Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias

<div><p>The activity of neural populations in the brains of humans and animals can exhibit vastly different spatial patterns when faced with different tasks or environmental stimuli. The degrees of similarity between these neural activity patterns in response to different events are used to characterize the representational structure of cognitive states in a neural population. The dominant methods of investigating this similarity structure first estimate neural activity patterns from noisy neural imaging data using linear regression, and then examine the similarity between the estimated patterns. Here, we show that this approach introduces spurious bias structure in the resulting similarity matrix, in particular when applied to fMRI data. This problem is especially severe when the signal-to-noise ratio is low and in cases where experimental conditions cannot be fully randomized in a task. We propose Bayesian Representational Similarity Analysis (BRSA), an alternative method for computing representational similarity, in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data. By marginalizing over the unknown activity patterns, we can directly estimate this covariance structure from imaging data. This method offers significant reductions in bias and allows estimation of neural representational similarity with previously unattained levels of precision at low signal-to-noise ratio, without losing the possibility of deriving an interpretable distance measure from the estimated similarity. The method is closely related to Pattern Component Model (PCM), but instead of modeling the estimated neural patterns as in PCM, BRSA models the imaging data directly and is suited for analyzing data in which the order of task conditions is not fully counterbalanced. The probabilistic framework allows for jointly analyzing data from a group of participants. The method can also simultaneously estimate a signal-to-noise ratio map that shows where the learned representational structure is supported more strongly. Both this map and the learned covariance matrix can be used as a structured prior for maximum <i>a posteriori</i> estimation of neural activity patterns, which can be further used for fMRI decoding. Our method therefore paves the way towards a more unified and principled analysis of neural representations underlying fMRI signals. We make our tool freely available in Brain Imaging Analysis Kit (BrainIAK).</p></div

The other side of recovery: validation of the Portuguese version of the subjective experiences of psychosis scale.

BACKGROUND: The aim of this study was to develop and validate a Portuguese version of The Subjective Experiences of Psychosis Scale (SEPS) for use in Portuguese-speaking populations in order to provide a self-report instrument to assess and monitor dimensions of psychotic experiences, translating patient's perspective and experience in terms of recovery from psychosis. METHODS: The sample consisted of 30 participants with psychotic disorders who had recently experienced delusions or hallucinations. The SEPS was completed along with other observer-based assessments and self-report questionnaires, such as the Brief Psychiatric Rating Scale, the Insight and Treatment Attitudes Questionnaire and the Function Assessment Short Test. RESULTS: Two main factors representing the positive and negative components of each subscale were identified. We obtained good internal consistency and test-retest reliability for the positive and negative components of all subscales. The subscales of SEPS correlated with observer-based assessments and self-report questionnaires. CONCLUSIONS: The Portuguese version of the SEPS is a useful tool in the assessment and monitoring of psychotic symptoms

Neural processes mediating contextual influences on human choice behaviour

Contextual influences on choice are ubiquitous in ecological settings. Current evidence suggests that subjective values are normalized with respect to the distribution of potentially available rewards. However, how this context-sensitivity is realised in the brain remains unknown. To address this, here we examine functional magnetic resonance imaging (fMRI) data during performance of a gambling task where blocks comprise values drawn from one of two different, but partially overlapping, reward distributions or contexts. At the beginning of each block (when information about context is provided), hippocampus is activated and this response is enhanced when contextual influence on choice increases. In addition, response to value in ventral tegmental area/substantia nigra (VTA/SN) shows context-sensitivity, an effect enhanced with an increased contextual influence on choice. Finally, greater response in hippocampus at block start is associated with enhanced context sensitivity in VTA/SN. These findings suggest that context-sensitive choice is driven by a brain circuit involving hippocampus and dopaminergic midbrain

