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28th International Congress of Psychology August 8 ... - U-netSURF

28th International Congress of Psychology August 8 ... - U-netSURF

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2074.5 Search asymmetries across feature dimensions, Jing Sun, Dingguo Gao, Hengyi Rao,<br />

Debin Huang, Sun Yat-Sen University, Guangzhou, China<br />

This study aimed to examine the search asymmetries <strong>of</strong> primitive features across feature<br />

dimensions through manipulating terminators in three experiments. In Experiment 1, searching L<br />

in Ss was faster than searching S in Ls, and the number <strong>of</strong> distractor affected searching S but not<br />

searching L. In Experiment 2, when search targets shared same terminators but different curvature,<br />

searching curve in line background was faster than searching line in curve background. Adopting<br />

different stimuli, Experiment 3 replicated the effects obtained in Experiment 1. The above results<br />

suggest terminator be more primitive than curvature in visual search.<br />

2074.6 Recognition <strong>of</strong> rotated objects: Paying attention to orientation, Irina Harris, Paul Dux,<br />

Macquarie University, Australia<br />

Recognition costs incurred by rotated objects have been interpreted as evidence for<br />

viewpoint-dependent recognition. We review findings which suggest that recognition is actually<br />

mediated by orientation-invariant representations and that these costs reflect a post-recognition<br />

process <strong>of</strong> determining the object’s orientation in space. We propose a two-stage theory <strong>of</strong> object<br />

recognition. In Stage1, the object’s identity is retrieved from memory. Strictly speaking, this is the<br />

recognition process, and is orientation-invariant. However, a second stage <strong>of</strong> processing is<br />

necessary, in which identity is bound to orientation information, in order to deliver a conscious<br />

percept anchored in space and time.<br />

2074.7 Functional asymmetry between upper and lower visual fields revealed by an<br />

event-related potential study, Zhe Qu 1 , Fan Silu 1 , Ding Yulong 2 , Song Yan 3 , Chen Lin 1 ,<br />

1 2<br />

Graduate School and Institute <strong>of</strong> Biophysics, Chinese Academy <strong>of</strong> Sciences; Sun Yat-Sen<br />

University, China; 3 Beijing Normal University, China<br />

To investigate the functional asymmetry between upper and lower visual fields (UVF and LVF),<br />

we recorded event-related potentials from 16 subjects while they were performing a simple visual<br />

discrimination task under sustained attention. The LVF stimuli elicited smaller P1, but larger N1<br />

than UVF stimuli. The latency <strong>of</strong> N1 was earlier for LVF than UVF. Moreover, the attentional<br />

effect on P1’s amplitude was smaller for LVF than UVF. These results, from visual perception and<br />

sustained attention, showed clear evidence for the functional asymmetry between UVF and LVF,<br />

suggesting LVF with more sensitivity <strong>of</strong> perception and less consumption <strong>of</strong> attentional resource.<br />

2074.8 Neural correlates <strong>of</strong> fast and slow visual perceptual learning: An event-related potential<br />

study, Yulong Ding 1 , Yan Song 2 , Zhe Qu 3 , Silu Fan 3 , 1 Sun Yat-Sen University, China; 2 Beijing<br />

Normal University, China; 3 Graduate School and Institute <strong>of</strong> Biophysics, Chinese Academy <strong>of</strong><br />

Sciences, China<br />

The present study investigated the time course <strong>of</strong> visual perceptual learning by recording<br />

event-related potentials (ERPs) from adult subjects while they were trained with a visual<br />

discrimination task. Behavioral data revealed both fast learning effect (occurred during daily<br />

training) and slow learning effect (occurred between days) on reaction times. For ERPs, fast<br />

learning effect was shown on decrement <strong>of</strong> N1 and increment <strong>of</strong> P2, while slow learning effect<br />

was shown on increment <strong>of</strong> N1 and decrement <strong>of</strong> N2. These results provide clear evidence<br />

supporting that different neural processes are involved in fast and slow visual perceptual learning.<br />

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