Do Python and Java lead students to construct different mental models of memory? How are novices' mental models of variable storage affected by programming language choices? Most Intro to CS teachers (I think) do not delve with breadth and depth into details of how variable names are tied to data and datatype. As a result, some students create pieces of their own mental model of how the computer is storing these variable names, data, and data type. 

What mental models of memory storage in general (and for sequences in particular) are typical in students who learn Python first vs. students who learn another language first- one that treats arrays more like C does?

Background

Several single-university studies (e.g. Jayal et al. 2015) have found that a post-secondary sequence that starts out with Python and then transitions to Java performs as well as a sequence that starts in Java. Another question asks how to optimize that transition.

Python has particular elements that do not correspond well to Java, however. Negative indices (with which a=[1, 2, 4, 6] means that a[-1] is 6) and slices (with which a[1:3] is [2, 4]) are commonly found in code students stumble upon on the Internet, making it somewhat useful to mention these language features when teaching about lists and strings.

Python's native types don't include a type in which pointer arithmetic can be exploited for speed, though the numpy library offers an array. I suspect that when students learn about Java arrays, they probably build a mental model that fairly closely matches how arrays are stored in memory in C. Python does not actually store lists in a sequence of equally sized memory blocks; [3, 'a', 5.2] is a perfectly valid list. 

Does the mixed-type sequence or slicing or negative indexing in Python change a student's initial mental model for lists, arrays, and other sequences?
I am not aware that cognitive modeling of memory structures in early programming education has been directly studied, so anything that I say here is entirely speculative.  However, I suspect that the models themselves would somewhat depend on teacher presentation, and would otherwise be relatively stable.

Take negative indexing as an example.  A reasonable initial cognitive model for a list would be some sort of cycle.  If it is later explained as an array with indexing performed with a modular operation, then we have moved our conception to some sort of line.  When we even later explain about memory references and data size, then we can finish the picture as an array (line) of fixed size memory references, each pointing to a different object in the heap.

There are only so many simple physical shapes that we can use to represent these ideas.  Which actual conceptualization a student takes will depend on their maturity in the subject matter and on how the data structure is presented by the instructor.  I don't see any reason to believe that the initial choice of language would have any substantive, lasting impact on these cognitive models as the student matures in their understanding.