Source Memory for Mental Imagery: Influences of the Stimuli’s Ease of Imagery.

The present study investigated how ease of imagery influences source monitoring accuracy. Two experiments were conducted in order to examine how ease of imagery influences the probability of source confusions of perceived and imagined completions of natural symmetric shapes. The stimuli consisted of binary pictures of natural objects, namely symmetric pictures of birds, butterflies, insects, and leaves. The ease of imagery (indicating the similarity of the sources) and the discriminability (indicating the similarity of the items) of each stimulus were estimated in a pretest and included as predictors of the memory performance for these stimuli. It was found that confusion of the sources becomes more likely when the imagery process was relatively easy. However, if the different processes of source monitoring-item memory, source memory and guessing biases-are disentangled, both experiments support the assumption that the effect of decreased source memory for easily imagined stimuli is due to decision processes and misinformation at retrieval rather than encoding processes and memory retention. The data were modeled with a Bayesian hierarchical implementation of the one high threshold source monitoring model.

Internal Models, Vestibular Cognition, and Mental Imagery: Conceptual Considerations

Vestibular cognition has recently gained attention. Despite numerous experimental and clinical demonstrations, it is not yet clear what vestibular cognition really is. For future research in vestibular cognition, adopting a computational approach will make it easier to explore the underlying mech- anisms. Indeed, most modeling approaches in vestibular science include a top-down or a priori component. We review recent Bayesian optimal observer models, and discuss in detail the conceptual value of prior assumptions, likelihood and posterior estimates for research in vestibular cognition. We then consider forward models in vestibular processing, which are required in order to distinguish between sensory input that is induced by active self-motion, and sensory input that is due to passive self-motion. We suggest that forward models are used not only in the service of estimating sensory states but they can also be drawn upon in an offline mode (e.g., spatial perspective transformations), in which interaction with sensory input is not desired. A computational approach to vestibular cogni- tion will help to discover connections across studies, and it will provide a more coherent framework for investigating vestibular cognition.

Disentangling mental imagery and perceptual expectation

Mental imagery and perception are thought to rely on similar neural circuits, and many recent behavioral studies have attempted to demonstrate interactions between actual physical stimulation and sensory imagery in the corresponding sensory modality. However, there has been a lack of theoretical understanding of the nature of these interactions, and both interferential and facilitatory effects have been found. Facilitatory effects appear strikingly similar to those that arise due to experimental manipulations of expectation. Using a self-motion discrimination task, we try to disentangle the effects of mental imagery from those of expectation by using a hierarchical drift diffusion model to investigate both choice data and response times. Manipulations of expectation are reasonably well understood in terms of their selective influence on parameters of the drift diffusion model, and in this study, we make the first attempt to similarly characterize the effects of mental imagery. We investigate mental imagery within the computational framework of control theory and state estimation. • Mental imagery and perception are thought to rely on similar neural circuits; however, on more theoretical grounds, imagery seems to be closely related to the output of forward models (sensory predictions). • We reanalyzed data from a study of imagined self-motion. • Bayesian modeling of response times may allow us to disentangle the effects of mental imagery on behavior from other cognitive (top-down) effects, such as expectation.

Evaluating alternative therapies. Support/imagery and immune function in breast cancer: A pilot study

