We used concurrent TMS-fMRI to test directly for hemispheric differences in causal influences of the right or left fronto-parietal cortex on activity (BOLD signal) in the human occipital cortex. Clinical data and some behavioral TMS studies have been taken to suggest right-hemisphere specialization for top-down modulation of vision in humans, based on deficits such as spatial neglect or extinction in lesioned patients, or findings that TMS to right (vs. left) fronto-parietal structures can elicit stronger effects on visual performance. But prior to the recent advent of concurrent TMS and neuroimaging, it was not possible to directly examine the causal impact of one (stimulated) brain region upon others in humans. Here we stimulated the frontal or intraparietal cortex in the left or right hemisphere with TMS, inside an MR scanner, while measuring with fMRI any resulting BOLD signal changes in visual areas V1-V4 and V5/MT+. For both frontal and parietal stimulation, we found clear differences between effects of right- versus left-hemisphere TMS on activity in the visual cortex, with all differences significant in direct statistical comparisons. Frontal TMS over either hemisphere elicited similar BOLD decreases for central visual field representations in V1-V4, but only right frontal TMS led to BOLD increases for peripheral field representations in these regions. Hemispheric differences for effects of parietal TMS were even more marked: Right parietal TMS led to strong BOLD changes in V1-V4 and V5/MT+, but left parietal TMS did not. These data directly confirm that the human frontal and parietal cortex show right-hemisphere specialization for causal influences on the visual cortex.
We show that strictly competitive, finite games of perfect information that may end in one of three possible ways can be solved by applying only two rounds of elimination of dominated strategies.
In recent years, a number of governments and consumer groups in rich countries have tried to discourage the use of child labor in poor countries through measures such as product boycotts and the imposition of international labor standards. The purported objective of such measures is to reduce the incidence of child labor in developing countries and thereby improve children’s welfare. In this paper, we examine the effects of such policies from a political-economy perspective. We show that these types of international action on child labor tend to lower domestic political support within developing countries for banning child labor. Hence, international labor standards and product boycotts may delay the ultimate eradication of
child labor.
Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This "best model" approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data.
Both perceptual inference and motor responses are shaped by learned probabilities. For example, stimulus-induced responses in sensory cortices and preparatory activity in premotor cortex reflect how (un)expected a stimulus is. This is in accordance with predictive coding accounts of brain function, which posit a fundamental role of prediction errors for learning and adaptive behavior. We used functional magnetic resonance imaging and recent advances in computational modeling to investigate how (failures of) learned predictions about visual stimuli influence subsequent motor responses. Healthy volunteers discriminated visual stimuli that were differentially predicted by auditory cues. Critically, the predictive strengths of cues varied over time, requiring subjects to continuously update estimates of stimulus probabilities. This online inference, modeled using a hierarchical Bayesian learner, was reflected behaviorally: speed and accuracy of motor responses increased significantly with predictability of the stimuli. We used nonlinear dynamic causal modeling to demonstrate that striatal prediction errors are used to tune functional coupling in cortical networks during learning. Specifically, the degree of striatal trial-by-trial prediction error activity controls the efficacy of visuomotor connections and thus the influence of surprising stimuli on premotor activity. This finding substantially advances our understanding of striatal function and provides direct empirical evidence for formal learning theories that posit a central role for prediction error-dependent plasticity.
Theories of the positive symptoms of schizophrenia hypothesize a role for aberrant reinforcement signaling driven by dysregulated dopamine transmission. Recently, we provided evidence of aberrant reward learning in symptomatic, but not asymptomatic patients with schizophrenia, using a novel paradigm, the Salience Attribution Test (SAT). The SAT is a probabilistic reward learning game that employs cues that vary across task-relevant and task-irrelevant dimensions; it provides behavioral indices of adaptive and aberrant reward learning. As an initial step prior to future clinical studies, here we used functional magnetic resonance imaging to examine the neural basis of adaptive and aberrant reward learning during the SAT in healthy volunteers. As expected, cues associated with high relative to low reward probabilities elicited robust hemodynamic responses in a network of structures previously implicated in motivational salience; the midbrain, in the vicinity of the ventral tegmental area, and regions targeted by its dopaminergic projections, i.e. medial dorsal thalamus, ventral striatum and prefrontal cortex (PFC). Responses in the medial dorsal thalamus and polar PFC were strongly correlated with the degree of adaptive reward learning across participants. Finally, and most importantly, differential dorsolateral PFC and middle temporal gyrus (MTG) responses to cues with identical reward probabilities were very strongly correlated with the degree of aberrant reward learning. Participants who showed greater aberrant learning exhibited greater dorsolateral PFC responses, and reduced MTG responses, to cues erroneously inferred to be less strongly associated with reward. The results are discussed in terms of their implications for different theories of associative learning.
Currently, most studies that employ dynamic causal modeling (DCM) use random-effects (RFX) analysis to make group inferences, applying a second-level frequentist test to subjects' parameter estimates. In some instances, however, fixed-effects (FFX) analysis can be more appropriate. Such analyses can be implemented by combining the subjects' posterior densities according to Bayes' theorem either on a multivariate (Bayesian parameter averaging or BPA) or univariate basis (posterior variance weighted averaging or PVWA), or by applying DCM to time-series averaged across subjects beforehand (temporal averaging or TA). While all these FFX approaches have the advantage of allowing for Bayesian inferences on parameters a systematic comparison of their statistical properties has been lacking so far. Based on simulated data generated from a two-region network we examined the effects of signal-to-noise ratio (SNR) and population heterogeneity on group-level parameter estimates. Data sets were simulated assuming either a homogeneous large population (N=60) with constant connectivities across subjects or a heterogeneous population with varying parameters. TA showed advantages at lower SNR but is limited in its applicability. Because BPA and PVWA take into account posterior (co)variance structure, they can yield non-intuitive results when only considering posterior means. This problem is relevant for high SNR data, pronounced parameter interdependencies and when FFX assumptions are violated (i.e. inhomogeneous groups). It diminishes with decreasing SNR and is absent for models with independent parameters or when FFX assumptions are appropriate. Group results obtained with these FFX approaches should therefore be interpreted carefully by considering estimates of dependencies among model parameters.
Reward representation in ventral striatum is boosted by perceptual novelty, although the mechanism of this effect remains elusive. Animal studies indicate a functional loop (Lisman and Grace, 2005) that includes hippocampus, ventral striatum, and midbrain as being important in regulating salience attribution within the context of novel stimuli. According to this model, reward responses in ventral striatum or midbrain should be enhanced in the context of novelty even if reward and novelty constitute unrelated, independent events. Using fMRI, we show that trials with reward-predictive cues and subsequent outcomes elicit higher responses in the striatum if preceded by an unrelated novel picture, indicating that reward representation is enhanced in the context of novelty. Notably, this effect was observed solely when reward occurrence, and hence reward-related salience, was low. These findings support a view that contextual novelty enhances neural responses underlying reward representation in the striatum and concur with the effects of novelty processing as predicted by the model of Lisman and Grace (2005).
