List-length Effect

Details:

A smaller percentage of items are remembered in a longer list, but as the length of the list increases, the absolute number of items remembered increases as well. For example, consider a list of 30 items (“L30”) and a list of 100 items (“L100”). An individual may remember 15 items from L30, or 50%, whereas the individual may remember 40 items from L100, or 40%. Although the percent of L30 items remembered (50%) is greater than the percent of L100 (40%), more L100 items (40) are remembered than L30 items (15).

Datasets:

(Datasets labeled with this bias will go here.)

Structural Analysis:

(Results of dataset analysis for patterns in structure, placement, tone, context, coreference, correlatives, and so on will go here.)

Proposed Algorithms:

(Proposed combinations of factors from the above analyses in algorithmic form for automated detection will go here.)

Positive Detection:

(Algorithms for bias-positive detection will go here.)

False-positive Detection:

(Algorithms for false-positive detection/filtering of the above bias-positive algorithms will go here.)

Certainty Mapping:

(Algorithms for the probability mapping between N mathematical dimensions for positive and false-positive algorithms will go here.)

Successful Algorithms:

(Tested successful algorithms will go here, along with their respective accuracy metrics and error data for further refinement.)