In everyday life, eye movements enable the eyes to gather the information required for motor actions. They are thus proactive, anticipating actions rather than just responding to stimuli. This means that the oculomotor system needs to know where to look and what to look for. Using examples from table tennis, driving and music reading we show that the information the eye movement system requires is very varied in origin and highly task speci¢c, and it is suggested that the control program or schema for a particular action must include directions for the oculomotor and visual processing systems. In many activities (reading text and music, typing, steering) processed information is held in a memory bu¡er for a period of about a second. This permits a match between the discontinuous input from the eyes and continuous motor output, and in particular allows the eyes to be involved in more than one task.
The eye^hand span (EHS) is the separation between eye position and hand position when sight-reading music. It can be measured in two ways: in notes (the number of notes between hand and eye; the`note index'), or in time (the length of time between ¢xation and performance; the`time index'). The EHSs of amateur and professional pianists were compared while they sight-read music. The professionals showed signi¢cantly larger note indexes than the amateurs (approximately four notes, compared to two notes), and all subjects showed similar variability in the note index. Surprisingly, the di¡erent groups of pianists showed almost identical mean time indexes (ca. 1s), with no signi¢cant di¡erences between any of the skill levels. However, professionals did show signi¢cantly less variation than the amateurs. The time index was signi¢cantly a¡ected by the performance tempo: when fast tempos were imposed on performance, all subjects showed a reduction in the time index (to ca. 0.7 s), and slow tempos increased the time index (to ca. 1.3 s). This means that the length of time that information is stored in the bu¡er is related to performance tempo rather than ability, but that professionals can ¢t more information into their bu¡ers.
We investigated the visual strategy of a subject without eye movements (AI), comparing her with normal subjects on the 'real-life' task of making a cup of tea. Differences in overall performance were surprisingly few. She took no more time than the controls to complete the tea-making task and the division of the task into object-related actions was essentially similar. However, the way AI took in visual information was very different from the normal subjects who used a typical 'saccade and fixate' strategy when moving between and scrutinizing objects. AI made saccades with the head, which were on average 1.5 times larger than the eye-head saccades of the controls and lasted four times as long, meaning that AI would have had impaired vision for more of the time than the controls. She also made only approximately one-third as many saccades as normals during the same task. However, she had another strategy, 'slow drift', in which she allowed her eyes to move smoothly across the scene at speeds of up to 30 degrees /s. Such movements were never seen in the controls, and we assume that AI used them to offset the cost in time of the slow head saccades, even though they had their own cost in terms of reduced resolution. We demonstrate that these differences have a minimal effect on the timings of events during an object-related action. We discuss supervisory checking operations within actions, and consider what information is needed for appropriate gaze control during object-related actions.
We investigated the visual strategy of a subject without eye movements (AI), comparing her with normal subjects on the 'real-life' task of making a cup of tea. Differences in overall performance were surprisingly few. She took no more time than the controls to complete the tea-making task and the division of the task into object-related actions was essentially similar. However, the way AI took in visual information was very different from the normal subjects who used a typical 'saccade and fixate' strategy when moving between and scrutinizing objects. AI made saccades with the head, which were on average 1.5 times larger than the eye-head saccades of the controls and lasted four times as long, meaning that AI would have had impaired vision for more of the time than the controls. She also made only approximately one-third as many saccades as normals during the same task. However, she had another strategy, 'slow drift', in which she allowed her eyes to move smoothly across the scene at speeds of up to 30°/s. Such movements were never seen in the controls, and we assume that AI used them to offset the cost in time of the slow head saccades, even though they had their own cost in terms of reduced resolution. We demonstrate that these differences have a minimal effect on the timings of events during an object-related action. We discuss supervisory checking operations within actions, and consider what information is needed for appropriate gaze control during object-related actions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.