on tactile and visual recognition

2
Perception & Psychophysics 1980,27 (6),579-580 Notes and Comment On tactile and visual recognition S. M. GUPTA and L. H. GEYER Department of Industrial Engineering and Information Systems Northeastern University Boston, MtlSSilchusetts02115 Craig (1979) reported confusion matrix data for the uppercase English alphabet presented tactually via an Optacon transmitter applied to the index fingertip of the left hand. The letters were presented as the appropriate elements of a 6 by 18 array, energized simultaneously for a controlled interval. Correct recognition averaged .52. He invited explora- tion of the question of whether or not these data were explainable in terms of visual feature processingrecog- nition models. One such model (Geyer & DeWald, 1973)demonstrated that a particular feature set pro- posed by Geyer (1970) and a feature processing model generally compatible with Selfridge's (1959) pande- monium model accounted for more than 91070 of the total sum squares variance of each of the three visual confusion matrices for capital letters reported by Townsend (1971a, 1971b). The same model and feature set, when applied to the Craig (1979) data, performed very poorly, accounting for only 83% of total sum squares variance. These results seem to cast doubt on Craig's (1979) suggestion that visual and tactile recognition may tap similar processes, but leave aside the puzzling fact, reported by Craig, that his main diagonal vector cor- related .88 with the main diagonal vector reported by Gilmore, Hersh, Caramazzo, and Griffin (1979) for recognition of block capital letters presented as the appropriate elements of a 5 by 7 dot matrix on a videoscreen. In fact, the Geyer and DeWald (1973) model is similarly poor at accounting for the Gilmore et al. (1979) data-80% of total sum squares. The contrast between the Craig (1979) and Gilmore et al. (1979) data, on the one hand, and the Townsend (1971a, 1971b) data, on the other, is quite visible in Table 1, which shows the sum square error for each pairing of one matrix as a predictor of another. Table 1 does not illuminate the question as to why the Gilmore et al. (1979) visual data should be more similarto Craig's (1979) tactual data than to Townsend's (1971a, 1971b)visualdata. In a further effort to understand this question, Fisher, Monty, and Glucksberg's (1969) data were also analyzed. Since they used tachistoscopic pre- sentations of capital letters, it seemed plausible that, if they could be compared on some common basis to Townsend's (1971a, 1971b), the results should be similar. The difficulty is that Townsend controlled his average correct recognition to .5, whereas it was .59 and .78 for Fisher et al.'s (1969) two matrices. As an approximation, these two matrices were nor- malized to .5 by multiplying all main diagonal ele- ments by the appropriate factors (.85 and .64) and proportionally correcting all off-diagonal cells. These reconstructed matrices were then compared to Townsend's (1971a, 1971b), Craig's (1979), and Fisher et al.'s (1969) matrices. The results are summarized in Table 1. While not totally clear-cut, it appears that the dif- ferences between either of the two Fisher et al. (1969) matrices and anyone of Townsend's (1971a, 1971b) three matrices are only slightly larger than the differ- ences within either subset, and substantially smaller than the differences between anyone of these five and either the Craig (1979) or Gilmore et al. (1979) data. Mewhort and Dow (1979) point out that Gilmore et al.' s main diagonal vector correlates -.873 with the number of dots used in the representation of each character. They suggest that differential brightness controlled performance, but Gilmore and Hirsh (1979) reply by pointing out that Mewhort and Dow's (1979) assumption of a slow dot-brightening algorithm is not correct. An alternative explanation, Table 1 Total Sum Squares Differences Between Pairs of Confusion Matrices Reported in Studies Cited Townsend's MJ Townsend's VF Craig Gilmore et a1. Fisher et al, No.1 Fisher et al, No.2 av s .667 .911 1.969 2.246 1.086 1.393 Townsend (I 971a, 1971b) MJ .820 2.350 2.985 1.108 1.195 VF 2.665 3.052 1.545 1.973 Craig (1979) 1.065 2.562 2.526 Gilmore et a1. (1979) 2.896 2.872 Fisher et al, No.1 (1969) .797 Copyright 1980 Psychonomic Society, Inc. 579 0031-5117/80/060579.02$00.45/0

