Incorporating Ethics into Artificial Intelligence

This article reviews the reasons scholars hold that driverless cars and many other AI equipped machines must be able to make ethical decisions, and the difficulties this approach faces. It then shows that cars have no moral agency, and that the term ‘autonomous’, commonly applied to these machines, is misleading, and leads to invalid conclusions about the ways these machines can be kept ethical. The article’s most important claim is that a significant part of the challenge posed by AI-equipped machines can be addressed by the kind of ethical choices made by human beings for millennia. Ergo, there is little need to teach machines ethics even if this could be done in the first place. Finally, the article points out that it is a grievous error to draw on extreme outlier scenarios—such as the Trolley narratives—as a basis for conceptualizing the ethical issues at hand.     

Negative transfer in human associative learning

Models of attentional allocation in associative learning are typically structured according to one of two guiding principles: the predictiveness principle, which posits that attention is paid to cues that have reliably predicted an outcome in the past, or the uncertainty principle, which states that attention is paid to cues about which little is known. Both principles are well supported by studies of animals. However, in studies of human learning, there is very little direct empirical support for the uncertainty principle. In the study reported here, we addressed this gap by investigating negative transfer, a phenomenon that may provide unique support for the uncertainty principle. In two human learning experiments using an allergist task, we replicated the primary findings of previous research on animal learning. We believe that these data provide the first direct evidence for the uncertainty principle in human associative learning.     

Too late to coordinate: Contextual influences on behavioral synchrony

The temporal coupling of behavior serves as a foundation for effective social exchange with synchronized actions moderating core components of social‐cognitive functioning. Questions remain, however, regarding the precise conditions under which this form of behavioral coordination emerges. In particular, do social factors moderate the extent to which people synchronize their movements with others? Given that synchrony serves as an important non‐verbal route through which interpersonal connections can be forged, the current investigation considered whether contextual influences moderate the emergence of behavioral coupling. To explore this issue, movements were recorded while participants performed a repetitive activity (i.e., stepping) with an interaction partner who either turned up for the experiment on time or was 15 minutes late. Results revealed that coordination (i.e., in‐phase synchrony) was substantially reduced when participants interacted with a tardy partner, a finding that highlights the impact that social factors exert on the spontaneous emergence of behavioral synchrony. Copyright © 2009 John Wiley & Sons, Ltd.     

Subjective randomness and natural scene statistics

Accounts of subjective randomness suggest that people consider a stimulus random when they cannot detect any regularities characterizing the structure of that stimulus. We explored the possibility that the regularities people detect are shaped by the statistics of their natural environment. We did this by testing the hypothesis that people’s perception of randomness in two-dimensional binary arrays (images with two levels of intensity) is inversely related to the probability with which the array’s pattern would be encountered in nature. We estimated natural scene probabilities for small binary arrays by tabulating the frequencies with which each pattern of cell values appears. We then conducted an experiment in which we collected human randomness judgments. The results show an inverse relationship between people’s perceived randomness of an array pattern and the probability of the pattern appearing in nature.     

Aggressive behaviour in adult slot machine gamblers: an interpretative phenomenological analysis

A growing number of studies have reported a link between gambling and aggressive behaviour. The aim of this study was to contextualize objective findings of a previous observational study regarding slot machine gambling and aggressive behaviour (Parke & Griffiths, Psychological Reports, 95, 109–114, 2004). Interpretative Phenomenological Analysis was applied using the Idiographic Case Study method. The data revealed three superordinate themes regarding slot machine gambling‐induced aggression (i.e. Competitive Advantage Reduction, Self‐esteem Reduction, and Cognitive Regret). Within these superordinate themes, subordinate themes emerged identifying how environmental factors and structural characteristics of slot machine gambling, along with the consequences of losing, produced aggressive behaviour. It is concluded that gambling‐induced aggression is a manifestation of the underlying conflict of engaging in dysfunctional behaviour while consciously acknowledging its detrimental effects. Copyright © 2005 John Wiley & Sons, Ltd.     

Online therapy for addictive behaviors.

Clinicians working in the field of addictive behaviors cannot afford to ignore the Internet. Psychological advice, help, and treatment for addicts are no exceptions, with both counseling and psychotherapy entering the computer age. This paper reviews the main issues in the area and approaches the discussion acknowledging that online therapy has to be incorporated within the overall framework of the need for clinical assistance. The paper also provides brief overviews of some websites as illustrative examples of what types of online therapy are available.     

Modeling individual differences using Dirichlet processes

We introduce a Bayesian framework for modeling individual differences, in which subjects are assumed to belong to one of a potentially infinite number of groups. In this model, the groups observed in any particular data set are not viewed as a fixed set that fully explains the variation between individuals, but rather as representatives of a latent, arbitrarily rich structure. As more people are seen, and more details about the individual differences are revealed, the number of inferred groups is allowed to grow. We use the Dirichlet process—a distribution widely used in nonparametric Bayesian statistics—to define a prior for the model, allowing us to learn flexible parameter distributions without overfitting the data, or requiring the complex computations typically required for determining the dimensionality of a model. As an initial demonstration of the approach, we present three applications that analyze the individual differences in category learning, choice of publication outlets, and web-browsing behavior.     

Online data collection from video game players: methodological issues.

The paper outlines the advantages and disadvantages of using the Internet to collect data concerning both online and offline gamers. Drawing from experience of a number of studies carried out online by the authors and by reviewing the available literature, the authors discuss the main issues concerning data collected from video game players. The paper examines a number of areas, including recruiting and utilizing participants, validity, suitable methods of data collection (i.e., questionnaire studies, online tests, participant observation, online interviews), and ethical issues. It is concluded that online research methods can be a useful way of examining the psychosocial aspects of video game playing.     

A Bayesian framework for word segmentation: Exploring the effects of context

Since the experiments of Saffran et al. [Saffran, J., Aslin, R., & Newport, E. (1996). Statistical learning in 8-month-old infants. Science, 274, 1926-1928], there has been a great deal of interest in the question of how statistical regularities in the speech stream might be used by infants to begin to identify individual words. In this work, we use computational modeling to explore the effects of different assumptions the learner might make regarding the nature of words - in particular, how these assumptions affect the kinds of words that are segmented from a corpus of transcribed child-directed speech. We develop several models within a Bayesian ideal observer framework, and use them to examine the consequences of assuming either that words are independent units, or units that help to predict other units. We show through empirical and theoretical results that the assumption of independence causes the learner to undersegment the corpus, with many two- and three-word sequences (e.g. what’s that, do you, in the house) misidentified as individual words. In contrast, when the learner assumes that words are predictive, the resulting segmentation is far more accurate. These results indicate that taking context into account is important for a statistical word segmentation strategy to be successful, and raise the possibility that even young infants may be able to exploit more subtle statistical patterns than have usually been considered.