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Biology subjects

Sudmann, S. S.

Publications and source records attributed to Sudmann, S. S..

2 recordsLinked to original sources

Decision-making processes in perceptual learning depend on effectors

Visual perceptual learning is traditionally thought to arise in visual cortex. However, typical perceptual learning tasks also involve systematic mapping of visual information onto motor actions. Because the motor system contains both effector-specific and effector-unspecific representations, the question arises whether visual perceptual learning is effector-specific itself, or not. Here, we study this question in an orientation discrimination task. Subjects learn to indicate their choices either with joystick movements or with manual reaches. After training, we challenge them to perform the same task with eye movements. We dissect the decision-making process using the drift diffusion model. We find that learning effects on the rate of evidence accumulation depend on effectors, albeit not fully. This suggests that during perceptual learning, visual information is mapped onto effector-specific integrators. Overlap of the populations of neurons encoding motor plans for these effectors may explain partial generalization. Taken together, visual perceptual learning is not limited to visual cortex, but also affects sensorimotor mapping at the interface of visual processing and decision making.

neuroscience↗

Pupil diameter tracks statistical structure in the environment

Pupil diameter determines how much light hits the retina, and thus, how much information is available for visual processing. This is regulated by a brainstem reflex pathway. Here, we investigate whether this pathway is under the control of internal models about the environment. If so, this would allow adjusting pupil dynamics to environmental statistics, and hence optimize information transmission. We manipulate environmental temporal statistics by presenting sequences of images that contain internal temporal structure to humans and macaque monkeys. We then measure whether the pupil tracks this structure not only at the rate that immediately arises from variations in luminance, but also at the rate of higher order statistics that are not available from luminance information alone. We find entrainment to environmental statistics in both species during the image sequences. Furthermore, pupil entrainment predicts later performance in an offline task that taps into the same internal models. Thus, the dynamics of the pupil are under control of internal models which adaptively match pupil diameter to the temporal structure of the environment, in line with an active sensing account.

neuroscience↗