The visual system efficiently processes complex and redundant information in a scene despite its limited capacity. One strategy for coping with the complexity and redundancy of a scene is to summarize it by using average information. However, despite its importance, the mechanism of averaging is not well understood. Here, a distributed attention model of averaging is proposed. Human percept for an object can be disturbed by various sources of internal noise, which can occur either before (early noise) or after (late noise) forming an ensemble perception. The model assumes these noises and reflects noise cancellation by averaging multiple items. The model predicts increased precision for more items with decelerated increments for large set-sizes resulting from late noise. Importantly, the model incorporates mechanisms of attention, which modulate each item’s contribution to the averaging process. The attention in the model also results in saturation of performance increments for small set-sizes because the amount of attention allocated to each item is greater for small set-sizes than for large set-sizes. To evaluate the proposed model, a psychophysical experiment was conducted in which observers’ ability to discriminate average sizes of two displays was measured. The observers’ averaging performance increased at a decreasing rate with small set-sizes and it approached an asymptote for large set-sizes. The model accurately predicted the observed pattern of data. It provides a theoretical framework for interpreting behavioral data and leads to an understanding of the characteristics of ensemble perception.
Bibliographical noteFunding Information:
This research was supported by the Brain Research Program (NRF-2017M3C7A1029658) through the National Research Foundation of Korea (NRF) to SC and the ICT Consilience Creative program (IITP-2017-2017-0-01015) through the Institute for Information and Communications Technology Promotion (IITP) funded by the Ministry of Science and ICT. Some of the data was presented at the 2018 Vision Sciences Society. For helpful comments and discussion about this manuscript, we thank Jüri Allik, Randolph Blake, Matthew Inverso, Min-Suk Kang, Joshua A. Solomon, and an anonymous reviewer. Anne Treisman was a wonderful mentor and role model for SC, and SC is eternally grateful for her guidance.
© 2019, The Psychonomic Society, Inc.
All Science Journal Classification (ASJC) codes
- Language and Linguistics
- Experimental and Cognitive Psychology
- Sensory Systems
- Linguistics and Language