Dopamine Increases a Value-Independent Gambling Propensity

Although the impact of dopamine on reward learning is well documented, its influence on other aspects of behavior remains the subject of much ongoing work. Dopaminergic drugs are known to increase risk-taking behavior, but the underlying mechanisms for this effect are not clear. We probed dopamine’s role by examining the effect of its precursor L-DOPA on the choices of healthy human participants in an experimental paradigm that allowed particular components of risk to be distinguished. We show that choice behavior depended on a baseline (ie, value-independent) gambling propensity, a gambling preference scaling with the amount/variance, and a value normalization factor. Boosting dopamine levels specifically increased just the value-independent baseline gambling propensity, leaving the other components unaffected. Our results indicate that the influence of dopamine on choice behavior involves a specific modulation of the attractiveness of risky options—a finding with implications for understanding a range of reward-related psychopathologies including addiction

Valence-dependent influence of serotonin depletion on model-based choice strategy.

Human decision-making arises from both reflective and reflexive mechanisms, which underpin goal-directed and habitual behavioural control. Computationally, these two systems of behavioural control have been described by different learning algorithms, model-based and model-free learning, respectively. Here, we investigated the effect of diminished serotonin (5-hydroxytryptamine) neurotransmission using dietary tryptophan depletion (TD) in healthy volunteers on the performance of a two-stage decision-making task, which allows discrimination between model-free and model-based behavioural strategies. A novel version of the task was used, which not only examined choice balance for monetary reward but also for punishment (monetary loss). TD impaired goal-directed (model-based) behaviour in the reward condition, but promoted it under punishment. This effect on appetitive and aversive goal-directed behaviour is likely mediated by alteration of the average reward representation produced by TD, which is consistent with previous studies. Overall, the major implication of this study is that serotonin differentially affects goal-directed learning as a function of affective valence. These findings are relevant for a further understanding of psychiatric disorders associated with breakdown of goal-directed behavioural control such as obsessive-compulsive disorders or addictions.This research was funded by Wellcome Trust Grants awarded to VV (Intermediate WT Fellowship) and Programme Grant (089589/Z/09/Z) awarded to TWR, BJE, ACR, JWD and BJS. It was conducted at the Behavioural and Clinical Neuroscience Institute, which is supported by a joint award from the Medical Research Council and Wellcome Trust (G00001354). YW was supported by the Fyssen Foundation. SP is supported by Marie Curie Intra-European Fellowship (FP7-People-2012-IEF).This is the final version of the article. It first appeared from NPG via http://dx.doi.org/10.1038/mp.2015.4

Finding minimal action sequences with a simple evaluation of actions

Animals are able to discover the minimal number of actions that achieves an outcome (the minimal action sequence). In most accounts of this, actions are associated with a measure of behavior that is higher for actions that lead to the outcome with a shorter action sequence, and learning mechanisms find the actions associated with the highest measure. In this sense, previous accounts focus on more than the simple binary signal of “was the outcome achieved?”; they focus on “how well was the outcome achieved?” However, such mechanisms may not govern all types of behavioral development. In particular, in the process of action discovery (Redgrave and Gurney, 2006), actions are reinforced if they simply lead to a salient outcome because biological reinforcement signals occur too quickly to evaluate the consequences of an action beyond an indication of the outcome’s occurrence. Thus, action discovery mechanisms focus on the simple evaluation of “was the outcome achieved?” and not “how well was the outcome achieved?” Notwithstanding this impoverishment of information, can the process of action discovery find the minimal action sequence? We address this question by implementing computational mechanisms, referred to in this paper as no-cost learning rules, in which each action that leads to the outcome is associated with the same measure of behavior. No-cost rules focus on “was the outcome achieved?” and are consistent with action discovery. No-cost rules discover the minimal action sequence in simulated tasks and execute it for a substantial amount of time. Extensive training, however, results in extraneous actions, suggesting that a separate process (which has been proposed in action discovery) must attenuate learning if no-cost rules participate in behavioral development. We describe how no-cost rules develop behavior, what happens when attenuation is disrupted, and relate the new mechanisms to wider computational and biological context

