Skip to main content
SpringerLink
Log in

When remembering less is more: Unfiltered items are associated with reduced memory fidelity in visual short-term memory

  • Published:
Attention, Perception, & Psychophysics Aims and scope Submit manuscript

Abstract

Visual short-term memory (VSTM), the ability to store information no longer visible, is essential for human behavior. VSTM limits vary across the population and are correlated with overall cognitive ability. It has been proposed that low-memory individuals are unable to select only relevant items for storage and that these limitations are greatest when memory demands are high. However, it is unknown whether these effects simply reflect task difficulty and whether they impact the quality of memory representations. Here we varied the number of items presented, or set size, to investigate the effect of memory demands on the performance of visual short-term memory across low- and high-memory groups. Group differences emerged as set size exceeded memory limits, even when task difficulty was controlled. In a change-detection task, the low-memory group performed more poorly when set size exceeded their memory limits. We then predicted that low-memory individuals encoding items beyond measured memory limits would result in the degraded fidelity of memory representations. A continuous report task confirmed that low, but not high, memory individuals demonstrated decreased memory fidelity as set size exceeded measured memory limits. The current study demonstrates that items held in VSTM are stored distinctly across groups and task demands. These results link the ability to maintain high quality representations with overall cognitive ability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Adam, K. C., Mance, I., Fukuda, K., & Vogel, E. K. (2015). The contribution of attentional lapses to individual differences in visual working memory capacity. Journal of Cognitive Neuroscience, 27, 1601–1616.

    Article  PubMed  PubMed Central  Google Scholar 

  • Alvarez, G. A., & Cavanagh, P. (2004). The capacity of visual short-term memory is set both by visual information load and by number of objects. Psychological Science, 15, 106–111.

    Article  PubMed  Google Scholar 

  • Awh, E., Barton, B., & Vogel, E. K. (2007). Visual working memory represents a fixed number of items regardless of complexity. Psychological Science, 18, 622–628.

    Article  PubMed  Google Scholar 

  • Awh, E., & Vogel, E. K. (2008). The bouncer in the brain. Nature Neuroscience, 11, 5–6.

    Article  PubMed  Google Scholar 

  • Barton, B., Ester, E. F., & Awh, E. (2009). Discrete resource allocation in visual working memory. Journal of Experimental Psychology: Human Perception and Performance, 35, 1359–1367.

    PubMed  Google Scholar 

  • Bays, P. M., Catalao, R. F. G., & Husain, M. (2009). The precision of visual working memory is set by allocation of a shared resource. Journal of Vision, 9, 1–11.

    Article  Google Scholar 

  • Bays, P. M., & Husain, M. (2008). Dynamic shifts of limited working memory resources in human vision. Science, 321, 851–854.

    Article  PubMed  PubMed Central  Google Scholar 

  • Brainard, D. H. (1997). The Psychophysics Toolbox. Spatial Vision, 10, 433–436.

    Article  PubMed  Google Scholar 

  • Conway, A. R., Cowan, N., Bunting, M. F., Therriault, D. J., & Minkoff, S. R. (2002). A latent variable analysis of working memory capacity, short-term memory capacity, processing speed, and general fluid intelligence. Intelligence, 30, 163–183.

    Article  Google Scholar 

  • Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87–185.

    Article  PubMed  Google Scholar 

  • Cowan, N. (2005). Working memory capacity limits in a theoretical context. In C. Izawa & N. Ohta (Eds.), Human learning and memory: Advances in theory and application (pp. 155–175). Erlbaum.

    Google Scholar 

  • Cowan, N., Fristoe, N. M., Elliott, E. M., Brunner, R. P., & Saults, J. S. (2006). Scope of attention, control of attention, and intelligence in children and adults. Memory & Cognition, 34, 1754–1768.

    Article  Google Scholar 

  • Dux, P. E., & Marois, R. (2008). Distractor inhibition predicts individual differences in the attentional blink. PLOS ONE, 3, e3330.

    Article  PubMed  PubMed Central  Google Scholar 

  • Engle, R. W., Kane, M. J., & Tuholski, S. W. (1999). Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence, and functions of the prefrontal cortex. In A. Miyake & P. Shah (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 102–134). Cambridge University Press.

    Chapter  Google Scholar 

  • Ester, E. F., Sprague, T. C., & Serences, J. T. (2015). Parietal and frontal cortex encode stimulus-specific mnemonic representations during visual working memory. Neuron, 87, 893–905.

