Dennis Norris

Memory for verbal material improves when words form familiar chunks. But how does the improvement due to chunking come about? Two possible explanations are that the input might be actively recoded into chunks, each of which takes up less memory capacity than items not forming part of a chunk (a form of data compression), or that chunking is based on redintegration. If chunking is achieved by redintegration, representations of chunks exist only in long-term memory (LTM) and help to reconstructing degraded traces in short-term memory (STM). In 6 experiments using 2-alternative forced choice recognition and immediate serial recall we find that when chunks are small (2 words) they display a pattern suggestive of redintegration, whereas larger chunks (3 words), show a pattern consistent with data compression. This concurs with previous data showing that there is a cost involved in recoding material into chunks in STM. With smaller chunks this cost seems to outweigh the benefits of recoding words into chunks. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

In Norris (2017), I explained why the notion of activated LTM (long-term memory) combined with a focus of attention was unable to perform the computations required to support short-term memory (STM) and argued that those extra computations must require a separate STM system. Cowan (2019) made the alternative proposal that this full set of computations is better conceptualized as a unitary system of activated LTM. To this he added a pointer system, the ability to perform variable binding, and an unspecified model of STM that acts as a front end to LTM. This appears to be simply an exercise in relabeling. Furthermore, without a computational specification of how the components work, the model lacks the ability to simulate even the most basic STM phenomena. If the model were specified in more detail it seems almost inevitable that it would contain something instantly recognizable as an STM system. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Is the capacity of short-term memory fixed, or does it improve with practice? It is already known that training on complex working memory tasks is more likely to transfer to untrained tasks with...

Following Conrad (1965, Journal of Verbal Learning and Verbal Behavior, 4, 161–169) it is often assumed that backward verbal serial recall is perform

Running span can be performed by either passively listening to to-be-remembered items or actively updating the target set during presentation. This choice of strategy is influenced by the rate of presentation in the task. Previous research suggests that the active updating process is demanding and time-consuming. It is favored at relatively slow rates of presentation, while the passive strategy is more successful when applied at fast rates. In two experiments the time course of resource demand during task performance and its sensitivity to presentation rate was examined. We hypothesized that running span imposes a high cognitive load only when active updating is employed. Participants performed running span simultaneously with a spatial reaction time (RT) task, and RTs on the concurrent task were used to index the resource demands of the memory task. A slow-paced running span exhibited a large overall resource demand in comparison with the serial recall tasks (Experiment 1) and fast-paced running span (Experiment 2). This demand was observed from the position in the list from which participants are presumed to start updating, suggesting a cognitive shift to a demanding mode of updating. In addition, a demand burst was found approximately 1000ms following item onset at these later positions. These data establish that the process of active updating in running span task is slow and cognitively demanding and indicate that this limits its application during fast presentation rates.