The Relative Influences of Priors and Sensory Evidence on an Oculomotor Decision Variable During Perceptual Learning

Gold, Joshua I.; Law, Chi-Tat; Connolly, Patrick; Bennur, Sharath

doi:10.1152/jn.90629.2008

Cited by 116 publications

(166 citation statements)

References 71 publications

Supporting

Mentioning

152

Contrasting

Order By: Relevance

“…Thus, although the single-trial estimates are based on more information than the single-trial RT, one might wonder whether the transformation is necessary at all; that is, perhaps raw RT might serve as a simpler proxy for fluctuations in response caution, with high response caution indicated by slow RT and vice versa. Indeed previous studies have made this assumption (Gold et al, 2008;Summerfield and Koechlin, 2010). Therefore, as a control on the STLBA method, we also computed correlations between raw RT and HR.…”

Section: Comparison To Response Time As a Proxy For Response Cautionmentioning

confidence: 95%

Neural Correlates of Trial-to-Trial Fluctuations in Response Caution

Maanen¹,

Brown²,

Eichele³

et al. 2011

J. Neurosci.

177

228

View full text Add to dashboard Cite

Trial-to-trial variability in decision making can be caused by variability in information processing as well as by variability in response caution. In this paper, we study which neural components code for trial-to-trial adjustments in response caution using a new computational approach that quantifies response caution on a single-trial level. We found that the frontostriatal network updates the amount of response caution. In particular, when human participants were required to respond quickly, we found a positive correlation between trial-to-trial fluctuations in response caution and the hemodynamic response in the presupplementary motor area and dorsal anterior cingulate. In contrast, on trials that required a change from a speeded response mode to a more accurate response mode or vice versa, we found a positive correlation between response caution and hemodynamic response in the anterior cingulate proper. These results indicate that for each decision, response caution is set through corticobasal ganglia functioning, but that individual choices differ according to the mechanisms that trigger changes in response caution.

show abstract

Section: Comparison To Response Time As a Proxy For Response Cautionmentioning

confidence: 95%

Neural Correlates of Trial-to-Trial Fluctuations in Response Caution

Maanen¹,

Brown²,

Eichele³

et al. 2011

J. Neurosci.

177

228

View full text Add to dashboard Cite

show abstract

“…To address these questions, we developed a probabilistic model of choice that takes into account both an internal neural response to contrast and systematic influences of nonvisual factors on the choices of the animal. To obtain this model, we adopted a framework that had been developed to study value-based decision making (Corrado et al, 2005;Lau and Glimcher, 2005) and endowed this framework with a sensory term (Seidemann, 1998;Gold et al, 2008). The result is a powerful model that accounts for both sensory and nonsensory determinants of choice.…”

Section: Probabilistic Choice Modelmentioning

confidence: 99%

The Detection of Visual Contrast in the Behaving Mouse

Busse¹,

Ayaz²,

Dhruv³

et al. 2011

J. Neurosci.

290

374

View full text Add to dashboard Cite

The mouse is becoming a key species for research on the neural circuits of the early visual system. To relate such circuits to perception, one must measure visually guided behavior and ask how it depends on fundamental stimulus attributes such as visual contrast. Using operant conditioning, we trained mice to detect visual contrast in a two-alternative forced-choice task. After 3-4 weeks of training, mice performed hundreds of trials in each session. Numerous sessions yielded high-quality psychometric curves from which we inferred measures of contrast sensitivity. In multiple sessions, however, choices were influenced not only by contrast, but also by estimates of reward value and by irrelevant factors such as recent failures and rewards. This behavior was captured by a generalized linear model involving not only the visual responses to the current stimulus but also a bias term and history terms depending on the outcome of the previous trial. We compared the behavioral performance of the mice to predictions of a simple decoder applied to neural responses measured in primary visual cortex of awake mice during passive viewing. The decoder performed better than the animal, suggesting that mice might not use optimally the information contained in the activity of visual cortex.

show abstract

“…For monkeys Cy and ZZ, training was combined with unit recordings in areas MT and LIP Law and Gold 2008). For At and Av, training was combined with a technique for assessing ongoing oculomotor activity during decision formation using saccadic eye movements evoked with electrical microstimulation of the frontal eye field (FEF; Connolly et al 2009;Gold et al 2008).…”

Section: E T H O D Smentioning

confidence: 99%

“…2;Gold and Shadlen 2003;Gold et al 2008;Law and Gold 2008). Briefly, visual stimuli were generated in MATLAB on a Macintosh computer, using the Psychophysics Toolbox software (Brainard 1997;Pelli 1997) with custom additions to draw the motion stimulus, and presented on a 21-in.…”

Section: Behavioral Task and Trainingmentioning

confidence: 99%

Relationships Between the Threshold and Slope of Psychometric and Neurometric Functions During Perceptual Learning: Implications for Neuronal Pooling

Gold

Law

Connolly

et al. 2010

Journal of Neurophysiology

Self Cite

View full text Add to dashboard Cite

Perceptual learning involves long-lasting improvements in the ability to perceive simple sensory stimuli. Some forms of perceptual learning are thought to involve an increasingly selective readout of sensory neurons that are most sensitive to the trained stimulus. Here we report novel changes in the relationship between the threshold and slope of the psychometric function during learning that are consistent with such changes in readout and can provide insights into the underlying neural mechanisms. In monkeys trained on a direction-discrimination task, perceptual improvements corresponded to lower psychometric thresholds and slightly shallower slopes. However, this relationship between threshold and slope was much weaker in comparable, ideal-observer "neurometric" functions of neurons in the middle temporal (MT) area, which represent sensory information used to perform the task and whose response properties did not change with training. We propose a linear/nonlinear pooling scheme to account for these results. According to this scheme, MT responses are pooled via linear weights that change with training to more selectively read out responses from the most sensitive neurons, thereby reducing predicted thresholds. An additional nonlinear (power-law) transformation does not change with training and causes the predicted psychometric function to become shallower as uninformative neurons are eliminated from the pooled signal. We show that this scheme is consistent with the measured changes in psychometric threshold and slope throughout training. The results suggest that some forms of perceptual learning involve improvements in a process akin to selective attention that pools the most informative neural signals to guide behavior.

show abstract

The Relative Influences of Priors and Sensory Evidence on an Oculomotor Decision Variable During Perceptual Learning

Cited by 116 publications

References 71 publications

Neural Correlates of Trial-to-Trial Fluctuations in Response Caution

Neural Correlates of Trial-to-Trial Fluctuations in Response Caution

The Detection of Visual Contrast in the Behaving Mouse

Relationships Between the Threshold and Slope of Psychometric and Neurometric Functions During Perceptual Learning: Implications for Neuronal Pooling

Contact Info

Product

Resources

About