have now answered this question by using a brain scanner to measure receptive fields in healthy volunteers. But does the movement of receptive fields also depend on what we are attending to at a given location? Paying attention to tiny details, for example, might require many receptive fields to move by large amounts to produce vision with high enough resolution. Unlike a digital camera, the brain is thus much more than a passive recording device. Moving receptive fields in this way enables the visual system to generate more detailed vision at the new attended location. We do not need to move our eyes for this to happen, just the focus of our attention. If we switch our attention to a different area of the scene in front of us, visual neurons move their receptive fields to cover that area instead. But whereas digital pixels have fixed locations, the receptive fields of neurons do not. A neuron’s receptive field is the area of visual space – the scene in front of our eyes – to which that neuron responds. Much like digital cameras record images using a grid of tiny pixels, our own visual experience results from the activity of many neurons, each with its own receptive field. Our results demonstrate that the deployment of spatial attention is tailored to the spatial sampling properties of units that are sensitive to the attended feature. These findings are parsimoniously explained by variations in the precision of an attentional gain field. However, the interaction did depend on spatial sampling properties of voxels that prefer the attended feature. This interaction occurred similarly throughout visual cortex, regardless of an area's overall feature preference. Our results show that spatial and feature-based attention interacted: resampling of visual space depended on both the attended location and feature (color vs. We investigate this hypothesis by estimating spatial sampling in visual cortex while independently varying both feature-based and spatial attention. Behavioral studies suggest that feature-based attention modulates this resampling to optimize the attended feature's sampling. Note: you do not need to set pos=(0,0) and mask=None as that is the default value of these parameters.Spatial attention changes the sampling of visual space. Change max to min if you want this to apply to the minimum length instead of the maximum length. Multiplying maintains aspect ratio as above and the righthand side is the multiplication factor to ensure scale. Replace the last line above with this: faceRGB.size *= scale / max(faceRGB.size) Say you want to set the image size so that scale is the maximum length along either the x- og y-axis and scale the other axis proportionally. If you want to control size directly and not just proportionally, see this discussion on the users list. add one line after initiating the ImageStim: scale = 0.7įaceRGB = visual.ImageStim(win, 'male.jpg')įaceRGB.size *= scale # scale the image relative to initial size Scaling the image will preserve this aspect ratio, so that's an easy solution. If you don't change the size of the image, PsychoPy maintains the correct aspect ratio.

You may have a situation where you (1) change the size of the image and (2) the image just happens to have the correct dimensions when presented in the default 800 x 800 pixels Window but appears stretched when you go fullscreen because your monitor has another aspect ratio. By default, PsychoPy uses 'norm' as units, which is size normalized to the window dimensions.

Psychopy fixed aspect ratio gratings