V. S. Chandrasekhar Pammi

From Bad to Worse: Striatal Coding of the Relative Value of Painful Decisions

The majority of decision-related research has focused on how the brain computes decisions over outcomes that are positive in expectation. However, much less is known about how the brain integrates information when all possible outcomes in a decision are negative. To study decision-making over negative outcomes, we used fMRI along with a task in which participants had to accept or reject 50/50 lotteries that could result in more or fewer electric shocks compared to a reference amount. We hypothesized that behaviorally, participants would treat fewer shocks from the reference amount as a gain, and more shocks from the reference amount as a loss. Furthermore, we hypothesized that this would be reflected by a greater BOLD response to the prospect of fewer shocks in regions typically associated with gain, including the ventral striatum and orbitofrontal cortex. The behavioral data suggest that participants in our study viewed all outcomes as losses, despite our attempt to induce a status quo. We find that the ventral striatum showed an increase in BOLD response to better potential gambles (i.e., fewer expected shocks). This lends evidence to the idea that the ventral striatum is not solely responsible for reward processing but that it might also signal the relative value of an expected outcome or action, regardless of whether the outcome is entirely appetitive or aversive. We also find a greater response to worse gambles in regions previously associated with aversive valuation, suggesting an opposing but simultaneous valuation signal to that conveyed by the striatum.

Role of affect in decision making.

Emotion plays a major role in influencing our everyday cognitive and behavioral functions, including decision making. We introduce different ways in which emotions are characterized in terms of the way they influence or elicited by decision making. This chapter discusses different theories that have been proposed to explain the role of emotions in judgment and decision making. We also discuss incidental emotional influences, both long-duration influences like mood and short-duration influences by emotional context present prior to or during decision making. We present and discuss results from a study with emotional pictures presented prior to decision making and how that influences both decision processes and postdecision experience as a function of uncertainty. We conclude with a summary of the work on emotions and decision making in the context of decision-making theories and our work on incidental emotions.

Neural loss aversion differences between depression patients and healthy individuals: A functional MRI investigation.

Neuroeconomics employs neuroscience techniques to explain decision-making behaviours. Prospect theory, a prominent model of decision-making, features a value function with parameters for risk and loss aversion. Recent work with normal participants identified activation related to loss aversion in brain regions including the amygdala, ventral striatum, and ventromedial prefrontal cortex. However, the brain network for loss aversion in pathologies such as depression has yet to be identified. The aim of the current study is to employ the value function from prospect theory to examine behavioural and neural manifestations of loss aversion in depressed and healthy individuals to identify the neurobiological markers of loss aversion in economic behaviour. We acquired behavioural data and fMRI scans while healthy controls and patients with depression performed an economic decision-making task. Behavioural loss aversion was higher in patients with depression than in healthy controls. fMRI results revealed that the two groups shared a brain network for value function including right ventral striatum, ventromedial prefrontal cortex, and right amygdala. However, the neural loss aversion results revealed greater activations in the right dorsal striatum and the right anterior insula for controls compared with patients with depression, and higher activations in the midbrain region ventral tegmental area for patients with depression compared with controls. These results suggest that while the brain network for loss aversion is shared between depressed and healthy individuals, some differences exist with respect to differential activation of additional areas. Our findings are relevant to identifying neurobiological markers for altered decision-making in the depressed.

Understanding decision neuroscience: a multidisciplinary perspective and neural substrates.

The neuroscience of decision making is a rapidly evolving multidisciplinary research area that employs neuroscientific techniques to explain various parameters associated with decision making behavior. In this chapter, we emphasize the role of multiple disciplines such as psychology, economics, neuroscience, and computational approaches in understanding the phenomenon of decision making. Further, we present a theoretical approach that suggests understanding the building blocks of decision making as bottom-up processes and integrate these with top-down modulatory factors. Relevant neurophysiological and neuroimaging findings that have used the building-block approach are reviewed. A unifying framework emphasizing multidisciplinary views would bring further insights into the active research area of decision making. Pointing to future directions for research, we focus on the role of computational approaches in such a unifying framework.

Cyber security: A game-theoretic analysis of defender and attacker strategies in defacing-website games

The rate at which cyber-attacks are increasing globally portrays a terrifying picture upfront. The main dynamics of such attacks could be studied in terms of the actions of attackers and defenders in a cyber-security game. However currently little research has taken place to study such interactions. In this paper we use behavioral game theory and try to investigate the role of certain actions taken by attackers and defenders in a simulated cyber-attack scenario of defacing a website. We choose a Reinforcement Learning (RL) model to represent a simulated attacker and a defender in a 2×4 cyber-security game where each of the 2 players could take up to 4 actions. A pair of model participants were computationally simulated across 1000 simulations where each pair played at most 30 rounds in the game. The goal of the attacker was to deface the website and the goal of the defender was to prevent the attacker from doing so. Our results show that the actions taken by both the attackers and defenders are a function of attention paid by these roles to their recently obtained outcomes. It was observed that if attacker pays more attention to recent outcomes then he is more likely to perform attack actions. We discuss the implication of our results on the evolution of dynamics between attackers and defenders in cyber-security games.

