Fig. S1.

Stimuli varying in object size and numerosity over time yield different responses captured by different tuning models. (A) Two example fMRI time courses from sites in right posterior parietal cortex, about 2 cm apart, elicited by the presented object size sequence (Top). Points represent mean response amplitudes; error bars represent the SE over repeated runs. In the Upper panel, the largest response amplitude occurs after the presentation of small objects, whereas in the Lower panel the largest response occurs with larger objects, considering the hemodynamic response delay. The tuning model prediction captures much of the variance (R²) in the time courses, indicated by the colored lines. However, different tuning models capture different amounts of this variance. (B) Representation of the tuning models that best fit each time course. The best-fitting models describe linear Gaussian tuning functions with inhibitory surrounds. Tuning models describing other tuning functions perform less well, failing to capture features of the fMRI time courses in A. (C) fMRI time courses from the same two sites in A, elicited by the presented numerosity sequence (Top). Although these time courses are very different, the largest response amplitude in the Upper panel again occurs after the presentation of small numerosities, whereas in the Lower panel the largest response occurs with larger numerosities, considering the hemodynamic response delay. Again, tuning models capture much of the variance in the time courses, and different models capture different amounts of variance. (D) Representation of the tuning models that best fit each time course. The best-fitting models describe logarithmic Gaussian tuning functions. Tuning models describing linear tuning functions perform less well, failing to capture features of the fMRI time courses in C. Dashed lines show the continuation of tuning functions outside the presented object size range.