1. Introduction to Concepts & Categories copy.docx

CRITICAL READING: CORNELL NOTES Introduction to Concepts & Categories Name: Date: 14 August 2023 Section: Lecture 1 Period: Questions/Main Ideas/Vocabulary Notes/Answers/Definitions/Examples/Sentences Input & Output Input: Masses of information from the environment. Output: R...

CRITICAL READING: CORNELL NOTES Introduction to Concepts & Categories Name: Date: 14 August 2023 Section: Lecture 1 Period: Questions/Main Ideas/Vocabulary Notes/Answers/Definitions/Examples/Sentences Input & Output Input: Masses of information from the environment. Output: Rational behaviour (mostly). Cognitive Processes We have no direct access to these processes. They are occurring within the ‘black box’ that is our brain. We can make inferences about these processes by forming theories or models of how they might work. We can test these theories or models empirically by doing research. This often leads us to modify our theory, which will lead to new experiments to test the theory and so on. Conceptual Representations It might seem trivial that we are able to categorise stimuli so easily. Nonetheless, it’s not entirely clear how we are able to do this as quickly and easily as we do. The basic assumption is that we have some sort of mental representation of the concepts cats and fish. We compare the stimuli to these conceptual representations and we categorise them accordingly. Presumably, this involves a number of interesting cognitive processes. It is worth noting that you don’t just have mental representations of cats and fish. We have a mental representation for each concept stored in our LTM. LTM Diagram The Classic View of Concept Formation Dominated our understanding of conceptual representation until the 1970s. Arose from philosophy rather than psychology. Based upon the premise that categories are defined by the presence or absence of specific properties. Categories are defined by necessary and sufficient features: Necessity: If any of these features are missing, it is definitely not a member of this category. sufficiency If all of them are present, then it’s definitely a member of this category. It therefore follows that: There are no in-between cases. All members are equal. Main claims of the classical view (false): Concepts are mentally represented as definitions. A definition provides characteristics that are necessary and jointly sufficient for membership of that category. Every object is either in or not in a given category, there are no in-between cases. Every member of a category is considered to be an equally representative member of the category as all other members of the category. Defining Properties Membership of the category squares is defined by the following properties: has four sides of equal length and all inner angles are equal. Membership of the category even numbers is defined by being divisible by 2 without a remainder. In order for a number to be even, it is necessary for the number to have this property. Furthermore, the presence of this property is sufficient evidence to classify a number as being even. Sometimes the properties aren’t immediately obvious but they can still be defined. Different colours can be defined by the length of their light waves. Objects with wavelengths in the range of 630 – 740 nm are red. Binary Category Membership Every object is either in or not in a given category, there are no in-between cases. Hampton (1979) presented participants with lists of potential numbers of 8 categories and asked them to rate their membership on a 7-point scale. Category membership ratings formed a continuum ranging from 0 to 100%. Some category members were always included or excluded, others were borderline cases. This result could be due to different people having different conceptual representations: If person A says a tomato is definitely a vegetable (100%), and person B says it definitely isn’t (0%), then on average they’re not sure. But McCloskey and Glucksberg (1978) demonstrated that even individuals change their inclusion criteria over time: Asked participants to make repeated category judgments (is an olive a fruit? Is chess a sport?). For 22% of the borderline cases, the participants changed their mind. Conclusion: Category membership isn’t binary. Equality of Category Members Intuitively, it seems that some category members are more typical or representative category members than others: A swallow seems like a more typical bird than a penguin. Rosch (1975) asked participants to rate the typicality of members of 10 different categories: The inter-rater reliability was extremely high (r = 0.97). This intuition seems to be consistent across individuals. McCloskey & Gluckberg (1978) found that category membership decisions could be predicted by typicality findings: Tables (mean typicality = 9.83): always furniture. Windows (mean typicality = 2.53): never furniture. Wastebaskets (mean typicality = 4.7): furniture 30% of the time. Rips, Shoben & Smith (1973) found categorisation decision times could be predicted by typicality: People are faster to confirm that a robin is a bird than a penguin. Battig & Montague (1969) asked participants to generate members of categories: Mervis, Caitin & Rosch (1976) later shows that generation frequency and typicality correlated around r = 0.63. Rips (1975) found that participants were influence by typicality when making inferences about category members: If a typical bird has a disease, then we are more likely to infer that other birds can catch the disease if an a-typical bird has the disease. Concepts Are Mentally Represented as Definitions Armstrong, Gleitman & Gleitman (1983) demonstrated that even classically definable categories show typicality effects: A square is a more typical polygon than an ellipse, a mother is a more typical female than a cowgirl and the number 3 is a more typical odd number than 91. An Alternative Approach – Family Resemblance Categories aren’t defined by necessary or sufficient features but by overlapping distributions of features. Members of a category come to be viewed as typical of the category as a whole in proportion to the extent to which they bear a family resemblance to other members of the category. Rosch & Mervis (1975) 20 exemplars (category members) from 6 different categories. One group of participants rated the typicality of the exemplars (how typical is X as a member of category Y). Another group generated features for the exemplars (X has the feature ‘something’). A third group rated the applicability of the features in relation to each of the exemplars (the feature ‘something’ is applicable to X, M, C, T, A and B). Family resemblance is a weighted sum of featural overlap amongst category members. Strong correlation between typicality and family resemblance for the categories furniture, vehicles, weapons, fruit, vegetables and clothing. The graded typicality structure that we see in the categories is well described by the featural overlap of the category members. Importantly, the features generated by the participants tended to apply to a small subset of the category members. Very few features appeared to apply to all of the members. None of the features could be construed as necessary and sufficient. Main Claims of the Classical View (True) Concepts are mentally represented as distributions of overlapping features or properties. Category boundaries aren’t clear cut; they’re fuzzy around the edges. Category membership doesn’t imply equality: some category members are more typical or ‘better’ category members than others.