In the hit Netflix movie Happy Gilmore 2 (2025), Happy's golf doubles partner gets injured after falling on ice and he needs a substitute. Luckily, behind him is Shooter McGavin, his former golf rival. It only takes Happy a few words to ignite Shooter's passion for golf enough to get him to say yes. With Shooter's help, they both win the round. Happy's efforts to convince Shooter, although successful, included insults and jabs. Plus, Shooter seemed eager to participate either way. Happy's general effort to get Shooter to do what he wants is a typical demonstration of the Apperception attribute.
In 2010, YouTuber Ed Bassmaster uploaded a prank video where he was buying a car from an alleged stranger. Bassmaster repeatedly uses the phrase "just look at it" alongside other variations of the verb "to look." The repeated use of verbs related to visual sense perception emphasizes the Sensation attribute.
In season 6, episode 10 of the hit HBO show called Game of Thrones (2003), Cersei Lannister is about to execute a plan to blow up the location of her trial – and she's notably absent. Of all people present, only Margaery Tyrell knows what's really going on. Margaery interrupts the high priest and explains that Cersei's absence is not intentional, but calculated. Margaery infers there is danger and urges everyone to leave, even at the expense of her normally polite demeanor. Sure enough, the building soon exploded as Cersei watched in satisfaction. Margaery's ability to engage in 'mental time travel' is demonstrated with her explanation as to why Cersei was not there, as well as her urge to exit the premises. This dialogue and behavior highlights the Inference attribute, even if it was too late for her character.
In the hit poker movie Rounders (1998), soon-to-be dropout law school student Mike McDermott is facing a stressful poker game against Teddy KGB, a Russian mobster with his own poker club. Mike previously lost his funds to Teddy KGB and has loan sharks after him. During the final poker showdown between the two, Mike is folding good hands because he notices that Teddy KGB has him beat when he splits open and eats an Oreo cookie. This cue allowed Mike to dominate most of the hands until Teddy KGB figured it out. The outburst indicates that the Russian realized his own tell. In using phrases like "Lays down a monster. The f*** did you lay that down. Should have paid me off ...," Teddy vocalizes his own inability to use cues, logical reasoning, and predictive consideration. This is reinforced by the expletives. Teddy KGB's self-admitted mistake after speaking in a confident manner demonstrates a near-bottom example of the Inference attribute.
An ultra low attribute score is exceptionally rare because it represents 5% of the entire population. In a room with 100 other people, a person with an ultra low attribute score would be lower than 95 of them and higher than none of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.
Very Low
5–10% percentile
A very low attribute score is rare because it represents 5% of the entire population. In a room with 100 other people, a person with a very low attribute score would be higher than five of them and lower than 90 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.
Low
10–20% percentile
A low attribute score is somewhat uncommon and represents 10% of the entire population. In a room with 100 other people, a person with a low attribute score would be higher than ten of them and lower than 80 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.
Slightly Low
20–40% percentile
A slightly low attribute score is common and represents 20% of the entire population. In a room with 100 other people, a person with a slightly low attribute score would be higher than 20 of them and lower than 60 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.
Average
40–60% percentile
An average attribute score is typical and represents 20% of the entire population. In a room with 100 other people, a person with an average attribute score would be higher than 40 of them and lower than 40 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.
Slightly High
60–80% percentile
A slightly high attribute score is common and represents 20% of the entire population. In a room with 100 other people, a person with a slightly high attribute score would be higher than 60 of them and lower than 20 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.
High
80–90% percentile
A high attribute score is somewhat uncommon and represents 10% of the entire population. In a room with 100 other people, a person with a high attribute score would be higher than 80 of them and lower than 10 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.
Very High
90–95% percentile
A very high attribute score is rare because it represents 5% of the entire population. In a room with 100 other people, a person with a very high attribute score would be higher than 90 of them and lower than five of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.
Ultra High
95–100% percentile
An ultra high attribute score is exceptionally rare because it represents 5% of the entire population. In a room with 100 other people, a person with an ultra high attribute score would be higher than 95 of them and lower than none of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.