Materials and Methods
Participants. Fourteen adults (aged 20–35) gave informed consent and par-
ticipated in the experiment. All of the participants were tested simulta-
neously, by using computer workstations that were closely matched for
monitor size and viewing distance.
Stimuli. Stimuli were gathered by using both a commercially available database
(Hemera Photo-Objects, Vol. I and II) and internet searches by using Google Image
Search. Overall, 2,600 categorically distinct images were gathered for the main
database, plus 200 paired exemplar images and 200 paired state images drawn
from categories not represented in the main database. The experimental stimuli
are available from the authors. Once these images had been gathered, 200 were
selected at random from the 2,600 objects to serve in the novel test condition.
Thus, all participants were tested with the same 300 pairs of novel, exemplar, and
state images. However, the item seen during the study session and the item used
as the foil at test were randomized across participants.
Study Blocks. The experiment was broken up into 10 study blocks of ⬇20 min
each, followed by a 30 min of testing session. Between blocks participants were
given a 5-min break, and were not allowed to discuss any of the images they had
seen. During a block, ⬇300 images were shown, with 2,896 images shown overall:
2,500 new and 396 repeated images. Each image (subtending 7.5 by 7.5° of visual
angle) was presented for 3 s, followed by an 800-ms fixation cross.
Repeat-Detection Task. To maintain attention and to probe online memory
capacity, participants performed a repeat-detection task during the 10 study
blocks. Repeated images were inserted into the stream such that there were
between 0 and 1,023 intervening items, and participants were told to respond by
using the spacebar anytime that an image repeated throughout the entire study
period. They were not informed of the structure of the repeat conditions.
Participants were given feedback only when they responded, with the fixation
cross turning red if they had incorrectly pressed the space bar (false alarm) or
green if they had correctly detected a repeat (hit), and were given no feedback
for misses or correct rejections.
Overall, 56 images were repeated immediately (1-back), 52 were repeated
with 1 intervening item (2-back), 48 were repeated with 3 intervening items
(4-back), 44 were repeated with 7 intervening items (8-back), and so forth, down
to 16 repeated with 1,023 intervening items (1,024-back). Repeat items were
inserted into the stream uniformly, with the constraint that all of the lengths of
n-backs (1-back, 2-back, 4-back, and 1,024-back) had to occur equally in the first
half of the experiment and the second half. This design ensured that fatigue
would not differentially affect images that were repeated from further back in
the stream. Due to the complexity of generating a properly counterbalanced set
of repeats, all participants had repeated images appear at the same places within
the stream. However, each participant saw a different order of the 2,500 objects,
and the specific images repeated in the n-back conditions were also different
across participants. Images that would later be tested in one of the three memory
conditions were never repeated during the study period.
Forced-Choice Tests. Following a 10-min break after the study period, we
probed the fidelity with which objects were remembered. Two items were
presented on the screen, one previously seen old item, and one new foil item.
Observers reported which item they had seen before in a two-alternative
forced-choice task.
Participants were allowed to proceed at their own pace and were told to
emphasize accuracy, not speed, in making their judgments. The 300 test trials
were presented in a random order for each participant, with the three types
of test trials (novel, exemplar, and state) interleaved. The images that would
later be tested were distributed uniformly throughout the study period.
ACKNOWLEDGMENTS. We thank P. Cavanagh, M. Chun, M. Greene, A.
Hollingworth, G. Kreiman, K. Nakayama, T. Poggio, M. Potter, R. Rensink, A.
Schachner, T. Thompson, A. Torralba, and J. Wolfe for helpful conversation
and comments on the manuscript. This work was partly funded by National
Institutes of Health Training Grant T32-MH020007 (to T.F.B.), a National
Defense Science and Engineering Graduate Fellowship (T.K.), National Re-
search Service Award Fellowship F32-EY016982 (to G.A.A.), and National
Science Foundation (NSF) Career Award IIS-0546262 and NSF Grant IIS-
0705677 (to A.O.).
1. Sperling G (1960) The information available in brief visual presentations. Psychol
Monogr 74:1–29.
2. Phillips WA (1974) On the distinction between sensory storage and short-term visual
memory. Percept Psychophys 16:283–290.
3. Brainerd CJ, Reyna VF (2005) The Science of False Memory (Oxford Univ Press, New
York)
4. Standing L (1973) Learning 10,000 pictures. Q J Exp Psychol 25:207–222.
5. Shepard RN (1967) Recognition memory for words, sentences, and pictures. J Verb
Learn Verb Behav 6:156 –163.
6. Standing L, Conezio J, Haber RN (1970) Perception and memory for pictures: Single-trial
learning of 2500 visual stimuli. Psychon Sci 19:73–74.
7. Rensink RA, O’Regan JK, Clark JJ (1997) To see or not to see: The need for attention to
perceive changes in scenes. Psychol Sci 8:368 –373.
8. Simons DJ, Levin DT (1997) Change blindness. Trends Cogn Sci 1:261–267.
9. Loftus EF (2003) Our changeable memories: Legal and practical implications. Nat Rev
Neurosci 4:231–234.
10. Chun MM (2003) in Cognitive Vision. Psychology of Learning and Motivation: Advances
in Research and Theory, eds Irwin D, Ross BH (Academic, San Diego, CA), Vol 42, pp
79–108.
