인지과학 협동과정 송이구 visual working memory as decision making: compensation for...
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인 지 과 학 협 동 과 정 송 이 구
Visual Working Memory as deci-sion making: compensation for memory uncertainty in reach
planning
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Visual Working Memory (VWM)
System that actively maintains visual information to serve the needs of ongo-ing tasks. (Luck & Vogel, 2013)
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Visual Working Memory
Online movement controlIntegration of visual information across eye
movementsVisual searchGaze correction following saccadic error
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Visual Working Memory Studies
Since Luck & Vogel’s study in 1997, there has been an numerous researches about VWM
“Change Blindness Test” was able to notice the limitations of VWM
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Visual Working Memory Studies
“Change Detection Task” quantify the capac-ity of VWM
WM capacity is correlated with individual differences in broad cognitive functions
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Visual Working Memory Capacity
Researches in Working Memory capacity fo-cused on How quickly the information is refreshed How quickly the information decays
In VWM capacity, focuses on How many items can be remembered Recently, focus on content of working memory repre-
sentation
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Visual Working Memory Capacity
Luck& Vogel (1997) used change detection task to estimate the capacity of the visual working memory. As a result found that about 3 or 4 items –was able
to accurately detect changes.
But shows only little information on how much we know Need to increase the amount of information Measure fidelity
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Models of Visual Working Memory Capacity
How is the memory stored then?
Discrete Slots (Item-limited) Idea that memory is encoded as Slots
If limit is N, no more representations can be encoded All or None
Continuous Resource (Information limited) No limit like slots, however, precision of memory is
decreased as set size increase
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Discrete Slots
Item limited
Change Detection paradigm Accordingly subjects accurately detected
the changes when fewer 3-4 items were shown displayed
Zhang & Luck(2008) Used a Mathematical Model to differentiate
Noisy memory representation OR Random guesses More than 3 items, rate of random guessing
increased
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Continuous Resources
Information limited
Alvarez & Cavanagh used the term “Trade-off” between the relation of number of items and its resolution
Poor Memory representation Not a completeabsence
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Integrated vs Independent
Object-basedLuck & Vogel(1997) – The number of features
in an object did not matter during change de-tection task (either 1,2, or even 4 features) “objects” are the units of visual working memory
Feature-basedXu(2002), Bays, Wu, &Husain(2011) – Objects
are not always encoded entirely
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Better to have 2 features in 1 object than 1 feature in 2 objects
Then…? Possibilities Hierarchical feature bundles
Higher level – Integrated object Lower level – Independent features
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How does the Visual Working Memory capac-ity reaching its limit influence motor planning and execution? (Ecologically relevant tasks)
-External cost bias the contents of visual memory?-External cost influence how people act on the basis of
uncertain memory information
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Decision Making (Wine-glass problem)
Wine-Glass Problem Better to underestimate than to overestimate !...
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Hypothesis
1. As set sizes increase, memory precision is lower
2. Subjects will undershoot during the over-shoot-penalty session and overshoot during undershoot-penalty session
3. As memory uncertainty grows, subjects will aim further away from the penalty area.
4. Contents of memory is biased due to the monetary costs related with memory error
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Experiment
Method
Participants – 12 individuals (8 female) Age (18 – 35) / Normal Vision(corrected-to-normal) Two experimental sessions (conducted on separate
days) Minimum $20 + additional monetary incentives
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Stimuli
“Smart Table” Glass surface with projection film Digital projector shooting arrays on the table
Subjects were to use the Stylus to aim at the intended area
Two Sessions Undershoot-penalty Overshoot-penalty
Two types targets1. One colored targets 2. Three colored targets
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Procedure
Every trial began by pressing the “Start-cross”
Display the target (either 1 or 3 object) for 1500 ms Retention interval was presented for 1000 ms Subjects then completed odd/even digit judgment
Recall trial or Discrimination trial
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Procedure
Recall Trial Subjects were told to touch the area where the cue
was previously given Given feedback - Monetary payoff or penalty Hit = 10 ¢ Penalty area = 20 ¢ Neither
= 0 ¢ 100 trials per block – 400 trials in total
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Procedure
Discrimination Trial Given cue, judge whether the cue is further or
closer than the previously given stimuli No feedback 50 trials per block – 200 trials in total Designed to observe if the memory was biased by
penalty condition
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Results
2 x 2 Within subject ANOVA
Mean relative aim differed as penalty condition differs F(1,11) = 19.62 p=0.001 η^2=0.44
Set size influenced by penalty condition F(1,11) = 10.15 p=0.009 η^2 = 0.120 The bigger the set size, the further the mean aim
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Results
Since, discrimination trials did not get any feed-back nor had anything to do with monetary incen-tives, participants had no reason to apply post-mnemonic decision strategy
No significant shift across penalty conditions F(1,11) = 1.38 p=0.27 η^2 = 0.02
Meaning during recall trials subjects applied Adap-tive decision strategy!
Post-experiment survey 9 out of 12 participants claimed to aim way from penalty
regions
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Is the result a in motor planning? Fixed Heuristic (aim away from penalty area)
Or Consideration of costs derived from memory error and
memory uncertainty
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Discussion
Any other factors that should be considered to bias the working memory? Or was the monetary cost in the experiment not an appropriate exam-ple for costs by memory error? -Since the monetary cost is actually not an “negative cost”
to the participants. (Just earning less instead of losing)
Would categorical perception bias the informa-tion encoding into the working memory?
What is more likely as VWM encoding process?
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Thank You
끝 !!
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Reference
• Brady, T., Konkle, T., & Alvarez, G. (2011). A review of visual mem-ory capacity: Beyond individual items and toward structured rep-resentations. Journal of Vision, 4-4.
• Hartshorne, J. (2008). Visual Working Memory Capacity and Proac-tive Interference. PLoS ONE.
• Hollingworth, A., Richard, A., & Luck, S. (2008). Understanding the function of Visual Short-Term Memory: Transsaccadic memory, object correspondence, and gaze correction. Journal of Experimen-tal Psychology: General, 137(1), 163-163.
• Kawasaki, M., & Yamaguchi, Y. (2014). Individual visual working memory capacities and related brain oscillatory activities are mod-ulated by color preferences. Frontiers in Human Neuroscience Front. Hum. Neurosci.
• Kording, K. (2007). Decision Theory: What "Should" the nervous system do? Science, 318(5850), 606-610.
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• Lerch, R., & Sims, C. (2015). Visual Working Memory as Decision Mak-ing: Compensation for Memory Uncertainty in Reach Planning. Re-trieved October 14, 2015.
• Luck, S., & Vogel, E. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390, 279-281.
• Luck, S., & Vogel, E. (2013). Visual Working Memory Capacity: From Psychophysics and Neurobiology to Individual Differences. Trends in Cognitive Sciences, 17(8), 391-400.
• Ma, W., Husain, M., & Bays, P. (2014). Changing concepts of working memory. Nature Neuroscience, 17(3), 347-356.
• Makovski, T., & Jiang, Y. (2009). The role of visual working memory in attentive tracking of unique objects. Journal of Experimental Psychol-ogy: Human Perception and Performance, 1687-1697.
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• Sims, C., Jacobs, R., & Knill, D. (2012). An ideal observer analysis of visual working memory. Psychological Review, 807-830.