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A brief version of: “Microstimulation in Visual Area MT: Effects of Direction Discrimination Performance” C.D. Salzman et al. 1992. The Journal of Neuroscience, 12, 2331 – 2355. Updated 11th October, 2007 Aim To investigate the relationship between behavioural responses in psychophysical tasks and neuronal responses by attempting to influence a monkey’s choice on a direction discrimination task by directly stimulating specific MT neurons. The theory: The direct activity of a specific cluster of MT neurons determines a direction judgment, therefore, if the appropriate cluster of neurons are stimulated, judgments will be made accordingly (even if these judgments are incorrect). Method The monkey was trained, using operant conditioning techniques (reward with juice) over a period of several months to identify the direction of motion from a random dot kinematogram (RDK). A RDK is a psychophysical stimulus which consists of a patch of moving dots. Some of the dots move in a random direction (noise dots) while others move coherently in one particular direction. The more dots that move coherently together, the stronger the perception of motion is and the easier it is to identify the direction of motion. The monkeys were trained to identify the direction of motion from these stimuli, and to make their response by moving their eyes in the direction of the coherent motion. Surgical preparation Area MT was identified on the basis of its characteristic location within the superior temporal sulcus, its preponderance of responsive, directionally selective neurons and its characteristic topography. Surgery was performed so that micro-electrodes could be inserted into the cortex. These micro-electrodes emitted a stimulating pulse of 10 µA in amplitude and 200 Hz (this frequency was thought to match the responses observed from single-cell recording). As the goal of the study was to stimulate neurons with a preferred direction selectivity, initial measurements were made to identify appropriate clusters of neurons. Behavioural paradigm Monkeys performed direction detection tasks using RDKs. On some trials, the specific neurons were stimulated (by applying the stimulating pulse); other trials were completed without stimulation. Performance (number of trials in which the direction of motion is correctly identified) was then compared between trials where no microstimulation takes place and trials where micro-stimulation does take place. Results The predominant effect of micro-stimulation was to bias the monkey’s decisions in favour of the direction preferred by the neurons at the stimulation site. The data therefore established a causal relationship between the activity of the stimulated neurons and perceptual judgments of motion direction. This result provides direct support for the linking hypothesis associating direction selectivity with motion perception and demonstrates a major functional role of the “motion pathway” within visual cortex. The figure illustrates that the monkey makes more judgments that match the preferred direction of the stimulated neurons when the cortex is stimulated than when it is not. Monkey J Monkey E Monkey E Null Direction 0% Preferred Direction Monkey J Null Direction 0% Preferred Direction Conclusion These data establish a causal relationship between the activity of stimulated neurons in area MT, and perceptual judgments of motion direction. Further points to note: The paper reports a series of experiments carried out on 3 separate monkeys. In total over 100 separate experiments were carried out. The results were comparable, so this paper presents a selection of the data. An important goal of the methodology was to activate selectively neurons that had a similar preferred direction (e.g. to the right). Therefore the experimenters positioned the electrode in the middle of a cluster of neurons that had previously been identified as having similar response properties such as preferred directions, preferred speeds and receptive field locations (i.e. it is not a single neuron that is being stimulated, but a cluster of neurons with similar turning curves). The paradigm presented RDKs which varied in correlation strength. The highest correlation was less than 100% and was set so that the monkeys made about 80 – 90% correct responses in the un-stimulated conditions. Importantly, in 50% of the trials the direction of motion in the RDK was the same as the preferred direction of the cell (right). However in the other half of the trials, the direction of motion in the RDK was in the opposite, or null, direction (left). That is why the behavioural data in the plots above show less than chance performance, and why the dependent variable is proportion of judgments in the preferred direction rather than proportion of correct responses. For example, in figure A below, when the stimulus is highly correlated, but moving in the null direction, as depicted by the open circle in the bottom left corner of the plot, the monkey is actually making 100% correct responses as on every trial they are responding toward the direction of the preferred cell (which, in our example, is right). This relates to the point that Adam made and clarifies why the behavioural results are described as being biased towards the preferred direction, rather than necessarily being correct. The line fitted through the data points is the logistic regression function describing the monkey’s performance. The plots show different effect sizes of micro-stimulation. The site at which the stimulation took place is not really the key factor here. On one hand the plots show a range of results from the many different experiments that were carried out (and note the results are from two of the monkeys, monkey J and monkey E). Although not very clearly presented, the plots also show results from experiments which ranged in difficulty of the motion task. Task difficulty depended on several variables: eccentricity of motion display, the size of the aperture it was presented through & the speed of the motion stimulus. When the monkey makes less correct responses the slope of the psychometric function appears flatter. Therefore, plot D presents data from a hard condition. For more details on the methodology I suggest you consult the primary source. The key details summarised above are presented on pages 2332 to 2336.