Neural computation of log likelihood in control of saccadic eye movements

Carpenter, R. H. S.; Williams, M. L. L.

doi:10.1038/377059a0

Cited by 754 publications

(756 citation statements)

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“…The data tend to lie along a straight line with a variable early component, as described previously [ Fig. 1 and (Carpenter and Williams 1995)], although the median latencies, slope of the main distribution and number and slope of early saccades is very variable between different subjects irrespective of disease status. The 'on/off' study revealed that the shape of the distribution of latencies was very similar in the two testing sessions, although L-dopa caused a swivel of the distribution in some patients (discussed below).…”

Section: Resultssupporting

confidence: 79%

“…Over the last decade it has been possible to test the theory by altering the prior probability (Carpenter and Williams 1995) information supply (Reddi et al 2003) and decision criterion level (Reddi and Carpenter 2000) with predictable effects on the distribution of saccadic latencies, and so far the model has held true. Of particular interest to this study it appears that an increase in the threshold criterion level required before a decision is made results in an anticlockwise swivel of the distribution (Fig.…”

Section: Reciprobit Plots Of Saccadic Latencymentioning

confidence: 99%

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Saccadic latency distributions in Parkinson’s disease and the effects of l-dopa

Michell

Fritz

et al. 2006

Exp Brain Res

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Parkinson's disease (PD) is associated with a loss of central dopaminergic pathways in the brain leading to an abnormality of movement, including saccades. In PD, analysis of saccadic latency distributions, rather than mean latencies, can provide much more information about how the neural decision process that precedes movement is affected by disease or medication. Subject to the constraints of intersubject variation and reproducibility, latency distribution may represent an attractive potential biomarker of PD. Here we report two studies that provide information about these parameters, and demonstrate a novel effect of dopamine on saccadic latency, implying that it influences the neural decision process itself. We performed a detailed cross-sectional study of saccadic latency distributions during a simple step task in 22 medicated patients and 27 agematched controls. This revealed high intersubject variability and an overlap of PD and control distributions. A second study was undertaken on a different population specifically to investigate the effects of dopamine on saccadic latency distributions in 15 PD patients. L-dopa was found to prolong latency, although the magnitude of the effect varied between subjects. The implications of these observations for the use of saccadic latency distributions as a potential biomarker of PD are discussed, as are the effects of L-dopa on neural decision making, where it is postulated to increase the criterion level of evidence required before the decision to move is made.

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Section: Resultssupporting

confidence: 79%

Section: Reciprobit Plots Of Saccadic Latencymentioning

confidence: 99%

Saccadic latency distributions in Parkinson’s disease and the effects of l-dopa

Michell

Fritz

et al. 2006

Exp Brain Res

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“…Reaching threshold for either pathway can be modified either by changing the rate at which activation of the pathway rises to threshold, or the baseline activation in the pathway prior to stimulus onset (see Carpenter, 1981;Carpenter & Williams, 1995;Hanes & Carpenter, 1999). Variations in baseline activation have been suggested for simple reflexive saccadic tasks: inter-trial effects have been interpreted as reflecting residual activation in topographic salience maps that persist beyond the duration of a single trial (Fecteau and Munoz, 2003).…”

