Perceptual Effects of New Category Learning: An Online Study M. Gregory1, D. Rivas2, C. Prévost3, H. Sabri4, F. Pérez Gay Juárez5, S. Harnad6 Departments of Cognitive Science1, Neuroscience5, and Psychology6, McGill University. Laboratoire de Communication et Cognition, UQAM, 1, 2, 3, 4, 5, 7. Background Introduction Methods Stimuli Categorization and Categorical Perception Difficulty 1 Difficulty 4 5 co-varying microcomponent pairs 0 random microcomponent pairs 2 co-varying microcomponent pairs 3 random microcomponent pairs In order to interact with the world, humans and other animals have developed the ability to sort objects and situations that surround them into categories. Categories are kinds of things; we behave in different way when we encounter objects or situations that pertain to different categories. Immediate Learner: Subject who learned within the first 20 trials . Compression and Separation K L K L Available microcomponent pairs: Tasks Other examples of innate categories are phonemes or facial expressions. But in fact, most of our categories are learned through experience and/or verbal instruction. So, how does our brain learn all these new categories? 1. Similarity judgment - 40 trials Our brain must develop a filter that will selectively highlight the invariant features and ignore the features that are not relevant for categorizing. This produces what we refer to as Categorical Perception (CP). Both compression and separation observed in learners. Separation observed in non-learners can be explained by the mere exposure effect 2. Categorization with feedback - 4 blocks (100 trials each) -Short pause between blocks in which participants are asked whether or not they have learned and to report the strategy they are using 500 ms 1.25 s 750 ms CP is produced when physical differences of the same size within a category are perceived as smaller than when they cross the boundary between categories. These perceptual changes are referred to as compression and separation. Compression is when stimuli within a category appear more similar and separation is when stimuli of different categories appear more different. 3. Second similarity judgment Colours are an example of innate categorical perception. This is when the brain can detect some categories by inborn mechanisms in our brains or peripheral sensory organs. Sliding bar used during similarity judgments While separation across difficulties showed a positive linear trend, compression appeared to be non-linear and especially low for difficulty 2 (somewhat easy). Learning curves -We divided our subjects in Immediate Learners, Learners, Non-Learners and Borderlines (attained 80% correct answers at least once but did not maintain it) - Learning criterion: reach and maintain 80% correct answers So when we look at the visible spectrum, shades of green are perceived as more similar (compression), and shades of blue and green are perceived as more different (separation), even though there may be a greater physical difference between the two green shades than the green and the blue shades. Thanks to Color CP, we perceive the rainbow as qualitative "bands" of different colors, instead of perceiving a continuum gradual change. Given than most of our categories are learned rather than innate, we are interested in studying if CP effects can be induced by learning a new category. Compression observed in learners and immediate learners, separation observed in all four groups. Results Categorization Task Experiment was run on 57 subjects , 37 of them were successful in learning the categories, 4 were borderline. There were 4 difficulties: 1 (easy), 2 (somewhat easy), 3 (somewhat difficult) and 4 (difficult) Conclusions Our computer-generated stimuli appear to appropriately reflect the difficulty level: more subjects learnt the easier difficulties in less trials. Reaction time tends to decrease across trials (regardless of difficulty). Learners spend more time on each trial. With our stimuli, we obtained significant effects of compression and separation in learners (separation was more significant than compression) according to the similarity judgments. We also obtained a less significant effect of separation in non-learners (mere exposure effect) Difficulty # participants %Successful Subjects %Unsuccessful Subjects %Borderline #Trials to learn (average for Learners) 1 15 93.4 6.7 57.79 2 13 84.6 15.4 125.29 3 16 56.25 25 18.75 193.33 4 53.8 38.5 7.7 193.71 Objectives To train human subjects to categorize non-familiar stimuli (black and white textures) through trial and error with corrective feedback. To observe the behavioural effects of categorical perception as demonstrated through the results of similarity judgement tasks before and after the training task. To contribute more data to the main ongoing project at the Laboratoire de Communication et Cognition (PhD Thesis of Fernanda Perez Gay Juarez), which shares the above mentioned objectives, as well as additional additional objectives pertaining to electrophysiological (EEG) data. To create a website platform that hosts online psychological experiments. **If you are interested in collaborating with us to put your experiment online, please don’t hesitate to contact us at cognitionlabmtl@gmail.com. As well, feel free to check the website platform at https://cogcommtl.ca/ References -Kay, P., & Kempton, W. (1984). What is the Sapir‐Whorf hypothesis?. American Anthropologist, 86(1), 65-79. -Categorical perception: The groundwork of cognition, Cambridge University Press, New York (1987). 10-28 pp. -Kaiser, P. K., & Boynton, R. M. (1996). Human color vision. http://philpapers.org -Berlin B & Kay P (1969) Basic color terms: Their universality and evolution. University of California Press, BerkrleyRichler, J. J., & Palmeri, T. J. (2014, January 26). Visual category learning. Wiley Interdisciplinary Reviews: Cognitive Science. doi:10.1002/wcs.1268 -Harnad, Stevan (2005) To Cognize is to Categorize: Cognition is Categorization. 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