As an AI language model, I can analyze the movie taste based on the given rating history. Here are some TASTE-REASON pairs for the user's movie preferences:

TASTE: Classic Comedy Lover
REASON: I rated movies like "Airplane!" (1980), "March of the Wooden Soldiers" (1934), and "Young Frankenstein" (1974) highly, indicating my fondness for classic comedy films.

TASTE: Adventure Sci-Fi Enthusiast
REASON: Movies such as "Star Wars: Episode VI - Return of the Jedi" (1983), "E.T. the Extra-Terrestrial" (1982), and "2001: A Space Odyssey" (1968) received high ratings from me, suggesting my preference for adventure and science fiction genres.

TASTE: Quirky and Offbeat Fan
REASON: I enjoyed movies like "Pee-wee's Big Adventure" (1985), "Beetlejuice" (1988), and "The Fifth Element" (1997), indicating my taste for quirky and offbeat films.

TASTE: Coming-of-Age Drama Admirer
REASON: Movies such as "Ferris Bueller's Day Off" (1986), "Parenthood" (1989), and "Boys Don't Cry" (1999) received positive ratings from me, suggesting my appreciation for coming-of-age dramas.

TASTE: Action and Thriller Fan
REASON: Films like "Grosse Pointe Blank" (1997), "Stargate" (1994), and "The Mask" (1994) were rated highly by me, indicating my inclination towards action and thriller genres.

Now, let's analyze the user's tendencies for high and low ratings:

HIGH RATINGS: The user tends to give high ratings (above 3) to classic comedies, adventure sci-fi films, quirky and offbeat movies, coming-of-age dramas, and action thrillers.

LOW RATINGS: The user has not given any low ratings (below 2) based on the provided rating history.

Please note that these conclusions are based on the given movie-rating history and may not represent the user's complete movie taste.