Do you share your personally useless information if others may benefit from it?

Information is personally useless if its beholder cannot individually benefit from it further unless she shares it with those who can exploit that information to increase their mutual outcome. We study sharing such information anonymously in a non-strategic and non-competitive setting, where selfish and cooperative motives align. Although sharing information was cost-free and resulted in expected mutual payoff, almost all subjects showed some levels of hesitancy toward sharing information, and it was more severe in the introverts. According to our mechanistic model, this irrationality could arise because of the excessive subjective value of personally useless information and low other-regarding motives, that necessitated over-attainable personal benefit to drive sharing. Interestingly, other-regarding element correlated with the subjects' belief about how others are cooperative in general. In addition, sensitivity to the value of information correlated with their extraversion level. The results open a new window towards understanding inefficient motives that deprive people of collective benefit.

Implicit Counterfactual Effect in Partial Feedback Reinforcement Learning: Behavioral and Modeling Approach.

Context remarkably affects learning behavior by adjusting option values according to the distribution of available options. Displaying counterfactual outcomes, the outcomes of the unchosen option alongside the chosen one (i.e., providing complete feedback), would increase the contextual effect by inducing participants to compare the two outcomes during learning. However, when the context only consists of the juxtaposition of several options and there is no such explicit counterfactual factor (i.e., only partial feedback is provided), it is not clear whether and how the contextual effect emerges. In this research, we employ Partial and Complete feedback paradigms in which options are associated with different reward distributions. Our modeling analysis shows that the model that uses the outcome of the chosen option for updating the values of both chosen and unchosen options in opposing directions can better account for the behavioral data. This is also in line with the diffusive effect of dopamine on the striatum. Furthermore, our data show that the contextual effect is not limited to probabilistic rewards, but also extends to magnitude rewards. These results suggest that by extending the counterfactual concept to include the effect of the chosen outcome on the unchosen option, we can better explain why there is a contextual effect in situations in which there is no extra information about the unchosen outcome.

Effects of methylphenidate on reinforcement learning depend on working memory capacity.

RATIONALE: Brain catecholamines have long been implicated in reinforcement learning, exemplified by catecholamine drug and genetic effects on probabilistic reversal learning. However, the mechanisms underlying such effects are unclear. OBJECTIVES AND METHODS: Here we investigated effects of an acute catecholamine challenge with methylphenidate (20 mg, oral) on a novel probabilistic reversal learning paradigm in a within-subject, double-blind randomised design. The paradigm was designed to disentangle effects on punishment avoidance from effects on reward perseveration. Given the known large individual variability in methylphenidate's effects, we stratified our effects by working memory capacity and trait impulsivity, putatively modulating the effects of methylphenidate, in a large sample (n = 102) of healthy volunteers. RESULTS: Contrary to our prediction, methylphenidate did not alter performance in the reversal phase of the task. Our key finding is that methylphenidate altered learning of choice-outcome contingencies in a manner that depended on individual variability in working memory span. Specifically, methylphenidate improved performance by adaptively reducing the effective learning rate in participants with higher working memory capacity. CONCLUSIONS: This finding emphasises the important role of working memory in reinforcement learning, as reported in influential recent computational modelling and behavioural work, and highlights the dependence of this interplay on catecholaminergic function.

Feedback From Mono-Articular Muscles is Sufficient for Exoskeleton Torque Adaptation.

In this paper, by using a biomechanical model of the human body, we prove that (1) due to the existence of bi-articular muscles and compliant-elements, blind full-torque-compensation at joint level leads to muscles' activity amplification and consequently online adaptation methods are required for exoskeleton torque optimization. Moreover, (2) we state a new hypothesis that "reducing the net torque of two antagonistic mono-articular muscles is sufficient for involved muscles' total effort reduction" and analytically discuss its validity condition. Using this hypothesis, (3) we develop an adaptation rule which optimizes the exoskeleton torque using EMG signals of only two antagonistic mono-articular muscles. Furthermore, (4) the stability, convergence, optimality, and robustness of our adaptation method are proved in the presence of electromyography's intrinsic noisy behavior. Finally, (5) we experimentally validate our EMG-based adaptation method on six healthy subjects. We show that adaptation of the elbow compliance in a 2-DOF semi-active assistive arm in a cyclic task results in significant muscles activity reduction in all our subjects.

Attention Cueing and Activity Equally Reduce False Alarm Rate in Visual-Auditory Associative Learning through Improving Memory.

In our daily life, we continually exploit already learned multisensory associations and form new ones when facing novel situations. Improving our associative learning results in higher cognitive capabilities. We experimentally and computationally studied the learning performance of healthy subjects in a visual-auditory sensory associative learning task across active learning, attention cueing learning, and passive learning modes. According to our results, the learning mode had no significant effect on learning association of congruent pairs. In addition, subjects' performance in learning congruent samples was not correlated with their vigilance score. Nevertheless, vigilance score was significantly correlated with the learning performance of the non-congruent pairs. Moreover, in the last block of the passive learning mode, subjects significantly made more mistakes in taking non-congruent pairs as associated and consciously reported lower confidence. These results indicate that attention and activity equally enhanced visual-auditory associative learning for non-congruent pairs, while false alarm rate in the passive learning mode did not decrease after the second block. We investigated the cause of higher false alarm rate in the passive learning mode by using a computational model, composed of a reinforcement learning module and a memory-decay module. The results suggest that the higher rate of memory decay is the source of making more mistakes and reporting lower confidence in non-congruent pairs in the passive learning mode.

