Feelings of knowing in the Ranschburg effect.

In two experiments, we assessed feelings of knowing (FOKs) for the Ranschburg effect to examine the types of retrieval ease that affect FOKs. In the Ranschburg effect, retrieval performance for repeated items differs from nonrepeated items in supramemory span tasks. We found that FOKs are affected by memory manipulations that affect recall processes, but not by manipulations that affect recognition. This suggests that processes that affect recognition, such as target familiarity, do not affect FOKs, whereas processes that affect recall, such as response suppression and guessing factors, affect FOKs. We propose that an integrated theory of FOKs must include mechanisms responsive to both encoding and retrieval factors (such as retrieval accessibility and cue familiarity), which are highly susceptible to output interference.

Using artificial bat sonar neural networks for complex pattern recognition: recognizing faces and the speed of a moving target.

Two sets of studies examined the viability of using bat-like sonar input for artificial neural networks in complex pattern recognition tasks. In the first set of studies, a sonar neural network was required to perform two face recognition tasks. In the first task, the network was trained to recognize different faces regardless of facial expressions. Following training, the network was tested on its ability to generalize and correctly recognize faces using echoes of novel facial expressions that were not included in the training set. The neural network was able to recognize novel echoes of faces almost perfectly (above 96% accuracy) when it was required to recognize up to five faces. In the second face recognition task, a sonar neural network was trained to recognize the sex of 16 faces (eight males and eight females). After training, the network was able to correctly recognize novel echoes of those faces as 'male' or as 'female' faces with accuracy levels of 88%. However, the network was not able to recognize novel faces as 'male' or 'female' faces. In the second set of studies, a sonar neural network was required to learn to recognize the speed of a target that was moving towards the viewer. During training, the target was presented in a variety of orientations, and the network's performance was evaluated when the target was presented in novel orientations that were not included in the training set. The different orientations dramatically affected the amplitude and the frequency composition of the echoes. The neural network was able to learn and recognize the speed of a moving target, and to generalize to new orientations of the target. However, the network was not able to generalize to new speeds that were not included in the training set. The potential and limitations of using bat-like sonar as input for artifical neural networks are discussed.