital and fusiform gyri (active during encoding) showed increased FC with the left inferior frontal gyrus and the left middle temporal gyrus (MTG) during post-encoding rest. Importantly, the left MTG subsequently also mediated successful retrieval. This finding might reflect the formation of functional connections between encoding- and retrieval-related regions during undisturbed post-encoding rest. These connections were vulnerable to experimental modulation: Cognitive interference disrupted FC changes during post-encoding rest resulting in poorer memory performance. The presentation of reminders also inhibited FC increases but without affecting memory performance. Our results contribute to a better understanding of the mechanisms by which post-encoding rest bridges the gap between encoding- and retrieval-related networks. (shrink)

A neuroscience-based approach has recently been proposed for the relation between the mind and the brain. The proposal is that events at the sub-neuronal, neuronal, and neuronal network levels take place simultaneously to perform a computation that can be described at a high level as a mental state, with content about the world. It is argued that as the processes at the different levels of explanation take place at the same time, they are linked by a non-causal supervenient (...) relationship: causality can best be described in brains as operating within but not between levels. This mind-brain theory allows mental events to be different in kind from the mechanistic events that underlie them; but does not lead one to argue that mental events cause brain events, or vice versa: they are different levels of explanation of the operation of the computational system. Here, some implications are developed. It is proposed that causality, at least as it applies to the brain, should satisfy three conditions. First, interventionist tests for causality must be satisfied. Second, the causally related events should be at the same level of explanation. Third, a temporal order condition must be satisfied, with a suitable time scale in the order of 10 ms. Next, although it may be useful for different purposes to describe causality involving the mind and brain at the mental level, or at the brain level, it is argued that the brain level may sometimes be more accurate, for sometimes causal accounts at the mental level may arise from confabulation by the mentalee, whereas understanding exactly what computations have occurred in the brain that result in a choice or action will provide the correct causal account for why a choice or action was made. Next, it is argued that possible cases of “downward causation” can be accounted for by a within-levels-of-explanation account of causality. This computational neuroscience approach provides an opportunity to proceed beyond Cartesian dualism and physical reductionism in considering the relations between the mind and the brain. (shrink)

Personhood is a foundational concept in ethics, yet defining criteria have been elusive. In this article we summarize attempts to define personhood in psychological and neurological terms and conclude that none manage to be both specific and non-arbitrary. We propose that this is because the concept does not correspond to any real category of objects in the world. Rather, it is the product of an evolved brain system that develops innately and projects itself automatically and irrepressibly onto the world whenever (...) triggered by stimulus features such as a human-like face, body, or contingent patterns of behavior. We review the evidence for the existence of an autonomous person network in the brain and discuss its implications for the field of ethics and for the implicit morality of everyday behavior. (shrink)

The goal of computational cognitive neuroscience is to understand how the brain embodies the mind by using biologically based computational models comprised of networks of neuronlike units. This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the field. The neural units in the simulations use equations based directly on the ion channels that govern the behavior of real neurons and the neural (...) networks incorporate anatomical and physiological properties of the neocortex. Thus the text provides the student with knowledge of the basic biology of the brain as well as the computational skills needed to simulate large-scale cognitive phenomena. (shrink)

Anthropology of Consciousness, Volume 33, Issue 1, Page 79-95, Spring 2022.

This paper reports on the investigation of the effects of neuronal shape, at both individual cell and network level, on the behavior of neuronal systems. More specifically, two-dimensional biologically realistic neuronal networks are obtained that take explicity into account the position and morphology of neuronal cells, with the respective behavior for associative recall being simulated through a diluted version of Hopfield's model. While a specific probability density function is used for the placement of the cell bodies, images of real (...) neuronal cells (namely alpha and beta ganglion cells from the cat retina) are used to obtain biologically realistic models. Several morphological measures – including fractal dimension, the excluded volume, and integral geometry functionals–are estimated for the considered cells, and their values are correlated with the potential of the network for associative recall, which is quantified in terms of the overlap between distorted version of the trained patterns and their original version. Such an approach allows the quantitative and objective characterization of the relationship between neuronal shape and function, an important issue in neuroscience. The obtained results substantiate interesting relationships between the neural morphology and function as determined by the performance of the network. (shrink)

Mechanist philosophers have examined several strategies scientists use for discovering causal mechanisms in neuroscience. Findings about the anatomical organization of the brain play a central role in several such strategies. Little attention has been paid, however, to the use of network analysis and causal modeling techniques for mechanism discovery. In particular, mechanist philosophers have not explored whether and how these strategies incorporate information about the anatomical organization of the brain. This paper clarifies these issues in the light of (...) the distinction between structural, functional and effective connectivity. Specifically, we examine two quantitative strategies currently used for causal discovery from functional neuroimaging data: dynamic causal modeling and probabilistic graphical modeling. We show that dynamic causal modeling uses findings about the brain’s anatomical organization to improve the statistical estimation of parameters in an already specified causal model of the target brain mechanism. Probabilistic graphical modeling, in contrast, makes no appeal to the brain’s anatomical organization, but lays bare the conditions under which correlational data suffice to license reliable inferences about the causal organization of a target brain mechanism. The question of whether findings about the anatomical organization of the brain can and should constrain the inference of causal networks remains open, but we show how the tools supplied by graphical modeling methods help to address it. (shrink)

