Fig 1.
The motor chunking framework (mcf) description of expert performance as chunk accumulation.
In the novice, actions are preceded by cognitive-motor planning. As action sequences are unitized into chunks, planning is done at the beginning of sequences only, thus saving time on subsequent actions and producing the well-established timing pattern within chunks of slower first actions and faster subsequent actions. As expertise accumulates, chunks increase in size and number, taking up a larger and larger proportion of the actions produced, further speeding performance.
Fig 2.
Mean action latency by skill level.
Mean action latency by skill level, where 1 is the least skilled. This pattern of speeded expert performance is one of the empirical phenomena the MCF is attempting to explain.
Fig 3.
The number of chunks by skill level, where 1 is the least skilled. Each grey point is one game, and the shaded region shows the density of values. The black circle shows the mean value for each skill level.
Fig 4.
Proportions of actions chunked by skill.
The percentage of actions that fall within chunks by skill level.
Fig 5.
Randomness of sequences by league.
Sequence randomness is the number of unique sequences less the number of uniques sequences expected via random sampling. Smaller numbers thus reflect less sequence diversity (and more sequence repetition), and larger numbers reflect more diversity. See the text for further details.
Fig 6.
Number of total seconds saved by chunking by league.
This is calculated as the average of the duration of chunked sequences less the time for equivalent non-chunked actions for each game by skill level.
Fig 7.
First versus inter-action time savings.
Time saved by chunks (in seconds) split for first actions and subsequent actions for each skill level.
Fig 8.
Schematic illustration of how action sequences change with expertise in our data.
As skill is acquired, speed is gained through a general speedup of all actions.