Time-varying decision boundaries: insights from optimality analysis.

The most widely used account of decision-making proposes that people choose between alternatives by accumulating evidence in favor of each alternative until this evidence reaches a decision boundary. It is frequently assumed that this decision boundary stays constant during a decision, depending on the evidence collected but not on time. Recent experimental and theoretical work has challenged this assumption, showing that constant decision boundaries are, in some circumstances, sub-optimal. We introduce a theoretical model that facilitates identification of the optimal decision boundaries under a wide range of conditions. Time-varying optimal decision boundaries for our model are a result only of uncertainty over the difficulty of each trial and do not require decision deadlines or costs associated with collecting evidence, as assumed by previous authors. Furthermore, the shape of optimal decision boundaries depends on the difficulties of different decisions. When some trials are very difficult, optimal boundaries decrease with time, but for tasks that only include a mixture of easy and medium difficulty trials, the optimal boundaries increase or stay constant. We also show how this simple model can be extended to more complex decision-making tasks such as when people have unequal priors or when they can choose to opt out of decisions. The theoretical model presented here provides an important framework to understand how, why, and whether decision boundaries should change over time in experiments on decision-making.

A tutorial on bridge sampling.

The marginal likelihood plays an important role in many areas of Bayesian statistics such as parameter estimation, model comparison, and model averaging. In most applications, however, the marginal likelihood is not analytically tractable and must be approximated using numerical methods. Here we provide a tutorial on bridge sampling (Bennett, 1976; Meng & Wong, 1996), a reliable and relatively straightforward sampling method that allows researchers to obtain the marginal likelihood for models of varying complexity. First, we introduce bridge sampling and three related sampling methods using the beta-binomial model as a running example. We then apply bridge sampling to estimate the marginal likelihood for the Expectancy Valence (EV) model-a popular model for reinforcement learning. Our results indicate that bridge sampling provides accurate estimates for both a single participant and a hierarchical version of the EV model. We conclude that bridge sampling is an attractive method for mathematical psychologists who typically aim to approximate the marginal likelihood for a limited set of possibly high-dimensional models.

Drosophila PINK1 and parkin loss-of-function mutants display a range of non-motor Parkinson's disease phenotypes.

Parkinson's disease (PD) is more commonly associated with its motor symptoms and the related degeneration of dopamine (DA) neurons. However, it is becoming increasingly clear that PD patients also display a wide range of non-motor symptoms, including memory deficits and disruptions of their sleep-wake cycles. These have a large impact on their quality of life, and often precede the onset of motor symptoms, but their etiology is poorly understood. The fruit fly Drosophila has already been successfully used to model PD, and has been used extensively to study relevant non-motor behaviours in other contexts, but little attention has yet been paid to modelling non-motor symptoms of PD in this genetically tractable organism. We examined memory performance and circadian rhythms in flies with loss-of-function mutations in two PD genes: PINK1 and parkin. We found learning and memory abnormalities in both mutant genotypes, as well as a weakening of circadian rhythms that is underpinned by electrophysiological changes in clock neurons. Our study paves the way for further work that may help us understand the mechanisms underlying these neglected aspects of PD, thus identifying new targets for treatments to address these non-motor problems specifically and perhaps even to halt disease progression in its prodromal phase.

Overcoming indecision by changing the decision boundary.

The dominant theoretical framework for decision making asserts that people make decisions by integrating noisy evidence to a threshold. It has recently been shown that in many ecologically realistic situations, decreasing the decision boundary maximizes the reward available from decisions. However, empirical support for decreasing boundaries in humans is scant. To investigate this problem, we used an ideal observer model to identify the conditions under which participants should change their decision boundaries with time to maximize reward rate. We conducted 6 expanded-judgment experiments that precisely matched the assumptions of this theoretical model. In this paradigm, participants could sample noisy, binary evidence presented sequentially. Blocks of trials were fixed in duration, and each trial was an independent reward opportunity. Participants therefore had to trade off speed (getting as many rewards as possible) against accuracy (sampling more evidence). Having access to the actual evidence samples experienced by participants enabled us to infer the slope of the decision boundary. We found that participants indeed modulated the slope of the decision boundary in the direction predicted by the ideal observer model, although we also observed systematic deviations from optimality. Participants using suboptimal boundaries do so in a robust manner, so that any error in their boundary setting is relatively inexpensive. The use of a normative model provides insight into what variable(s) human decision makers are trying to optimize. Furthermore, this normative model allowed us to choose diagnostic experiments and in doing so we present clear evidence for time-varying boundaries. (PsycINFO Database Record

Context-dependent decision-making: a simple Bayesian model.

