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Below the figure, state that the figure shows your own data. State whether your data show expected results or not with a reason. In case your data do not show the expected results, speculate why you could not produce reasonable discrimination data. the global summary data. If you don't see a list, it means you haven't completed any labs yet. Categorical Perception Discrimination Debriefing You have successfully submitted this activity. What methods did we employ in this experiment? You heard two sounds and were asked to judge whether they sounded the same or different. A positive score indicates more "Same" responses; a negative score indicates more "Different" responses. What do we predict participants will do? Why? opposite sides of the boundary (especially comparisons between stimuli 4 and 6 ), you should have a negative discrimination score (more "Different" responses). How robust is this effect? Are there limits to this effect? Data as of 2023-10-30 16:30:23 Pacific Daylight Time perceived as the same (and have high positive scores) whereas comparisons involving tokens from opposite sides of the category boundary will be perceived as different (and will have scores below 0 ). 1. Describe the expected results of the CogLab experiment with the reason. You would need to explain VOT, how the nine original stimuli (BA1 - BA9) were created, how the seven pairs were constructed, and how the discrimination score is calculated in the process of describing the reason. Pay attention to the borderline VOT between /ba/ and /pa/. Please write in sentences a you would in APA format. Global Data Below the figure, state that the figure shows the global data. State whether the global data show expected results or not with a reason. The table shows the discrimination score for each comparison. If you said 'Same', you get +1 added to your discrimination score; if you said 'Different' you got 1 subtracted from your score. A discrimination score of 0 means you gave the same number of 'Same' and 'Different' responses. A positive number means you gave more 'Same' responses; a negative number means you gave more 'Different' responses. The expected result is that comparisons involving tokens on the same side of the category boundary will be perceived as the same (and have high positive scores) whereas comparisons involving tokens from opposite sides of the category boundary will be perceived as different (and will have scores below 0 )