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Page 1: On tactile and visual recognition

Perception & Psychophysics1980,27 (6),579-580

Notes and Comment

On tactile and visual recognition

S. M. GUPTA and L. H. GEYERDepartment ofIndustrial Engineering

and Information SystemsNortheastern University

Boston, MtlSSilchusetts02115

Craig (1979) reported confusion matrix data forthe uppercase English alphabet presented tactuallyvia an Optacon transmitter applied to the indexfingertip of the left hand. The letters were presentedas the appropriate elements of a 6 by 18 array,energized simultaneously for a controlled interval.Correct recognition averaged .52. He invited explora­tion of the question of whether or not these data wereexplainable in terms of visual feature processing recog­nition models. One such model (Geyer & DeWald,1973)demonstrated that a particular feature set pro­posed by Geyer (1970) and a feature processing modelgenerally compatible with Selfridge's (1959) pande­monium model accounted for more than 91070 ofthe total sum squares variance of each of the threevisual confusion matrices for capital letters reportedby Townsend (1971a, 1971b). The same model andfeature set, when applied to the Craig (1979) data,performed very poorly, accounting for only 83%of total sum squares variance.

These results seem to cast doubt on Craig's (1979)suggestion that visual and tactile recognition may tapsimilar processes, but leave aside the puzzling fact,reported by Craig, that his main diagonal vector cor­related .88 with the main diagonal vector reported byGilmore, Hersh, Caramazzo, and Griffin (1979) forrecognition of block capital letters presented as theappropriate elements of a 5 by 7 dot matrix on avideoscreen. In fact, the Geyer and DeWald (1973)model is similarly poor at accounting for the Gilmoreet al. (1979) data-80% of total sum squares. Thecontrast between the Craig (1979) and Gilmore et al.

(1979) data, on the one hand, and the Townsend(1971a, 1971b) data, on the other, is quite visible inTable 1, which shows the sum square error for eachpairing of one matrix as a predictor of another.Table 1 does not illuminate the question as to whythe Gilmore et al. (1979) visual data should be moresimilarto Craig's (1979) tactual data than to Townsend's(1971a, 1971b)visualdata.

In a further effort to understand this question,Fisher, Monty, and Glucksberg's (1969) data werealso analyzed. Since they used tachistoscopic pre­sentations of capital letters, it seemed plausible that,if they could be compared on some common basisto Townsend's (1971a, 1971b), the results shouldbe similar. The difficulty is that Townsend controlledhis average correct recognition to .5, whereas it was.59 and .78 for Fisher et al.'s (1969) two matrices.As an approximation, these two matrices were nor­malized to .5 by multiplying all main diagonal ele­ments by the appropriate factors (.85 and .64) andproportionally correcting all off-diagonal cells.These reconstructed matrices were then compared toTownsend's (1971a, 1971b), Craig's (1979), and Fisheret al.'s (1969) matrices. The results are summarizedin Table 1.

While not totally clear-cut, it appears that the dif­ferences between either of the two Fisher et al. (1969)matrices and anyone of Townsend's (1971a, 1971b)three matrices are only slightly larger than the differ­ences within either subset, and substantially smallerthan the differences betweenanyone of these five andeither the Craig (1979) or Gilmore et al. (1979) data.

