49 research outputs found
Individual differences in eyewitness accuracy across multiple lineups of faces
Theories of face recognition in cognitive psychology stipulate that the hallmark of accurate identification is the ability to recognize a person consistently, across different encounters. In this study, we apply this reasoning to eyewitness identification by assessing the recognition of the same target person repeatedly, over six successive lineups. Such repeat identifications are challenging and can be performed only by a proportion of individuals, both when a target exhibits limited and more substantial variability in appearance across lineups (Experiments 1 and 2). The ability to do so correlates with individual differences in identification accuracy on two established tests of unfamiliar face recognition (Experiment 3). This indicates that most observers have limited facial representations of target persons in eyewitness scenarios, which do not allow for robust identification in most individuals, partly due to limitations in their ability to recognize unfamiliar faces. In turn, these findings suggest that consistency of responses across multiple lineups of faces could be applied to assess which individuals are accurate eyewitnesses
Pan-Arctic linkages between snow accumulation and growing-season air temperature, soil moisture and vegetation
Arctic field studies have indicated that the air temperature, soil
moisture and vegetation at a site influence the quantity of snow
accumulated, and that snow accumulation can alter growing-season soil
moisture and vegetation. Climate change is predicted to bring about
warmer air temperatures, greater snow accumulation and northward
movements of the shrub and tree lines. Understanding the responses of
northern environments to changes in snow and growing-season land
surface characteristics requires: (1) insights into the present-day
linkages between snow and growing-season land surface characteristics;
and (2) the ability to continue to monitor these associations over
time across the vast pan-Arctic. The objective of this study was
therefore to examine the pan-Arctic (north of 60° N)
linkages between two temporally distinct data products created from
AMSR-E satellite passive microwave observations: GlobSnow snow water
equivalent (SWE), and NTSG growing-season AMSR-E Land Parameters (air
temperature, soil moisture and
vegetation transmissivity). Due to the complex and interconnected
nature of processes determining snow and growing-season land surface
characteristics, these associations were analyzed using the modern
nonparametric technique of alternating conditional expectations
(ACE), as this approach does not impose a predefined analytic
form. Findings indicate that regions with lower vegetation
transmissivity (more biomass) at the start and end of the growing
season tend to accumulate less snow at the start and end of the snow
season, possibly due to interception and sublimation. Warmer air
temperatures at the start and end of the growing season were
associated with diminished snow accumulation at the start and end of
the snow season. High latitude sites with warmer mean annual growing-season temperatures tended to accumulate more snow, probably due to
the greater availability of water vapor for snow season precipitation
at warmer locations. Regions with drier soils preceding snow onset
tended to accumulate greater quantities of snow, likely because drier
soils freeze faster and more thoroughly than wetter
soils. Understanding and continuing to monitor these linkages at the
regional scale using the ACE approach can allow insights to be gained
into the complex response of Arctic ecosystems to climate-driven
shifts in air temperature, vegetation, soil moisture and snow
accumulation
Mean absolute error of all model formulations at Ivotuk for the snow seasons of years 2004 (dark gray) to 2007 (light gray)
<p><strong>Figure 6.</strong> Mean absolute error of all model formulations at Ivotuk for the snow seasons of years 2004 (dark gray) to 2007 (light gray).</p> <p><strong>Abstract</strong></p> <p>The Arctic net ecosystem exchange (NEE) of CO<sub>2</sub> between the land surface and the atmosphere is influenced by the timing of snow onset and melt. The objective of this study was to examine whether uncertainty in model estimates of NEE could be reduced by representing the influence of snow on NEE using remote sensing observations of snow cover area (SCA). Observations of NEE and time-lapse images of SCA were collected over four locations at a low Arctic site (Daring Lake, NWT) in May–June 2010. Analysis of these observations indicated that SCA influences NEE, and that good agreement exists between SCA derived from time-lapse images, Landsat and MODIS. MODIS SCA was therefore incorporated into the vegetation photosynthesis respiration model (VPRM). VPRM was calibrated using observations collected in 2005 at Daring Lake. Estimates of NEE were then generated over Daring Lake and Ivotuk, Alaska (2004–2007) using VPRM formulations with and without explicit representations of the influence of SCA on respiration and/or photosynthesis. Model performance was assessed by comparing VPRM output against unfilled eddy covariance observations from Daring Lake and Ivotuk (2004–2007). The uncertainty in VPRM estimates of NEE was reduced when respiration was estimated as a function of air temperature when SCA ≤ 50% and as a function of soil temperature when SCA > 50%.</p
NEE from 1 May to 7 June of years 2004–2007 at Daring Lake MT (left) and Ivotuk (right) as observed using the eddy covariance technique (black), and as estimated by the RESP<sub>0</sub> & GEE<sub>0</sub> (orange) and RESP<sub>s</sub> & GEE<sub>0</sub> (blue)
<p><strong>Figure 5.</strong> NEE from 1 May to 7 June of years 2004–2007 at Daring Lake MT (left) and Ivotuk (right) as observed using the eddy covariance technique (black), and as estimated by the RESP<sub>0</sub> & GEE<sub>0</sub> (orange) and RESP<sub>s</sub> & GEE<sub>0</sub> (blue). Within each plot, the date where estimates from the two models appear to merge represents the day at which SCA initially decreases below 50%.</p> <p><strong>Abstract</strong></p> <p>The Arctic net ecosystem exchange (NEE) of CO<sub>2</sub> between the land surface and the atmosphere is influenced by the timing of snow onset and melt. The objective of this study was to examine whether uncertainty in model estimates of NEE could be reduced by representing the influence of snow on NEE using remote sensing observations of snow cover area (SCA). Observations of NEE and time-lapse images of SCA were collected over four locations at a low Arctic site (Daring Lake, NWT) in May–June 2010. Analysis of these observations indicated that SCA influences NEE, and that good agreement exists between SCA derived from time-lapse images, Landsat and MODIS. MODIS SCA was therefore incorporated into the vegetation photosynthesis respiration model (VPRM). VPRM was calibrated using observations collected in 2005 at Daring Lake. Estimates of NEE were then generated over Daring Lake and Ivotuk, Alaska (2004–2007) using VPRM formulations with and without explicit representations of the influence of SCA on respiration and/or photosynthesis. Model performance was assessed by comparing VPRM output against unfilled eddy covariance observations from Daring Lake and Ivotuk (2004–2007). The uncertainty in VPRM estimates of NEE was reduced when respiration was estimated as a function of air temperature when SCA ≤ 50% and as a function of soil temperature when SCA > 50%.</p