    Article  PubMed  PubMed Central  Google Scholar 

  • Fukuda, K., & Vogel, E. K. (2009). Human variation in overriding attentional capture. Journal of Neuroscience, 29, 8726–8733.

    Article  PubMed  Google Scholar 

  • Fukuda, K., & Vogel, E. K. (2011). Individual differences in recovery time from attentional capture. Psychological Science, 22, 361–368.

    Article  PubMed  Google Scholar 

  • Fukuda, K., Vogel, E., Mayr, U., & Awh, E. (2010). Quantity, not quality: The relationship between fluid intelligence and working memory capacity. Psychonomic Bulletin & Review, 17, 673–679.

    Article  Google Scholar 

  • Fukuda, K., Woodman, G. F., & Vogel, E. K. (2015). Individual differences in visual working memory capacity: Contributions of attentional control to storage. In P. Jolicoeur, C. Lefebvre, & J. Martinez-Trujillo (Eds.), Mechanisms of sensory working memory: Attention and performance XXV (pp. 105–119). ScienceDirect.

    Chapter  Google Scholar 

  • Gaspar, J. M., Christie, G. J., Prime, D. J., Jolicœur, P., & McDonald, J. J. (2016). Inability to suppress salient distractors predicts low visual working memory capacity. Proceedings of the National Academy of Sciences, 113, 3693–3698.

    Article  Google Scholar 

  • Hardman, K. O., Vergauwe, E., & Ricker, T. J. (2017). Categorical working memory representations are used in delayed estimation of continuous colors. Journal of Experimental Psychology: Human Perception and Performance, 43(1), 30–54.

    PubMed  Google Scholar 

  • Harrison, S. A., & Tong, F. (2009). Decoding reveals the contents of visual working memory in early visual areas. Nature, 458, 632–635.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences, 105, 6829–6833.

    Article  Google Scholar 

  • Kamitani, Y., & Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nature Neuroscience, 8, 679–685.

    Article  PubMed  PubMed Central  Google Scholar 

  • Kumle, L., Võ, M. L. H., & Draschkow, D. (2021). Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R. Behavior Research Methods, 53(6), 2528–2543.

    Article  PubMed  PubMed Central  Google Scholar 

  • Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 309, 279–281.

    Article  Google Scholar 

  • MacCallum, R. C., Zhang, S., Preacher, K. J., & Rucker, D. D. (2002). On the practice of dichotomization of quantitative variables. Psychological Methods, 7(1), 19–40.

    Article  PubMed  Google Scholar 

  • McNab, F., & Klingberg, T. (2008). Prefrontal cortex and basal ganglia control access to working memory. Nature Neuroscience, 11, 103–107.

    Article  PubMed  Google Scholar 

  • Murray, J. D., Bernacchia, A., Roy, N. A., Constantinidis, C., Romo, R., & Wang, X. J. (2017). Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex. Proceedings of the National Academy of Sciences, 114(2), 394–399.

    Article  Google Scholar 

  • Pashler, H. (1988). Familiarity and visual change detection. Perception & Psychophysics, 44, 369–378.

    Article  Google Scholar 

  • Peirce, J. W. (2009). Generating stimuli for neuroscience using PsychoPy. Frontiers in Neuroinformatics, 2, 343

  • Pelli, D. G. (1997). The VideoToolbox software for visual psychophysics: Transforming numbers into movies. Spatial Vision, 10, 437–442.

    Article  PubMed  Google Scholar 

  • Pinheiro, J., Bates, D., R Core Team. (2023). Nlme: linear and nonlinear mixed effects models. R package version 3.1-162, https://CRAN.R-project.org/package=nlme

  • Rademaker, R. L., Chunharas, C., & Serences, J. T. (2019). Coexisting representations of sensory and mnemonic information in human visual cortex. Nature Neuroscience, 22, 1336–1344.

    Article  PubMed  PubMed Central  Google Scholar 

  • Robison, M. K., Miller, A. L., & Unsworth, N. (2018). Individual differences in working memory capacity and filtering. Journal of Experimental Psychology: Human Perception and Performance, 44, 1038–1053.

    PubMed  Google Scholar 

  • Schurgin, M. W., Wixted, J. T., & Brady, T. F. (2020). Psychophysical scaling reveals a unified theory of visual memory strength. Nature Human Behaviour, 4(11), 1156–1172.

