University of Illinois at Urbana-Champaign
Is attention necessary for the representational advantage of good exemplars over bad exemplars?
Abstract
dc:descriptionReal-world (rw-) statistical regularities, or expectations about the visual world learned over a lifetime, have been found to be associated with scene perception efficiency. For example, good (i.e., highly representative) exemplars of basic scene categories, one example of a rw-statistical regularity, are detected more readily than bad exemplars of the category. Similarly, good exemplars are more accurately decoded than bad exemplars in scene-responsive regions, particularly in the parahippocampal place area (PPA). Here we ask whether the observed neural advantage of the good scene exemplars requires full attention. We used a dual-task paradigm to manipulate attention and exemplar representativeness while recording neural responses with fMRI. Both univariate and multivariate pattern analyses were adopted to examine the effect of representativeness. In the attend-to-scenes condition, our results replicated an earlier study showing that good exemplars evoke less activity but a clearer category representation than bad exemplars. Importantly, similar advantages of the good exemplars were also observed when participants were distracted by a serial visual search task demanding a high attention load. In addition, a cross-decoding method between attended and distracted representations revealed that attention resulted in a quantitative rather than qualitative improvement of the category representation, particularly for good exemplars. We, therefore, conclude that the effect of category representativeness on neural representations does not require full attention.
Degree
thesis:*- Name thesis:degree_name
- M.S.
- Level thesis:degree_level
- Thesis
- Discipline thesis:degree_discipline
- Psychology
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2023
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Shao, Zhenan
- Contributors dc:contributor
-
- Beck, Diane M.
- Federmeier, Kara
Subjects
dc:subject × 6Rights
dc:rights- Statement dc:rights
-
- Copyright 2023 Zhenan Shao
- Language dc:language
- en, eng
Identifiers
dc:identifier.*- Handle dc:identifier
- https://hdl.handle.net/2142/120459