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In computer science, software transactional memory (STM) is a concurrency control mechanism analogous to database transactions for controlling access to shared memory in concurrent computing. It is an alternative to lock-based synchronization. STM is a strategy implemented in software, rather than as a hardware component.
Sensory memory (SM) allows individuals to retain impressions of sensory information after the original stimulus has ceased. [2] A common demonstration of SM is a child's ability to write letters and make circles by twirling a sparkler at night. When the sparkler is spun fast enough, it appears to leave a trail which forms a continuous image.
[2] Transactional memory is limited in that it requires a shared-memory abstraction. Although transactional memory programs cannot produce a deadlock, programs may still suffer from a livelock or resource starvation. For example, longer transactions may repeatedly revert in response to multiple smaller transactions, wasting both time and energy ...
Delos Wickens discovered that proactive interference build-up is released when there is a change to the category of items being learned, leading to increased processing in STM. [9] Presenting new skills later in practice can considerably reduce proactive interference desirable for participants to have the best opportunity to encode fresh new ...
The usefulness of the SAM model and in particular its model of the short-term store is often demonstrated by its application to the recency effect in free recall. When serial-position curves are applied to SAM, a strong recency effect is observed, but this effect is strongly diminished when a distractor, usually arithmetic, is placed in between ...
In a backward digit span task, the procedure is largely the same, except that subjects being tested are asked to recall the digits in backward order (e.g., if presented with the following string of numbers "1 5 9 2 3," the subject would be asked to recall the digits in reverse order; in the case, the correct response would be "3 2 9 5 1").
Long short-term memory (LSTM) [1] is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem [2] commonly encountered by traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models , and other sequence learning methods.
In his article, Miller discussed a coincidence between the limits of one-dimensional absolute judgment and the limits of short-term memory. In a one-dimensional absolute-judgment task, a person is presented with a number of stimuli that vary on one dimension (e.g., 10 different tones varying only in pitch) and responds to each stimulus with a corresponding response (learned before).