Sentence examples for memory items from inspiring English sources
Theoretical results are presented for storage/retrieval of memory items represented by feature vectors made up of 1000 randomly selected bivalent components.
Our modeling approach is based on the idea that uncompleted or partially completed intentions are available as context in the current goal, and they prime related memory items while inhibiting unrelated memory items.
The cognitive neuroscience of human memory tries to comprehend how we encode, store, and retrieve memory items within and across those systems.
However, once the intended tasks are completed, they are removed from the current goal, which produces an inhibitory effect on memory items associated with them.
Sparse distributed memory (SDM) is a content-addressable memory technique that relies on close memory items tending to be clustered together.
In our model overlapping random configurations of sparse cell populations correspond to memory items that are stored by simple Hebbian coincidence learning.
That is, the times to retrieve memory items related to completed intentions are slower than for those with no associated intentions.
They had subjects perform the same cued memory task, then applied a broad TMS pulse just after the signature of the uncued memory item had faded.
Memory was evaluated through its components: working memory (item 1&2), episodic memory (item 3 though 9) and semantic memory (item 10&11).
Thus, the visual features of the memory item, and not the semantic association between the memory item and the second exemplar, may have been what biased attention.
On neutral trials, the search items were unrelated to the memory item.
