Source code understandability is a desirable quality factor affecting long-term code maintenance. Understandability of source code can be assessed in a variety of ways, including subjective evaluation of code fragments (perceived understandability), correctness, and response time to tasks performed. It can also be assessed using various source code metrics, such as cyclomatic complexity or cognitive complexity. Programming languages are evolving, giving programmers new ways to do the same things, e.g., iterating over collections. Functional solutions (lambda expressions and streams) are added to typical imperative constructs like iterators or for-each statements. This research aims to check if there is a correlation between perceived understandability, understandability measured by task correctness, and predicted by source code metrics for typical tasks that require iteration over collections implemented in Java. The answer is based on the results of an experiment. The experiment involved 99 participants of varying ages, declared Java knowledge and seniority measured in years. Functional code was perceived as the most understandable, but only in one case, the subjective assessment was confirmed by the correctness of answers. In two examples with the highest perceived understandability, streams received the worst correctness scores. Cognitive complexity and McCabe's complexity had the lowest values in all tasks for the functional approach, but - unfortunately - they did not correlate with answer correctness. The main finding is that the functional approach to collection manipulation is the best choice for the filter-map-reduce idiom and its alternatives (e.g., filter-only). It should not be used in more complex tasks, especially those with higher complexity metrics.