Disentangling narrow and coarse semantic networks in the brain: The role of computational models of word meaning

There has been a recent boom in research relating semantic space computational models to fMRI data, in an effort to better understand how the brain represents semantic information. In the first study reported here, we expanded on a previous study to examine how different semantic space models and modeling parameters affect the abilities of these computational models to predict brain activation in a data-driven set of 500 selected voxels. The findings suggest that these computational models may contain distinct types of semantic information that relate to different brain areas in different ways. On the basis of these findings, in a second study we conducted an additional exploratory analysis of theoretically motivated brain regions in the language network. We demonstrated that data-driven computational models can be successfully integrated into theoretical frameworks to inform and test theories of semantic representation and processing. The findings from our work are discussed in light of future directions for neuroimaging and computational research.     

Computational approaches to the development of perceptual expertise

Dog experts, ornithologists, radiologists and other specialists are noted for their remarkable abilities at categorizing, identifying and recognizing objects within their domain of expertise. A complete understanding of the development of perceptual expertise requires a combination of thorough empirical research and carefully articulated computational theories that formalize specific hypotheses about the acquisition of expertise. A comprehensive computational theory of the development of perceptual expertise remains elusive, but we can look to existing computational models from the object-recognition, perceptual-categorization, automaticity and related literatures for possible starting points. Arguably, hypotheses about the development of perceptual expertise should first be explored within the context of existing computational models of visual object understanding before considering the creation of highly modularized adaptations for particular domains of perceptual expertise.     

The Debate on the Moral Responsibilities of Online Service Providers

Online service providers (OSPs)—such as AOL, Facebook, Google, Microsoft, and Twitter—significantly shape the informational environment (infosphere) and influence users’ experiences and interactions within it. There is a general agreement on the centrality of OSPs in information societies, but little consensus about what principles should shape their moral responsibilities and practices. In this article, we analyse the main contributions to the debate on the moral responsibilities of OSPs. By endorsing the method of the levels of abstract (LoAs), we first analyse the moral responsibilities of OSPs in the web (LoA_IN). These concern the management of online information, which includes information filtering, Internet censorship, the circulation of harmful content, and the implementation and fostering of human rights (including privacy). We then consider the moral responsibilities ascribed to OSPs on the web (LoA_ON) and focus on the existing legal regulation of access to users’ data. The overall analysis provides an overview of the current state of the debate and highlights two main results. First, topics related to OSPs’ public role—especially their gatekeeping function, their corporate social responsibilities, and their role in implementing and fostering human rights—have acquired increasing relevance in the specialised literature. Second, there is a lack of an ethical framework that can (a) define OSPs’ responsibilities, and (b) provide the fundamental sharable principles necessary to guide OSPs’ conduct within the multicultural and international context in which they operate. This article contributes to the ethical framework necessary to deal with (a) and (b) by endorsing a LoA enabling the definition of the responsibilities of OSPs with respect to the well-being of the infosphere and of the entities inhabiting it (LoA_For).     

“Bridge to the Literature”? Third-Party Genetic Interpretation Tools and the Views of Tool Developers

Patients and health care consumers can obtain access to their “raw,” or uninterpreted, genetic data from direct-to-consumer genetic testing companies, researchers, or providers and pursue self-directed analysis via third-party interpretation tools. Yet relatively little is known about the nature of currently available interpretation tools or the motivations of tool developers. We conducted a structured content analysis of 23 third-party interpretation tool websites and supporting information, tracking features such as types of information returned, modes of generating and presenting that information, and privacy and security measures. We additionally conducted qualitative interviews with a subset of 10 tool developers. A majority of tools (16 of 23, or 70%) offer some type of health or wellness-related information, often extracted from publicly available variant annotation databases. Half of those interviewed characterized their activities as “bridging” users to the scientific literature rather than interpretation, for which they gave a variety of scientific, ethical, and regulatory justifications. The scale, heterogeneity, and complexity of information available from third-party interpretation are unprecedented. While developers aim to enlighten and empower tool users, interpretation-free “bridging” to rapidly evolving databases may instead impose burdens on genetic counselors and other health care providers asked to provide further contextualization and explanation.     

