Lambda-calculus,combinators and applicative computational technologies

Applicative computing systems and technologies have taken a strong position in modern computing. In this paper, the basic applicative system, whether it is a lambda calculus or a system of combinators, is considered as a prototype concept system, using which it is possible to build individual systems that are practically significant for mathematics, computing, or programming. They are families of computational models that have both their own semantics and applied areas. This conceptualization/individualization technique is characteristic of the field of semantic studies. As it turns out, the applicative approach forms a metatheoretical framework that provides the basis for cognitive systems that consider abstract objects and interpret their properties and behavior in the environment of modern computing.     

The enactive computational basis of cognition and the explanatory cognitive basis for computing

The computational theory of cognition, or computationalism, holds that cognition is a form of computation. Two issues related to this view are comprised by the goal of this paper: A) Computing systems are traditionally seen as representational systems, but functional and enactive approaches support non-representational theories; B) Recently, a sociocultural theory against computationalism was proposed with the aim of ontologically reducing computing to cognition. We defend, however, that cognition and computation are in action, thus cognition is just a form of computing and that cognition is the explanatory basis for computation. We state that: 1. Representational theories of computing recurring to intentional content run into metaphysical problems. 2. Functional non-representational theories do not incur this metaphysical problem when describing computing in terms of the abstract machine. 3. Functional theories are consistent with enactive in describing computing machines not in a strictly functional way, but especially in terms of their organization. 4. Enactive cognition is consistent with the computationalism in describing Turing machines as functionally and organizationally closed systems. 5. The cognitive explanatory basis for computing improves the computational theory of cognition. When developed in the human linguistic domain, computer science is seen as a product of human socionatural normative practices, however, cognition is just an explanatory, not ontological, basis for computing. The paper concludes by supporting that computation is in action, that cognition is just one form of computing in the world and the explanatory basis for computation.     

Are Computational Transitions Sensitive to Semantics?

The formal conception of computation (FCC) holds that computational processes are not sensitive to semantic properties. FCC is popular, but it faces well-known difficulties. Accordingly, authors such as Block and Peacocke pursue a ‘semantically-laden’ alternative, according to which computation can be sensitive to semantics. I argue that computation is insensitive to semantics within a wide range of computational systems, including any system with ‘derived’ rather than ‘original’ intentionality. FCC yields the correct verdict for these systems. I conclude that there is only one promising strategy for semantically-laden theorists: identify special computational systems that help generate their own semantic properties, and then show that computation within those systems is semantically-laden. Unfortunately, the few existing discussions that pursue this strategy are problematic.     

Abstract versus Causal Explanations?

In the recent literature on causal and non-causal scientific explanations, there is an intuitive assumption (which we call the ‘abstractness assumption’) according to which an explanation is non-causal by virtue of being abstract. In this context, to be ‘abstract’ means that the explanans in question leaves out many or almost all causal microphysical details of the target system. After motivating this assumption, we argue that the abstractness assumption, in placing the abstract and the causal character of an explanation in tension, is misguided in ways that are independent of which view of causation or causal explanation one takes to be most accurate. On major accounts of causation, as well as on major accounts of causal explanation, the abstractness of an explanation is not sufficient for it being non-causal. That is, explanations are not non-causal by dint of being abstract.     

Steps Toward a Computational Metaphysics

In this paper, the authors describe their initial investigations in computational metaphysics. Our method is to implement axiomatic metaphysics in an automated reasoning system. In this paper, we describe what we have discovered when the theory of abstract objects is implemented in prover9 (a first-order automated reasoning system which is the successor to otter). After reviewing the second-order, axiomatic theory of abstract objects, we show (1) how to represent a fragment of that theory in prover9’s first-order syntax, and (2) how prover9 then finds proofs of interesting theorems of metaphysics, such as that every possible world is maximal. We conclude the paper by discussing some issues for further research.     

Physical Oracles: The Turing Machine and the Wheatstone Bridge

Earlier, we have studied computations possible by physical systems and by algorithms combined with physical systems. In particular, we have analysed the idea of using an experiment as an oracle to an abstract computational device, such as the Turing machine. The theory of composite machines of this kind can be used to understand (a) a Turing machine receiving extra computational power from a physical process, or (b) an experimenter modelled as a Turing machine performing a test of a known physical theory T. Our earlier work was based upon experiments in Newtonian mechanics. Here we extend the scope of the theory of experimental oracles beyond Newtonian mechanics to electrical theory. First, we specify an experiment that measures resistance using a Wheatstone bridge and start to classify the computational power of this experimental oracle using non-uniform complexity classes. Secondly, we show that modelling an experimenter and experimental procedure algorithmically imposes a limit on our ability to measure resistance by the Wheatstone bridge. The connection between the algorithm and physical test is mediated by a protocol controlling each query, especially the physical time taken by the experimenter. In our studies we find that physical experiments have an exponential time protocol; this we formulate as a general conjecture. Our theory proposes that measurability in Physics is subject to laws which are co-lateral effects of the limits of computability and computational complexity.     

