Fig. 1.

Stimuli and pRF modeling. (A) Example stimuli. Objects were placed either randomly or pseudorandomly to lie entirely within 0.75° of fixation. Using purely random placements, smaller objects can take larger steps between consecutive placements (variable step condition). Therefore, we introduced a condition where objects always made steps of the same length in random directions (constant step condition). These two conditions gave very similar responses. (B) pRF modeling procedure (9, 11). A candidate neural tuning model describes a tuning function of an fMRI recording site, characterized by a preferred object size, tuning width, and suppressive surround width. Convolving the tuning model’s response amplitude with the time course of presented object sizes and the hemodynamic response function (HRF) predicts the fMRI response for this tuning model. For each recording site, we find the best-fitting tuning model parameters by minimizing the squared difference between the predicted fMRI response and the recorded data. (C) Two example fMRI time courses from sites in right posterior parietal cortex, about 2 cm apart, elicited by the presented object size time course (Top). Points represent mean response amplitudes; error bars represent the SE over repeated runs. In the Upper panel, the largest responses occur after presentation of small objects, whereas in the Lower panel the largest responses occur for larger objects, considering the hemodynamic response delay. The tuning model predictions (colored lines) capture over 75% of the variance (R²) in the time courses. BOLD, blood oxygen level-dependent. (D) The tuning models that explain the most variance in each time course. The model describes a linear Gaussian tuning function with a suppressive surround, characterized by two parameters: preferred object size and tuning width summarized by the function’s full width at half maximum (FWHM). Different tuning model parameters explain the different responses seen in C, capturing similar amounts of variance. Dashed lines show the continuation of tuning functions outside the presented object size range. (E) Linear one-Gaussian object size tuning models explain more response variance in most recording sites than logarithmic tuning models. Goodness of fit is evaluated by twofold cross-validation. Comparisons of difference of Gaussian tuning models give similar results. (F) Linear DoG object size tuning models explain more response variance in most recording sites than linear one-Gaussian models.