Place learning in virtual space III: Investigation of spatial navigation training procedures and their application to fMRI and clinical neuropsychology

This paper describes the utilization of a desktop virtual environment task, the Computer-Generated (C-G) Arena, in the study of human spatial navigation. First, four experiments examined the efficacy of various training procedures in the C-G Arena. In Experiment 1, participants efficiently located a hidden target after only observing the virtual environment from a fixed position (placement learning). In Experiment 2, participants efficiently located a hidden target after only observing an experimenter search the virtual environment (observational learning). In Experiment 3, participants failed to display alatent learning effect in the virtual environment. In Experiment 4, all training procedures effectively taught participants the layout of the virtual environment, but the observational learning procedure most effectivelytaught participants the location of a hidden target within the environment. Finally, two experiments demonstrated the application of C-G Arena procedures to neuroimaging (Experiment 5) and neuropsychological (Experiment 6) investigations of human spatial navigation.     

ON MISREADING AND MISLEADING PATIENTS: SOME REFLECTIONS ON COMMUNICATIONS,MISCOMMUNICATIONS AND COUNTERTRANSFERENCE ENACTMENTS1

This author focuses on an aspect of transference countertransference interaction that enacted covertly is often overlooked. He argues that conflicts, needs and biases that may go undetected for lengthy periods of time are not infrequently contained within the analyst's accurate and technically correct interventions and that for defensive reasons, patients often suppress, deny or rationalise their accurate perceptions of these countertransference elements and fail to confront their analysts with them. The mistakes, miscommunications and misperceptions that arise as a consequence of the unconscious collusions that develop between patient and analyst can have a profound effect on the analytic work. Several clinical examples are presented to illustrate the operation of such covert communications in analysis and their impact on the treatment process.     

Individual differences and the use of nonspecifying variables in learning to perceive distance and size: Comments on McConnell, Muchisky, and Bingham (1998)

McConnell, Muchisky, and Bingham (1998) showed that observers are able to judge the distance and size of falling, rolling, and swinging balls and that performance improves after practice with feedback. They concluded that observers use information that specifies the spatial scales of the different event types—namely, event duration in combination with event-specific constants. The improvement was interpreted as the calibration of the event-specific constants. We argue that their analyses should have considered the use of optical variables that do not specify the to-be-perceived metrics and individual differences in variable use. Furthermore, we propose convergence on the more useful variables as an alternative explanation for the observed improvement. The viability of these arguments is demonstrated with an experiment in which participants are trained with feedback to judge the distance and size of freely falling balls.     

Phonology as the source of syllable frequency effects in visual word recognition: Evidence from French

In order to investigate whether syllable frequency effects in visual word recognition can be attributed to phonologically or orthographically defined syllables, we designed one experiment that allowed six critical comparisons. Whereas only a weak effect was obtained when both orthographic and phonological syllable frequency were conjointly manipulated in Comparison 1, robust effects for phonological and null effects for orthographic syllable frequency were found in Comparisons 2 and 3. Comparisons 4 and 5 showed that the syllable frequency effect does not result from a confound with the frequency of letter or phoneme clusters at the beginning of words. The syllable frequency effect was shown to diminish with increasing word frequency in Comparison 6. These results suggest that visually presented polysyllabic words are parsed into phonologically defined syllables during visual word recognition. Materials and links may be accessed at www.psychonomic.org/archive.     

PyEPL: A cross-platform experiment-programming library

PyEPL (the Python Experiment-Programming Library) is a Python library which allows cross-platform and object-oriented coding of behavioral experiments. It provides functions for displaying text and images onscreen, as well as playing and recording sound, and is capable of rendering 3-D virtual environments forspatial-navigation tasks. It is currently tested for Mac OS X and Linux. It interfaces with Activewire USB cards (on Mac OS X) and the parallel port (on Linux) for synchronization of experimental events with physiological recordings. In this article, we first present two sample programs which illustrate core PyEPL features. The examples demonstrate visual stimulus presentation, keyboard input, and simulation and exploration of a simple 3-D environment. We then describe the components and strategies used in implementing PyEPL.     

Choosing the best method for local validity estimation: relative accuracy of meta-analysis versus a local study versus Bayes-analysis

This study assessed the relative accuracy of 3 techniques--local validity studies, meta-analysis, and Bayesian analysis--for estimating test validity, incremental validity, and adverse impact in the local selection context. Bayes-analysis involves combining a local study with nonlocal (meta-analytic) validity data. Using tests of cognitive ability and personality (conscientiousness) as predictors, an empirically driven selection scenario illustrates conditions in which each of the 3 estimation techniques performs best. General recommendations are offered for how to estimate local parameters, based on true population variability and the number of studies in the meta-analytic prior. Benefits of empirical Bayesian analysis for personnel selection are demonstrated, and equations are derived to help guide the choice of a local validity technique (i.e., meta-analysis vs. local study vs. Bayes-analysis).     

Visual and phonological codes in letter and word recognition: Evidence from incremental priming

Critical issues in letter and word priming were investigated using the novel incremental priming technique . This technique adds a parametric manipulation of prime duration (or prime intensity) to the traditional design of a fast masked priming study. By doing so, additional information on the time course and nature of priming effects can be obtained. In Experiment 1, cross-case letter priming (a-A) was investigated in both alphabetic decision (letter/non-letter classification) and letter naming. In Experiment 2, cross-case word priming was investigated in lexical decision and naming. Whereas letter priming in alphabetic decision was most strongly determined by visual overlap between prime and target, word priming in lexical decision was facilitated by both orthographic and phonological information. Orthographic activation was stronger and occurred earlier than phonological activation. In letter and word naming, in contrast, priming effects were most strongly determined by phonological/articulatory information. Differences and similarities between letter and word recognition are discussed in the light of the incremental priming data.     

Avoid violence,rioting, and outrage; approach celebration,delight, and strength: Using large text corpora to compute valence,arousal, and the basic emotions

Ever since Aristotle discussed the issue in Book II of his Rhetoric, humans have attempted to identify a set of “basic emotion labels”. In this paper we propose an algorithmic method for evaluating sets of basic emotion labels that relies upon computed co-occurrence distances between words in a 12.7-billion-word corpus of unselected text from USENET discussion groups. Our method uses the relationship between human arousal and valence ratings collected for a large list of words, and the co-occurrence similarity between each word and emotion labels. We assess how well the words in each of 12 emotion label sets—proposed by various researchers over the past 118 years—predict the arousal and valence ratings on a test and validation dataset, each consisting of over 5970 items. We also assess how well these emotion labels predict lexical decision residuals (LDRTs), after co-varying out the effects attributable to basic lexical predictors. We then demonstrate a generalization of our method to determine the most predictive “basic” emotion labels from among all of the putative models of basic emotion that we considered. As well as contributing empirical data towards the development of a more rigorous definition of basic emotions, our method makes it possible to derive principled computational estimates of emotionality—specifically, of arousal and valence—for all words in the language.