Equality bias impairs collective decision-making across cultures.

We tend to think that everyone deserves an equal say in a debate. This seemingly innocuous assumption can be damaging when we make decisions together as part of a group. To make optimal decisions, group members should weight their differing opinions according to how competent they are relative to one another; whenever they differ in competence, an equal weighting is suboptimal. Here, we asked how people deal with individual differences in competence in the context of a collective perceptual decision-making task. We developed a metric for estimating how participants weight their partner's opinion relative to their own and compared this weighting to an optimal benchmark. Replicated across three countries (Denmark, Iran, and China), we show that participants assigned nearly equal weights to each other's opinions regardless of true differences in their competence-even when informed by explicit feedback about their competence gap or under monetary incentives to maximize collective accuracy. This equality bias, whereby people behave as if they are as good or as bad as their partner, is particularly costly for a group when a competence gap separates its members.

FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model.

A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well.

Context transfer in reinforcement learning using action-value functions.

This paper discusses the notion of context transfer in reinforcement learning tasks. Context transfer, as defined in this paper, implies knowledge transfer between source and target tasks that share the same environment dynamics and reward function but have different states or action spaces. In other words, the agents learn the same task while using different sensors and actuators. This requires the existence of an underlying common Markov decision process (MDP) to which all the agents' MDPs can be mapped. This is formulated in terms of the notion of MDP homomorphism. The learning framework is Q-learning. To transfer the knowledge between these tasks, the feature space is used as a translator and is expressed as a partial mapping between the state-action spaces of different tasks. The Q-values learned during the learning process of the source tasks are mapped to the sets of Q-values for the target task. These transferred Q-values are merged together and used to initialize the learning process of the target task. An interval-based approach is used to represent and merge the knowledge of the source tasks. Empirical results show that the transferred initialization can be beneficial to the learning process of the target task.

Computational model of excitatory/inhibitory ratio imbalance role in attention deficit disorders.

Impairments in attentional behaviors, including over-selectivity, under-selectivity, distractibility and difficulty in shift of attention, are widely reported in several developmental disorders, including autism. Uncharacteristic inhibitory to excitatory neuronal number ratio (IER) and abnormal synaptic strength levels in the brain are two broadly accepted neurobiological disorders observed in autistic individuals. These neurobiological findings are contrasting and their relation to the atypical attentional behaviors is not clear yet. In this paper, we take a computational approach to investigate the relation of imbalanced IER and abnormal synaptic strength to some well-documented spectrum of attentional impairments. The computational model is based on a modified version of a biologically plausible neural model of two competing minicolumns in IT cortex augmented with a simple model of top-down attention. Top-down attention is assumed to amplify (attenuates) attended (unattended) stimulus. The inhibitory synaptic strength parameter in the model is set such that typical attentional behavior is emerged. Then, according to related findings, the parameter is changed and the model's attentional behavior is considered. The simulation results show that, without any change in top-down attention, the abnormal inhibitory synaptic strength values--and IER imbalance- result in over-selectivity, under-selectivity, distractibility and difficulty in shift of attention in the model. It suggests that the modeled neurobiological abnormalities can be accounted for the attentional deficits. In addition, the atypical attentional behaviors do not necessarily point to impairments in top-down attention. Our simulations suggest that limited changes in the inhibitory synaptic strength and variations in top-down attention signal affect the model's attentional behaviors in the same way. So, limited deficits in the inhibitory strength may be alleviated by appropriate change in top-down attention biasing. Nevertheless, our model proposes that this compensation is not possible for very high and very low values of the inhibitory strength.

Differences in white matter reflect atypical developmental trajectory in autism: A Tract-based Spatial Statistics study.

Autism is a neurodevelopmental disorder in which white matter (WM) maturation is affected. We assessed WM integrity in 16 adolescents and 14 adults with high-functioning autism spectrum disorder (ASD) and in matched neurotypical controls (NT) using diffusion weighted imaging and Tract-based Spatial Statistics. Decreased fractional anisotropy (FA) was observed in adolescents with ASD in tracts involved in emotional face processing, language, and executive functioning, including the inferior fronto-occipital fasciculus and the inferior and superior longitudinal fasciculi. Remarkably, no differences in FA were observed between ASD and NT adults. We evaluated the effect of age on WM development across the entire age range. Positive correlations between FA values and age were observed in the right inferior fronto-occipital fasciculus, the left superior longitudinal fasciculus, the corpus callosum, and the cortical spinal tract of ASD participants, but not in NT participants. Our data underscore the dynamic nature of brain development in ASD, showing the presence of an atypical process of WM maturation, that appears to normalize over time and could be at the basis of behavioral improvements often observed in high-functioning autism.