精加工推理中的文章结构清晰度效应

本研究旨在探讨文章结构清晰度对读者在阅读时所构建的精加工推理的水平和阶段的影响。研究采用让被试阅读后做推理测试题的方法 ,通过分析其答题成绩得出的结果表明 :文章的结构越清晰 ,越有利于读者在阅读的过程中进行精加工推理。     

The Epistemological Status of Vision and its Implications for Design

Computational theories of vision typically rely on the analysis of two aspects of human visual function: (1) object and shape recognition (2) co-calibration of sensory measurements. Both these approaches are usually based on an inverse-optics model, where visual perception is viewed as a process of inference from a 2D retinal projection to a 3D percept within a Euclidean space schema. This paradigm has had great success in certain areas of vision science, but has been relatively less successful in understanding perceptual representation, namely, the nature of the perceptual encoding. One of the drawbacks of inverse-optics approaches has been the difficulty in defining the constraints needed to make the inference computationally tractable (e.g. regularity assumptions, Bayesian priors, etc.). These constraints, thought to be learned assumptions about the nature of the physical and optical structures of the external world, have to be incorporated into any workable computational model in the inverse-optics paradigm. But inference models that employ an inverse optics plus structural assumptions approach inevitably result in a naïve realist theory of perceptual representation. Another drawback of inference models for theories of perceptual representation is their inability to explain central features of the visual experience. The one most evident in the process and visual understanding of design is the fact that some visual configurations appear, often spontaneously, as perceptually more coherent than others. The epistemological consequences of inferential approaches to vision indicate that they fail to capture enduring aspects of our visual experience. Therefore they may not be suited to a theory of perceptual representation, or useful for an understanding of the role of perception in the design process and product.     

Speaking Your Mind: Large Inarticulateness Constitutional and Circumstantial

When someone is asked to speak his mind, it is sometimes possible for him to furnish what his utterance appears to have omitted. In such cases we might say that he had a mind to speak. Sometimes, however, the opposite is true. Asked to speak his mind, our speaker finds that he has no mind to speak. When it is possible to speak one's mind and when not is largely determined by the kinds of beings we are and by the kinds of resources we are able to draw upon. In either case, not speaking one's mind is leaving something out whose articulation would or could matter for the purposes for which one was speaking in the first place. Inarticulation is no fleetingly contingent and peripheral phenomenon in human thinking and discourse. It is a substantial and dominant commonplace. In Part One I attempt to say something about what it is about the human agent that makes inarticulateness so rife. In Part Two, I consider various strategies for making the unarticulated explicit, and certain constraints on such processes. I shall suggest, among other things, that standard treatments of enthymematic reconstruction are fundamentally misconceived.     

Predictive coding explains binocular rivalry: an epistemological review

Binocular rivalry occurs when the eyes are presented with different stimuli and subjective perception alternates between them. Though recent years have seen a number of models of this phenomenon, the mechanisms behind binocular rivalry are still debated and we still lack a principled understanding of why a cognitive system such as the brain should exhibit this striking kind of behaviour. Furthermore, psychophysical and neurophysiological (single cell and imaging) studies of rivalry are not unequivocal and have proven difficult to reconcile within one framework. This review takes an epistemological approach to rivalry that considers the brain as engaged in probabilistic unconscious perceptual inference about the causes of its sensory input. We describe a simple empirical Bayesian framework, implemented with predictive coding, which seems capable of explaining binocular rivalry and reconciling many findings. The core of the explanation is that selection of one stimulus, and subsequent alternation between stimuli in rivalry occur when: (i) there is no single model or hypothesis about the causes in the environment that enjoys both high likelihood and high prior probability and (ii) when one stimulus dominates, the bottom-up, driving signal for that stimulus is explained away while, crucially, the bottom-up signal for the suppressed stimulus is not, and remains as an unexplained but explainable prediction error signal. This induces instability in perceptual dynamics that can give rise to perceptual transitions or alternations during rivalry.     

Taking a closer look: On the operation of nonconscious impression formation

In this article, we analyzed the information processing that underlies nonconscious impression formation. In the first experiment, the nonconscious activation of the impression formation goal led to a faster analysis of the trait implications of behaviors, compared with a control group. In Experiment 2, participants who were nonconsciously primed with an impression formation goal were more likely than those in a control condition to form associations in memory between behaviors and implied traits. In Experiment 3, nonconsciously primed participants were more sensitive than those in a control condition to whether inconsistent trait information was relevant or irrelevant to the actor’s disposition. Moreover, in Experiments 2 and 3, those with a nonconscious goal showed just as much evidence of impression formation as those who were consciously and intentionally trying to form an impression. Implications for nonconscious goal-pursuit and impression formation are discussed.     

A user’s guide to the evolutionary argument against naturalism

Alvin Plantinga has famously argued that metaphysical naturalism is self-defeating, and cannot be rationally accepted. I distinguish between two different ways of understanding this argument, which I call the "probabilistic inference conception", and the "process characteristic conception". I argue that the former is what critics of the argument usually presuppose, whereas most critical responses fail when one assumes the latter conception. To illustrate this, I examine three standard objections to Plantinga's evolutionary argument against naturalism: the Perspiration Objection, the Tu Quoque Objection, and the "Why Can't the Naturalist Just Add a Little Something?" Objection. I show that Plantinga's own responses to these objections fail, and propose counterexamples to his first two principles of defeat. I then go on to construct more adequate responses to these objections, using the distinctions I develop in the first part of the paper.     

Epistemic instrumentalism,exceeding our grasp

In the concluding chapter of Exceeding our Grasp Kyle Stanford outlines a positive response to the central issue raised brilliantly by his book, the problem of unconceived alternatives. This response, called "epistemic instrumentalism", relies on a distinction between instrumental and literal belief. We examine this distinction and with it the viability of Stanford's instrumentalism, which may well be another case of exceeding our grasp.     

Model structure adequacy analysis: selecting models on the basis of their ability to answer scientific questions

Models carry the meaning of science. This puts a tremendous burden on the process of model selection. In general practice, models are selected on the basis of their relative goodness of fit to data penalized by model complexity. However, this may not be the most effective approach for selecting models to answer a specific scientific question because model fit is sensitive to all aspects of a model, not just those relevant to the question. Model Structural Adequacy analysis is proposed as a means to select models based on their ability to answer specific scientific questions given the current understanding of the relevant aspects of the real world.     

The error statistical philosopher as normative naturalist

We argue for a naturalistic account for appraising scientific methods that carries non-trivial normative force. We develop our approach by comparison with Laudan’s (American Philosophical Quarterly 24:19–31, 1987, Philosophy of Science 57:20–33, 1990) “normative naturalism” based on correlating means (various scientific methods) with ends (e.g., reliability). We argue that such a meta-methodology based on means–ends correlations is unreliable and cannot achieve its normative goals. We suggest another approach for meta-methodology based on a conglomeration of tools and strategies (from statistical modeling, experimental design, and related fields) that affords forward looking procedures for learning from error and for controlling error. The resulting “error statistical” appraisal is empirical—methods are appraised by examining their capacities to control error. At the same time, this account is normative, in that the strategies that pass muster are claims about how actually to proceed in given contexts to reach reliable inferences from limited data.