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People who have no experience with programming can create informal programs to rearrange the order of cars in trains. To find out whether they rely on kinematic mental simulations, the current studies examined participants’ eye movements in two experiments in which participants performed various moves and rearrangements on a railway consisting of a main track running from left to right and a siding entered from and exited to the left track. In Experiment 1, they had to imagine different sorts of single moves of cars on the railway. The sequences of their fixations resembled iconic gestures: they tended to look at the starting location of the imagined move, and then at its final location. In Experiment 2, the task was to create descriptions of how to solve four sorts of rearrangements that differ in their Kolmogorov complexity. It predicted the time to find the correct solution and the relative number and duration of fixations recorded during the description of each move for rearrangements of different complexity. Participants were more likely to fixate on the symbols on the cars than anything else, and they fixated longer when the rearrangement was more difficult. They also tended to fixate regions of the tracks where a car’s movement began or ended, as if they were imagining a car moving along the tracks. The results suggest that humans rely on a kinematic mental simulation when creating informal algorithms.

Introduction: This study examines the consistency between subjective similarity evaluations and the theoretical predictions derived from Tversky's ratio model of similarity, alongside the impact of additional positive and negative features on perceived similarity to ideal and bad politicians. Methods: Using a sample of 120 participants, we assessed the similarity of eight candidate profiles to an ideal and bad politician, varying in positive and negative features. Participants' subjective evaluations were compared with theoretical predictions derived from Tversky's ratio model. The analysis focused on how candidate and referent valence influenced observed versus theoretical similarity. Results: Subjective similarity judgments deviated systematically from theoretical predictions, especially for positively featured candidates, indicating a negativity effect. Additional positive features decreased the perceived similarity of favorable candidates to an ideal politician, while additional negative features did not significantly affect similarity judgments of unfavorable candidates. Discussion: Our findings underscore a significant disparity between subjective and objective similarity judgments, notably for favorable candidates. While the ratio model performs well for unfavorable candidates, its applicability diminishes for favorable ones, emphasizing the role of feature valence in decisionmaking. Further research on feature valence is crucial for a comprehensive understanding across contexts. [ABSTRACT FROM AUTHOR]

Recursive loops in informal algorithms are difficult to formulate, even for na ̈ıve adults (Khemlani et al., 2013).Children can formulate algorithms that do not require loops (Bucciarelli et al., 2016), and anecdotal evidence suggests thatthey can understand loops. As there were no previous studies, we examined how they made deductions of the consequencesof loops, and how they abduced loops in creating informal algorithms in everyday language. We therefore tested fifth-gradechildren’s ability carry out both these tasks in algorithms that rearrange the order of cars on a toy railway track with onesiding. Experiment 1 showed that they could deduce rearrangements from algorithms containing loops, and Experiment 2showed that they could formulate at least some algorithms that contained loops. These abilities are the likely precursors to thecomprehension of recursion and to computer programming.