Can confidentiality of data pay off?

Ss were administered a self-report personality measure and a social desirability scale under conditions of Confidentiality, Anonymity or Nonanonymity alone. The tester's written assurance that the Ss' disclosures would be kept strictly confidential had a substantial moderating effect (when compared with Nonanonymity alone) on correlations between some subscales of the personality measure and the social desirability scale. In contrast to an earlier finding, there were no appreciable effects of Anonymity when compared with Nonanonymity alone. It appears that confidentiality of data can pay off in terms of increased predictive validity of some self-report personality measures by moderating the potential biasing effect of test response distortion due to socially desirable responding.     

Husserl’s hyletic data and phenomenal consciousness

In the Logical Investigations, Ideas I and many other texts, Husserl maintains that perceptual consciousness involves the intentional “animation” or interpretation of sensory data or hyle, e.g., “color-data,” “tone-data,” and algedonic data. These data are not intrinsically representational nor are they normally themselves objects of representation, though we can attend to them in reflection. These data are “immanent” in consciousness; they survive the phenomenological reduction. They partly ground the intuitive or “in-the-flesh” aspect of perception, and they have a determinacy of character that we do not create but can only discover. This determinate, non-representational stratum of perceptual consciousness also serves as a bridge between consciousness and the world beyond it. I articulate and defend this conception of perceptual consciousness. I locate the view in the space of contemporary positions on phenomenal character and argue for its superiority. I close by briefly arguing that the Husserlian account is perfectly compatible with physicalism (this involves disarming the Grain Problem).     

An MS-DOS-compatible data logger for recording subjects’ self-ratings

Some research designs require subjects to make ratings of their own internal cognitive or affective states or the behavior of other subjects. An ideal device for this purpose should allow for electronic data storage and be easy to use for research subjects. The communication box (Markman & Poltrock, 1982) has been popular for this purpose but consists of dated technology. Other data-logging devices are too complicated for subjects’ use. This paper describes an electronic data logger which uses readily available hardware. A dual-tone multiple-frequency (DTMF) telephone keypad encoder is used to generate unique tones that are stored on the audio track of a videotape or on an audio tape. A DTMF receiver decoder IC is used to decode the audio signals. The decoded signals can be read on a light-emitting diode display or entered directly into a personal computer file via an RS-232 serial interface.     

Who owns the data in a clinical trial?

Data gathered by investigators are used to test the validity of a specific scientific hypothesis. When the hypothesis relates to the biology of a disease or its treatment, then data sets may contain specific and identifiable medical information. Since the information in a clinical data set was gathered to test a specific hypothesis and there is usually a sponsor interested in the outcome, the issue of who owns the data is a critical one. In my opinion, data from both publicly and privately funded research should be made available, in a format that protects confidentiality and intellectual property rights, to interested and responsible parties within a reasonable period of time after publication. An earlier version of this paper was presented at an International Conference on “Conflict of Interest and its Significance in Science and Medicine” held in Warsaw, Poland on 5–6 April, 2002. J. M. Drazen is Editor-in-Chief of The New England Journal of Medicine and Professor of Medicine at Harvard Medical School.     

Can cognitive processes be inferred from neuroimaging data?

There is much interest currently in using functional neuroimaging techniques to understand better the nature of cognition. One particular practice that has become common is 'reverse inference', by which the engagement of a particular cognitive process is inferred from the activation of a particular brain region. Such inferences are not deductively valid, but can still provide some information. Using a Bayesian analysis of the BrainMap neuroimaging database, I characterize the amount of additional evidence in favor of the engagement of a cognitive process that can be offered by a reverse inference. Its usefulness is particularly limited by the selectivity of activation in the region of interest. I argue that cognitive neuroscientists should be circumspect in the use of reverse inference, particularly when selectivity of the region in question cannot be established or is known to be weak.     

