Separate, Causal Roles of the Caudate in Saccadic Choice and Execution in a Perceptual Decision Task

Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA.
Neuron (Impact Factor: 15.05). 09/2012; 75(5):865-74. DOI: 10.1016/j.neuron.2012.07.021
Source: PubMed

ABSTRACT In contrast to the well-established roles of the striatum in movement generation and value-based decisions, its contributions to perceptual decisions lack direct experimental support. Here, we show that electrical microstimulation in the monkey caudate nucleus influences both choice and saccade response time on a visual motion discrimination task. Within a drift-diffusion framework, these effects consist of two components. The perceptual component biases choices toward ipsilateral targets, away from the neurons' predominantly contralateral response fields. The choice bias is consistent with a nonzero starting value of the diffusion process, which increases and decreases decision times for contralateral and ipsilateral choices, respectively. The nonperceptual component decreases and increases nondecision times toward contralateral and ipsilateral targets, respectively, consistent with the caudate's role in saccade generation. The results imply a causal role for the caudate in perceptual decisions used to select saccades that may be distinct from its role in executing those saccades. VIDEO ABSTRACT:

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    • "fMRI studies also show that compared to non-food-related pictures, food-related pictures activate the striatum [92] in healthy individuals. Consistent with this finding, we observed right striatum activation when responses to high-calorie food pictures were compared to responses to low-calorie food pictures, although previous studies showed that the dorsal striatum is not strictly dedicated to habit behaviors, and that it can be involved in decision-making [28, 82, 93–95]. Animal studies also have shown that direct pharmacological activation of the striatum, amygdalo-hypothalamic circuit produced hyperphagia and increased preferentially the intake of foods high in fat and sugar, even in animals fed beyond apparent satiety [96]. "
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    ABSTRACT: Background The loss of self-control or inability to resist tempting/rewarding foods, and the development of less healthful eating habits may be explained by three key neural systems: (1) a hyper-functioning striatum system driven by external rewarding cues; (2) a hypo-functioning decision-making and impulse control system; and (3) an altered insula system involved in the translation of homeostatic and interoceptive signals into self-awareness and what may be subjectively experienced as a feeling. Methods The present study examined the activity within two of these neural systems when subjects were exposed to images of high-calorie versus low-calorie foods using functional magnetic resonance imaging (fMRI), and related this activity to dietary intake, assessed by 24-hour recall. Thirty youth (mean BMI = 23.1 kg/m2, range = 19.1 - 33.7; age =19.7 years, range = 14 - 22) were scanned using fMRI while performing food-specific go/nogo tasks. Results Behaviorally, participants more readily pressed a response button when go trials consisted of high-calorie food cues (HGo task) and less readily pressed the response button when go trials consisted of low-calorie food cues (LGo task). This habitual response to high-calorie food cues was greater for individuals with higher BMI and individuals who reportedly consume more high-calorie foods. Response inhibition to the high-calorie food cues was most difficult for individuals with a higher BMI and individuals who reportedly consume more high-calorie foods. fMRI results confirmed our hypotheses that (1) the "habitual" system (right striatum) was more activated in response to high-calorie food cues during the go trials than low-calorie food go trials, and its activity correlated with participants’ BMI, as well as their consumption of high-calorie foods; (2) the prefrontal system was more active in nogo trials than go trials, and this activity was inversely correlated with BMI and high-calorie food consumption. Conclusions Using a cross-sectional design, our findings help increase understanding of the neural basis of one’s loss of ability to self-control when faced with tempting food cues. Though the design does not permit inferences regarding whether the inhibitory control deficits and hyper-responsivity of reward regions are individual vulnerability factors for overeating, or the results of habitual overeating.
    Nutrition Journal 09/2014; 13(1):92. DOI:10.1186/1475-2891-13-92 · 2.60 Impact Factor
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    • "The oculomotor loop caudate neurons in this region are sensitive to many of the same factors as LIP and FEF (Watanabe and Munoz, 2013). Ding and Gold (2010, 2012, 2013) found that multiple relevant variables for perceptual decision making were coded for in the body of the caudate, including cells sensitive to information accumulation, decision threshold, and bias before actual stimulus toward a left or right saccade. Harsay et al. (2011) have examined the system in humans, and found that functional connectivity between the cortical oculomotor regions and caudate predicts learning in a saccade task. "
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    ABSTRACT: Although high level visual cortex projects to a specific region of the striatum, the tail of the caudate, and participates in corticostriatal loops, the function of this visual corticostriatal system is not well understood. This article first reviews what is known about the anatomy of the visual corticostriatal loop across mammals, including rodents, cats, monkeys, and humans. Like other corticostriatal systems, the visual corticostriatal system includes both closed loop components (recurrent projections that return to the originating cortical location) and open loop components (projections that terminate in other neural regions). The article then reviews what previous empirical research has shown about the function of the tail of the caudate. The article finally addresses the possible functions of the closed and open loop connections of the visual loop in the context of theories and computational models of corticostriatal function.
    Frontiers in Systems Neuroscience 12/2013; 7:104. DOI:10.3389/fnsys.2013.00104
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    • "This approach revealed clear task-dependent effects on adaptive learning [9]. In principle, congruence between these kinds of direct analyses of behavioral data and fit model parameters can help support interpretations of those parameters and has the advantage of testing modeling assumptions and predictions explicitly rather than via comparisons of different model sets [8], [33], [34]. In contrast, inconsistencies between direct analyses and fit model parameters can help guide how the model can be modified or expanded—keeping in mind, of course, that adding to a model's complexity can improve its overall fit to the data but often by overfitting to specious features of the data and making it more difficult to interpret the contributions of individual parameters [35]. "
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    ABSTRACT: Fitting models to behavior is commonly used to infer the latent computational factors responsible for generating behavior. However, the complexity of many behaviors can handicap the interpretation of such models. Here we provide perspectives on problems that can arise when interpreting parameter fits from models that provide incomplete descriptions of behavior. We illustrate these problems by fitting commonly used and neurophysiologically motivated reinforcement-learning models to simulated behavioral data sets from learning tasks. These model fits can pass a host of standard goodness-of-fit tests and other model-selection diagnostics even when the models do not provide a complete description of the behavioral data. We show that such incomplete models can be misleading by yielding biased estimates of the parameters explicitly included in the models. This problem is particularly pernicious when the neglected factors are unknown and therefore not easily identified by model comparisons and similar methods. An obvious conclusion is that a parsimonious description of behavioral data does not necessarily imply an accurate description of the underlying computations. Moreover, general goodness-of-fit measures are not a strong basis to support claims that a particular model can provide a generalized understanding of the computations that govern behavior. To help overcome these challenges, we advocate the design of tasks that provide direct reports of the computational variables of interest. Such direct reports complement model-fitting approaches by providing a more complete, albeit possibly more task-specific, representation of the factors that drive behavior. Computational models then provide a means to connect such task-specific results to a more general algorithmic understanding of the brain.
    PLoS Computational Biology 04/2013; 9(4):e1003015. DOI:10.1371/journal.pcbi.1003015 · 4.62 Impact Factor
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