Concept: Philosophy of mind
People often discount evidence that contradicts their firmly held beliefs. However, little is known about the neural mechanisms that govern this behavior. We used neuroimaging to investigate the neural systems involved in maintaining belief in the face of counterevidence, presenting 40 liberals with arguments that contradicted their strongly held political and non-political views. Challenges to political beliefs produced increased activity in the default mode network-a set of interconnected structures associated with self-representation and disengagement from the external world. Trials with greater belief resistance showed increased response in the dorsomedial prefrontal cortex and decreased activity in the orbitofrontal cortex. We also found that participants who changed their minds more showed less BOLD signal in the insula and the amygdala when evaluating counterevidence. These results highlight the role of emotion in belief-change resistance and offer insight into the neural systems involved in belief maintenance, motivated reasoning, and related phenomena.
What is the level of consciousness of the psychedelic state? Empirically, measures of neural signal diversity such as entropy and Lempel-Ziv (LZ) complexity score higher for wakeful rest than for states with lower conscious level like propofol-induced anesthesia. Here we compute these measures for spontaneous magnetoencephalographic (MEG) signals from humans during altered states of consciousness induced by three psychedelic substances: psilocybin, ketamine and LSD. For all three, we find reliably higher spontaneous signal diversity, even when controlling for spectral changes. This increase is most pronounced for the single-channel LZ complexity measure, and hence for temporal, as opposed to spatial, signal diversity. We also uncover selective correlations between changes in signal diversity and phenomenological reports of the intensity of psychedelic experience. This is the first time that these measures have been applied to the psychedelic state and, crucially, that they have yielded values exceeding those of normal waking consciousness. These findings suggest that the sustained occurrence of psychedelic phenomenology constitutes an elevated level of consciousness - as measured by neural signal diversity.
Humans in all societies form and participate in cooperative alliances. To successfully navigate an alliance-laced world, the human mind needs to detect new coalitions and alliances as they emerge, and predict which of many potential alliance categories are currently organizing an interaction. We propose that evolution has equipped the mind with cognitive machinery that is specialized for performing these functions: an alliance detection system. In this view, racial categories do not exist because skin color is perceptually salient; they are constructed and regulated by the alliance system in environments where race predicts social alliances and divisions. Early tests using adversarial alliances showed that the mind spontaneously detects which individuals are cooperating against a common enemy, implicitly assigning people to rival alliance categories based on patterns of cooperation and competition. But is social antagonism necessary to trigger the categorization of people by alliance-that is, do we cognitively link A and B into an alliance category only because they are jointly in conflict with C and D? We report new studies demonstrating that peaceful cooperation can trigger the detection of new coalitional alliances and make race fade in relevance. Alliances did not need to be marked by team colors or other perceptually salient cues. When race did not predict the ongoing alliance structure, behavioral cues about cooperative activities up-regulated categorization by coalition and down-regulated categorization by race, sometimes eliminating it. Alliance cues that sensitively regulated categorization by coalition and race had no effect on categorization by sex, eliminating many alternative explanations for the results. The results support the hypothesis that categorizing people by their race is a reversible product of a cognitive system specialized for detecting alliance categories and regulating their use. Common enemies are not necessary to erase important social boundaries; peaceful cooperation can have the same effect.
Intentions, including their temporal properties and semantic content, are receiving increased attention, and neuroscientific studies in humans vary with respect to the topography of intention-related neural responses. This may reflect the fact that the kind of intentions investigated in one study may not be exactly the same kind investigated in the other. Fine-grained intention taxonomies developed in the philosophy of mind may be useful to identify the neural correlates of well-defined types of intentions, as well as to disentangle them from other related mental states, such as mere urges to perform an action. Intention-related neural signals may be exploited by brain-machine interfaces (BMIs) that are currently being developed to restore speech and motor control in paralyzed patients. Such BMI devices record the brain activity of the agent, interpret (“decode”) the agent’s intended action, and send the corresponding execution command to an artificial effector system, e.g., a computer cursor or a robotic arm. In the present paper, we evaluate the potential of intention concepts from philosophy of mind to improve the performance and safety of BMIs based on higher-order, intention-related control signals. To this end, we address the distinction between future-, present-directed, and motor intentions, as well as the organization of intentions in time, specifically to what extent it is sequential or hierarchical. This has consequences as to whether these different types of intentions can be expected to occur simultaneously or not. We further illustrate how it may be useful or even necessary to distinguish types of intentions exposited in philosophy, including yes- vs. no-intentions and oblique vs. direct intentions, to accurately decode the agent’s intentions from neural signals in practical BMI applications.
Recent findings link fronto-temporal gamma electroencephalographic (EEG) activity to conscious awareness in dreams, but a causal relationship has not yet been established. We found that current stimulation in the lower gamma band during REM sleep influences ongoing brain activity and induces self-reflective awareness in dreams. Other stimulation frequencies were not effective, suggesting that higher order consciousness is indeed related to synchronous oscillations around 25 and 40 Hz.