Many phenomena in animal learning can be explained by a context-learning process whereby an animal learns about different patterns of relationship between environmental variables. Differentiating between such environmental regimes or 'contexts' allows an animal to rapidly adapt its behaviour when context changes occur. The current work views animals as making sequential inferences about current context identity in a world assumed to be relatively stable but also capable of rapid switches to previously observed or entirely new contexts. We describe a novel decision-making model in which contexts are assumed to follow a Chinese restaurant process with inertia and full Bayesian inference is approximated by a sequential-sampling scheme in which only a single hypothesis about current context is maintained. Actions are selected via Thompson sampling, allowing uncertainty in parameters to drive exploration in a straightforward manner. The model is tested on simple two-alternative choice problems with switching reinforcement schedules and the results compared with rat behavioural data from a number of T-maze studies. The model successfully replicates a number of important behavioural effects: spontaneous recovery, the effect of partial reinforcement on extinction and reversal, the overtraining reversal effect, and serial reversal-learning effects.

A day-hospital approach to treatment of pediatric complex regional pain syndrome: initial functional outcomes.

OBJECTIVES: To examine clinical outcomes of an interdisciplinary day-hospital treatment program (comprised of physical, occupational, and cognitive-behavioral therapies with medical and nursing services) for pediatric complex regional pain syndrome (CRPS). METHODS: The study is a longitudinal case series of consecutive patients treated in a day-hospital pediatric pain rehabilitation program. Participants were 56 children and adolescents with ages 8 to 18 years (median=14 y) with CRPS spectrum conditions who failed to progress sufficiently with a previous outpatient and/or inpatient treatments. Patients participated in daily physical therapy, occupational therapy, and psychological treatment and received nursing and medical care as necessary. The model places equal emphasis on physical and cognitive-behavioral approaches to pain management. Median duration of stay was 3 weeks. Outcome measures included assessments of physical, occupational, and psychological functioning at program admission, discharge, and at posttreatment follow-up at a median of 10 months after discharge. Scores at discharge and follow-up were compared with measures on admission by Wilcoxon tests, paired t tests, or analysis of variance as appropriate, with corrections for multiple comparisons. RESULTS: Outcomes demonstrate clinically and statistically significant improvements from admission to discharge in pain intensity (P<0.001), functional disability (P<0.001), subjective report of limb function (P<0.001), timed running (P<0.001), occupational performance (P<0.001), medication use (P<0.01), use of assistive devices (P<0.001), and emotional functioning (anxiety, P<0.001; depression, P<0.01). Functional gains were maintained or further improved at follow-up. DISCUSSION: A day-hospital interdisciplinary rehabilitation approach seems effective in reducing disability and improving physical and emotional functioning and occupational performance among children and adolescents with CRPSs that have failed to improve with outpatient treatment.

Posterior weighted reinforcement learning with state uncertainty.

Reinforcement learning models generally assume that a stimulus is presented that allows a learner to unambiguously identify the state of nature, and the reward received is drawn from a distribution that depends on that state. However, in any natural environment, the stimulus is noisy. When there is state uncertainty, it is no longer immediately obvious how to perform reinforcement learning, since the observed reward cannot be unambiguously allocated to a state of the environment. This letter addresses the problem of incorporating state uncertainty in reinforcement learning models. We show that simply ignoring the uncertainty and allocating the reward to the most likely state of the environment results in incorrect value estimates. Furthermore, using only the information that is available before observing the reward also results in incorrect estimates. We therefore introduce a new technique, posterior weighted reinforcement learning, in which the estimates of state probabilities are updated according to the observed rewards (e.g., if a learner observes a reward usually associated with a particular state, this state becomes more likely). We show analytically that this modified algorithm can converge to correct reward estimates and confirm this with numerical experiments. The algorithm is shown to be a variant of the expectation-maximization algorithm, allowing rigorous convergence analyses to be carried out. A possible neural implementation of the algorithm in the cortico-basal-ganglia-thalamic network is presented, and experimental predictions of our model are discussed.

