Visual long-term memory has a massive storage capacity for object details

By Timothy F. Brady, Talia Konkle, George A. Alvarez, Aude Oliva. Proceedings of the National Academy of Sciences of the United States of America, 2008

1,116 citations recorded by the source corpus.

One of the major lessons of memory research has been that human memory is fallible, imprecise, and subject to interference. Thus, although observers can remember thousands of images, it is widely assumed that these memories lack detail. Contrary to this assumption, here we show that long-term memory is capable of storing a massive number of objects with details from the image. Participants viewed pictures of 2,500 objects over the course of 5.5 h. Afterward, they were shown pairs of images and indicated which of the two they had seen. The previously viewed item could be paired with either an object from a novel category, an object of the same basic-level category, or the same object in a different state or pose. Performance in each of these conditions was remarkably high (92%, 88%, and 87%, respectively), suggesting that participants successfully maintained detailed representations of thousands of images. These results have implications for cognitive models, in which capacity limitations impose a primary computational constraint (e.g., models of object recognition), and pose a challenge to neural models of memory storage and retrieval, which must be able to account for such a large and detailed storage capacity.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8458e49fb08d2cca3a8d7355465e182c30785220

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Citations in the corpus

Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents (2018, background): “The default stimulus set for the continuous recognition and arbitrary visuomotor mapping tasks is from Brady et al. (2008).”
Robust Representation of Stable Object Values in the Oculomotor Basal Ganglia (2012, background): “Rather, efficient choices are likely based on the long-term memories of these objects, as suggested in behavioral studies in humans (Standing, 1973; Brady et al., 2008) and animals (Vaughan and Greene, 1984; Fagot and Cook, 2006).”
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning (2020, methodology): “ications, e.g., robot exploration and object search, which will be further explained in Sec. 6.3. 4Learning 4.1Long-term Learning Inspired by the fact that human has a massive memory storage capacity [4], we use an autoencoder in Fig. 2a for long-term learning for the following reasons. Unsupervised”
Sleep and the Price of Plasticity: From Synaptic and Cellular Homeostasis to Memory Consolidation and Integration (2014, background): “Regardless, it should be kept in mind that, even without any explicit instruction to learn, at the end of a typical waking day, we can recollect an extraordinary amount of events, facts, and scenes, including many irrelevant details (Brady et al., 2008; Standing, 1973).”
A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence (2021, methodology): “To ensure participant engagement and control cognitive state, we incorporated a continuous recognition task 26 in which participants were instructed to indicate whether they have seen each presented image at any point in the past.”
Understanding and Predicting Image Memorability at a Large Scale (2015, background): “One hallmark of human cognition is our massive capacity for remembering lots of different images [2, 20], many in great detail, and after only a single view.”
Computational principles of biological memory (2015, methodology): “This form of synaptic complexity can greatly extend memory lifetimes without sacrificing the initial memory strength, accounting for our remarkable ability to remember for long times a large number of details even when memories are learned in one shot (Brady et al., 2008).”
What Makes a Photograph Memorable? (2014, background): “The role of the fillers was two-fold: first, they provided spacing between the first and second repetition of a target; second, responses on repeated fillers constituted a ‘vigilance task’ that allowed us to continuously check that participants were attentive to the task [5], [3].”
What Makes a Visualization Memorable? (2013, methodology): “[9] tested the long-term memory capacity for storing details by detecting repeat object images when shown pairs of objects, one old and one new.”