July 2013
Volume 13, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   July 2013
The Multiple Change Detection Approach to Estimating Visual Working Memory Capacity
Author Affiliations
  • Nancy Carlisle
    Center for Mind and Brain, University of California Davis
  • Britta Hahn Hahn
    Maryland Psychiatric Research Center, University of Maryland
  • Benjamin Robinson
    Maryland Psychiatric Research Center, University of Maryland
  • James Gold
    Maryland Psychiatric Research Center, University of Maryland
  • Steven Luck
    Center for Mind and Brain, University of California Davis
Journal of Vision July 2013, Vol.13, 1350. doi:10.1167/13.9.1350
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Nancy Carlisle, Britta Hahn Hahn, Benjamin Robinson, James Gold, Steven Luck; The Multiple Change Detection Approach to Estimating Visual Working Memory Capacity. Journal of Vision 2013;13(9):1350. doi: 10.1167/13.9.1350.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Estimates of visual working memory capacity (K) vary widely across individuals (Vogel & Awh, 2008), and correlate with measures of intellectual ability. However, typical measures of K are potentially confounded with attentional lapses. It is unclear whether incorrect responses are due limitations in capacity, or a complete lapse of attention that results in no items being stored in working memory. Recently, methods using multiple set sizes and new analysis techniques (Rouder, Morey, Cowan, Zwilling, Morey, & Pratte, 2008; Morey, 2011) were introduced to dissociate attentional lapses and K. However, there is evidence that participants may exert reduced effort at larger array sizes, leading to a decreased estimate of K (Rouder, et al., 2008). Here, we evaluate a new method of assessing K-called Multiple Change Detection (MCD)-that provides concurrent measures of attentional lapses and K while eliminating differential effort allocation across set sizes (Gibson, Wasserman, & Luck, 2011). Rather than varying the set size and changing a single item, this method keeps set size constant and varies the number of items that change. The number of changes is not known in advance, so observers cannot strategically vary their level of effort.

To evaluate MCD, we performed Monte Carlo simulations contrasting MCD and traditional methods for estimating K. MCD-based estimates of K were less distorted by attentional lapses. We applied this new method to people with schizophrenia (PSZ) and healthy control subjects (HCS) to determine whether previous reports of reduced K in PSZ were an artifact of an increased lapse rate. We found that PSZ had more frequent attentional lapses than HCS, but capacity was still reduced in PSZ. By providing separate estimates of attentional lapses and capacity and eliminating the possibility of differential effort allocation across set sizes, MCD provides a promising new method for estimating visual working memory capacity.

Meeting abstract presented at VSS 2013

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×