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McMaster University

1. LeBarr, A. Nicole. A self-heuristic biases perception and representation of novel people and objects.

Degree: PhD, 2017, McMaster University

A robust associative self network automatically biases attention, memory, and impression formation in a heuristic-like way. This thesis examines whether this self-heuristic underlies association formation of novel person and object representations to the self network and how this structure influences perceptions. This was tested across three experiments. The first employed an implicit task to assess whether self-similar individuals were represented with greater association strength to self-concept than self-dissimilar individuals. The second used an implicit task to measure whether newly-owned, previously-owned, and unowned objects exhibited different association strength with self-concept. The third determined the impact of minimal self-similarity to another individual, presented either before or after encoding, on memory for encoded information about them. Results of these experiments support three conclusions summarizing how a self-heuristic affects perceptions of novel stimuli. First, self-relevance automatically biases cognitive representation of novel self-similar (versus self-dissimilar) people and owned (versus unowned) objects, evidenced by stronger implicit association strength between these stimuli and self-concept. Next, this representation biases memory accuracy and errors in favour of heuristic-consistent information, even in contexts of minimal self-similarity. Finally, representation of self-similar people and owned objects relative to the self network biases perception through first-order effects, whereby unrelated concepts sharing an association to the self-network can influence one-another. Owned objects were automatically more favourably evaluated due to a first-order association with self-positivity. Perception of well-established self-knowledge was malleable based on response pairing with first-order associated self-similar or self-dissimilar individuals. Finally, when memory retrieval for self-similar and self-dissimilar individuals failed, responses were predicted based on first-order associated personality traits. These conclusions provide novel support for the existence of an automatic and ubiquitous self-heuristic that biases representation formation and subsequent perception of novel people and objects.

Thesis

Doctor of Philosophy (PhD)

A highly accessible network of self-representation biases attention and memory in favour of self-relevant information. I investigated how this network mediates representation of novel people and novel objects, stimulus categories that have received little attention in the social cognitive literature. An implicit test of cognitive association strength (i.e. the Implicit Association Test) revealed that novel self-similar (versus self-dissimilar) people and owned (versus unowned) objects are immediately associated to the self network. The new representations led to perceptual biases through first-order associations, whereby strictly self-relevant information was generalized to self-similar people and owned objects. For instance, even minimal…

Advisors/Committee Members: Shedden, Judith M., Psychology.

Subjects/Keywords: self; social cognition; heuristic; ownership; Implicit Association Test; implicit; memory; association

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APA (6th Edition):

LeBarr, A. N. (2017). A self-heuristic biases perception and representation of novel people and objects. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/22028

Chicago Manual of Style (16th Edition):

LeBarr, A Nicole. “A self-heuristic biases perception and representation of novel people and objects.” 2017. Doctoral Dissertation, McMaster University. Accessed October 23, 2017. http://hdl.handle.net/11375/22028.

MLA Handbook (7th Edition):

LeBarr, A Nicole. “A self-heuristic biases perception and representation of novel people and objects.” 2017. Web. 23 Oct 2017.

Vancouver:

LeBarr AN. A self-heuristic biases perception and representation of novel people and objects. [Internet] [Doctoral dissertation]. McMaster University; 2017. [cited 2017 Oct 23]. Available from: http://hdl.handle.net/11375/22028.

Council of Science Editors:

LeBarr AN. A self-heuristic biases perception and representation of novel people and objects. [Doctoral Dissertation]. McMaster University; 2017. Available from: http://hdl.handle.net/11375/22028

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