Sports specialization is becoming the norm in youth sports for a variety of reasons. When sports specialization occurs too early, detrimental effects may occur, both physically and psychologically. If the timing is correct and sports specialization is performed under the correct conditions, the athlete may be successful in reaching specific goals. Young athletes who train intensively, whether specialized or not, can also be at risk of adverse effects on the mind and body. The purpose of this clinical report is to assist pediatricians in counseling their young athlete patients and their parents regarding sports specialization and intensive training. This report supports the American Academy of Pediatrics clinical report “Overuse Injuries, Overtraining, and Burnout in Child and Adolescent Athletes.”
The waking mind is often occupied with mental contents that are minimally constrained by events in the here and now. These self-generated thoughts-e.g., mind-wandering or daydreaming-interfere with external task performance and can be a marker for unhappiness and even psychiatric problems. They also occupy our thoughts for upwards of half of the time, and under non-demanding conditions they (i) allow us to connect our past and future selves together, (ii) help us make successful long-term plans and (iii) can provide a source of creative inspiration. The lengths that the mind goes to self-generate thought, coupled with its apparent functionality, suggest that the mind places a higher priority on such cognition than on many other mental acts. Although mind-wandering may be unpleasant for the individual who experiences it and disruptive to the tasks of the moment, self-generated thought allows consciousness freedom from the here and now and so reflects a key evolutionary adaptation for the mind. Here we synthesize recent literature from cognitive and clinical psychology and propose two formal hypotheses that (1) highlight task context and thought content as critical factors that constrain the costs and benefits of self-generated thought and (2) provide direction on ways to investigate the costs and benefits from an impartial perspective.
- Neural networks : the official journal of the International Neural Network Society
- Published almost 5 years ago
Efforts to create computational models of consciousness have accelerated over the last two decades, creating a field that has become known as artificial consciousness. There have been two main motivations for this controversial work: to develop a better scientific understanding of the nature of human/animal consciousness and to produce machines that genuinely exhibit conscious awareness. This review begins by briefly explaining some of the concepts and terminology used by investigators working on machine consciousness, and summarizes key neurobiological correlates of human consciousness that are particularly relevant to past computational studies. Models of consciousness developed over the last twenty years are then surveyed. These models are largely found to fall into five categories based on the fundamental issue that their developers have selected as being most central to consciousness: a global workspace, information integration, an internal self-model, higher-level representations, or attention mechanisms. For each of these five categories, an overview of past work is given, a representative example is presented in some detail to illustrate the approach, and comments are provided on the contributions and limitations of the methodology. Three conclusions are offered about the state of the field based on this review: (1) computational modeling has become an effective and accepted methodology for the scientific study of consciousness, (2) existing computational models have successfully captured a number of neurobiological, cognitive, and behavioral correlates of conscious information processing as machine simulations, and (3) no existing approach to artificial consciousness has presented a compelling demonstration of phenomenal machine consciousness, or even clear evidence that artificial phenomenal consciousness will eventually be possible. The paper concludes by discussing the importance of continuing work in this area, considering the ethical issues it raises, and making predictions concerning future developments.
We investigated links between persuasive behavior and theory of mind (ToM) understanding using a novel naturalistic peer persuasion task in which children were invited to convince an interactive puppet to eat raw broccoli or brush his teeth. Sixty-three 3- to 8-year-olds (M age = 6 years, 6 months) took part in the persuasion task and were also given a battery of first-order and advanced false belief tests. As predicted, the number of independent persuasive arguments children produced was significantly associated with false belief scores, even after controlling for age and verbal ability. (PsycINFO Database Record © 2013 APA, all rights reserved).
Consciousness remains a mystery-“a phenomenon that people do not know how to think about-yet” (Dennett, , p. 21). Here, I consider how the connectionist perspective on information processing may help us progress toward the goal of understanding the computational principles through which conscious and unconscious processing differ. I begin by delineating the conceptual challenges associated with classical approaches to cognition insofar as understanding unconscious information processing is concerned, and to highlight several contrasting computational principles that are constitutive of the connectionist approach. This leads me to suggest that conscious and unconscious processing are fundamentally connected, that is, rooted in the very same computational principles. I further develop a perspective according to which the brain continuously and unconsciously learns to redescribe its own activity itself based on constant interaction with itself, with the world, and with other minds. The outcome of such interactions is the emergence of internal models that are metacognitive in nature and that function so as to make it possible for an agent to develop a (limited, implicit, practical) understanding of itself. In this light, plasticity and learning are constitutive of what makes us conscious, for it is in virtue of our own experiences with ourselves and with other people that our mental life acquires its subjective character. The connectionist framework continues to be uniquely positioned in the Cognitive Sciences to address the challenge of identifying what one could call the “computational correlates of consciousness” (Mathis & Mozer, ) because it makes it possible to focus on the mechanisms through which information processing takes place.