The comorbidity of chronic pain and opioid addiction is a serious problem that has been growing with the practice of prescribing opioids for chronic pain. Neuroimaging research has shown that chronic pain and opioid dependence both affect brain structure and function, but this is the first study to evaluate the neurophysiological alterations in patients with comorbid chronic pain and addiction. Eighteen participants with chronic low back pain and opioid addiction were compared with eighteen age- and sex-matched healthy individuals in a (...) pain-induction fMRI task. Unified structural equation modeling (SEM) with Lagrange multiplier (LM) testing yielded a network model of pain processing for patient and control groups based on 19 a priori defined regions. Tests of differences between groups on specific regression parameters were determined on a path-by-path basis using z-tests corrected for the number of comparisons. Patients with the chronic pain and addiction comorbidity had increased connection strengths; many of these connections were interhemispheric and spanned regions involved in sensory, affective, and cognitive processes. The affected regions included those that are commonly altered in chronic pain or addiction alone, indicating that this comorbidity manifests with neurological symptoms of both disorders. Understanding the neural mechanisms involved in the comorbidity is crucial to finding a comprehensive treatment, rather than treating the symptoms individually. (shrink)

The pursuit of happiness is a preoccupation for many people — and probably has been ever since the emergence of Homo sapiens sapiens. The scientific understanding of the brain basis of happiness and its pursuit is, however, still in its infancy. Here we focus on recent scientific research on the closely related concepts of pleasure and desire, and discuss their underlying neural mechanisms and their roles in happiness. We also speculate on potential contributions of the brain's default networks to orchestrating (...) cognitive aspects of meaningfulness that are important to happiness. Finally, we discuss how the lack of pleasure, anhedonia, is a central feature of emotional disorders such as depression and thus perhaps the most important obstacle to happiness for many people. (shrink)

Neurofeedback is a complex learning scenario, as the task consists of trying out mental strategies while processing a feedback signal that signifies activation in the brain area to be self-regulated and acts as a potential reward signal. In an attempt to dissect these subcomponents, we obtained whole-brain networks associated with efficient self-regulation in two paradigms: parallel, where the task was performed concurrently, combining feedback with strategy execution; and serial, where the task was performed consecutively, separating feedback processing from strategy execution. (...) Twenty participants attempted to control their anterior midcingulate cortex using functional magnetic resonance imaging NF in 18 sessions over 2 weeks, using cognitive and emotional mental strategies. We analyzed whole-brain fMRI activations in the NF training runs with the largest aMCC activation for the serial and parallel paradigms. The equal length of the strategy execution and the feedback processing periods in the serial paradigm allows a description of the two task subcomponents with equal power. The resulting activation maps were spatially correlated with functionally annotated intrinsic connectivity brain maps. Brain activation in the parallel condition correlates with the basal ganglia network, the cingulo-opercular network, and the frontoparietal control network ; brain activation in the serial strategy execution condition with the default mode network, the FPCN, and the visual processing network; while brain activation in the serial feedback processing condition predominantly with the CON, the DMN, and the FPCN. Additional comparisons indicate that BG activation is characteristic to the parallel paradigm, while supramarginal gyrus and superior temporal gyrus activations are characteristic to the serial paradigm. The multifaceted view of the subcomponents allows describing the cognitive processes associated with strategy execution and feedback processing independently in the serial feedback task and as combined processes in the multitasking scenario of the conventional parallel feedback task. (shrink)

Bowers et al. propose to use controlled behavioral experiments when evaluating deep neural networks as models of biological vision. We agree with the sentiment and draw parallels to the notion that “neuroscience needs behavior.” As a promising path forward, we suggest complementing image recognition tasks with increasingly realistic and well-controlled task environments that engage real-world object recognition behavior.

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)

We consider the potential contribution of economics to an interdisciplinary research partnership between sociology and neuroscience. We correct a misunderstanding in previous literature over the understanding of humans as ‘social animals’, which has in turn led to misidentification of the potential relevance of game theory and the economics of networks to the social neuroscience project. Specifically, it has been suggested that these can be used to model mindreading. We argue that mindreading is at best a derivative and special (...) basis for social coordination, whereas the general and pervasive phenomenon on which it depends is mindshaping. We then outline the foundations of Conditional Game Theory as a mathematical model of mindshaping, which extends game theory without displacing its classic solution concepts, and which exploits economists’ experience in modeling networks. (shrink)

The pursuit of happiness is a preoccupation for many people — and probably has been ever since the emergence of Homo sapiens sapiens. The scientific understanding of the brain basis of happiness and its pursuit is, however, still in its infancy. Here we focus on recent scientific research on the closely related concepts of pleasure and desire, and discuss their underlying neural mechanisms and their roles in happiness. We also speculate on potential contributions of the brain's default networks to orchestrating (...) cognitive aspects of meaningfulness that are important to happiness. Finally, we discuss how the lack of pleasure, anhedonia, is a central feature of emotional disorders such as depression and thus perhaps the most important obstacle to happiness for many people. (shrink)