Cancer patients increasingly request alternative therapies such as imagery techniques and support groups. Although research suggests evidence of enhanced psychosocial functioning with supportive group therapy and enhanced immune function with imagery techniques, studies are anecdotal or limited to case studies or descriptive reports. The efficacy of these alternative therapies should be validated by randomized, controlled trials and the mechanisms of action mediating immune function and outcome examined.^ In a 12-month pilot study, we evaluate the feasibility of conducting a controlled study with clinical trial methodology to test the effects of imagery/relaxation and support on quality of life, emotional well-being, and immune function for women after breast cancer. Using a randomized pre-post test design with three intervention waves, we assigned women (n = 47) to either standard care (n = 15), standard care plus 6-weekly support sessions (n = 16) or imagery/relaxation sessions (n = 16).^ The primary aim of this pilot study is to determine the feasibility of conducting a clinical trial of alternative therapies in a clinical care setting. Secondary aims are to determine parameter estimates for the effects of the two treatment groups on quality of life, coping, social support, and immune function and describe methodology issues related to trials of alternative therapies.^ The research provides direction for future studies of alternative therapies by describing the recruitment, clinical trial experience, and related methodology issues. The study extends previous work by differentiating the effects of support group from mental imagery among outpatient groups who are homogeneous regarding cancer type and treatment stage. The study provides data for future longitudinal studies of disease progression by differentiating the effectiveness of interventions designed to enhance quality of life, coping, social support, and immune function and subsequently, alter the clinical course of disease. ^

SeaWiFS chlorophyll imagery of the iron-fertilized area of SOIREE

The growth of populations is known to be influenced by dispersal, which has often been described as purely diffusive (Kierstead and Slobodkin, 1953; Okubo, 1980). In the open ocean, however, the tendrils and filaments of phytoplankton populations provide evidence for dispersal by stirring (Gower et al., 1980, doi:10.1038/288157a0; Holligan et al., 1993, doi:10.1029/93GB01731). Despite the apparent importance of horizontal stirring for plankton ecology, this process remains poorly characterized. Here we investigate the development of a discrete phytoplankton bloom, which was initiated by the iron fertilization of a patch of water (7 km in diameter) in the Southern Ocean (Boyd et al., 2000, doi:10.1038/35037500). Satellite images show a striking, 150-km-long bloom near the experimental site, six weeks after the initial fertilization. We argue that the ribbon-like bloom was produced from the fertilized patch through stirring, growth and diffusion, and we derive an estimate of the stirring rate. In this case, stirring acts as an important control on bloom development, mixing phytoplankton and iron out of the patch, but also entraining silicate. This may have prevented the onset of silicate limitation, and so allowed the bloom to continue for as long as there was sufficient iron. Stirring in the ocean is likely to be variable, so blooms that are initially similar may develop very differently.

Characteristics and changes of thermokarst lakes in Canada, Russia and Sweden

Peat plateaus are widespread at high northern latitudes and are important soil organic carbon reservoirs. A warming climate can cause either increased ground subsidence (thermokarst) resulting in lake formation or increased drainage as the permafrost thaws. A better understanding of spatiotemporal variations in these landforms in relation to climate change is important for predicting the future thawing permafrost carbon feedback. In this study, dynamics in thermokarst lake extent during the last 35-50 years has been quantified through time series analysis of aerial photographs and high-resolution satellite images (IKONOS/QuickBird) in three peat plateau complexes, spread out across the northern circumpolar region along a climatic and permafrost gradient. From the mid-1970s until the mid-2000s there has been an increase in mean annual air temperature, winter precipitation, and ground temperature in all three study areas. The two peat plateaus located in the continuous and discontinuous permafrost zones, respectively, where mean annual air temperatures are below -5°C and ground temperatures are -2°C or colder, have experienced small changes in thermokarst lake extent. In the peat plateau located in the sporadic permafrost zone where the mean annual air temperature is around -3°C, and the ground temperature is close to 0°C, lake drainage and infilling with fen vegetation has been extensive and many new thermokarst lakes have formed. In a future progressively warmer and wetter climate permafrost degradation can cause significant impacts on landscape composition and greenhouse gas exchange also in areas with extensive peat plateaus, which presently still experience stable permafrost conditions.

