I think you should not try to solve the problem yourself for an intelligent system. We have more than two classes - 1. Investigates, 2. Does not investigate. There are more subclasses: 1.1, 1.2, etc. "How it explores". And similarly there are subclasses: 2.1, 2.2., etc. "In what way does not investigate". Then everything is done automatically. The system will identify which features of the relative position of the rat and the object carry the most information about the belonging and non-belonging of the situation to a particular class. This problem can be solved in intelligent systems, in particular in the Eidos system developed by me.

*The Eidos-X++ system differs from other artificial intelligence systems in the following parameters:

*- it was developed in a universal setting, independent of the subject area. Therefore, it is universal and can be applied in many subject areas (http://lc.kubagro.ru/aidos/index.htm);

*- it is in full open free access (http://lc.kubagro.ru/aidos/_Aidos-X.htm) and has all the relevant source texts (http://lc.kubagro.ru/__AIDOS-X.txt);

*- it is one of the first domestic systems of artificial intelligence of the personal level, i.e. it does not take special training in the field of technologies of artificial intelligence from the user (there is an act of introduction of system "Eidos" in 1987) (http://lc.kubagro.ru/aidos/aidos02/PR-4.htm);

*- it provides stable identification in a comparable form of strength and direction of cause-effect relationships in incomplete noisy interdependent (nonlinear) data of very large dimension of numerical and non-numerical nature, measured in different types of scales (nominal, ordinal and numerical) and in different units of measurement (i.e. does not impose strict requirements to the data that cannot be performed, and processes the data that can);

*- it contains a large number of local (supplied with the installation) and cloud educational and scientific applications (currently 31 and 252 (http://aidos.byethost5.com/Source_data_applications/WebAppls.htm), respectively) (http://lc.kubagro.ru/aidos/Presentation_Aidos-online.pdf);

*- it supports on-line environment of knowledge accumulation and is widely used all over the world (http://aidos.byethost5.com/map5.php);

*- it provides multilingual interface support in 51 languages. The language databases are included in the installation and can be replenished automatically;

*- the most time-consuming, computationally, are the operations of the synthesis models and implements recognition using graphic processing unit (GPU) where some tasks can only support up to several thousand times; the solution of these tasks is intelligent processing of big data, big information and big knowledge;

*- it provides transformation of the initial empirical data into information, and its knowledge and solution using this knowledge of classification problems, decision support and research of the subject area by studying its system-cognitive model, generating a very large number of tabular and graphical output forms (development of cognitive graphics), many of which have no analogues in other systems (examples of forms can be found in: http://lc.kubagro.ru/aidos/aidos18_LLS/aidos18_LLS.pdf);

*- it well imitates the human style of thinking: gives the results of the analysis, understandable to experts according to their experience, intuition and professional competence.

*- instead of making almost impossible demands on the source data (such as the normality of distribution, absolute accuracy and complete repetitions of all combinations of factor values and their complete independence and additivity), the automated system-cognitive analysis (ASC-analysis) offers to process this data without any preliminary processing and thereby transform it into information, and then transform this information into knowledge by applying it to achieve goals (i.e. for the management) and solving problems of classification, decision support, and meaningful empirical research of the domain being modeled.

*What is the strength of the approach implemented in Eidos system? The strength is implementing an approach whose effectiveness does not depend on what we think about the subject area or whether we think at all. It generates models directly based on empirical data, rather than based on our understanding of the mechanisms for implementing patterns in this data. This is why Eidos models are effective, even if our understanding of the subject area is incorrect or totally absent.

*And this as well is the weakness of this approach implemented in Eidos system. Models of the Eidos system are phenomenological models, i.e. they do not reflect the mechanisms of determination, but only the fact and nature of determination.

In the case of rats or let's say other organisms, thermal heating technology should be in place. Matter and Dark matter particle accelerators should also be considered because how do we know the images are not photoshopped and not real, we could go on a wild goose chase with images that some joker created on paintbrush app.

It is by opposing intent that we prove its existence. (That comes obliquely here from security operations which have a duty through unambiguous warnings or non-lethal opposition to prove hostile intent in challengers before the use of force).

So, if you dangle the object above the ground but in the limits of reach, then your subjects prove their exploratory intent by both stretching up and sniffing. All sniffers are then more obviously explorers, and hiders and recliners are ruled out.

More generally, if the subjects had to overcome discomfort or fear in order to venture ever closer to an object, then it's much more likely that their intent is to explore. The greater or more numerous the obstacles, the greater the intent.