The Influence of Knowledge and Expectations for Color on Episodic Memory

The Influence of Knowledge and Expectations for Color on Episodic Memory Kimele Persaud (Kimele.persaud@rutgers.edu) Pernille Hemmer (Pernille.hemmer@rutgers.edu) Department of Psychology, Rutgers University Abstract Expectations learned from our environment are known to exert strong influences on episodic memory. Furthermore, people have prior expectations for universal color labels and their associated hue space—a salient property of the environment. In three experiments, we assessed peoples’ color naming preferences, and expectation for color. Using a novel experimental paradigm, we then assessed free recall for color. We found that people’s color naming preferences were consistent with the universal color terms (Berlin & Kay, 1969), as well as a strong subjective agreement on the hue values associated with these color labels. We further found that free recall for color was biased towards the mean hue value for each preferred color. We modeled this relationship between prior expectation and episodic memory with a rational model under the simple assumption that people combine expectations for color with noisy memory representations. This model provided a strong qualitative fit to the data. Keywords: Episodic memory; color; prior knowledge and expectations; Bayesian models. Introduction “Mere color, unspoiled by meaning, and unallied with definite form, can speak to the soul in a thousand different ways.” ― Oscar Wilde Color is fundamental to how we identify, define, and organize the world around us. As such, color stands as an essential feature in many facets of society, ranging from the identification of individuals of a racial or ethnic group to the facilitation of simple communication among people. Similarly, colors also serve to represent and unite members of social groups as illustrated by the colors of a country’s flag and the colors of sports teams. Color not only holds cultural relevance, but is also an invaluable domain for investigating human cognition. Color has been employed in efforts to understand cognition ranging from modelling the emergence of language universals as a function of learning biases (Xu, Griffiths, & Dowman, 2010), to measuring the influence of categorization on perception (Webster & Kay, 2012). For example, color naming has been used to understand the perceptual commonalities among different languages and individuals of different cultures (e.g., Davies & Corbett, 1997; Xu, Griffiths, & Dowman, 2010). An important finding from cross cultural studies of the universality of basic color terms is the existence of 11 basic colors: white, black, brown, gray, red, orange, yellow, green, blue, purple, and pink (Berlin & Kay, 1969). Additional support for the 11 universal color terms comes from studies of perception (Hardin, 2005; Uchikawa & Shinoda, 1996; Webster & Kay, 2012). These terms can be utilized to adequately partition the color space into 11 regions (Uchikawa & Shinoda, 1996), and discretize this space into small sets for categorization and cognitive processing (Webster & Kay, 2012). Taken together, cognitive studies of color naming and color perception suggest that people have clear knowledge and expectations for colors. Across a broad range of domains, expectations learned from the underlying environment have been shown to influence performance on cognitive tasks, such as perceptual categorization (Huttenlocher, Hedges, and Duncan, 1991; Huttenlocher, Hedges, & Vevea 2000; Jern and Kemp, 2013; Galleguillos and Belongie, 2010 ), visual perception (Eckstein, Abbey, Pham, & Shimozaki, 2004; Epstein, 2008; Todorovic, 2010), color perception (Mitterer & de Ruiter, 2008), and long term memory (Bartlett, 1932; Hemmer and Steyvers, 2009a). People appear to have strong prior expectations for their natural environment, and use this knowledge optimally. For example, Huttenlocher et al. (1991) showed that having prior knowledge of the underlying stimulus distribution improved average recall. They found that responses regressed toward the mean of the overall stimulus distribution, which enhanced performance. Furthermore, this influence of prior knowledge has been shown to be hierarchical, such that the structure of the natural environment interacts with recall at multiple levels of abstraction. For instance, recall for the size of objects was shown to be biased towards the overall size distribution, or the distributions associated with specific objects, as a function of familiarity (Hemmer & Steyvers, 2009a; 2009b). Similarly, prior knowledge about the height of people influences recall, not only towards the general height of people, but also at a more fine-grained level based on gender - i.e., knowledge that females on average are shorter than males (Hemmer, Tauber & Steyvers, in revision). Such behavior is well modeled by a rational model of memory which assumes that noisy data in the mind is optimally combined with prior knowledge about the environment. The question is, how does an observer in a task integrate noisy and incomplete information stored in episodic memory with prior knowledge of the environment? In a real world example, imagine that an individual has witnessed a car accident. Later, when questioned by the police, that individual is asked to recall certain aspects of the event, such as the color of a car seen fleeing the scene. The witness might only have a vague recollection of the events that transpired - the car was greenish. However, they are also likely to have prior knowledge about the possible