A Meta-Analytic Method for Testing Hypotheses about Clusters of Decision Makers

Meta-analytic methods are adapted to the task of testing hypotheses about clusters of decision makers, and an empirical illustration of these methods is presented. These methods have a number of advantages. Meta-analytic methods enable the researcher to (a) hypothesize about factors which influence cue weights and empirically test their moderating influences, (b) estimate the true relative weight from the observed relative weight after correcting for statistical artifacts, and (c) partition the variability in the observed relative weights across decision makers into variability across decision makers caused by variations in the true relative weights and the variability across decision makers caused by statistical artifacts. Finally, these methods facilitate integration of the results of individual studies without the necessity for the raw data employed in the individual studies.     

EEG波形伪迹去除方法

EEG波形记录与ERP分析技术是认知科学和脑科学研究的新兴手段 ,但在实际研究和临床应用中 ,伪迹一直是困扰研究效度的重要因素。本文主要介绍了近年来兴起的回归方法、伪迹减法、主成分分析 (PCA)、独立成分分析 (ICA)、JADE分析等脑电伪迹去除技术。相对于传统方法 ,这些技术存在精度高、速度快、实用性强的优点 ,但它们都各自针对不同问题情境 ,均建立在特定假设基础上 ,所以应根据具体的研究目的和实验条件进行合理选择。通用性、实时性和稳定性将是伪迹去除技术的发展方向     

Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results.     

Methodological naturalism and epistemic internalism

Epistemic naturalism holds that the results or methodologies from the cognitive sciences are relevant to epistemology, and some have maintained that scientific methods are more compatible with externalist theories of justification than with internalist theories. But practically all discussions about naturalized epistemology are framed exclusively in terms of cognitive psychology, which is only one of the cognitive sciences. The question addressed in this essay is whether a commitment to naturalism really does favor externalism over internalism, and we offer reasons for thinking that naturalism in epistemology is compatible with both internalist and externalist conceptions of justification. We also argue that there are some distinctively internalist aims that are currently being studied scientifically and these notions, and others, should be studied by scientific methods. This essay is dedicated to Deborah Mayo, who has long advocated using error statistical techniques to analyze and resolve epistemological puzzles in the philosophy of science. This essay follows the same spirit by advocating that computational concepts and techniques be applied within the heart of traditional, analytic epistemology.     

Bidimensional regression: assessing the configural similarity and accuracy of cognitive maps and other two-dimensional data sets

Bidimensional regression is a method for comparing the degree of resemblance between 2 planar configurations of points and, more generally, for assessing the nature of the geometry (Euclidean and non-Euclidean) between 2-dimensional independent and dependent variables. For example, it can assess the similarity between location estimates from different tasks or participant groups, measure the fidelity between cognitive maps and actual locations, and provide parameters for psychological process models. The authors detail the formal similarity between uni- and bidimensional regression, provide computational methods and a new index of spatial distortion, outline the advantages of bidimensional regression over other techniques, and provide guidelines for its use. The authors conclude by describing substantive areas in psychology for which the method would be appropriate and uniquely illuminating.     

RECkoning with representational apriorism in evolutionary cognitive archaeology

In evolutionary cognitive archaeology, the school of thought associated with the traditional framework has been deeply influenced by cognitivist intuitions, which have led to the formulation of mentalistic and disembodied cognitive explanations to address the emergence of artifacts within the archaeological record of ancient hominins. Recently, some approaches in this domain have further enforced this view, by arguing that artifacts are passive means to broadcast/perpetuate meanings that are thoroughly internal to the mind. These meanings are conveyed either in the form of a Language of Thought, constituted by sub-personal, content-bearing mental representations, or in that of a natural language. In both cases, however, material culture stands as the physical derivative of computations run over representations, which include abstract concepts, semantic relationships, and meta-representations about intensional states, a conception hereby indicated as “representational apriorism”. In this paper, I will argue that such mentalistic models are plagued by the fundamental problems of content, substance, and origin, which affect the representational substrates required for the production of artifacts. At the same time, these models fail the criterion of minimalism at the crux of conditional cognitive archaeology, because they propose overly costly explanations which are insufficiently constrained by material evidence. An alternative proposal, based on the principles of radical enactive cognitive science, is hereby introduced in order to counter this mentalistic drift. It is concluded that a radical enactive cognitive archaeology is able to dissolve the deep problems confronting the mentalistic paradigm, while providing minimalistic cognitive explanations about the emergence of Paleolithic artifacts.     

