Comparison of monkey performance and AlexNet

Whiskers represent 99% credible intervals for the estimates, while shaded intervals represent 80% credible intervals. <b>Top.</b> Image classifications for each of the five categories by AlexNet, given the outputs at each of its seven convolutional layers. Each block assumes that a stimulus of the type given by the title has been presented, and cumulative proportions of the five classified categories are (plotted from bottom to top) birds (light red), cats (dark blue), flowers (yellow), people (dark purple), and hoofstock (light green). The proportion of correct classifications is also inscribed in the relevant block. <b>Bottom Left.</b> Subjects’ response accuracy for the pair AE at the end of each phase. “Full model fit” estimates estimate performance based on all trials across all phases. “Trial only” estimates are based on the proportion of correct responses to AE during the last session of each phase. <b>Bottom Right.</b> AlexNet classification accuracy in forced two-item classification as a function of the number of convolutional layers used. The uncertainty in the estimates arises across multiple simulations using different training and validation sets of stimuli

Response Data And Analytic Script For Categorical TI Experiment

Raw data for two monkeys performing a categorical transitive inference task, as well as the R script used to analyze the data

Distance effect parameter in a categorical transitive inference task (revised)

Whiskers represent 99% credible intervals for the estimates, while shaded intervals represent 80% credible intervals. <b>Left. </b>Session-by-session of the “distance effect on trial zero” parameter in the logistic regression analysis of performance during all-pairs sessions, averaged across subjects. Since parameters are measured in log-odds units, no distance effect at transfer would correspond to a parameter value of 0.0. <b>Right.</b> Proportion of correct responses for the critical test pair BD on its first presentation. “Trials only” estimates are based only on the first two BD presentations in each phase. “BD model fit” estimates are based on the intercept of a logistic regression of response accuracy that uses only BD trials. “Full model fit” estimates predict BD accuracy using all trials and their symbolic distances. Performance above chance indicates that transitive inference has occurred

Time-series analysis of transitive inference performance using changing categorical stimuli

Time series analysis of task performance, divided by symbolic distance, averaged across subjects. All sessions presented adjacent pairs (black), but only all-pairs sessions included symbolic distance of 2 (red), 3 (blue), and 4 (green). Discontinuities correspond to gaps between sessions. Shaded regions represent the 99% credible interval of the estimate

Procedure for Categorical Transitive Inference

<b>Left.</b> Trial structure for any single trial of the task. Subjects must touch a blue square to begin the trial, which is immediately replaced by two images. If a correct response is made, subjects see a green check mark and are immediately given a fluid reward. If an incorrect response is made, subjects see a red X, followed by a black screen for 2 seconds. Following feedback, the next trial begins with the start stimulus. <b>Middle.</b> Each phase of the experiment made use of a consistent category sequence (in this case, birds-cats-flowers-people-hooved). The stimuli themselves, however, were drawn at random from the image back during every trial. During adjacent-pair trial (using only AB, BC, CD, and DE), the identity of the stimulus changed for every trial, even when the same category appeared in two consecutive trials. The left-right position of stimuli was also counterbalanced. This was also the case during all-pairs sessions, which intermixed all possible stimulus pairings. <b>Right.</b> Two exemplars each from the five stimulus categories used in the experiment. In all categories, an effort was made to include category members from multiple distances and angles, with a mixture of both solitary and group photos, as well as both color and black-and-white. This stimulus diversity was intended to reduce subjects’ reliance on specific discrete features as categories cues

Session-by-session performance in reward gradient transitive inference transfer testing

Population-level estimates of session-by-session performance in Experiment 2. Shaded regions correspond to the 95% credible interval. <b>(Top)</b> Estimated response accuracy for an average subjects, presented on a logit scale. Empirical means for each subject are also provided. <b>(Bottom)</b> Estimated population parameters for the distance parameter (<i>β_D</i>) in blue and the reward parameter (<i>β_R</i>) in red

Session-by-session performance for reward gradient transitive inference

Population-level estimates of session-by-session performance in Experiment 1. Shaded regions correspond to the 95% credible interval. <b>(Top) </b>Estimated response accuracy for an average subjects, presented on a logit scale. <b>(Bottom)</b> Estimated population parameters for the distance parameter (<i>β_D</i>) in blue and the reward parameter (<i>β_R</i>) in red

Data & analysis script for "Reward associations do not explain transitive inference performance in monkeys" preprint

Event counts for Experiments 1 and 2, as well as the R script that analyzes the data using Stan