How do scientists select articles in the PubMed database? An empirical study of criteria and strategies

IntroductionThe present study investigated the strategies used by experienced researchers to find articles in an online bibliographic database.ObjectiveThe goal was to shed light on researchers’ actual search behaviour by empirically observing users during the search process.MethodWe asked 16 neuroscience researchers and 16 researchers in other disciplines of life sciences to perform five search tasks on neuroscience topics using the PubMed database. Think-aloud protocols were recorded while the participants performed the search tasks.ResultsAll researchers managed to find adequate references for the tasks in the limited amount of time allocated. Most participants used similar, very basic moves and tactics to perform the tasks. However, they behaved in many different ways when they had to decide whether to read the retrieved results or to modify their query (according to the number of references they obtained), or when selecting the one or two references they finally gave as answers among all possible answers.DiscussionVariability in participants’ behaviour may result from inter-individual differences in either the participants’ mental models of PubMed or cognitive abilities and styles, but the question requires further investigation. The data further suggest that developers of online search engines and databases should concentrate their efforts on improving simple-search interfaces and helping users to correct search errors in real time.     

The past,present, and future of computational models of cognitive development

Does modeling matter? We address this question by providing a broad survey of the computational models of cognitive development that have been proposed and studied over the last three decades. We begin by noting the advantages and limitations of computational models. We then describe four key dimensions across which models of development can be organized and classified. With this taxonomy in hand, we focus on how the modeling enterprise has evolved over time. In particular, we separate the timeline into three overlapping historical waves and highlight how each wave of models has not only been shaped by developmental theory and behavioral research, but in return also provided valuable insights and innovations to the study of cognitive development.     

Constraints on Analogical Mapping: A Comparison of Three Models

Three theories of analogy have been proposed that are supported by computational models and data from experiments on human analogical abilities. In this article we show how these theories can be unified within a common metatheoretical framework that distinguishes among levels of informational, behavioral, and hardware constraints. This framework clarifies the distinctions among three computational models in the literature: the Analogical Constraint Mapping Engine (ACME), the Structure-Mapping Engine (SME), and the Incremental Analogy Machine (IAM). We then go on to develop a methodology for the comparative testing of these models. In two different manipulations of an analogical mapping task we compare the results of computational experiments with these models against the results of psychological experiments. In the first experiment we show that increasing the number of similar elements in two analogical domains decreases the response time taken to reach the correct mapping for an analogy problem. In the second psychological experiment we find that the order in which the elements of the two domains are presented has significant facilitative effects on the ease of analogical mapping. Of the three models, only IAM embodies behavioral constraints and predicts both of these results. Finally, the immediate implications of these results for analogy research are discussed, along with the wider implications the research has for cognitive science methodology.     

Information system development: From user participation to contingent interaction among involved parties

Abstract

Over the last few decades many social scientists have pointed to the importance of user participation in information system development (SD). In study A the opinions of 314 Dutch system developers on this issue were examined. The conclusion is that the majority rate the value of user participation highly. However, these data confront us with three important problems. First, we cannot be sure that the stated preferences of system developers are reflected in their actual behaviour. Second, system developers may give a different meaning to the concept “user participation” than social scientists. Third, “user participation” does not necessarily imply “user influence” on SD. Moreover, the empirical relationship between user participation and the effectiveness of SD remains unclear. In our opinion this can be attributed both to the multidimensional nature of the former concept and to the contingency of this relationship to the context of SD. Therefore, in study B, we developed a multidimensional contingency model to study the interaction among parties in SD instead of the study of user participation. In this explorative study no linear relationship between the effectiveness of SD and the interaction between users and system developers was found. Instead, effective SD seemed to require a fit between the context of SD and the interactions with and among users.     

Mentoring and Research Misconduct: An Analysis of Research Mentoring in Closed ORI Cases

We are reporting on how involved the mentor was in promoting responsible research in cases of research misconduct. We reviewed the USPHS misconduct files of the Office of Research Integrity. These files are created by Institutions who prosecute a case of possible research misconduct; ORI has oversight review of these investigations. We explored the role of the mentor in the cases of trainee research misconduct on three specific behaviors that we believe mentors should perform with their trainee: (1) review source data, (2) teach specific research standards and (3) minimize stressful work situations. We found that almost three quarters of the mentors had not reviewed the source data and two thirds had not set standards. These two behaviors are positively correlated. We did not see convincing evidence in the records that mentors were causing stress, but it was apparent in the convicted trainees' confessions that over 50% experienced some kind of stress. Secondary data, while not created for this research purpose, allows us to look at concrete research behaviors that are otherwise not very researchable. We believe it is important for mentors and institutions to devote more attention to teaching mentors about the process of education and their responsibilities in educating the next generation of scientists. This becomes a critical issue for large research groups who need to determine who is in charge educating, supervising and assuring data integrity.     

