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.     

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.     

Using Cases with Contrary Facts to Illustrate and Facilitate Ethical Analysis

There has been increasing interest in developing practical, non-theoretical tools for analyzing ethical problems in public health, biomedicine, and other scientific disciplines so that professionals can make and justify ethical decisions in their own research or practice. Tools for ethical decisionmaking, together with case studies on ethics, are often used in graduate education programs and in continuing professional education. Students can benefit from opportunities to further develop their analytical skills, to recognize ethical issues, and to develop their moral sensitivity. One practical approach for illustrating and facilitating ethical analysis uses cases with contrary facts and circumstances, an approach which complements rather than replaces theoretical approaches to moral reasoning. Cases with contrary facts and circumstances are presented in two or more alternative ways so that the facts, circumstances, or framing of one version runs counter to that of the other version (s). Cases with contrary facts, together with practical steps for identifying and analyzing ethical issues, are likely to be useful tools for illustrating and facilitating ethics analysis and stimulating the moral imagination. The findings and conclusions in this article are those of the author and do not necessarily represent the views of the Centers for Disease Control and Prevention.     

社会认知神经科学研究的最新进展

社会认知神经科学是一门采用认知神经科学技术研究社会认知现象的交叉学科,目前已经形成认知神经科学与心理生理学的热点研究领域。该文回顾了近年来有关情绪、情绪障碍、自我意识、经济决策、道德等方面的脑机制研究,临床研究和脑成像研究表明,恐惧、厌恶、愤怒、惊讶、快乐、悲伤等基本情绪在表达和识别上具有普遍性与特殊性;孤独症与“男性脑”的特征相关,抗抑郁药百忧解对抑郁症儿童的治疗作用;在自我意识的研究领域,自我概念产生的脑区定位、灵长类的自我意象、精神疾病患者的自我概念变化等研究的热点问题;经济决策的研究最近成为亮点之一,主要包括强互惠合作、风险决策、跨期选择以及品牌决策等方面;在道德的脑机制研究中,重点涉及了道德判断与捐赠行为的神经基础。文章最后展望了社会认知神经科学在我国发展的前景     

Challenges of Evaluating Multilevel Interventions

This article uses the Comprehensive Mixed-Methods Participatory Evaluation (CMMPE; Nastasi and Hitchcock Transforming school mental health services: Population-based approaches to promoting the competency and wellness of children, Thousand Oaks, CA: Corwin Press with National Association of School Psychologists 2008; Nastasi et al. School-based mental health services: creating comprehensive and culturally specific programs. Washington, DC: American Psychological Association 2004) model as a framework for addressing the multiplicity of evaluation decisions and complex nature of questions related to program success in multilevel interventions. CMMPE defines program success in terms of acceptability, integrity, social or cultural validity, outcomes (impact), sustainability and institutionalization, thus broadening the traditional notions of program outcomes. The authors use CMMPE and an example of a community-based multilevel sexual risk prevention program with multiple outcomes to discuss challenges of evaluating multilevel interventions. The sexual risk program exemplifies what Schensul and Tricket (this issue) characterize as multilevel intervention–multilevel evaluation (M–M), with both intervention and evaluation at community, health practitioner, and patient levels. The illustration provides the context for considering several challenges related to M–M designs: feasibility of randomized controlled trials within community-based multilevel intervention; acceptability and social or cultural validity of evaluation procedures; implementer, recipient, and contextual variations in program success; interactions among levels of the intervention; unanticipated changes or conditions; multiple indicators of program success; engaging multiple stakeholders in a participatory process; and evaluating sustainability and institutionalization. The complexity of multilevel intervention and evaluation designs challenges traditional notions of evaluation research and experimental designs. Overcoming these challenges is critical to effective translation of research to practice in psychology and related disciplines.     

Neural mechanisms of social influence

The present investigation explores the neural mechanisms underlying the impact of social influence on preferences. We socially tagged symbols as valued or not – by exposing participants to the preferences of their peers – and assessed subsequent brain activity during an incidental processing task in which participants viewed popular, unpopular, and novel symbols. The medial prefrontal cortex (mPFC) differentiated between symbols that were and were not socially tagged – a possible index of normative influence – while aspects of the striatum (the caudate) differentiated between popular and unpopular symbols – a possible index of informational influence. These results suggest that integrating activity in these two brain regions may differentiate objects that have become valued as a result of social influence from those valued for non-social reasons.     

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.     

简评认知神经科学取向的智力观

随着认知神经科学与生物技术的发展,现当代智力理论的构建明显地受到其影响。这些智力理论被称之为认知神经科学取向的智力观,如Garlick的神经可塑性模型;Das等人根据Luria的神经模型提出的智力PASS模型;Kaufman提出的儿童评估成套测验（K-ABC）;Ceci的生物-生态学智力模型及Perkins的真智力理论等。这些智力观都直接将认知神经过程或神经生物学的某些研究成果融合于其理论模型之中。认知神经科学取向的智力观为智力研究提供了新的研究视角和思考维度,但同时也可能面临某种发展困境。第二代认知科学的具身认知思想可能是解决传统身-心争论的有效途径。     

Top-down predictions in the cognitive brain

The human brain is not a passive organ simply waiting to be activated by external stimuli. Instead, we propose that the brain continuously employs memory of past experiences to interpret sensory information and predict the immediately relevant future. The basic elements of this proposal include analogical mapping, associative representations and the generation of predictions. This review concentrates on visual recognition as the model system for developing and testing ideas about the role and mechanisms of top-down predictions in the brain. We cover relevant behavioral, computational and neural aspects, explore links to emotion and action preparation, and consider clinical implications for schizophrenia and dyslexia. We then discuss the extension of the general principles of this proposal to other cognitive domains.