Attentional modulation of blink startle at long, short, and very short lead intervals

The present research investigated attentional blink startle modulation at lead intervals of 60, 240 and 3500 ms. Letters printed in Gothic or standard fonts, which differed in rated interest, but not valence, served as lead stimuli. Experiment I established that identifying letters as vowels/consonants took longer than reading the letters and that performance in both tasks was slower if letters were printed in Gothic font. In Experiment 2, acoustic blink eliciting stimuli were presented 60, 240 and 3500 ms after onset of the letters in Gothic and in standard font and during intertrial intervals. Half the participants (Group Task) were asked to identify the letters as vowels/consonants whereas the others (Group No-Task) did not perform a task. Relative to control responses, blinks during letters were facilitated at 60 and 3500 ms lead intervals and inhibited at the 240 ms lead interval for both conditions in Group Task. Differences in blink modulation across lead intervals were found in Group No-Task only during Gothic letters with blinks at the 3500 ms lead interval facilitated relative to control blinks. The present results confirm previous findings indicating that attentional processes can modulate startle at very short lead intervals. (C) 2001 Elsevier Science B.V. All rights reserved.

Fuzzy modeling based recognition of multi-font numerals

In this paper, we present a new scheme for off-line recognition of multi-font numerals using the Takagi-Sugeno (TS) model. In this scheme, the binary image of a character is partitioned into a fixed number of sub-images called boxes. The features consist of normalized vector distances (gamma) from each box. Each feature extracted from different fonts gives rise to a fuzzy set. However, when we have a small number of fonts as in the case of multi-font numerals, the choice of a proper fuzzification function is crucial. Hence, we have devised a new fuzzification function involving parameters, which take account of the variations in the fuzzy sets. The new fuzzification function is employed in the TS model for the recognition of multi-font numerals.