    Article  PubMed  Google Scholar 

  • Serences, J. T., Ester, E. F., Vogel, E. K., & Awh, E. (2009). Stimulus-specific delay activity in human primary visual cortex. Psychological Science, 20, 207–214.

    Article  PubMed  Google Scholar 

  • Sheremata, S., & Shomstein, S. (2014). Hemifield asymmetries differentiate VSTM for single-and multiple-feature objects. Attention, Perception, & Psychophysics, 76, 1609–1619.

    Article  Google Scholar 

  • Shipstead, Z., Redick, T. S., Hicks, K. L., & Engle, R. W. (2012). The scope and control of attention as separate aspects of working memory. Memory, 20, 608–628.

    Article  PubMed  Google Scholar 

  • Tanner, W. P., Jr., & Swets, J. A. (1954). A decision-making theory of visual detection. Psychological Review, 61, 401–409.

    Article  PubMed  Google Scholar 

  • Todd, J. J., & Marois, R. (2004). Capacity limit of visual short-term memory in human posterior parietal cortex. Nature, 428, 751–754.

    Article  PubMed  Google Scholar 

  • Unsworth, N., Fukuda, K., Awh, E., & Vogel, E. K. (2014). Working memory and fluid intelligence: Capacity, attention control, and secondary memory retrieval. Cognitive Psychology, 71, 1–26.

    Article  PubMed  PubMed Central  Google Scholar 

  • Unsworth, N., Fukuda, K., Awh, E., & Vogel, E. K. (2015). Working memory delay activity predicts individual differences in cognitive abilities. Journal of Cognitive Neuroscience, 27, 853–865.

    Article  PubMed  Google Scholar 

  • Van den Berg, R., Shin, H., Chou, W. C., George, R., & Ma, W. J. (2012). Variability in encoding precision accounts for visual short-term memory limitations. Proceedings of the National Academy of Sciences, 109, 8780–8785.

    Article  Google Scholar 

  • Vogel, E. K., & Machizawa, M. G. (2004). Neural activity predicts individual differences in visual working memory capacity. Nature, 428, 748–751.

    Article  PubMed  Google Scholar 

  • Vogel, E. K., McCollough, A. W., & Machizawa, M. G. (2005). Neural measures reveal individual differences in controlling access to working memory. Nature, 438, 500–503.

    Article  PubMed  Google Scholar 

  • Vogel, E. K., Woodman, G. F., & Luck, S. J. (2001). Storage of features, conjunctions, and objects in visual working memory. Journal of Experimental Psychology: Human Perception and Performance, 27, 92–114.

    PubMed  Google Scholar 

  • von Allmen, D. Y., Wurmitzer, K., Martin, E., & Klaver, P. (2013). Neural activity in the hippocampus predicts individual visual short-term memory capacity. Hippocampus, 23, 606–615.

    Article  Google Scholar 

  • Wilken, P., & Ma, W. J. (2004). A detection theory account of change detection. Journal of Vision, 4, 1120–1135.

    Article  PubMed  Google Scholar 

  • Zhang, W., & Luck, S. J. (2008). Discrete fixed-resolution representations in visual working memory. Nature, 453, 233–235.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory for Rehabilitation Neuroscience, University of Florida, 1864 Stadium Road, Gainesville, FL, 32611, USA

    Young Seon Shin

  2. Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, 33431, USA

    Summer L. Sheremata

  3. Department of Psychology, Florida Atlantic University, Boca Raton, FL, 33431, USA

    Summer L. Sheremata

Authors
  1. Young Seon Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Summer L. Sheremata
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Young Seon Shin.

Ethics declarations

Conflict of interests

None

Additional information

Open practices statement

Neither experiment was preregistered. The data of this study are available from the corresponding author, Y.S.S, upon reasonable request.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Public significance statement

Visual short-term memory selects and holds a few items from the external world by constructing stable internal images for further analysis. However, the amount of information available is severely limited, with limits varying within the population. In the present study, we investigate (1) whether limits between individuals exist when controlling for overall task difficulty and (2) whether memory fidelity is affected by differences in memory limits. We demonstrate that when asked to remember many items, individuals are less likely to detect a change in the remembered items and remember more items at a cost of memory fidelity.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shin, Y.S., Sheremata, S.L. When remembering less is more: Unfiltered items are associated with reduced memory fidelity in visual short-term memory. Atten Percept Psychophys 86, 1248–1258 (2024). https://doi.org/10.3758/s13414-024-02891-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3758/s13414-024-02891-w

Keywords

Search

Navigation