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Time for Change: Implementation of Aksentijevic-Gibson Complexity in Psychology
Time for Change: Implementation of Aksentijevic-Gibson Complexity in Psychology
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Time for Change: Implementation of Aksentijevic-Gibson Complexity in Psychology
Time for Change: Implementation of Aksentijevic-Gibson Complexity in Psychology

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Time for Change: Implementation of Aksentijevic-Gibson Complexity in Psychology
Time for Change: Implementation of Aksentijevic-Gibson Complexity in Psychology
Journal Article

Time for Change: Implementation of Aksentijevic-Gibson Complexity in Psychology

2020
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Overview
Given that complexity is critical for psychological processing, it is somewhat surprising that the field was dominated for a long time by probabilistic methods that focus on the quantitative aspects of the source/output. Although the more recent approaches based on the Minimum Description Length principle have produced interesting and useful models of psychological complexity, they have not directly defined the meaning and quantitative unit of complexity measurement. Contrasted to these mathematical approaches are various ad hoc measures based on different aspects of structure, which can work well but suffer from the same problem. The present manuscript is composed of two self-sufficient, yet related sections. In Section 1, we describe a complexity measure for binary strings which satisfies both these conditions (Aksentijevic–Gibson complexity; AG). We test the measure on a number of classic studies employing both short and long strings and draw attention to an important feature—a complexity profile—that could be of interest in modelling the psychological processing of structure as well as analysis of strings of any length. In Section 2 we discuss different factors affecting the complexity of visual form and showcase a 2D generalization of AG complexity. In addition, we provide algorithms in R that compute the AG complexity for binary strings and matrices and demonstrate their effectiveness on examples involving complexity judgments, symmetry perception, perceptual grouping, entropy, and elementary cellular automata. Finally, we enclose a repository of codes, data and stimuli for our example in order to facilitate experimentation and application of the measure in sciences outside psychology.