Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model
Fig 4
Monkey and human performance on non-terminal stimulus pairs.
Trial zero is the point of transfer from adjacent-pair to all-pair trials. (A) Smoothed response accuracy for three rhesus macaques, divided into pairs with ordinal distance one (BC, CD, DE, and EF; red), two (BD, DE, and CF; orange), three (BE and DF; green), and four (BF; blue). Subjects show an immediate distance effect from the first transfer trial. (B) Simulated performance using betasort, using each monkey’s maximum-likelihood model parameters for each session. Hypothetical performance is plotted for all distances at all times, to show how the algorithm would respond had it been presented with trials of each type. Like the monkeys, the algorithm displays an immediate distance effect. (C) Simulated performance using betaQ, with maximum-likelihood parameters. Although a small distance effect is observed, performance remains close to chance throughout training. (D) Simulated performance using Q/softmax. Performance remains strictly at chance throughout adjacent-pair training, and only begins to display a distance effect after the onset of the all-pairs trials. (E) Performance of human participants given 36 trials of adjacent-pair training, followed by 90 trials of non-adjacent pairs only, and finally 42 trials of all pairs. Unlike the monkeys, participants rapidly acquire the adjacent pairs, and show only a mild distance effect at transfer. (F-H) Simulations based on human performance using the three algorithms, analogous to panels B through D. As in the monkey case, Q/softmax displays no distance effect at all until non-adjacent pairs are presented.