An investigation into cued recall of multi-attribute stimuli

Journal of Experimental Psychology: Learning, Memory and Cognition, Vol 23, No. 5, 1247-1260, 1997
An investigation into cued recall of multi-attribute stimuli
Geoff Ward, Elizabeth Churchill, Paul Musgrove

Memory performance for sequences of letters positioned in particular spatial locations in a 3 x 3 grid was examined by requiring participants to recall attributes of the target stimuli given 1 or 2 features of the stimuli as cues. Cuing asymmetry was observed between the serial-position curves of object and sequential-order information, and location and sequential-order information, when the stimuli were presented in both the same and different locations.

After correcting for response bias, this asymmetry was attenuated for the stimuli presented in different locations and was eliminated for the stimuli presented in the same location.

Contrary to the predictions of the fragmentation hypothesis (G. V. Jones, 1976), asymmetry was also observed between object and location information. The roles of spatial location and response bias are offered as explanations for previous contradictory claims for cuing symmetry between item and order information.

Another publication from the same category: Computer Vision

WACV, March, 2016

Fashion Apparel Detection: The Role of Deep Convolutional Neural Network and Pose-dependent Priors

Kota Hara, Vignesh Jagadeesh, Robinson Piramuthu

In this work, we propose and address a new computer vision task, which we call fashion item detection, where the aim is to detect various fashion items a person in the image is wearing or carrying. The types of fashion items we consider in this work include hat, glasses, bag, pants, shoes and so on.

The detection of fashion items can be an important first step of various e-commerce applications for fashion industry. Our method is based on state-of-the-art object detection method which combines object proposal methods with a Deep Convolutional Neural Network.

Since the locations of fashion items are in strong correlation with the locations of body joints positions, we incorporate contextual information from body poses in order to improve the detection performance. Through the experiments, we demonstrate the effectiveness of the proposed method.