The intrinsic difficulty of recursive functions

This paper deals with a philosophical question that arises within the theory of computational complexity: how to understand the notion of INTRINSIC complexity or difficulty, as opposed to notions of difficulty that depend on the particular computational model used. The paper uses ideas from Blum's abstract approach to complexity theory to develop an extensional approach to this question. Among other things, it shows how such an approach gives detailed confirmation of the view that subrecursive hierarchies tend to rank functions in terms of their intrinsic, and not just their model-dependent, difficulty, and it shows how the approach allows us to model the idea that intrinsic difficulty is a fuzzy concept. Jan Zygmunt     

A mechanistic perspective on canonical neural computation

Although it has been argued that mechanistic explanation is compatible with abstraction (i.e., that there are abstract mechanistic models), there are still doubts about whether mechanism can account for the explanatory power of significant abstract models in computational neuroscience. Chirimuuta has recently claimed that models describing canonical neural computations (CNCs) must be evaluated using a non-mechanistic framework. I defend two claims regarding these models. First, I argue that their prevailing neurocognitive interpretation is mechanistic. Additionally, a criterion recently proposed by Levy and Bechtel to legitimize mechanistic abstract models, and also a criterion proposed by Chirimuuta herself aimed to distinguish between causal and non-causal explanation, can be employed to show why these models are explanatory only under this interpretation (as opposed to a purely mathematical or non-causal interpretation). Second, I argue that mechanism is able to account for the special epistemic achievement implied by CNC models. Canonical neural components contribute to an integrated understanding of different cognitive functions. They make it possible for us to explain these functions by describing different mechanisms constituted by common basic components arranged in different ways.     

Desktop flight simulators: Simulation fidelity and pilot performance

Improvements in the computing power and visual resolution of modern desktop computing systems, as well as advances in software technology for displaying high-speed animations, have encouraged the development of relatively sophisticated real-time flight simulators for the PC and Macintosh. We review some of the factors that determine how well such programs capture the actual experience of flight. The most significant factor limiting the quality of performance in flying a simulated aircraft is the “frame rate” problem: at low altitudes and in highly detailed visual environments, as in approaching a runway threshold during landing, the computational demands of the animation may necessitate a reduction in the number of frames displayed per second on the screen. The delayed sensory feedback that results proves to be very detrimental to sustaining smooth control of the aircraft, especially during the flare to touchdown where such control is needed most. This finding parallels the well-known effects of delayed auditory feedback (Lee, 1950) and delayed visual feedback (Smith, 1962).     

人际互动中社会学习的计算神经机制

人类在社会互动中通过他人的行为对他人特质、意图及特定情境下的社会规范进行学习, 是优化决策、维护积极社会互动的重要条件。近年来, 越来越多的研究通过结合计算模型与神经影像技术对社会学习的认知计算机制及其神经基础进行了深入考察。已有研究发现, 人类的社会学习过程能够较好地被强化学习模型与贝叶斯模型刻画, 主要涉及的认知计算过程包括主观期望、预期误差和不确定性的表征以及信息整合的过程。大脑对这些计算过程的执行主要涉及奖惩加工相关脑区(如腹侧纹状体与腹内侧前额叶)、社会认知加工相关脑区(如背内侧前额叶和颞顶联合区)及认知控制相关脑区(如背外侧前额叶)。需要指出的是, 计算过程与大脑区域之间并不是一一映射的关系, 提示未来研究可借助多变量分析与脑网络分析等技术从系统神经科学的角度来考察大尺度脑网络如何执行不同计算过程。此外, 将来研究应注重生态效度, 利用超扫描技术考察真实互动下的社会学习过程, 并更多地关注内隐社会学习的计算与神经机制。     

具身概念表征的研究及理论述评

具身认知强调认知在本质上是具身的, 身体在认知的实现中发挥着关键作用。传统的符号加工理论认为, 概念表征独立于主体的知觉运动系统并以抽象符号的形式储存于语言记忆中。概念表征的具身理论则认为, 概念表征与知觉运动系统具有共同的神经基础, 概念在本质上是主体经验客体时知觉与运动体验的神经记录, 而概念加工的基本形式则是身体经验的模拟与还原。关于该理论的实证研究主要集中于概念加工引发的知觉动作变化、身体动作对概念加工的影响、抽象概念加工的具身特征等领域。今后的研究应关注符号加工理论与具身理论的整合等。     

Deictic codes for the embodiment of cognition

To describe phenomena that occur at different time scales, computational models of the brain must incorporate different levels of abstraction. At time scales of approximately 1/3 of a second, orienting movements of the body play a crucial role in cognition and form a useful computational level--more abstract than that used to capture natural phenomena but less abstract than what is traditionally used to study high-level cognitive processes such as reasoning. At this "embodiment level," the constraints of the physical system determine the nature of cognitive operations. The key synergy is that at time scales of about 1/3 of a second, the natural sequentiality of body movements can be matched to the natural computational economies of sequential decision systems through a system of implicit reference called deictic in which pointing movements are used to bind objects in the world to cognitive programs. This target article focuses on how deictic binding make it possible to perform natural tasks. Deictic computation provides a mechanism for representing the essential features that link external sensory data with internal cognitive programs and motor actions. One of the central features of cognition, working memory, can be related to moment-by-moment dispositions of body features such as eye movements and hand movements.     

