Working memory is traditionally viewed as having a fixed capacity, holding a limited number of items, such as Miller's "magical number" seven or Cowan's four. However, recent evidence suggests that working memory should be conceptualized as a limited resource that is flexibly distributed among all items to be maintained. This view emphasizes the quality rather than the quantity of memory representations, with performance determined by the precision of recall. Behavioral and neural data support this resource model, showing that recall precision decreases as the number of items increases, and that salient or goal-relevant stimuli are stored with higher precision at the expense of others. This model aligns with the idea that working memory is a continuous resource, not a fixed number of slots, and that precision varies across items and trials. Neural data, including fMRI and EEG, show that signals are sensitive to the number of items in memory, with increasing or inverted U-shaped responses to load. These findings challenge the traditional slot model, which assumes a fixed number of slots. Resource models also suggest that working memory is influenced by attention and that precision can be affected by noise in encoding, maintenance, and retrieval. Neural studies indicate that working memory resources are distributed across brain regions, with activity modulated by stimulus salience and task relevance. Recent research has shown that working memory precision can be influenced by both internal noise and external factors, and that models incorporating variable precision and Bayesian inference provide a better account of recall errors. These findings suggest that working memory is a dynamic, flexible system, with precision and resource allocation dependent on the task and the environment. The implications of these findings extend to understanding memory limitations in aging, disease, and natural scenes, and highlight the importance of considering both the quantity and quality of memory representations.Working memory is traditionally viewed as having a fixed capacity, holding a limited number of items, such as Miller's "magical number" seven or Cowan's four. However, recent evidence suggests that working memory should be conceptualized as a limited resource that is flexibly distributed among all items to be maintained. This view emphasizes the quality rather than the quantity of memory representations, with performance determined by the precision of recall. Behavioral and neural data support this resource model, showing that recall precision decreases as the number of items increases, and that salient or goal-relevant stimuli are stored with higher precision at the expense of others. This model aligns with the idea that working memory is a continuous resource, not a fixed number of slots, and that precision varies across items and trials. Neural data, including fMRI and EEG, show that signals are sensitive to the number of items in memory, with increasing or inverted U-shaped responses to load. These findings challenge the traditional slot model, which assumes a fixed number of slots. Resource models also suggest that working memory is influenced by attention and that precision can be affected by noise in encoding, maintenance, and retrieval. Neural studies indicate that working memory resources are distributed across brain regions, with activity modulated by stimulus salience and task relevance. Recent research has shown that working memory precision can be influenced by both internal noise and external factors, and that models incorporating variable precision and Bayesian inference provide a better account of recall errors. These findings suggest that working memory is a dynamic, flexible system, with precision and resource allocation dependent on the task and the environment. The implications of these findings extend to understanding memory limitations in aging, disease, and natural scenes, and highlight the importance of considering both the quantity and quality of memory representations.