Computation, reduction, and teleology of consciousness

This paper aims to explore mechanistic and teleological explanations of consciousness. In terms of mechanistic explanations, it critiques various existing views, especially those embodied by existing computational cognitive models. In this regard, the paper argues in favor of the explanation based on the distinction between localist (symbolic) representation and distributed representation (as formulated in the connectionist literature), which reduces the phenomenological difference to a mechanistic difference. Furthermore, to establish a teleological explanation of consciousness, the paper discusses the issue of the functional role of consciousness on the basis of the aforementioned mechanistic explanation. A proposal based on synergistic interaction between the conscious and the unconscious is advanced that encompasses various existing views concerning the functional role of consciousness. This two-step deepening explanation has some empirical support, in the form of a cognitive model and various cognitive data that it captures.     

Functional analysis and mechanistic explanation

Piccinini and Craver (Synthese 183:283–311, 2011) argue for the surprising view that psychological explanation, properly understood, is a species of mechanistic explanation. This contrasts with the ‘received view’ (due, primarily, to Cummins and Fodor) which maintains a sharp distinction between psychological explanation and mechanistic explanation. The former is typically construed as functional analysis, the analysis of some psychological capacity into an organized series of subcapacities without specifying any of the structural features that underlie the explanandum capacity. The latter idea, of course, sees explanation as a matter of describing structures that maintain (or produce) the explanandum capacity. In this paper, I defend the received view by criticizing Piccinini and Craver’s argument for the claim that psychological explanation is not distinct from mechanistic explanation, and by showing how psychological explanations can possess explanatory force even when nothing is known about the underlying neurological details. I conclude with a few brief criticisms about the enterprise of mechanistic explanation in general.     

“Looking Up” and “Looking Down”: On the Dual Character of Mechanistic Explanations

Mechanistic explanation is at present the received view of scientific explanation. One of its central features is the idea that mechanistic explanations are both “downward looking” and “upward looking”: they explain by offering information about the internal constitution of the mechanism as well as the larger environment in which the mechanism is situated. That is, they offer both constitutive and contextual explanatory information. Adequate mechanistic explanations, on this view, accommodate the full range of explanatory factors both “above” and “below” the target phenomenon. The aim of this paper is to demonstrate that mechanistic explanation cannot furnish both constitutive and contextual information simultaneously, because these are different types of explanation with distinctly different aims. Claims that they can, I argue, depend on several intertwined confusions concerning the nature of explanation. Particularly, such claims tend to conflate mechanistic and functional explanation, which I argue ought to be understood as distinct. Conflating them threatens to oversell the explanatory power of mechanisms and obscures the means by which they explain. I offer two broad reasons in favor of distinguishing mechanistic and functional explanation: the first concerns the direction of explanation of each, and the second concerns the type of questions to which these explanations offer answers. I suggest an alternative picture on which mechanistic explanation is understood as fundamentally constitutive, and according to which an adequate understanding of a phenomenon typically requires supplementing the mechanistic explanation with a functional explanation.     

Integrating psychology and neuroscience: functional analyses as mechanism sketches

We sketch a framework for building a unified science of cognition. This unification is achieved by showing how functional analyses of cognitive capacities can be integrated with the multilevel mechanistic explanations of neural systems. The core idea is that functional analyses are sketches of mechanisms, in which some structural aspects of a mechanistic explanation are omitted. Once the missing aspects are filled in, a functional analysis turns into a full-blown mechanistic explanation. By this process, functional analyses are seamlessly integrated with multilevel mechanistic explanations.     

A cross-order integration hypothesis for the neural correlate of consciousness

One major problem many hypotheses regarding the neural correlate of consciousness (NCC), face is what we might call "the why question": why would this particular neural feature, rather than another, correlate with consciousness? The purpose of the present paper is to develop an NCC hypothesis that answers this question. The proposed hypothesis is inspired by the cross-order integration (COI) theory of consciousness, according to which consciousness arises from the functional integration of a first-order representation of an external stimulus and a second-order representation of that first-order representation. The proposal comes in two steps. The first step concerns the "general shape" of the NCC and can be directly derived from COI theory. The second step is a concrete hypothesis that can be arrived at by combining the general shape with empirical considerations.     

Exemplarization: a solution to the problem of consciousness?

