Spatial experiment is combined with hyper-realism as we become intimate eavesdroppers on the collapse of a marriage.
To-date geographical research on encounters has primarily comprised observation of naturalistic settings (both micro publics and everyday public spaces) or narrative accounts of encounters generated by conventional methods, this paper focuses on a contrived spatial experiment to create meaningful contact across difference.
In particular, estimates requiring access to temporal (Experiments 1 & 2) and spatial (Experiment 3) conflicts between synesthetically congruent auditory and visual stimuli were found to be less reliable (i.e., higher discrimination thresholds) than those requiring access to conflicts between synesthetically incongruent stimuli.
In total, each subject performed 80 trials per condition (for CONST-to-VAR as well as for VAR-to-CONST conditions) for the contrast and spatial frequency experiments, and 96 trials per condition for the motion experiment.
Direct comparisons between the local context biconditional (Experiment 3) and distal spatial biconditional (Experiment 4) could be made using data from the final session of both experiments (Fig. 3).
In predictive spatial cueing experiments, trials begin with either a central or a peripheral cue, followed by a target, to which a speeded response is often required.
Taken together with our spatial rescue experiments, this result suggests that mushroom body Gal4 expression patterns in the pupal or early adult brain may be more relevant to understanding dunce rescue effects than patterns in the adult brain.
Endogenous spatial attention experiments have shown the largest cueing effects for both tactile and visual targets when attention is directed at the same external spatial location for both modalities, even when the hands are placed in a crossed position (Spence et al., 2000).
Since the development and refinement of spatial proteomics experiments, several classification methods have been used: partial least-square discriminant analysis (Dunkley et al., 2006), SVMs (Trotter et al., 2010), random forest (Ohta et al., 2010), neural networks (Tardif et al., 2012) and naive Bayes (Nikolovski et al., 2012), all available in pRoloc.