Mewhort and Dow (1979) point out that Gilmoreet al.' s main diagonal vector correlates -.873 withthe number of dots used in the representation of eachcharacter. They suggest that differential brightnesscontrolled performance, but Gilmore and Hirsh(1979) reply by pointing out that Mewhort andDow's (1979) assumption of a slow dot-brighteningalgorithm is not correct. An alternative explanation,

Table 1Total Sum Squares Differences Between Pairs of Confusion Matrices Reported in Studies Cited

Townsend's MJTownsend's VFCraigGilmore et a1.Fisher et al, No.1Fisher et al, No.2

av s

.667

.9111.9692.2461.0861.393

Townsend (I971a, 1971b)

MJ

.8202.3502.9851.1081.195

VF

2.6653.0521.5451.973

Craig(1979)

1.0652.5622.526

Gilmore et a1.(1979)

2.8962.872

Fisher et al,No.1 (1969)

.797

Copyright 1980 Psychonomic Society, Inc. 579 0031-5117/80/060579.02$00.45/0

Page 2: On tactile and visual recognition

580 GUPTA AND GEYER

both plausible and theoretically intriguing, is thatvisual presentation of a dot matrix pattern, limitedto a 5 by 7 array, does not trigger the neural featuredetection networks involved in conventional strokeletter recognition. This does not seem a far-fetchedconjecture, since feature detection research has beenprincipallyconcerned with "edge detection" (cf. Hubel& Wiesel, 1959, 1962; Lettvin, Maturana, McCulloch,& Pitts, 1959). If this conjecture is correct, thenvisual dot patterns would need to be processed insome alternative fashion, possibly similar to theprocessing of dot pressure patterns from tactual pre­sentations. While speculative, this idea gains modestsupport from the fact that Craig's (1979) maindiagonal data also correlate highly with the numberof pins actuated by his Optacon for each letter,r=-.84.\

REFERENCES

CRAIG, J. C. A confusion matrix for tactually presented letters.Perception & Psychophysics, 1979, 26, 409-411.

FISHER, D. F., MONTY, R. A., & GLUCKSBERG, S. Visual confu­sion matrices: Fact or artifact. Journal of Psychology, 1969,71,111-125.

GEYER, L. H. A two-channel theory of short-term visual storage(Doctoral dissertation, SUNY at Buffalo, 1970). DissertationAbstracts International, 1971, 31, 566OB. (University Micro­films No. 71-7165)

GEYER, L. H., & DEWALD, C. G. Feature lists and confusionmatrices. Perception & Psychophysics, 1973,14,471-482.

GILMORE, G. C., & HERSH, H. Multidimensional letter similarity:

A reply to Mewhort and Dow. Perception & Psychophysics,1979,26,501-502.

GILMORE, G. C., HERSH, H., CARAMAZZA, A., & GRIFFIN, J.Multidimensional letter similarity derived from recognitionerrors. Perception & Psychophysics, 1979, 25, 425-431.

HUBEL, D. H., & WIESEL, T. N. Receptive fields of singleneurones in the eat's striate cortex. Journal of Physiology,1959, 148,574-591.

HUBEL, D. H., & WIESEL, T. N. Receptive fields, binocularinteraction and functional architecture in the visual cortex.Journal ofPhysiology, 1962, 160, 106-154.

LETTVIN, J. Y., MATURANA, R. H., MCCULLOCH, W. S., &PITTS, W. H. What the frog's eye tells the frog's brain. Pro­ceedings of the Institute of Radio Engineers, 1959, 47,1940-1951.

MEWHORT, D. J. K., & Dow, M. L. Multidimensional letter simi­larity: A confound with brightness? Perception & Psychophysics,1979,26,325-326.

SELFRIDGE, O. G. Pandemonium: A paradigm for learning. InThe mechanization of thought processes. London: H. M. Sta­tionery Office, 1959.

TOWNSEND, J. T. Alphabetic confusion: A test of models forindividuals. Perception & Psychophysics, 1971, 9, 449-454. (a)

TOWNSEND, J. T. Theoretical analysis of an alphabetic confusionmatrix. Perception & Psychophysics, 1971,9,40-50. (b)

NOTE

I. We are grateful to James C. Craig for providing the pinletter patterns used by him for Optacon presentation of capitalletters.

(Received for publication February 29, 1980;accepted March 10, 1980.)