11. Wolfe JM (1998) Visual Memory: What do you know about what you saw? Curr Biol
8:R303–R304.
12. O’Regan JK, Noe¨ A (2001) A sensorimotor account of vision and visual consciousness.
Behav Brain Sci 24:939 –1011.
13. Hollingworth A (2004) Constructing visual representations of natural scenes: The roles
of short- and long-term visual memory. J Exp Psychol Hum Percept Perform 30:519 –
537.
14. Tatler BW, Melcher D (2007) Pictures in mind: Initial encoding of object properties
varies with the realism of the scene stimulus. Perception 36:1715–1729.
15. Vogt S, Magnussen S (2007) Long-term memory for 400 pictures on a common theme.
Exp Psycol 54:298 –303.
16. Castelhano M, Henderson J (2005) Incidental visual memory for objects in scenes. Vis
Cog 12:1017–1040.
17. Marmie WR, Healy AF (2004) Memory for common objects: Brief intentional study is
sufficient to overcome poor recall of US coin features. Appl Cognit Psychol 18:445– 453.
18. Koutstaal W, Schacter DL (1997) Gist-based false recognition of pictures in older and
younger adults. J Mem Lang 37:555–583.
19. Intraub H, Hoffman JE (1992) Remembering scenes that were never seen: Reading and
visual memory. Am J Psychol 105:101–114.
20. Landauer TK (1986) How much do people remember? Some estimates of the quantity
of learned information in long-term memory. Cognit Sci 10:477– 493.
21. Dudai Y (1997) How big is human memory, or on being just useful enough. Learn Mem
3:341–365.
22. Fagot J, Cook RG (2006) Evidence for large long-term memory capacities in baboons
and pigeons and its implications for learning and the evolution of cognition. Proc Natl
Acad Sci USA 103:17564 –17567.
23. Fuster JM (2003) More than working memory rides on long-term memory. Behav Brain
Sci 26:737.
24. Palmeri TJ, Tarr MJ (2008) Visual Memory, eds Luck SJ, Hollingworth A (Oxford Univ
Press, New York), 163–207.
25. Fodor JA (1975). The Language of Thought (Harvard Univ Press, Cambridge, MA).
26. Barsalou LW (1999) Perceptual symbol systems. Behav Brain Sci 22:577– 660.
27. Barsalou LW (1990) Content and Process Specificity in the Effects of Prior Experiences,
eds Srull TK, Wyer RS, Jr (Erlbaum, Hillsdale, NJ), pp 61– 88.
28. Collins AM, Loftus EF (1975) A spreading activation theory of semantic processing.
Psychol Rev 82:407– 428.
29. Anderson JR (1983) Retrieval of information from long-term memory. Science 220:25–30.
30. Sigala N, Gabbiani F, Logothetis NK (2002) Visual categorization and object represen-
tation in monkeys and humans. J Cog Neuro 14:187–198.
31. Mandler G (1980) Recognizing: The judgment of previous occurrence. Psychol Rev
87:252–271.
32. Yonelinas AP (2002) The nature of recollection and familiarity: A review of 30 years of
research. J Mem Lang 46:441–517.
33. Jacoby LL (1991) A process dissociation framework: Separating automatic from inten-
tional uses of memory. J Mem Lang 30:513–541.
34. Tarr MJ, Bulthoff HH (1998) Image-based object recognition in man, monkey and
machine. Cognition 67:1–20.
35. Riesenhuber M, Poggio T (1999) Hierarchical models of object recognition in cortex.
Nat Neurosci 2:1019 –1025.
36. Logothetis NK, Pauls J, Poggio T (1995) Shape representation in the inferior temporal
cortex of monkeys. Curr Biol 5:552–563.
37. Tarr MJ, Williams P, Hayward WG, Gauthier, I. (1998) Three-dimensional object recog-
nition is viewpoint dependent. Nat Neurosci 1:275–277.
38. Bulthoff HH, Edelman S (1992) Psychological support for a two-dimensional view
interpolation theory of object recognition. Proc Natl Acad Sci USA 89:60 – 64.
39. Torralba A, Fergus R, Freeman W, 80 million tiny images: A large dataset for non-
parametric object and scene recognition. IEEE Trans PAMI, in press.
40. Nosofsky RM (1986) Attention, similarity, and the identification-categorization rela-
tionship. J Exp Psychol Gen 115:39 – 61.
41. Ahissar M, Hochstein S (2004) The reverse hierarchy theory of visual perceptual
learning. Trends Cogn Sci 8:457– 464.
42. Wheeler ME, Petersen SE, Buckner RL (2000) Memory’s echo: Vivid remembering
reactivates sensory-specific cortex. Proc Natl Acad Sci USA 97:11125–11129.
43. Kosslyn SM, et al. (1999) The Role of Area 17 in Visual Imagery: Convergent Evidence
from PET and rTMS. Science 284:167–170.
44. Garoff RJ, Slotnick SD, Schacter DL (2005) The neural origins of specific and general
memory: The role of fusiform cortex. Neuropsychologia 43:847– 859.
Brady et al. PNAS
兩
September 23, 2008
兩
vol. 105
兩
no. 38
兩
14329
NEUROSCIENCEPSYCHOLOGY