The aim of this study was to investigate the factors influencing how Ss name subsets of stimuli. The stimuli in both experiments were selected from a set of eight different pictures (shown in Fig. 2) or a set of 16 different geometrical forms. In Exp. I, each S was presented in a random order all 36 stimulus subsets of one or two stimuli which could be constructed from the eight different pictures. The Ss gave each subset three different names, the one be considered the most appropriate, the second most appropriate, and the third most appropriate. In Exp. II, eight different subsets of three different pictures and ten different subsets of three geometrical forms were selected (shown in Fig. 3). For every subset of three stimuli, all three alternative classifications into two subsets were presented to each S. Thus, each S was presented 24 classifications of three stimuli into groups for the pictures and 30 similar classifications for the geometrical forms. Each subset to be classified was drawn on a separate white card and the order of presentation was randomized. For all 54 cards, each S rated the ‘goodness’ of the classification and then gave the most appropriate name to each subset. Different Ss were used in the two experiments. The results indicate: 1. There was greater agreement among Ss as to the most appropriate subset name than to less appropriate subset names. (Table 2) 2. There was greater agreement among Ss as to subset names for better classifications than for poorer classifications. The rank order correlation between the classification goodness and the agreement was significantly high. 3. If the subset names are organized hierarchically, the name considered the most appropriate name was generally a lower order name (e. g., mammals), the second most appropriate name was a higher order name (e. g., animals), and the third most appropriate name was a still higher order name (e. g., living things). Subsets of two stimuli normally received a higher order name than one-stimulus subsets. (Table 3) 4. when two subsets are presented together, names of the two subsets are usually at the same hierarchical level. However, when each subset was independently named, the names were generally at different levels since one subset consisted of two stimuli and the other subset consisted of only one stimulus. For example, suppose dog and bat were placed in one subset, and duck was placed in the other subset. In Exp. I, where each subset was independently named, the subset dog and bat would be named ‘mammals’ and the subset duck would be named ‘duck’. In Exp. II, the subset dog and bat would still be named ‘mammals’ but now the subset duck would be named ‘birds’. The name of the larger subset was also affected by the relationship between the subsets. Suppose the two-stimuli subset dog and bat was presented first with the one-stimulus subset duck and then was presented with the one-stimulus subset apple. When the subset dog and bat was presented with the subset duck, 64% of the Ss named dog and bat ‘mammals’ and only 6% of the Ss named this subset ‘animals’. However, when it was presented with the subset apple, 64% of the Ss named dog and bat ‘animals’ and 23% of the Ss named this subset ‘mammals’. Therefore, subset names depend both on intra-subset structure and on inter-subset structure. 5. The results 2 and 4 were supported by the results obtained from the geometrical form subsets.
The measures of meaningfulness (M) and association value can predict learningspeed only when experimental materials are of the same class. If different classes of test materials are used, the two measures are known to reduce their predictability. In a previous study where two differing classes of materials (two-letter syllables and digits with the same level of M) were used, more Ss were found to recall digits than two-letter syllables. (Imae & Umemoto, 1966) In order to find a general scale which can prove valid when used across different classes of test material, the “rated ease of learning” measure (REL) was tested in the present study. A 7-point scale was constructed, ranging from “very hard to memorize” to “very easy to memorize” and a total of 104 items were selected from among 4 different classes of material (26 items from each class). The 4 classes of stimuli used in the present study were Japanese two-letter syllables (corresponding to English CVC trigrams), digits, alphabetical monograms and digrams. Mvalues of the alphabetical monograms were not computed in Japan, but those of the items in the three remaining classes were all known. A total of 69 college students rated all the 104 items, arranged at random, during atest session. The scale average for each item was calculated by assigning values ranging from 7 to 1 to each corresponding point on the scale-7 to “very easy to memorize” and 1 to “very hard to memorize”. The RELs obtained for the alphabetical monograms are shown in Table 1. The Spearman's γ was computed between M's obtained from American Ss by Anderson and those corresponding RELs given in Table 1. The correlation was only. 10. The relationship between REL and M is shown in Fig. 1, and correlations between the two measures appear high within each class. As shown in Fig. 1, however, even at the same level of M, different materials have different RELs. Five minutes after the rating task, Ss were told to recall as many items as possible. The median percentage of Ss who incidentally recalled the items was then calculated. The relationship between the percentage of Ss who recalled the items and the M values is shown in Fig. 2. The M measure predicted the percentage of Ss who recalled the items within, one particular class of material. When stimuli were of different classes, however, the percentage of Ss who recalled the items was significantly different at the same level of M. Digits were recalled most often, alphabetical digrams next, followed by twoletter syllables. In Fig. 3, the percentage of Ss who recalled each item is shown as a joint function of the class of material and the REL. A group of dots enclosed by the dotted circle are not significantly different in the percentage of Ss who recalled the items. It is possible to combine all the items belonging to the different materials at each of the scale points, and, in Fig. 4, the median percentage of Ss who recalled the combined items are plotted as a function of the REL. In Fig. 4, it is shown that the percentage of Ss who recalled the combined items increases monotonously as a function of the REL, and all the 6 dots in Fig. 4 are significantly different from one another in terms of the percentage of Ss who recalled the items. It is concluded that the REL is very robust in predicting learningspeed across different classes of material, and sensitive enough to predict differences in learning-speed when the stimuli judged have a difference of only one scale unit on the REL scale.
The experimental apparatus used of three gas-filled tungsten lamps, three glass filters and a flashed opal glass. The flashed opal glass was illuminated by three colored lights actuated by the three lamps in conjunction with the three filters. The transmitted light through this glass was the stimulus light. The distance between the surface of the flashed opal glass and each of the lamps could be varied continuously by means of a driving motor. The chromaticity and the luminance of the stimulus light were calibrated by a spectrophotometer, a phototube, etc. The positions of the two of the three lamps were fixed so that the locus of the stimulus light was straight on the chromaticity diagram and the luminance in the white sensory region was about 20 abs. The observers changed the position of remaining lamp by the method of minimal changes to determine the white points. The results are summarized as follows. 1) The white points were determined which could be judged white with a probability of 0.5 by three observers experienced in color experiments, and the white sensory region was obtained from these white points by elliptical approximation. It was found that the white region varied in position in the different observers, but for every observer, it was located below the black body locus. This is different from a common opinion that the central point of white on the chromaticity diagram is the chromaticity coordinate of the C. I. E. standard source C. In the experiment, the light of the C. I. E. standard source C was recognized frequently as white only by one of the observers. 2) The white sensory region which was judged at least as white by any one of the three observers was a rather broad zone along the downside of the black body locus (color temperature 4, 500°K to 25, 000°K). 3) The white sensory region which was judged at least once as white by an observer inexperienced in color experiments was very wide and presented an approximate ellipse having the normal line on the black body locus (color temperature 4, 500°K to 30, 000°K). 4) When the surrounding field was illuminated by a red light or a green light or a blue light or a tungsten lamp, the white sensory region moved towards the chromaticity of the adaptation field in all the three experienced observers. 5) When the white judgement was made arbitrarily only in each experiment, the variation was 0.007 to 0.02 on the x-y chromaticity diagram. When the same judgement was made ten times in each experiment for a given time, the variation for the time was 0.004 to 0.008.