Change detection
In offline change point detection it is assumed that a sequence of length
Importantly, anomalous observations that differ from the ongoing behavior of the time series are not generally considered change points as long as the series returns to its previous behavior afterwards.
Basseville (1993, Section 2.6) discusses offline change-in-mean detection with hypothesis testing based on the works of Page[2] and Picard[3] and maximum-likelihood estimation of the change time, related to two-phase regression.
Other approaches employ clustering based on maximum likelihood estimation,[citation needed], use optimization to infer the number and times of changes,[4] via spectral analysis,[5] or singular spectrum analysis.
[6] Statistically speaking, change detection is often considered as a model selection problem.
Bayesian methods often quantify uncertainties of all sorts and answer questions hard to tackle by classical methods, such as what is the probability of having a change at a given time and what is the probability of the data having a certain number of changepoints.
[8] "Offline" approaches cannot be used on streaming data because they need to compare to statistics of the complete time series, and cannot react to changes in real-time but often provide a more accurate estimation of the change time and magnitude.
Additional research has found that focussing one's attention to the word that will be changed during the initial reading of the original sentence can improve detection.
This was shown using italicized text to focus attention, whereby the word that will be changing is italicized in the original sentence (Sanford, Sanford, Molle, & Emmott, 2006), as well as using clefting constructions such as "It was the tree that needed water."
These change-detection phenomena appear to be robust, even occurring cross-linguistically when bilinguals read the original sentence in their native language and the changed sentence in their second language (Kennette, Wurm & Van Havermaet, 2010).
Recently, researchers have detected word-level changes in semantics across time by computationally analyzing temporal corpora (for example: the word "gay" has acquired a new meaning over time) using change point detection.
Even though music is not a language, it is still written and people to comprehend its meaning which involves perception and attention, allowing change detection to be present.
When noticing one's appearance, change detection is vital, as faces are "dynamic" and can change in appearance due to different factors such as "lighting conditions, facial expressions, aging, and occlusion".
[15] Change detection algorithms use various techniques, such as "feature tracking, alignment, and normalization," to capture and compare different facial features and patterns across individuals in order to correctly identify people.
[16] The brain processes visual information from the eyes, compares it with previous knowledge stored in memory, and identifies differences between the two stimuli.
This process occurs rapidly and unconsciously, allowing individuals to respond to changing environments and make necessary adjustments to their behavior.
[19] With all three of these working together, change detection has a significantly increased success rate.
[19] It was previously believed that the posterior parietal cortex (PPC) played a role in enhancing change detection due to its focus on "sensory and task-related activity".
[20] Moreover, top-down processing plays an important role in change detection because it enables people to resort to background knowledge which then influences perception, which is also common in children.
Researchers have conducted a longitudinal study surrounding children's development and the change detection throughout infancy to adulthood.
[21] In this, it was found that change detection is stronger in young infants compared to older children, with top-down processing being a main contributor to this outcome.
