The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.

https://www.bmj.com/content/bmj/372/bmj.n71.full.pdf

The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

The majority of functional neuroscience studies have focused on the brain's response to a task or stimulus. However, the brain is very active even in the absence of explicit input or output. In this Article we review recent studies examining spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of functional magnetic resonance imaging as a potentially important and revealing manifestation of spontaneous neuronal activity. Although several challenges remain, these studies have provided insight into the intrinsic functional architecture of the brain, variability in behaviour and potential physiological correlates of neurological and psychiatric disease.

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Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging.

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

We outline a model for types and levels of automation that provides a framework and an objective basis for deciding which system functions should be automated and to what extent. Appropriate selection is important because automation does not merely supplant but changes human activity and can impose new coordination demands on the human operator. We propose that automation can be applied to four broad classes of functions: 1) information acquisition; 2) information analysis; 3) decision and action selection; and 4) action implementation. Within each of these types, automation can be applied across a continuum of levels from low to high, i.e., from fully manual to fully automatic. A particular system can involve automation of all four types at different levels. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design using our model. Secondary evaluative criteria include automation reliability and the costs of decision/action consequences, among others. Examples of recommended types and levels of automation are provided to illustrate the application of the model to automation design.

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A model for types and levels of human interaction with automation

Automation is often problematic because people fail to rely upon it appropriately. Because people respond to technology socially, trust influences reliance on automation. In particular, trust guides reliance when complexity and unanticipated situations make a complete understanding of the automation impractical. This review considers trust from the organizational, sociological, interpersonal, psychological, and neurological perspectives. It considers how the context, automation characteristics, and cognitive processes affect the appropriateness of trust. The context in which the automation is used influences automation performance and provides a goal-oriented perspective to assess automation characteristics along a dimension of attributional abstraction. These characteristics can influence trust through analytic, analogical, and affective processes. The challenges of extrapolating the concept of trust in people to trust in automation are discussed. A conceptual model integrates research regarding trust in automation and describes the dynamics of trust, the role of context, and the influence of display characteristics. Actual or potential applications of this research include improved designs of systems that require people to manage imperfect automation.

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Trust in Automation: Designing for Appropriate Reliance

The Akaike information criterion (AIC; Akaike, 1973) is a popular method for comparing the adequacy of multiple, possibly nonnested models. Current practice in cognitive psychology is to accept a single model on the basis of only the “raw” AIC values, making it difficult to unambiguously interpret the observed AIC differences in terms of a continuous measure such as probability. Here we demonstrate that AIC values can be easily transformed to so-called Akaike weights (e.g., Akaike, 1978, 1979; Bozdogan, 1987; Burnham & Anderson, 2002), which can be directly interpreted as conditional probabilities for each model. We show by example how these Akaike weights can greatly facilitate the interpretation of the results of AIC model comparison procedures.

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AIC model selection using Akaike weights

We introduce a distributed model of memory for serial order, called SOB, that produces ordered serial recall by relying on encoding and retrieval processes that are endogenous to the model. SOB explains the basic shape of the serial position curve, the pattern of errors during recall (including the balance between transpositions, omissions, intrusions, and erroneous repetitions), the effects of list length on the distribution of errors, the overall level of recall and response latency, and the effects of natural language frequency on recall performance. In addition, contrary to several recent suggestions, SOB demonstrates that distributed representations can support unambiguous recall, selective response suppression, and novelty-sensitive encoding.

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An endogenous distributed model of ordering in serial recall

Recent analyses of serial correlations in cognitive tasks have provided preliminary evidence of the presence of a particular form of long-range serial dependence known as 1/f noise. It has been argued that long-range dependence has been largely ignored in mainstream cognitive psychology even though it accounts for a substantial proportion of variability in behavior (see, e.g., Gilden, 1997, 2001). In this article, we discuss the defining characteristics of long-range dependence and argue that claims about its presence need to be evaluated by testing against the alternative hypothesis of short-range dependence. For the data from three experiments, we accomplish such tests with autoregressive fractionally integrated moving-average time series modeling. We find that long-range serial dependence in these experiments can be explained by any of several mechanisms, including mixtures of a small number of short-range processes.

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Estimation and interpretation of 1/fα noise in human cognition

This article introduces a new computational model for the complex-span task, the most popular task for studying working memory. SOB-CS is a two-layer neural network that associates distributed item representations with distributed, overlapping position markers. Memory capacity limits are explained by interference from a superposition of associations. Concurrent processing interferes with memory through involuntary encoding of distractors. Free time in-between distractors is used to remove irrelevant representations, thereby reducing interference. The model accounts for benchmark findings in four areas: (1) effects of processing pace, processing difficulty, and number of processing steps; (2) effects of serial position and error patterns; (3) effects of different kinds of item-distractor similarity; and (4) correlations between span tasks. The model makes several new predictions in these areas, which were confirmed experimentally.

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Modeling working memory: An interference model of complex span

Preface 1. Introduction 1.1 Models and Theories in Science 1.2 Why Quantitative Modeling? 1.3 Quantitative Modeling in Cognition 1.4 The Ideas Underlying Modeling and Its Distinct Applications 1.5 What Can We Expect From Models? 1.6 Potential Problems 2. From Words to Models: Building a Toolkit 2.1 Working Memory 2.2 The Phonological Loop: 144 Models of Working Memory 2.3 Building a Simulation 2.4 What Can We Learn From These Simulations? 2.5 The Basic Toolkit 2.6 Models and Data: Sufficiency and Explanation 3. Basic Parameter Estimation Techniques 3.1 Fitting Models to Data: Parameter Estimation 3.2 Considering the Data: What Level of Analysis? 4. Maximum Likelihood Estimation 4.1 Basics of Probabilities 4.2 What Is a Likelihood? 4.3 Defining a Probability Function 4.4 Finding the Maximum Likelihood 4.5 Maximum Likelihood Estimation for Multiple Participants 4.6 Properties of Maximum Likelihood Estimators 5. Parameter Uncertainty and Model Comparison 5.1 Error on Maximum Likelihood Estimates 5.2 Introduction to Model Selection 5.3 The Likelihood Ratio Test 5.4 Information Criteria and Model Comparison 5.5 Conclusion 6. Not Everything That Fits Is Gold: Interpreting the Modeling 6.1 Psychological Data and The Very Bad Good Fit 6.2 Parameter Identifiability and Model Testability 6.3 Drawing Lessons and Conclusions From Modeling 7. Drawing It All Together: Two Examples 7.1 WITNESS: Simulating Eyewitness Identification 7.2 Exemplar Versus Boundary Models: Choosing Between Candidates 7.3 Conclusion 8. Modeling in a Broader Context 8.1 Bayesian Theories of Cognition 8.2 Neural Networks 8.3 Neuroscientific Modeling 8.4 Cognitive Architectures 8.5 Conclusion References Author Index Subject Index About the Authors

Simon Farrell

Papers

AIC model selection using Akaike weights

An endogenous distributed model of ordering in serial recall

Estimation and interpretation of 1/fα noise in human cognition

Modeling working memory: An interference model of complex span

Computational Modeling in Cognition: Principles and Practice