Parameters, Predictions, and Evidence in Computational Modeling: A Statistical View Informed by ACT-R

Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive theories, encouraging a "model fixed, data variable" paradigm that makes it difficult to interpret model predictions and to account for individual differences. This article proposes a likelihood-based, "data fixed, model variable" paradigm in which models are treated as stochastic processes in experiments with participant-to-participant variation that can be applied to a broad range of mechanistic cognitive architectures. This article discusses the implementation and implications of this view in model validation, with a concrete focus on a simple class of ACT-R models of cognition. This article is not intended as a recipe for broad application of these preliminary, proof-of-concept methods, but as a framework for communication between statisticians searching for interesting problems in the cognitive modeling sphere, and cognitive modelers interested in generalizing from deterministic to stochastic model validation, in the face of random variation in human experimental data.     

基于非侵入性脑刺激的认知增强：方法、伦理和应用

作为人类追求卓越的方式之一,基于非侵入性脑刺激的认知增强成为众多学科和公众关注的问题。首先阐述了非侵入性脑刺激的两种主要技术手段(经颅磁刺激和经颅直流刺激)的技术原理及其在提升健康个体认知功能上的应用,并分析了这两种技术可能带来的安全、自主选择、公平等伦理问题,最后总结了该认知增强技术在体育和军事等两个具体领域的应用。未来可进一步提高技术手段的深部脑区刺激能力及该增强技术的持续和真实效果。     

Knowledge and cognitive integration

Cognitive integration is a defining yet overlooked feature of our intellect that may nevertheless have substantial effects on the process of knowledge-acquisition. To bring those effects to the fore, I explore the topic of cognitive integration both from the perspective of virtue reliabilism within externalist epistemology and the perspective of extended cognition within externalist philosophy of mind and cognitive science. On the basis of this interdisciplinary focus, I argue that cognitive integration can provide a minimalist yet adequate epistemic norm of subjective justification: so long as the agent’s belief-forming process has been integrated in his cognitive character, the agent can be justified in holding the resulting beliefs merely by lacking any doubts there was something wrong in the way he arrived at them. Moreover, since both externalist philosophy of mind and externalist epistemology treat the process of cognitive integration in the same way, we can claim that epistemic cognitive characters may extend beyond our organismic cognitive capacities to the artifacts we employ or even to other agents we interact with. This move is not only necessary for accounting for advanced cases of knowledge that is the product of the operation of epistemic artifacts or the interactive activity of research teams, but it can further lead to interesting ramifications both for social epistemology and philosophy of science.     

How does cognition evolve? Phylogenetic comparative psychology

Now more than ever animal studies have the potential to test hypotheses regarding how cognition evolves. Comparative psychologists have developed new techniques to probe the cognitive mechanisms underlying animal behavior, and they have become increasingly skillful at adapting methodologies to test multiple species. Meanwhile, evolutionary biologists have generated quantitative approaches to investigate the phylogenetic distribution and function of phenotypic traits, including cognition. In particular, phylogenetic methods can quantitatively (1) test whether specific cognitive abilities are correlated with life history (e.g., lifespan), morphology (e.g., brain size), or socio-ecological variables (e.g., social system), (2) measure how strongly phylogenetic relatedness predicts the distribution of cognitive skills across species, and (3) estimate the ancestral state of a given cognitive trait using measures of cognitive performance from extant species. Phylogenetic methods can also be used to guide the selection of species comparisons that offer the strongest tests of a priori predictions of cognitive evolutionary hypotheses (i.e., phylogenetic targeting). Here, we explain how an integration of comparative psychology and evolutionary biology will answer a host of questions regarding the phylogenetic distribution and history of cognitive traits, as well as the evolutionary processes that drove their evolution.     

Corvid caching: Insights from a cognitive model

Caching and recovery of food by corvids is well-studied, but some ambiguous results remain. To help clarify these, we built a computational cognitive model. It is inspired by similar models built for humans, and it assumes that memory strength depends on frequency and recency of use. We compared our model's behavior to that of real birds in previously published experiments. Our model successfully replicated the outcomes of two experiments on recovery behavior and two experiments on cache site choice. Our "virtual birds" reproduced declines in recovery accuracy across sessions, revisits to previously emptied cache sites, a lack of correlation between caching and recovery order, and a preference for caching in safe locations. The model also produced two new explanations. First, that Clark's nutcrackers may become less accurate as recovery progresses not because of differential memory for different cache sites, as was once assumed, but because of chance effects. And second, that Western scrub jays may choose their cache sites not on the basis of negative recovery experiences only, as was previously thought, but on the basis of positive recovery experiences instead. Alternatively, both "punishment" and "reward" may be playing a role. We conclude with a set of new insights, a testable prediction, and directions for future work.     

Towards a Vygotskyan cognitive robotics: The role of language as a cognitive tool

Cognitive Robotics can be defined as the study of cognitive phenomena by their modeling in physical artifacts such as robots. This is a very lively and fascinating field which has already given fundamental contributions to our understanding of natural cognition. Nonetheless, robotics has to date addressed mainly very basic, low-level cognitive phenomena like sensory-motor coordination, perception, and navigation, and it is not clear how the current approach might scale up to explain high-level human cognition. In this paper we argue that a promising way to do that is to merge current ideas and methods of ‘embodied cognition’ with the Russian tradition of theoretical psychology which views language not only as a communication system but also as a cognitive tool, that is by developing a Vygotskyan cognitive robotics. We substantiate this idea by discussing several domains in which language can improve basic cognitive abilities and permit the development of high-level cognition: learning, categorization, abstraction, memory, voluntary control, and mental life.     

