Sources and mechanisms of modality-specific distraction in visual short-term memory

ABSTRACT

Visual short-term and working memory can be disrupted by irrelevant, distracting input occurring after encoding. Distractors similar to the original memory are known to be interfering, but it is unclear whether dissimilar distractors have the same disruptive effect. The presence of dissimilar distraction would be problematic for views of similarity-based interference, hence the present study investigated modality-specific distraction using a procedure that required participants to compare single target and probe objects over a delay. An irrelevant distractor could be presented during the delay separating the target and probe, but it varied in its similarity to the target. In four experiments, recognition was disrupted by the presence of a distractor, even when the distractors were highly dissimilar to the target. Furthermore, the interference effect was not reduced when the same distractors were repeatedly used throughout the experiment, and interference from dissimilar distractors was only lessened when it was extremely predictable. These findings indicate that susceptibility to dissimilar distraction is a persistent limitation in visual short-term memory.

KEYWORDS:

• Retroactive interference
• distraction
• visual memory
• forgetting

Acknowledgements

The Fribble stimuli used in these experiments were provided courtesy of Michael J. Tarr, Center for the Neural Basis of Cognition and Department of Psychology, Carnegie Mellon University, http://www.tarrlab.org A number of undergraduate students have supported this project at different stages as part of the Research Opportunity Network. We would like to thank Bonnie Garner for her help with Experiment 1, particularly in sourcing the high familiarity distractors, as well as Ellie Batham, Lyndsey Howells and Dionne Wallace for support with participant testing. We are also grateful to Professor John Krantz for hosting Experiments 3 and 4 on the website “Psychological Research on the Net” (https://psych.hanover.edu/research/exponnet.html).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in the Open Science Framework project “Sources and Mechanisms of Modality-Specific Distraction in Visual Short-Term Memory” at https://doi.org/10.17605/OSF.IO/CA7U6.

Notes

1 As the paradigm used here involves passive maintenance of visual representations, without any need for manipulating that information, the phrase “short-term memory” is used instead of “working memory.”

2 The full set of these distractors included images of a washing machine (practice trial), a

toaster, toothpaste, sunglasses, a bicycle, a kettle, a coat hanger, an axe, a bed, a hairdryer, a car, a power drill, a hairbrush, a chair, a bus, an iron, a laptop, a toothbrush, a lamp, keys, a teapot, kitchen knives, a light switch, a pen, a tea cup, scissors, a fork, a tape measure, a tap, an umbrella, a light bulb, a lawn mower, a pan, a hammer, a smart phone, a game controller and a bar of soap.

3 To make specific comparisons, a half-normal distribution was assumed as an estimated RI effect could be obtained from Mercer (2018), which also used the Fribble stimulus set. In that study, the largest RI effect – based on subtracting performance in a distractor condition against the no distractor control – was 0.08. This occurred for a dissimilar distractor presented 1.5 s after the target. This difference was then used to calculate the Bayes factor (BF¹⁰), employing the calculator available at http://www.lifesci.sussex.ac.uk/home/Zoltan_Dienes/inference/bayes_factor.swf.

4 Here “surprise” is referred to within the context of distractor type, rather than its occurrence or position, which could be anticipated.

5 The approach to calculating the Bayes factor for these pairwise comparisons matched Experiment 1, anticipating an RI effect of 0.08 based on a half-normal distribution.

6 Although Gorilla software randomly allocated participants to each condition, such that group sizes should be equivalent, the higher drop-out rate in the changing condition led to an imbalance in group size.

7 Correlations between age and averaged A’ in each experiment were as follows: Experiment 1 – r(20) = 0.36, p = .099; Experiment 2 – r(75) = -0.11, p = .352; Experiment 3 – r(162) = 0.10, p = .206; Experiment 4 – r(80) = 0.11, p = .328.