Infomax Control of Eye Movements

Butko, Nicholas J.; Movellan, Javier R.

doi:10.1109/tamd.2010.2051029

Cited by 41 publications

(39 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Najemnik and Geisler (2005), 2008) show that eye movements in a visual search task are well modeled as being targeted to obtain the most useful disambiguating visual input about which location in an array contains the target (see also Butko and Movellan, 2010; Zelinsky, 2008, 2012, in this issue). Similarly, Itti and Baldi (2009) report that humans preferentially move their eyes to locations that are especially informative in scene perception (see also Torralba, Oliva, Castelhano, and Henderson, 2006, Zhang, Tong, Marks, Shan, and Cottrell, 2008, Kanan, Tong, Zhang, and Cottrell, 2009) and Renninger, Verghese, and Coughlan (2007) show that eye movements in a shape discrimination task are well described as maximizing the total information gained about the shape.…”

Section: Discussionmentioning

confidence: 99%

The utility of modelling word identification from visual input within models of eye movements in reading

Bicknell

Lévy

2012

Visual Cognition

View full text Add to dashboard Cite

Decades of empirical work have shown that a range of eye movement phenomena in reading are sensitive to the details of the process of word identification. Despite this, major models of eye movement control in reading do not explicitly model word identification from visual input. This paper presents a argument for developing models of eye movements that do include detailed models of word identification. Specifically, we argue that insights into eye movement behavior can be gained by understanding which phenomena naturally arise from an account in which the eyes move for efficient word identification, and that one important use of such models is to test which eye movement phenomena can be understood this way. As an extended case study, we present evidence from an extension of a previous model of eye movement control in reading that does explicitly model word identification from visual input, Mr. Chips (Legge, Klitz, & Tjan, 1997), to test two proposals for the effect of using linguistic context on reading efficiency.

show abstract

Section: Discussionmentioning

confidence: 99%

The utility of modelling word identification from visual input within models of eye movements in reading

Bicknell

Lévy

2012

Visual Cognition

View full text Add to dashboard Cite

show abstract

“…While there are several studies that show that maximizing the information gained with every fixation is an optimal way to gathering information with a foveated system, (Legge, Klitz, and Tjan 1997; Lee & Yu, 2000; Raj, Frazor, Geisler & Bovik, 2005; Butko and Movellan, 2010), fewer studies have compared these models to human performance. One counter-example is reading where Legge, Hooven, Klitz, Mansfield, and Tjan (2002) showed that that the characteristic eye movement patterns such as skipping small words, fixating the center of words, and backward saccades were consistent with the eyes moving to informative locations.…”

Section: Discussionmentioning

confidence: 99%

Efficient saccade planning requires time and clear choices

Ghahghaei

Verghese

2015

Vision Research

View full text Add to dashboard Cite

We use eye movements constantly to gather information. Saccades are efficient when they maximize the information required for the task, however there is controversy regarding the efficiency of eye movement planning. For example, saccades are efficient when searching for a single target (Nature, 434 (2005) 387–91), but are inefficient when searching for an unknown number of targets in noise, particularly under time pressure (Vision Research 74 (2012), 61–71). In this study, we used a multiple-target search paradigm and explored whether altering the noise level or increasing saccadic latency improved efficiency. Experiments used stimuli with two levels of discriminability such that saccades to the less discriminable stimuli provided more information. When these two noise levels corresponded to low and moderate visibility, most observers did not preferentially select informative locations, but looked at uncertain and probable target locations equally often. We then examined whether eye movements could be made more efficient by increasing the discriminability of the two stimulus levels and by delaying the first saccade so that there was more time for decision processes to influence the saccade choices. Some observers did indeed increase the proportion of their saccades to informative locations under these conditions. Others, however, made as many saccades as they could during the limited time and were unselective about the saccade goal. A clear trend that emerges across all experiments is that conditions with a greater proportion of efficient saccades are associated with a longer latency to initiate saccades, suggesting that the choice of informative locations requires deliberate planning.

show abstract

“…This biased and informed search mechanism is much more efficient. As a future extension, fovea movement would be learned as well [(Whitehead and Ballard, 1990; Schmidhuber and Huber, 1991); see also recent work by Butko and Movellan (2010)].…”

Section: Methodsmentioning

confidence: 99%

Confidence-based progress-driven self-generated goals for skill acquisition in developmental robots

et al. 2013

View full text Add to dashboard Cite

A reinforcement learning agent that autonomously explores its environment can utilize a curiosity drive to enable continual learning of skills, in the absence of any external rewards. We formulate curiosity-driven exploration, and eventual skill acquisition, as a selective sampling problem. Each environment setting provides the agent with a stream of instances. An instance is a sensory observation that, when queried, causes an outcome that the agent is trying to predict. After an instance is observed, a query condition, derived herein, tells whether its outcome is statistically known or unknown to the agent, based on the confidence interval of an online linear classifier. Upon encountering the first unknown instance, the agent “queries” the environment to observe the outcome, which is expected to improve its confidence in the corresponding predictor. If the environment is in a setting where all instances are known, the agent generates a plan of actions to reach a new setting, where an unknown instance is likely to be encountered. The desired setting is a self-generated goal, and the plan of action, essentially a program to solve a problem, is a skill. The success of the plan depends on the quality of the agent's predictors, which are improved as mentioned above. For validation, this method is applied to both a simulated and real Katana robot arm in its “blocks-world” environment. Results show that the proposed method generates sample-efficient curious exploration behavior, which exhibits developmental stages, continual learning, and skill acquisition, in an intrinsically-motivated playful agent.

show abstract

Infomax Control of Eye Movements

Cited by 41 publications

References 28 publications

The utility of modelling word identification from visual input within models of eye movements in reading

The utility of modelling word identification from visual input within models of eye movements in reading

Efficient saccade planning requires time and clear choices

Confidence-based progress-driven self-generated goals for skill acquisition in developmental robots

Contact Info

Product

Resources

About