Cognitive Neuroscience最新文献

Doerig and colleagues put forward the notion that we need hard and theory-neutral criteria by which to arbitrate between empirical (mechanistic) theories of consciousness. However, most of the criteria that they propose are not theory neutral because they focus on functional equivalence between systems. Because empirical theories of consciousness are mechanistic rather than functionalist, we think these criteria are not helpful when arbitrating between them.

Fear-generalization is a critical function for survival, in which an organism extracts information from a specific instantiation of a threat (e.g., the western diamondback rattlesnake in my front yard on Sunday) and learns to fear - and accordingly respond to - pertinent higher-order information (e.g., snakes live in my yard). Previous work investigating fear-conditioning in humans has used functional magnetic resonance imaging (fMRI) to demonstrate that activity patterns representing stimuli from an aversively-conditioned category (CS+) are more similar to each other than those of a neutral category (CS-). Here we used fMRI and multiple aversively-conditioned categories to ask whether we would find only similarity increases within the CS+ categories or also similarity increases between the CS+ categories. Using representational similarity analysis, we correlated several models to activity patterns underlying different brain regions and found that, following fear-conditioning, between-category and within-category similarity increased for the CS+ categories in the insula, superior frontal gyrus (SFG), and the right temporal pole. When specifically investigating fear-generalization, these between- and within-category effects were detected in the SFG. These results advance prior pattern-based neuroimaging work by exploring the effect of aversively-conditioning multiple categories and indicate an extended role for such regions in potentially representing supracategorical information during fear-learning.

Doerig et al. point out a number of shortcomings with existing theories of consciousness and argue they should be systematically constrained by empirical data. In this commentary I suggest a further constraint - the potential functions of (the contents of) consciousness. One such candidate function in humans is the social sharing of reportable mental states. The social function of consciousnessprovides a general framework within which to understand the evolution and neurobiology of conscious awareness.

I consider the Doerig, Schruger, Herzog (this issue) paper to be a very important paper on the multiple empirical theories of consciousness (ToCs). The paper compared 13 different approaches. It would be wonderful to create a forum for all interested parties to discuss the many issues this paper brought up. Equally important is what the paper didn't bring up. Namely, how do the different empirical approaches connect to qualia, the subjective aspects of consciousness. In my mind, qualia is the biggest mystery facing present science. It would be good to see how the multiple interpretations might connect to qualia.

Consciousness is now a well-established field of empirical research. A large body of experimental results has been accumulated and is steadily growing. In parallel, many Theories of Consciousness (ToCs) have been proposed. These theories are diverse in nature, ranging from computational to neurophysiological and quantum theoretical approaches. This contrasts with other fields of natural science, which host a smaller number of competing theories. We suggest that one reason for this abundance of extremely different theories may be the lack of stringent criteria specifying how empirical data constrains ToCs. First, we argue that consciousness is a well-defined topic from an empirical point of view and motivate a purely empirical stance on the quest for consciousness. Second, we present a checklist of criteria that, we propose, empirical ToCs need to cope with. Third, we review 13 of the most influential ToCs and subject them to the criteria. Our analysis helps to situate these different ToCs in the theoretical landscapeand sheds light on their strengths and weaknesses from a strictly empirical point of view.

Adaptive Resonance Theory does more than satisfy 'hard criteria' for ToCs.

Doerig et al. have set several criteria that theories of consciousness need to fulfill. By these criteria, higher-order theories fare better than most existing theories. But they also argue that higher-order theories may not be able to answer both the 'small network argument' and the 'other systems argument'. In response, we focus on the case of the Perceptual Reality Monitoring theory to explain why higher-order theories do just fine.

In their paper Doerig et al. argue that we should put the hard problem aside and focus on empirical data to solve the 'easy' problems of consciousness - finding the neural and functional correlates of consciousness. In other words 'shut up and measure'. This has worked well with other thorny issues, such as explaining life, so why not adopt this approach here? We argue that despite the popularity of this view it is not feasible. In order to collect any consciousness data one needs to take an implicit or explicit stance on the hard problem.

Actions are guided by a combination of external cues, internal intentions, and stored knowledge. Self-initiated voluntary actions, produced without immediate external cues, may be preceded by a slow EEG Readiness Potential (RP) that progressively increases prior to action. The cognitive significance of this neural event is controversial. Some accounts link the RP to the fact that timing of voluntary actions is generated endogenously, without external constraints. Others link it to the unique role of a planning process, and therefore of temporal expectation, in voluntary actions. In many previous experiments, actions are unconstrained by external cues, but also potentially involve preplanning and anticipation. To separate these factors, we developed a reinforcement learning paradigm where participants learned, through trial and error, the optimal time to act. If the RP reflects freedom from external constraint, its amplitude should be greater early in learning, when participants do not yet know when to act. Conversely, if the RP reflects planning, it should be greater later on, when participants have learned, and plan in advance, the time of action. We found that RP amplitudes grew with learning, suggesting that this neural activity reflects planning and anticipation for the forthcoming action, rather than freedom from external constraint.