In recent publications, Keith Lehrer developed the intriguing idea of a special mental process??exemplarization??and applied it in a sophisticated manner to different phenomena such as intentionality, representation of the self, the knowledge of ineffable content (of art works) and the problem of phenomenal consciousness. In this paper I am primarily concerned with the latter issue. The target of this paper is to analyze whether exemplarization, besides explaining epistemic phenomena such as immediate and ineffable knowledge of experiences, can also solve the ontological problem of consciousness. In particular, Lehrer suggests that if we consider exemplarization, zombies cannot provide an argument for anti-physicalism. I argue that exemplarization offers neither a physicalist explanation of the conceivability of zombies nor an elucidating physicalist account of their impossibility. Therefore, exemplarization cannot offer a physicalist solution to the ??hard problem?? of consciousness.     

Formalization of Damasio's theory of emotion, feeling and core consciousness

This paper contributes an analysis and formalization of Damasio's theory on core consciousness. Three important concepts in this theory are 'emotion', 'feeling' and 'feeling a feeling' (or core consciousness). In particular, a simulation model is described of the dynamics of basic mechanisms leading via emotion and feeling to core consciousness, and dynamic properties are formally specified that hold for these dynamics at a more global level. These properties have been automatically checked for the simulation model. Moreover, a formal analysis is made of relevant notions of representation used by Damasio. As part of this analysis, specifications of representation relations have been verified and confirmed against the simulation model.     

Mechanism Discovery and Design Explanation: Where Role Function Meets Biological Advantage Function

In the recent literature on explanation in biology, increasing attention is being paid to the connection between design explanation and mechanistic explanation, viz. the role of design principles and heuristics for mechanism discovery and mechanistic explanation. In this paper we extend the connection between design explanation and mechanism discovery by prizing apart two different types of design explanation and by elaborating novel heuristics that one specific type offers for mechanism discovery across species. We illustrate our claims in terms of two lines of biological research on the biological advantages of organismal traits, one on the eye-size of giant squid, the other on foraging habits of specific bat species. We argue that this research illustrates useful heuristics for mechanism discovery across species, viz. reasoning strategies to infer likely mechanisms for a certain biological role based on assessments of the environmental conditions in which the role is performed efficiently (i.e., offers a biological advantage) and less or in-efficiently. We bring out the novel features of our analysis in terms of a comparison with mechanistic approaches to mechanism discovery, amongst which graph-theoretical ones, and by comparing the different types of design explanation and the discovery heuristics they support.     

Extended Mechanistic Explanations: Expanding the Current Mechanistic Conception to Include More Complex Biological Systems

Mechanistic accounts of explanation have recently found popularity within philosophy of science. Presently, we introduce the idea of an extended mechanistic explanation, which makes explicit room for the role of environment in explanation. After delineating Craver and Bechtel’s (2007) account, we argue this suggestion is not sufficiently robust when we take seriously the mechanistic environment and modeling practices involved in studying contemporary complex biological systems. Our goal is to extend the already profitable mechanistic picture by pointing out the importance of the mechanistic environment. It is our belief that extended mechanistic explanations, or mechanisms that take into consideration the temporal sequencing of the interplay between the mechanism and the environment, allow for mechanistic explanations regarding a broader group of scientific phenomena.     

Negative causation in causal and mechanistic explanation

Instances of negative causation—preventions, omissions, and the like—have long created philosophical worries. In this paper, I argue that concerns about negative causation can be addressed in the context of causal explanation generally, and mechanistic explanation specifically. The gravest concern about negative causation is that it exacerbates the problem of causal promiscuity—that is, the problem that arises when a particular account of causation identifies too many causes for a particular effect. In the explanatory context, the problem of promiscuity can be solved by characterizing the phenomenon to be explained as a contrast between two or more events or non-events. This contrastive strategy also can solve other problems that negative causation presents for the leading accounts of mechanistic explanation. Along the way, I argue that to be effective, accounts of causal explanation must incorporate negative causation. I also develop a taxonomy of negative causation and incorporate each variety of negative causation into the leading accounts of mechanistic explanation.     

