This is an extremely powerful interface, but there’s no option to ‘skip’ part of the flow diagram – everything is run in the order in which it appears from left-to-right. The loops around parts of the flow panel indicate that the bits inside the loops are run multiple times (i.e. The experiment proceeds from left to right, and each part of the flow panel is executed in turn. One common use of conditional branching in psychology experiments is to repeat trials that the subject got incorrect for instance, one might want one’s subjects to achieve 90% correct on a block of trials before they continue to the next one, so the program would have something in it which said ‘if (correct trials > 90%) then continue to the next block, else if (correct trials < 90%) repeat the incorrect trials’.Īt the bottom of the PsychoPy builder is a time-line graphic (the ‘flow panel’) which shows the parts of the experiment: Essentially the program says, ‘if A is true: do X, otherwise (or ‘else’ in programming jargon) if B is true do Y’. which branch to follow) based on some value or ‘condition’. A ‘conditional branch’ is where the computer decides what to do out of two of more alternatives (i.e. In programming logic, a ‘branch’ is a point in a program which causes the computer to start executing a different set of instructions. The PsychoPy ‘builder’ interface (a generally brilliant, friendly, GUI front-end) does have one pretty substantial drawback though it doesn’t support conditional branching. I’ve been using it a lot recently, and I’m happy to report my initial ardour for it is still lambently undimmed. Taken together, these findings indicate that the power spectrum per se cannot explain the main behavioural signature of Weber-like encoding of numerosities (the ratio effect), at least over the tested numerical range, partially challenging alternative indirect accounts of numerosity processing.Regular readers will know that I’m a big fan of PsychoPy, which (for non-regular readers *tsk*) is a piece of free, open-source software for designing and programming experiments, built on the Python language. Moreover, this effect was found to be independent of the stimulus type, although the overall performance was better with the original rather than the SF equalised stimuli (Experiment 2). In both experiments, the results clearly showed a ratio-dependence of the performance: numerosity discrimination became harder and slower as the ratio between numerosities increased. ![]() In Experiment 2, participants performed the same task, but they were presented with both the original and SF equalised stimuli. In Experiment 1, participants had to select the numerically larger of two briefly presented moderate-range numerical sets (i.e., 8–18 dots) carefully matched for SF the ratio between numerosities was manipulated by levels of increasing difficulty (e.g., 0.66, 0.75, 0.8). Here, to disentangle these accounts, we tested whether the well-known behavioural signature of numerosity encoding (ratio effect) is preserved despite the equalisation of the SF content. Alternative accounts propose that, whatever the range, numerosity is indirectly derived from summary texture-statistics of the raw image such as spatial frequency (SF). Most evidence suggests that numerosity is directly extracted on individual objects following Weber’s law, at least for a moderate numerical range. How non-symbolic numerosity is visually extracted remains a matter of intense debate.
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