The psychology of environmental concern—some Australian data

An attempt is made to find out what psychological traits (if any) are characteristic of the environmentalism supporter in the community at large. In an Australia-wide mail survey, ‘environmentalists’ were found to be consumer-conscious, anti-authoritarian, anti-Australia, less interested in personal ‘success’, anti-fashion, counter-culturally oriented and hedonistic. All except the first were however very weak tendencies. They were found to be equally likely to go either way on social conservatism, economic conservatism, sociability and misanthropy.     

When are item response models consistent with observed data?

The problem of deciding whether a set of mental test data is consistent with any one of a large class of item response models is considered. The classical assumption of locla independence is weakened to a new condition, local nonnegative dependence (LND). Necessary and sufficient conditions are derived for a LND item response model to fit a set of data. This leads to a condition that a set of data must satisfy if it is to be representable by any item response model that assumes both local independence and monotone item characteristic curves. An example is given to show that LND is strictly weaker than local independence. Thus rejection of LND models implies rejection of all item response models that assume local independence for a given set of data.This research was supported in part by Grant NIE-G-78-0157 to ETS from the NIE, by the Program Statistics Research Project, and by TOEFL Program Research. I would like to thank Dr. Douglas Jones of ETS for stimulating discussions during the early stages of this research, Dr. Frederick Lord of ETS for his encouragement of this work and comments on earlier drafts of this paper and Professor Robert Berk of Rutgers University for pointing out that conditions (a), (b) and (c) of Theorem 2 were also sufficient for LND and Monotonicity. Dr. Donald Alderman of ETS provided financial support for the development of a computer program to apply these results to data from the TOEFL program.     

Does number data entry rely on the phonological loop?

Two experiments investigated effects of articulatory processing on number data entry. Participants entered four‐digit numbers presented as either words or numerals on a keyboard, either under an articulatory condition or in silence. In Experiment 1, the articulatory condition was articulatory suppression; in Experiment 2, it was vocalisation. In Experiment 1, the articulatory suppression group typed initial digits faster than the silent group, but for subsequent digits, the opposite pattern occurred at least with word stimuli. In Experiment 2, the silent group typed initial digits faster but typed subsequent digits somewhat slower than the vocalisation group. Thus, articulation of numbers, which promotes entry into the phonological loop of working memory, retards processing of initial digits but enhances processing of subsequent digits.     

A similarity-based range—frequency model for two-category rating data

This research examines whether an extension of range-frequency theory (Parducci, 1965) could successfully predict context effects on discrimination between stimuli for two-category rating data. The new model proposes that similarity comparisons between stimulus values and rating category prototypes take place on an implicit range—frequency scale, and thus it entails effects of context on both mean ratings and discrimination indices. Predictions of the similarity-based range—frequency model were tested in an experiment in which the frequency distribution was manipulated by varying the range and relative frequencies of squares, with participants rating square sizes on a two-category scale. The model provided a parsimonious account of how discriminability of common stimuli increases with a decrease in stimulus range or an increase in the density of stimuli within a subrange.     

Is dyslexia a visual perceptive disorder? New empirical data

Several studies have shown that a phonological deficit is the origin of developmental dyslexia, because dyslexics have important difficulties in mapping orthographic to phonological codes. However, visual criteria are still used for the diagnosis of dyslexia and to develop methods of intervention. This study attempts to determine whether there are visual problems in dyslexic children. To this aim, dyslexic children and children without reading difficulties, matched by chronological age, participated in two experiments. One study was based on the Reversal test and the other was a visual decision task in which participants had to decide whether two letters were the same or different. There were 40 pairs of letters, to measure reaction times and mistakes. The results showed that dyslexics had similar performance to controls in the detection of different visual stimuli. Developmental dyslexics do not appear to have visual perceptual problems, but a particular difficulty to retrieve the phonological code of graphemes.     

Must psychologists change the way they analyze their data?

