Evaluating persistence of shape information using a matching protocol

Ernest Greene; Hautus Michael J

doi:10.3934/Neuroscience.2018.1.81

AIMS Neuroscience, 2018, 5(1): 81-96. doi: 10.3934/Neuroscience.2018.1.81. Research article

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Evaluating persistence of shape information using a matching protocol

Ernest Greene, Hautus Michael J

¹ Department of Psychology, University of Southern California, Los Angeles, USA
² School of Psychology, University of Auckland, Auckland, New Zealand

Received: , Accepted: , Published:

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Many laboratories have studied persistence of shape information, the goal being to better understand how the visual system mediates recognition of objects. Most have asked for recognition of known shapes, e.g., letters of the alphabet, or recall from an array. Recognition of known shapes requires access to long-term memory, so it is not possible to know whether the experiment is assessing short-term encoding and working memory mechanisms, or has encountered limitations on retrieval from memory stores. Here we have used an inventory of unknown shapes, wherein a string of discrete dots forms the boundary of each shape. Each was displayed as a target only once to a given respondent, with recognition being tested using a matching task. Analysis based on signal detection theory was used to provide an unbiased estimate of the probability of correct decisions about whether comparison shapes matched target shapes. Four experiments were conducted, which found the following: a) Shapes were identified with a high probability of being correct with dot densities ranging from 20% to 4%. Performance dropped only about 10% across this density range. b) Shape identification levels remained very high with up to 500 milliseconds of target and comparison shape separation. c) With one-at-a-time display of target dots, varying the total time for a given display, the proportion of correct decisions dropped only about 10% even with a total display time of 500 milliseconds. d) With display of two complementary target subsets, also varying the total time of each display, there was a dramatic decline of proportion correct that reached chance levels by 500 milliseconds. The greater rate of decline for the two-pulse condition may be due to a mechanism that registers when the number of dots is sufficient to create a shape summary. Once a summary is produced, the temporal window that allows shape information to be added may be more limited.

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Keywords: shape recognition; shape encoding; visual persistence; working memory

Citation: Ernest Greene, Hautus Michael J. Evaluating persistence of shape information using a matching protocol. AIMS Neuroscience, 2018, 5(1): 81-96. doi: 10.3934/Neuroscience.2018.1.81

References:

1. Lollo VD (1980) Temporal integration in visual memory. J Exp Psychol Gen 109: 75–97.
2. Schlangen D, Barenholtz E (2015) Intrinsic and contextual features in object recognition. J Vis 15: 1–15.
3. Pan JS, Bingham GP (2013) With an eye to low vision: Optic flow enables perception despite image blur. Optom Vis Sci 90: 1119–1127.
4. Giladgutnick S, Yovel G, Sinha P (2012) Recognizing degraded faces: The contribution of configural and featural cues. Percept 41: 1497–1511.
5. Gollin ES (1960) Developmental studies of visual recognition of incomplete objects. Percept Mot Skills 11: 289–298.
6. Gollin ES (1961) Further studies of visual recognition of incomplete objects. Percept Mot Skills 13: 307–314.
7. Gollin ES (1962) Factors affecting the visual recognition of incomplete objects. Percept Mot Skills 15: 583–590.
8. Greene E (2007) Recognition of objects displayed with incomplete sets of discrete boundary dots. Percept Mot Skills 104: 1043–1059.
9. Greene E (2007) Retinal encoding of ultrabrief shape recognition cues. Plos One 2: e871.
10. Greene E, Visani A (2015) Recognition of letters displayed as briefly flashed dot patterns. Atten Percept Psychphys 77: 1955–1969.
11. Greene E (2016) Information persistence evaluated with low-density dot patterns. Acta Psychol 170: 215–225.
12. Greene E, Hautus MJ (2017) Demonstrating invariant encoding of shapes using a matching judgment protocol. AIMS Neurosci, 4: 120–147.
13. Green DM, Swets JA (1966) Signal detection theory and psychophysics. Robert E. Krieger 25: 1478–1481.
14. Hautus MJ, Hout DV, Lee HS (2009) Variants of A Not-A and 2AFC tests: Signal Detection Theory models. Food Qual Prefer 20: 222–229.
15. Hautus MJ (1995) Corrections for extreme proportions and their biasing effects on estimated values of d^′. Behav Res Methods Instrum Comput 27: 46–51.
16. Miller J (1996) The sampling distribution of d^'. Percept Psychophys 58: 65–72.
17. Hautus J (2012) SDT Assistant (version 1.0) [Software]. Available from: http://hautus.org.
18. Hautus MJ (1997) Calculating estimates of sensitivity from group data: Pooled versus averaged estimators. Behav Res Methods Instrum Comput 29: 556–562.
19. Macmillan NA, Creelman CD (2005) Detection theory: A user's guide, 2nd Ed.
20. Greene E, Onwuzulike O (2017) What constitutes elemental shape information for biological vison. Trends Artif Intel 1: 22–26.
21. Greene E, Ogden RT (2012) Evaluating the contribution of shape attributes to recognition using the minimal transient discrete cue protocol. Behav Brain Funct 8: 53.
22. Phillips WA (1974) On the distinction between sensory storage and short-term visual memory. Percept Psychophys 16: 283–290.
23. Coltheart M (1980) Iconic memory and visible persistence. Percept Psychophys 27: 183–228.
24. Greene E (2007) Information persistence in the integration of partial cues for object recognition. Percept Psychophys 69: 772–784.
25. Greene E (2007) The integration window for shape cues is a function of ambient illumination. Behav Brain Funct 3: 15.

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This article has been cited by:

1. Ernest Greene, New encoding concepts for shape recognition are needed, AIMS Neuroscience, 2018, 5, 3, 162, 10.3934/Neuroscience.2018.3.162
2. Andrew Geoly, Ernest Greene, Masking the Integration of Complementary Shape Cues, Frontiers in Neuroscience, 2019, 13, 10.3389/fnins.2019.00178

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