操作表征在被试操作效应中的调节作用：ERPs研究

本研究考察操作表征(operational representation)在被试操作效应中的调节作用。选取结构性/功能性操作表征短语各160个,分析再认判断中的脑电差异。结果表明,动作编码比语词编码诱发更显著的P1、N2pb和N400;在500~700ms时间窗,相比于语词编码,对结构性和功能性操作表征短语进行动作编码均可显著诱发了中央区更负的平均波幅;在700~900ms时间窗,仅对于结构性操作表征短语,动作编码比语词编码诱发了前额叶更正的平均波幅。研究为动作编码的语义反馈加工机制提供了数据支撑,也为动作信息再激活与动作表征观点的整合提供解释方向。     

Minimal models and canonical neural computations: the distinctness of computational explanation in neuroscience

In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I discuss work in computational neuroscience that creates difficulties for the mechanist project. Carandini and Heeger (Nat Rev Neurosci 13:51–62, 2012) propose that many neural response properties can be understood in terms of canonical neural computations. These are “standard computational modules that apply the same fundamental operations in a variety of contexts.” Importantly, these computations can have numerous biophysical realisations, and so straightforward examination of the mechanisms underlying these computations carries little explanatory weight. Through a comparison between this modelling approach and minimal models in other branches of science, I argue that computational neuroscience frequently employs a distinct explanatory style, namely, efficient coding explanation. Such explanations cannot be assimilated into the mechanistic framework but do bear interesting similarities with evolutionary and optimality explanations elsewhere in biology.     

Computation without Representation

The received view is that computational states are individuated at least in part by their semantic properties. I offer an alternative, according to which computational states are individuated by their functional properties. Functional properties are specified by a mechanistic explanation without appealing to any semantic properties. The primary purpose of this paper is to formulate the alternative view of computational individuation, point out that it supports a robust notion of computational explanation, and defend it on the grounds of how computational states are individuated within computability theory and computer science. A secondary purpose is to show that existing arguments for the semantic view are defective.     

Putting the psychology back into psychological models: Mechanistic versus rational approaches

Two basic approaches to explaining the nature of the mind are the rational and the mechanistic approaches. Rational analyses attempt to characterize the environment and the behavioral outcomes that humans seek to optimize, whereas mechanistic models attempt to simulate human behavior using processes and representations analogous to those used by humans. We compared these approaches with regard to their accounts of how humans learn the variability of categories. The mechanistic model departs in subtle ways from rational principles. In particular, the mechanistic model incrementally updates its estimates of category means and variances through error-driven learning, based on discrepancies between new category members and the current representation of each category. The model yields a prediction, which we verify, regarding the effects of order manipulations that the rational approach does not anticipate. Although both rational and mechanistic models can successfully postdict known findings, we suggest that psychological advances are driven primarily by consideration of process and representation and that rational accounts trail these breakthroughs.     

内隐学习中表征与意识的关系

近年来研究者分别从表征的质量和表征的外显等级两个方面探讨了内隐学习中表征与意识的关系。有关表征质量与意识的研究,清晰地揭示了影响表征进入意识的因素;而有关表征外显等级与意识的研究,则明确地阐明了什么样的表征才是有意识的。但表征与意识的关系是一个十分复杂的问题,目前仍存在着很大的争议,还有待于进一步的探讨     

Models and mechanisms in network neuroscience

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 long-standing 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 nonexplanatory, 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.     

Whistles,bells, and cogs in machines: Thomas Huxley and epiphenomenalism

In this paper I try to shed some historical light upon the doctrine of epiphenomenalism, by focusing on the version of epiphenomenalism championed by Thomas Huxley, which is often treated as a classic statement of the doctrine. I argue that it is doubtful if Huxley held any form of metaphysical epiphenomenalism, and that he held a more limited form of empirical epiphenomenalism with respect to consciousness but not with respect to mentality per se. Contrary to what is conventionally supposed, Huxley's empirical epiphenomenalism with respect to consciousness was not simply based upon the demonstration of the neurophysiological basis of conscious mentality, or derived from the extension of mechanistic and reflexive principles of explanation to encompass all forms of animal and human behavior, but was based upon the demonstration of purposive and coordinated animal and human behavior in the absence of consciousness. Given Huxley's own treatment of mentality, his characterization of animals and humans as “conscious automata” was not well chosen. © 2010 Wiley Periodicals, Inc.     

文本阅读中文本表征的意识性研究

文本表征的意识性是文本阅读研究领域的一个新的热点问题,该问题研究的理论基础来自于多年以前研究者就提出的再认双加工模型,该问题研究的主要范式有记得-知道范式、独立记得-知道范式和加工分离范式,三者各有自己的特点,在很多研究中经常结合使用。当前,研究者使用最新的研究范式对文本阅读中文本表征的意识性进行了初步研究,已经初步得出了一些重要结论,但当前研究中仍存在一些问题,需研究者在未来的研究中加以关注。