A priori entailment and conceptual analysis: Making room for type-C physicalism 1

According to pancomputationalism, everything is a computing system. In this paper, I distinguish between different varieties of pancomputationalism. I find that although some varieties are more plausible than others, only the strongest variety is relevant to the philosophy of mind, but only the most trivial varieties are true. As a side effect of this exercise, I offer a clarified distinction between computational modelling and computational explanation.     

Multiple Systems in Decision Making: A Neurocomputational Perspective

ABSTRACT— Various psychological models posit the existence of two systems that contribute to decision making. The first system is bottom-up, automatic, intuitive, emotional, and implicit, while the second system is top-down, controlled, deliberative, and explicit. It has become increasingly evident that this dichotomy is both too simplistic and too vague. Here we consider insights gained from a different approach, one that considers the multiple computational demands of the decision-making system in the context of neural mechanisms specialized to accomplish some of that system's more basic functions. The use of explicit computational models has led to (a) identification of core trade-offs imposed by a single-system solution to cognitive problems that are solved by having multiple neural systems, and (b) novel predictions that can be tested empirically and that serve to further refine the models.     

Learning a theory of causality

The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be learned from co-occurrence of events. We begin by phrasing the causal Bayes nets theory of causality and a range of alternatives in a logical language for relational theories. This allows us to explore simultaneous inductive learning of an abstract theory of causality and a causal model for each of several causal systems. We find that the correct theory of causality can be learned relatively quickly, often becoming available before specific causal theories have been learned--an effect we term the blessing of abstraction. We then explore the effect of providing a variety of auxiliary evidence and find that a collection of simple perceptual input analyzers can help to bootstrap abstract knowledge. Together, these results suggest that the most efficient route to causal knowledge may be to build in not an abstract notion of causality but a powerful inductive learning mechanism and a variety of perceptual supports. While these results are purely computational, they have implications for cognitive development, which we explore in the conclusion.     

Clarifying the task

Those who would enquire into therelationship between “health conceptions” and “health care consequences” are faced with a formidable task. In order to make this challenge manageable it is necessary to define the scope of the task as precisely as possible. Are we, for instance, faced with a purely theoretical challenge; a task for applied philosophy, or must we employ multi-disciplinary methods? This paper argues that while philosophy has a central clarifying role, inquiry into the relationship between “health conceptions” and “health care organisation” can be done properly only through the combined efforts of several disciplines. Unless we are to be concerned only with abstract models it is essential to take account of the reality of health care situations. Given this it is suggested that the study of “health conceptions” is only a part of a greater task (unless all conceptions are to count as “health conceptions”). What is needed is understanding of the possible and actual purposes of health care, and detailed study of their practical implications.     

Computationalism

What counts as a computation and how it relates to cognitive function are important questions for scientists interested in understanding how the mind thinks. This paper argues that pragmatic aspects of explanation ultimately determine how we answer those questions by examining what is needed to make rigorous the notion of computation used in the (cognitive) sciences. It (1) outlines the connection between the Church-Turing Thesis and computational theories of physical systems, (2) differentiates merely satisfying a computational function from true computation, and finally (3) relates how we determine a true computation to the functional methodology in cognitive science. All of the discussion will be directed toward showing that the only way to connect formal notions of computation to empirical theory will be in virtue of the pragmatic aspects of explanation.     

情感计算技术如何推动犯罪风险评估工具的发展？

摘 要 随着近年来人工智能深度学习技术的发展,情感计算与人格计算技术日渐成熟,在许多实际应用场景中取得了良好的表现,当前人工智能情感计算技术应用于犯罪风险评估领域,能够有效解决目前主流的风险评估工具难以解决的个体内差异性的预测因子评估问题以及被测评参与者因社会赞许性而导致结果失真的问题。本文在详细阐述目前主流评估工具的局限性基础上,详细阐述了以情感计算技术为支撑的动态风险评估工具的设计思路、目前已有的技术方案以及设计细节的理论依据,在此基础上最后提出以人工智能技术为支撑的新型评估工具的未来发展方向。     

Causality in naturally occurring computational systems

Some recent results from the study of chaotic and complex dynamical systems are invoked to assess the status of causality as it applies to naturally occurring computational systems. The evidence would support Bunge's concept of general determinism and therefore the doctrine of semicausalism. A foundation of statistical determinism must be supplemented with other notions such as interactive, emergent, contextual and structural determinism. These forms of determinism are both noncausal and nonreductionist. Contextual factors play a prominent role. Thus the likelihood of finding general methods for the prediction and control of NOCS is low.