Challenges of functional imaging research of pain in children.

Functional imaging has revolutionized the neurosciences. In the pain field it has dramatically altered our understanding of how the brain undergoes significant functional, anatomical and chemical changes in patients with chronic pain. However, most studies have been performed in adults. Because functional imaging is non-invasive and can be performed in awake individuals, applications in children have become more prevalent, but only recently in the pain field. Measures of changes in the brains of children have important implications in understanding neural plasticity in response to acute and chronic pain in the developing brain. Such findings may have implications for treatments in children affected by chronic pain and provide novel insights into chronic pain syndromes in adults. In this review we summarize this potential and discuss specific concerns related to the imaging of pain in children.

Serum lipids regulate dendritic cell CD1 expression and function.

Dendritic cells (DCs) are highly potent antigen-presenting cells (APCs) and play a vital role in stimulating naïve T cells. Treatment of human blood monocytes with the cytokines granulocyte-macrophage colony-stimulating factor (GM-CSF) and interleukin (IL)-4 stimulates them to develop into immature dendritic cells (iDCs) in vitro. DCs generated by this pathway have a high capacity to prime and activate resting T cells and prominently express CD1 antigen-presenting molecules on the cell surface. The presence of human serum during the differentiation of iDCs from monocytes inhibits the expression of CD1a, CD1b and CD1c, but not CD1d. Correspondingly, T cells that are restricted by CD1c showed poor responses to DCs that were generated in the presence of human serum, while the responses of CD1d-restricted T cells were enhanced. We chemically fractionated human serum to isolate the bioactive factors that modulate surface expression of CD1 proteins during monocyte to DC differentiation. The human serum components that affected CD1 expression partitioned with polar organic soluble fractions. Lysophosphatidic acid and cardiolipin were identified as lipids present in normal human serum that potently modulate CD1 expression. Control of CD1 expression was mediated at the level of gene transcription and correlated with activation of the peroxisome proliferator-activated receptor (PPAR) nuclear hormone receptors. These findings indicate that the ability of human DCs to present lipid antigens to T cells through expression of CD1 molecules is sensitively regulated by lysophosphatidic acid and cardiolipin in serum, which are ligands that can activate PPAR transcription factors.

CD1-mediated gamma/delta T cell maturation of dendritic cells.

Immature myeloid dendritic cells (DCs) express only low levels of major histocompatibility complex (MHC) class II but express high levels of CD1 a, b, and c antigen-presenting molecules at the cell surface. As Vdelta1+ gamma/delta T cells are the main tissue subset of gamma/delta T cells and they are known to recognize CD1c in the absence of specific foreign antigen recognition, we examined the possible interaction of these T cells with immature DCs. We show that CD1-restricted gamma/delta T cells can mediate the maturation of DCs. DC maturation required cell-cell contact and could be blocked by antibodies against CD1c. The maturation process was partially mediated by tumor necrosis factor alpha. Importantly, immature DCs matured in the presence of lipopolysaccharide and CD1-restricted gamma/delta T cells produced bioactive interleukin-12p70. In addition, these DCs were able to efficiently present peptide antigens to naive CD4+ T cells. CD1-restricted gamma/delta T cell recognition of immature DCs provides the human immune system with the capacity to rapidly generate a pool of mature DCs early during microbial invasion. This may be an important source of critical host signals for T helper type 1 polarization of antigen-specific naive T cells and the subsequent adaptive immune response.

CD1-dependent dendritic cell instruction.

Both microbial products and T cell factors influence dendritic cell (DC) maturation. However, it is not known which T cells are capable of interacting with DCs at the initiation of adaptive immunity, when foreign antigen-specific T cells are rare. We show here that self-reactive CD1-restricted T cells can promote DC maturation by recognizing CD1 in the absence of foreign antigens. T cell recognition of all four CD1 isoforms can trigger DC maturation, but their distinct mechanisms of costimulation lead to profound differences in concomitant interleukin 12 p70 production. Distinct CD1-reactive T cells may thus differentially direct DC development early in the immune response, thereby controlling subsequent polarization of acquired immunity.