Toward Modeling and Automating Ethical Decision Making: Design, Implementation, Limitations, and Responsibilities

One recent priority of the U.S. government is developing autonomous robotic systems. The U.S. Army has funded research to design a metric of evil to support military commanders with ethical decision-making and, in the future, allow robotic military systems to make autonomous ethical judgments. We use this particular project as a case study for efforts that seek to frame morality in quantitative terms. We report preliminary results from this research, describing the assumptions and limitations of a program that assesses the relative evil of two courses of action. We compare this program to other attempts to simulate ethical decision-making, assess possibilities for overcoming the trade-off between input simplification and output reliability, and discuss the responsibilities of users and designers in implementing such programs. We conclude by discussing the implications that this project highlights for the successes and challenges of developing automated mechanisms for ethical decision making.     

Extrapolating human judgments from skip-gram vector representations of word meaning

There is a growing body of research in psychology that attempts to extrapolate human lexical judgments from computational models of semantics. This research can be used to help develop comprehensive norm sets for experimental research, it has applications to large-scale statistical modelling of lexical access and has broad value within natural language processing and sentiment analysis. However, the value of extrapolated human judgments has recently been questioned within psychological research. Of primary concern is the fact that extrapolated judgments may not share the same pattern of statistical relationship with lexical and semantic variables as do actual human judgments; often the error component in extrapolated judgments is not psychologically inert, making such judgments problematic to use for psychological research. We present a new methodology for extrapolating human judgments that partially addresses prior concerns of validity. We use this methodology to extrapolate human judgments of valence, arousal, dominance, and concreteness for 78,286 words. We also provide resources for users to extrapolate these human judgments for three million English words and short phrases. Applications for large sets of extrapolated human judgments are demonstrated and discussed.     

Brain mechanisms for predictive control by switching internal models: implications for higher-order cognitive functions

Humans can guide their actions toward the realization of their intentions. Flexible, rapid and precise realization of intentions and goals relies on the brain learning to control its actions on external objects and to predict the consequences of this control. Neural mechanisms that mimic the input–output properties of our own body and other objects can be used to support prediction and control, and such mechanisms are called internal models. We first summarize functional neuroimaging, behavioral and computational studies of the brain mechanisms related to acquisition, modular organization, and the predictive switching of internal models mainly for tool use. These mechanisms support predictive control and flexible switching of intentional actions. We then review recent studies demonstrating that internal models are crucial for the execution of not only immediate actions but also higher-order cognitive functions, including optimization of behaviors toward long-term goals, social interactions based on prediction of others’ actions and mental states, and language processing. These studies suggest that a concept of internal models can consistently explain the neural mechanisms and computational principles needed for fundamental sensorimotor functions as well as higher-order cognitive functions. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Reinforcement learning in the brain

A wealth of research focuses on the decision-making processes that animals and humans employ when selecting actions in the face of reward and punishment. Initially such work stemmed from psychological investigations of conditioned behavior, and explanations of these in terms of computational models. Increasingly, analysis at the computational level has drawn on ideas from reinforcement learning, which provide a normative framework within which decision-making can be analyzed. More recently, the fruits of these extensive lines of research have made contact with investigations into the neural basis of decision making. Converging evidence now links reinforcement learning to specific neural substrates, assigning them precise computational roles. Specifically, electrophysiological recordings in behaving animals and functional imaging of human decision-making have revealed in the brain the existence of a key reinforcement learning signal, the temporal difference reward prediction error. Here, we first introduce the formal reinforcement learning framework. We then review the multiple lines of evidence linking reinforcement learning to the function of dopaminergic neurons in the mammalian midbrain and to more recent data from human imaging experiments. We further extend the discussion to aspects of learning not associated with phasic dopamine signals, such as learning of goal-directed responding that may not be dopamine-dependent, and learning about the vigor (or rate) with which actions should be performed that has been linked to tonic aspects of dopaminergic signaling. We end with a brief discussion of some of the limitations of the reinforcement learning framework, highlighting questions for future research.     