Online k-taxi via double coverage and time-reverse primal-dual

We consider the online k-taxi problem, a generalization of the k-server problem, in which k servers are located in a metric space. A sequence of requests is revealed one by one, where each request is a pair of two points, representing the start and destination of a travel request by a passenger. The goal is to serve all requests while minimizing the distance traveled without carrying a passenger. We show that the classic Double Coverage algorithm has competitive ratio 2 k- 1 on HSTs, matching a recent lower bound for deterministic algorithms. For bounded depth HSTs, the competitive ratio turns out to be much better and we obtain tight bounds. When the depth is d≪ k, these bounds are approximately kd/ d!. By standard embedding results, we obtain a randomized algorithm for arbitrary n-point metrics with (polynomial) competitive ratio O(kcΔ 1/clog Δn), where Δ is the aspect ratio and c≥ 1 is an arbitrary positive integer constant. The only previous known bound was O(2 klog n). For general (weighted) tree metrics, we prove the competitive ratio of Double Coverage to be Θ (kd) for any fixed depth d, but unlike on HSTs it is not bounded by 2 k- 1. We obtain our results by a dual fitting analysis where the dual solution is constructed step-by-step backwards in time. Unlike the forward-time approach typical of online primal-dual analyses, this allows us to combine information from the past and the future when assigning dual variables. We believe this method can be useful also for other problems. Using this technique, we also provide a dual fitting proof of the k-competitiveness of Double Coverage for the k-server problem on trees

Goal-directed and habitual control in the basal ganglia: implications for Parkinson's disease

Progressive loss of the ascending dopaminergic projection in the basal ganglia is a fundamental pathological feature of Parkinson's disease. Studies in animals and humans have identified spatially segregated functional territories in the basal ganglia for the control of goal-directed and habitual actions. In patients with Parkinson's disease the loss of dopamine is predominantly in the posterior putamen, a region of the basal ganglia associated with the control of habitual behaviour. These patients may therefore be forced into a progressive reliance on the goal-directed mode of action control that is mediated by comparatively preserved processing in the rostromedial striatum. Thus, many of their behavioural difficulties may reflect a loss of normal automatic control owing to distorting output signals from habitual control circuits, which impede the expression of goal-directed action. © 2010 Macmillan Publishers Limited. All rights reserved

Motivational modulation of bradykinesia in Parkinson's disease off and on dopaminergic medication.

Motivational influence on bradykinesia in Parkinson's disease may be observed in situations of emotional and physical stress, a phenomenon known as paradoxical kinesis. However, little is known about motivational modulation of movement speed beyond these extreme circumstances. In particular, it is not known if motivational factors affect movement speed by improving movement preparation/initiation or execution (or both) and how this effect relates to the patients' medication state. In the present study, we tested if provision of motivational incentive through monetary reward would speed-up movement initiation and/or execution in Parkinson's disease patients and if this effect depended on dopaminergic medication. We studied the effect of monetary incentive on simple reaction time in 11 Parkinson's disease patients both "off" and "on" dopaminergic medication and in 11 healthy participants. The simple reaction time task was performed across unrewarded and rewarded blocks. The initiation time and movement time were quantified separately. Anticipation errors and long responses were also recorded. The prospect of reward improved initiation times in Parkinson's disease patients both "off" and "on" dopaminergic medication, to a similar extent as in healthy participants. However, for "off" medication, this improvement was associated with increased frequency of anticipation errors, which were eliminated by dopamine replacement. Dopamine replacement had an additional, albeit small effect, on reward-related improvement of movement execution. Motivational strategies are helpful in overcoming bradykinesia in Parkinson's disease. Motivational factors may have a greater effect on bradykinesia when patients are "on" medication, as dopamine appears to be required for overcoming speed-accuracy trade-off and for improvement of movement execution. Thus, medication status should be an important consideration in movement rehabilitation programmes for patients with Parkinson's disease