Multi-temporal mapping of seagrass cover, species and biomass of the Eastern Banks, Moreton Bay, Australia, with links to shapefiles

The spatial and temporal dynamics of seagrasses have been studied from the leaf to patch (100 m**2) scales. However, landscape scale (> 100 km**2) seagrass population dynamics are unresolved in seagrass ecology. Previous remote sensing approaches have lacked the temporal or spatial resolution, or ecologically appropriate mapping, to fully address this issue. This paper presents a robust, semi-automated object-based image analysis approach for mapping dominant seagrass species, percentage cover and above ground biomass using a time series of field data and coincident high spatial resolution satellite imagery. The study area was a 142 km**2 shallow, clear water seagrass habitat (the Eastern Banks, Moreton Bay, Australia). Nine data sets acquired between 2004 and 2013 were used to create seagrass species and percentage cover maps through the integration of seagrass photo transect field data, and atmospherically and geometrically corrected high spatial resolution satellite image data (WorldView-2, IKONOS and Quickbird-2) using an object based image analysis approach. Biomass maps were derived using empirical models trained with in-situ above ground biomass data per seagrass species. Maps and summary plots identified inter- and intra-annual variation of seagrass species composition, percentage cover level and above ground biomass. The methods provide a rigorous approach for field and image data collection and pre-processing, a semi-automated approach to extract seagrass species and cover maps and assess accuracy, and the subsequent empirical modelling of seagrass biomass. The resultant maps provide a fundamental data set for understanding landscape scale seagrass dynamics in a shallow water environment. Our findings provide proof of concept for the use of time-series analysis of remotely sensed seagrass products for use in seagrass ecology and management.

A study on the lateralization of the effect of musical imagery on spontaneous otoacoustic emissions

It has been suggested that different pathways through the brain are followed depending on the type of information that is being processed. Although it is now known that there is a continuous exchange of information through both hemispheres, language is considered to be processed by the left hemisphere, where Broca?s and Wernicke?s areas are located. On the other hand, music is thought to be processed mainly by the right hemisphere. According to Sininger Y.S. & Cone- Wesson, B. (2004), there is a similar but contralateral specialization of the human ears; due to the fact that auditory pathways cross-over at the brainstem. A previous study showed an effect of musical imagery on spontaneous otoacoustic emissions (SOAEs) (Perez-Acosta and Ramos-Amezquita, 2006), providing evidence of an efferent influence from the auditory cortex on the basilar membrane. Based on these results, the present work is a comparative study between left and right ears of a population of eight musicians that presented SOAEs. A familiar musical tune was chosen, and the subjects were trained in the task of evoking it after having heard it. Samples of ear-canal signals were obtained and processed in order to extract frequency and amplitude data on the SOAEs. This procedure was carried out before, during and after the musical image creation task. Results were then analyzed to compare the difference between SOAE responses of left and right ears. A clear asymmetrical SOAEs response to musical imagery tasks between left and right ears was obtained. Significant changes of SOAE amplitude related to musical imagery tasks were only observed on the right ear of the subjects. These results may suggest a predominant left hemisphere activity related to a melodic image creation task.

Foreground segmentation in depth imagery using depth and spatial dynamic models for video surveillance applications

Low-cost systems that can obtain a high-quality foreground segmentation almostindependently of the existing illumination conditions for indoor environments are verydesirable, especially for security and surveillance applications. In this paper, a novelforeground segmentation algorithm that uses only a Kinect depth sensor is proposedto satisfy the aforementioned system characteristics. This is achieved by combininga mixture of Gaussians-based background subtraction algorithm with a new Bayesiannetwork that robustly predicts the foreground/background regions between consecutivetime steps. The Bayesian network explicitly exploits the intrinsic characteristics ofthe depth data by means of two dynamic models that estimate the spatial and depthevolution of the foreground/background regions. The most remarkable contribution is thedepth-based dynamic model that predicts the changes in the foreground depth distributionbetween consecutive time steps. This is a key difference with regard to visible imagery,where the color/gray distribution of the foreground is typically assumed to be constant.Experiments carried out on two different depth-based databases demonstrate that theproposed combination of algorithms is able to obtain a more accurate segmentation of theforeground/background than other state-of-the art approaches.