A Computational Model of Linguistic Humor in Puns

Humor plays an essential role in human interactions. Precisely what makes something funny, however, remains elusive. While research on natural language understanding has made significant advancements in recent years, there has been little direct integration of humor research with computational models of language understanding. In this paper, we propose two information‐theoretic measures—ambiguity and distinctiveness—derived from a simple model of sentence processing. We test these measures on a set of puns and regular sentences and show that they correlate significantly with human judgments of funniness. Moreover, within a set of puns, the distinctiveness measure distinguishes exceptionally funny puns from mediocre ones. Our work is the first, to our knowledge, to integrate a computational model of general language understanding and humor theory to quantitatively predict humor at a fine‐grained level. We present it as an example of a framework for applying models of language processing to understand higher level linguistic and cognitive phenomena.     

Reporting structural equation modeling results in Psychology and Aging: some proposed guidelines.

Structural equation modeling (SEM) is now widely used in social and behavioral science research. SEM provides the possibility of fitting, and evaluating the fit, of well-specified, theoretical models to empirical data--more generally, of testing elaborated psychological theories. The options available to users of these approaches are many and varied. Popular SEM computational software packages, such as LISREL and EQS, provide a large amount of information, and there is some uncertainty as to what should be routinely reported. A series of guidelines are proposed for reporting SEM results in articles submitted to Psychology and Aging. The suggested guidelines ask authors using SEM methodology to provide important analysis information that will enable readers to evaluate the findings.     

Universal moral grammar: theory, evidence and the future

Scientists from various disciplines have begun to focus attention on the psychology and biology of human morality. One research program that has recently gained attention is universal moral grammar (UMG). UMG seeks to describe the nature and origin of moral knowledge by using concepts and models similar to those used in Chomsky's program in linguistics. This approach is thought to provide a fruitful perspective from which to investigate moral competence from computational, ontogenetic, behavioral, physiological and phylogenetic perspectives. In this article, I outline a framework for UMG and describe some of the evidence that supports it. I also propose a novel computational analysis of moral intuitions and argue that future research on this topic should draw more directly on legal theory.     

How using a paper versus mobile calendar influences everyday planning and plan fulfillment

This research examines how using a paper versus mobile calendar influences everyday planning and plan fulfillment behavior. Consumers are rapidly moving from paper to mobile calendars for convenience, but this research shows that doing so may lead individuals to be less successful in effectively developing and implementing their plans. In three studies, we demonstrate that compared with mobile calendar users, paper calendar users develop higher-quality plans and are more successful in plan fulfillment. We provide evidence that this happens because paper calendar users take a broader, big-picture perspective during planning, which also leads to higher-quality plan development and a greater likelihood of plan fulfillment. When mobile calendar users use a mode that also provides a broader perspective, their plan quality increases.     

Adding Value to Second-Language Listening and Reading Subscores: Using a Score Augmentation Approach

This study examined whether reporting subscores for groups of items within a test section assessing a second-language modality (specifically reading or listening comprehension) added value from a measurement perspective to the information already provided by the section scores. We analyzed the responses of 116,489 test takers to reading and listening items from operational administrations of two large-scale international tests of English as a foreign language. To “strengthen” the reliability of the subscores, and thus improve their added value, we applied a score augmentation method (Haberman, 2008). In doing so, our aim was to examine whether reporting augmented subscores for specific groups of reading and listening items could improve the added value of these subscores and consequently justify providing more fine-grained information about test taker performance. Our analysis indicated that in general, there was lack of support for reporting subscores from a psychometric perspective, and that score augmentation marginally improved the added value of the subscores. We discuss several implications of our findings for test developers wishing to report more fine-grained information about test performance. We conclude by arguing that research on how to best report such refined feedback should remain the focus of future efforts related to second-language proficiency tests.     

Accountability,Collaboration, and Social Change: Ethical Tensions in an Action Research Project to Address Untested Sexual Assault Kits (SAKs)

We conducted a collaborative action research project with stakeholders in Detroit, Michigan, to develop long‐term policy strategies to resolve ~11,000 untested rape kits that were discovered in a police storage facility in August 2009. In our research, we uncovered overwhelming evidence of victim‐blaming behaviors and fundamental disrespect of rape survivors by the police, which directly contributed to their decisions not to submit kits for forensic testing. We had an ethical responsibility to report these negative findings accurately and completely, and in doing so, we were concerned that police stakeholders might disengage from the action research project and hamper our other ethical responsibilities to promote general and public welfare. In this article, we examine the ethical challenges of balancing accountability, collaboration, and social change.