Wagenmakers, Wetzels, Borsboom, and van der Maas (2011) argued that psychologists should replace the familiar "frequentist" statistical analyses of their data with bayesian analyses. To illustrate their argument, they reanalyzed a set of psi experiments published recently in this journal by Bem (2011), maintaining that, contrary to his conclusion, his data do not yield evidence in favor of the psi hypothesis. We argue that they have incorrectly selected an unrealistic prior distribution for their analysis and that a bayesian analysis using a more reasonable distribution yields strong evidence in favor of the psi hypothesis. More generally, we argue that there are advantages to bayesian analyses that merit their increased use in the future. However, as Wagenmakers et al.'s analysis inadvertently revealed, they contain hidden traps that must be better understood before being more widely substituted for the familiar frequentist analyses currently employed by most research psychologists.     

BEHAVE—An automated data analysis system for observed events

This paper first defines the context of automated data analysis systems and then mentions approaches to analysis of data by systems of this type. It then briefly describes the BEHAVE data structure and proceeds to a discussion of the BEHAVE data analysis package which provides the user with summary information concerning organization of data.     

Does number data entry rely on the phonological loop?

Two experiments investigated effects of articulatory processing on number data entry. Participants entered four-digit numbers presented as either words or numerals on a keyboard, either under an articulatory condition or in silence. In Experiment 1, the articulatory condition was articulatory suppression; in Experiment 2, it was vocalisation. In Experiment 1, the articulatory suppression group typed initial digits faster than the silent group, but for subsequent digits, the opposite pattern occurred at least with word stimuli. In Experiment 2, the silent group typed initial digits faster but typed subsequent digits somewhat slower than the vocalisation group. Thus, articulation of numbers, which promotes entry into the phonological loop of working memory, retards processing of initial digits but enhances processing of subsequent digits.     

A data–information–knowledge cycle for modeling driving behavior

When talking about automation, “autonomous vehicles”, often abbreviated as AVs, come to mind. In transitioning from the “driver” mode to the different automation levels, there is an inevitable need for modeling driving behavior. This often happens through data collection from experiments and studies, but also information extraction, a key step in behavioral modeling. Particularly, naturalistic driving studies and field operational trials are used to collect meaningful data on drivers’ interactions in real–world conditions. On the other hand, information extraction methods allow to predict or mimic driving behavior, by using a set of statistical learning methods. In simple words, the way to understand drivers’ needs and wants in the era of automation can be represented in a data–information cycle, starting from data collection, and ending with information extraction. To develop this cycle, this research reviews studies with keywords “data collection”, “information extraction”, “AVs”, while keeping the focus on driving behavior. The resulting review led to a screening of about 161 papers, out of which about 30 were selected for a detailed analysis. The analysis included an investigation of the methods and equipment used for data collection, the features collected, the size and frequency of the data along with the main problems associated with the different sensory equipment; the studies also looked at the models used to extract information, including various statistical techniques used in AV studies. This paved the way to the development of a framework for data analytics and fusion, allowing the use of highly heterogeneous data to reach the defined objectives; for this paper, the example of impacts of AVs on a network level and AV acceptance is given. The authors suggest that such a framework could be extended and transferred across the various transportation sectors.     

Should I test more babies? Solutions for transparent data peeking

Research with infants is often slow and time-consuming, so infant researchers face great pressure to use the available participants in an efficient way. One strategy that researchers sometimes use to optimize efficiency is data peeking (or “optional stopping”), that is, doing a preliminary analysis (whether a formal significance test or informal eyeballing) of collected data. Data peeking helps researchers decide whether to abandon or tweak a study, decide that a sample is complete, or decide to continue adding data points. Unfortunately, data peeking can have negative consequences such as increased rates of false positives (wrongly concluding that an effect is present when it is not). We argue that, with simple corrections, the benefits of data peeking can be harnessed to use participants more efficiently. We review two corrections that can be transparently reported: one can be applied at the beginning of a study to lay out a plan for data peeking, and a second can be applied after data collection has already started. These corrections are easy to implement in the current framework of infancy research. The use of these corrections, together with transparent reporting, can increase the replicability of infant research.