Section: Repetition Priming In the Antisaccade Taskmentioning

confidence: 99%

Trial by trial effects in the antisaccade task

Tatler

Hutton

2006

Exp Brain Res

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The antisaccade task requires participants to inhibit the reflexive tendency to look at a sudden onset target and instead direct their gaze to the opposite hemifield. As such it provides a convenient tool with which to investigate the cognitive and neural systems that support goaldirected behaviour. Recent models of cognitive control suggest that antisaccade performance on a single trial should vary as a function of the outcome (correct antisaccade or erroneous prosaccade) of the previous trial. In addition, repetition priming effects suggest that the spatial location of the target on the previous trial may also influence current trial performance. Thus an analysis of contingency effects in antisaccade performance may provide new insights into the factors that influence the monitoring and modulation of the antisaccade task and other ongoing behaviours. Using a multilevel modelling analysis we explored previous trial effects on current trial performance in a large antisaccade dataset. We found (1) repetition priming effects following correct antisaccades; (2) contrary to models of cognitive control antisaccade error rates were increased on trials following an error, suggesting that failures to adequately maintain the task goal can persist across more than one trial; and (3) current trial latencies varied according to the previous trial outcome (correct antisaccade, slowly corrected error or rapidly corrected error). These results are discussed in terms of current models of antisaccade performance and cognitive control and further demonstrate the utility of multilevel modelling for providing a powerful statistical technique for analysing antisaccade data.

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“…Interestingly, in some cases it might be not only possible, but also in fact optimal, to incorporate psychological factors like reward anticipation and prior probabilities (i.e. uncertainty about the potential outcomes before any stimulus appears) into this framework by simply adding that information to the accumulating sensory signal [10,11]. For the BST and similar visually guided saccade tasks, it is tempting to speculate that this quantity, reflecting several sensory and non-sensory factors, is accumulated in oculomotor areas, such as the superior colliculus.…”

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Linking reward expectation to behavior in the basal ganglia

Gold

2003

Trends in Neurosciences

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Psychological factors such as expected reward value can have strong effects on behavior. Although signals related to reward have been found in numerous brain regions, how these signals are used by the circuits that control action is unknown. A recent study suggests that neurons in the caudate nucleus of the basal ganglia could play a role in transforming expected reward into a spatially selective behavioral bias.Poor Paris was asked to make a decision that even Zeus had avoided. Who was the most beautiful goddess: Hera, Athena or Aphrodite? Fearful and uncertain about how his decision would be received by the losers, he triedtactfully but unsuccessfully -to declare a three-way tie. Told to make a decision, Paris received assurances that there would be no reprisals and, even better, was offered rewards by the goddesses to try to influence him. Hera promised that if he picked her, he would become the King of Asia, as well as the richest man alive. Athena promised that Paris would be victorious in all battles, and that he would become the most handsome and wise man in the world. Aphrodite offered, simply, the beautiful Helen of Troy. It was no contest: Aphrodite's reward easily biased Paris' decision in her favor.The fact that 'psychological' factors, including uncertainty and anticipated costs and benefits, biased the judgment of Paris and can affect similarly subjective decisions is to be expected. Perhaps more surprising is that these factors can also shape our ability to perform even mundane sensory-motor tasks, influencing how well and how quickly we perceive sensory stimuli and execute appropriate motor commands [1,2]. However, as central as these influences are to both complex and simple behaviors, little is known about the underlying neural mechanisms. For example, despite a growing body of work identifying neural circuits that represent information related to reward in the context of sensory -motor tasks [3], how these kinds of signals might influence behavior is a mystery. A recent study by Lauwereyns and colleagues has begun to shed light on this difficult issue [4]. The results describe neural signals in a region of the basal ganglia called the caudate nucleus that appear to relate anticipation of an uncertain reward with the preparation for a goal-directed eye movement.Caudate responses correspond to an advance bias In their study, Lauwereyns and colleagues trained monkeys to perform a 'biased saccade task' (BST). On a given trial, the monkey fixated a central spot of light until a target (another spot of light) appeared to the left or to the right. Once the target appeared, the monkey made a saccadic eye movement to it as quickly as possible. Then came the crucial feature of the task: after correctly making the saccade, the monkey was rewarded (with a sound plus a drop of liquid) only if the target was in one of the two possible locations. The rewarded location was fixed in blocks of 20 consecutive trials, such that only correct leftward saccades were rewarded in one block, only correct rightward sacc...

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Neural computation of log likelihood in control of saccadic eye movements

Cited by 754 publications

References 14 publications

Saccadic latency distributions in Parkinson’s disease and the effects of l-dopa

Saccadic latency distributions in Parkinson’s disease and the effects of l-dopa

Trial by trial effects in the antisaccade task

Linking reward expectation to behavior in the basal ganglia

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