Hierarchical Approximate Bayesian Computation

Approximate Bayesian computation (ABC) is a powerful technique for estimating the posterior distribution of a model’s parameters. It is especially important when the model to be fit has no explicit likelihood function, which happens for computational (or simulation-based) models such as those that are popular in cognitive neuroscience and other areas in psychology. However, ABC is usually applied only to models with few parameters. Extending ABC to hierarchical models has been difficult because high-dimensional hierarchical models add computational complexity that conventional ABC cannot accommodate. In this paper, we summarize some current approaches for performing hierarchical ABC and introduce a new algorithm called Gibbs ABC. This new algorithm incorporates well-known Bayesian techniques to improve the accuracy and efficiency of the ABC approach for estimation of hierarchical models. We then use the Gibbs ABC algorithm to estimate the parameters of two models of signal detection, one with and one without a tractable likelihood function.     

When a good fit can be bad

How should we select among computational models of cognition? Although it is commonplace to measure how well each model fits the data, this is insufficient. Good fits can be misleading because they can result from properties of the model that have nothing to do with it being a close approximation to the cognitive process of interest (e.g. overfitting). Selection methods are introduced that factor in these properties when measuring fit. Their success in outperforming standard goodness-of-fit measures stems from a focus on measuring the generalizability of a model's data-fitting abilities, which should be the goal of model selection.     

Cognitive solutions for security and cryptography

This paper describes the new security solutions based on cognitive approaches and new computing paradigm called cognitive cryptography. This new security area establish a new generation of computational methods and security systems, focused on creation intelligent cryptographic algorithms and security protocols using cognitive information processing approaches. Such systems are designed for semantic evaluation of encrypted data, and allow to select the most appropriate techniques of its encryption. This paper presents a possible application of such techniques for different security tasks like authentication, secret sharing, secure data management etc. Additionally, some cryptographic solutions inspired by biological models will be presented.     

Feature selection for inductive generalization

Judging similarities among objects, events, and experiences is one of the most basic cognitive abilities, allowing us to make predictions and generalizations. The main assumption in similarity judgment is that people selectively attend to salient features of stimuli and judge their similarities on the basis of the common and distinct features of the stimuli. However, it is unclear how people select features from stimuli and how they weigh features. Here, we present a computational method that helps address these questions. Our procedure combines image-processing techniques with a machine-learning algorithm and assesses feature weights that can account for both similarity and categorization judgment data. Our analysis suggests that a small number of local features are particularly important to explain our behavioral data.     

Dealing with artifacts: The EOG contamination of the event-related brain potential

Eye movements and blinks represent a major source of artifacts in the electroencephalogram (EEG) and event-related brain potentials (ERPs). The origin of this artifact is the large difference in potential that exists between the cornea and the retina. Eye movements and blinks produce shifts of the electric fields that propagate across the whole head and that can be several times larger than the activity generated by the brain. Ocular activity can be monitored by electrodes located near the eyes (electrooculogram, or EOG). The electric fields associated with eye movements and blinks are somewhat different. The simplest procedure for dealing with ocular artifacts is to eliminate trials on which EOG activity is detected (rejection). However, this technique may result in data loss and biased data samples, especially when one is comparing clinical populations or tasks involving large amounts of eye movements. Another approach involves estimation and correction of the ocular artifact on the EEG and ERP traces. Several techniques have been proposed. Some of them are reviewed in the present paper. Issues related to the accuracy of the various techniques, as well as other advantages and limitations, are also discussed. Finally, general guidelines for how to deal with ocular artifacts are proposed.     

Beyond Rhetoric: Techniques for Using the Black Aesthetic in Group Counseling and Guidance

This article presents a small sample of the kinds of things that can be done in group work with Blacks seeking to advance our fight for freedom and dignity. We have used these techniques in a variety of settings, and we go beyond rhetorical assertiveness in saying that, “It does indeed work.” Black children, adolescents, and adults all benefit greatly from guidance stemming from a philosophical orientation anchored in an African way of life. These techniques are a practical extension of the prophetic wisdom of the Black artist, coupled with the expressive styles of Black people. Just as we searched and found a philosophical base in the cultural artifacts of Black life, we have, too, searched and found cathartic, curative, and educational aids in music, drama, poetry, folklore, expressive movement, and graphic expression, making our methods consistent with the creative Black spirit. The techniques that follow are not meant to be complete in any sense, but, rather, are offered to stimulate readers to unleash their creative potential as they seek to assist Blacks with their problems to enable them to persist in struggle.