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Experiments on blue jays suggest they form a search image for certain prey.. Visual predators may form what is termed a search image of certain prey.. Predators need not locate their host directly: Kestrels, for instance, are able to detect the faeces and urine of their prey (which reflect ultraviolet), allowing them to identify areas where there are large numbers of voles, for example.
The neural basis of prey detection, recognition, and orientation was studied in depth by Jörg-Peter Ewert in a series of experiments that made the toad visual system a model system in neuroethology (neural basis of natural behavior). He began by observing the natural prey catching behavior of the common European toad (Bufo bufo).
The psychology scholar Robert Mitchell identifies four levels of deception in animals. At the first level, as with protective mimicry like false eyespots and camouflage, the action or display is inbuilt. At the second level, an animal performs a programmed act of behaviour, as when a prey animal feigns death to avoid being eaten.
Thus, prey feature detection is not an all-or-nothing condition, but rather a matter of degree: the greater an object's releasing value as a prey stimulus, the stronger is prey-selective T5.2 neuron's discharge frequency, the shorter is toad's prey-catching response latency, and the higher is the number of prey-catching responses during a ...
Tinbergen suggested that this prey selection was caused by an attentional bias that improved detection of one type of insect while suppressing detection of others. This "attentional priming" is commonly said to result from a pretrial activation of a mental representation of the attended object, which Tinbergen called a "searching image".
Detection theory or signal detection theory is a means to measure the ability to differentiate between information-bearing patterns (called stimulus in living organisms, signal in machines) and random patterns that distract from the information (called noise, consisting of background stimuli and random activity of the detection machine and of the nervous system of the operator).
Furthermore, researchers involved in exploring learning algorithms for neural networks are gradually uncovering generic principles that allow a learning machine to be successful. For example, Bengio and LeCun (2007) wrote an article regarding local vs non-local learning, as well as shallow vs deep architecture. [231]
Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.