Effects of Motivation: Rewarding Hackers for Undetected Attacks Cause Analysts to Perform Poorly

Objective: The aim of this study was to determine how monetary motivations influence decision making of humans performing as security analysts and hackers in a cybersecurity game. Background: Cyberattacks are increasing at an alarming rate. As cyberattacks often cause damage to existing cyber infrastructures, it is important to understand how monetary rewards may influence decision making of hackers and analysts in the cyber world. Currently, only limited attention has been given to this area. Method: In an experiment, participants were randomly assigned to three between-subjects conditions (n = 26 for each condition): equal payoff, where the magnitude of monetary rewards for hackers and defenders was the same; rewarding hacker, where the magnitude of monetary reward for hacker’s successful attack was 10 times the reward for analyst’s successful defense; and rewarding analyst, where the magnitude of monetary reward for analyst’s successful defense was 10 times the reward for hacker’s successful attack. In all conditions, half of the participants were human hackers playing against Nash analysts and half were human analysts playing against Nash hackers. Results: Results revealed that monetary rewards for human hackers and analysts caused a decrease in attack and defend actions compared with the baseline. Furthermore, rewarding human hackers for undetected attacks made analysts deviate significantly from their optimal behavior. Conclusions: If hackers are rewarded for their undetected attack actions, then this causes analysts to deviate from optimal defend proportions. Thus, analysts need to be trained not become overenthusiastic in defending networks. Application: Applications of our results are to networks where the influence of monetary rewards may cause information theft and system damage.

Evolution of Cooperation with Heterogeneous Conditional Cooperators

Conditional cooperation declines over time if heterogeneous ideal conditional agents are involved in repeated interactions. With strict assumptions of rationality and a population consisting of ideal conditional agents who strictly follow a decision rule, cooperation is not expected. However, cooperation is commonly observed in human societies. Hence, we propose a novel evolutionary agent-based model where agents rely on social information. Each agent interacts only once either as a donor or as a receiver. In our model, the population consists of either non-ideal or ideal heterogeneous conditional agents. Their donation decisions are stochastically based on the comparison between the number of donations in the group and their conditional cooperative criterion value. Non-ideal agents occasionally cooperate even if the conditional rule of the agent is not satisfied. The stochastic decision and selection rules are controlled with decision intensity and selection intensity, respectively. The simulations show that high levels of cooperation (more than 90%) are established in the population with non-ideal agents for a particular range of parameter values. The emergence of cooperation needs non-ideal agents and a heterogeneous population. The current model differs from existing models by relying on social information and not on individual agent’s prior history of cooperation.

Promises and pitfalls of relating alteration of white matter pathways causing improvement in cognitive performance

ABSTRACT Here, we argue systematically about the promises and pitfalls of relating Human Connectome to cognitive enhancement. We also highlight three key areas where further resolution is required before the generalization of the white-matter-related cause of cognitive enhancement across a variety of cognitive modalities is made. These key areas are: (a) inherent limitations in estimating of diffusion-weighted anisotropy index near volumes with high abundance of crossing fibers; (b) species differences in cell types and only a putative link between brain rhythms and modulation of activity in precursor cells in rodents; (c) sparse evidence of finding white matter structural connectivity change between two remote brain networks causing cognitive enhancement.

Modelling Social Dilemmas: A Regret-driven Neural Network Model

Abstract Marchiori and Warglien (2008, Science, 319(5866), 1111–1113) showed that a simple regret-driven neural network model outperforms almost all competing models when predicting human choice behaviour in games with ‘unique equilibrium in mixed strategies’. Considering its effectiveness in this class of games, we scale up the model to account for strategically more important decision-making scenarios like prisoners’ dilemma (PD). The modification is based on the assumption that the trajectory of behaviour observed in a repeated PD experiment is the result of the bidirectional attraction between pareto-optimal (mutual cooperation) versus self-interested defection (mutual defection) in repeated PD game. The simulation results significantly capture the qualitative trends in behaviour over time.

Influence of Motivational Factors on Hackers’ and Analysts’ Decisions in Dynamic Security Games

Cyber-attacks may be studied as a non-cooperative game between hackers and analysts. However, current game-theoretic approaches have disregarded how motivational factors (cost and benefit of hacker’s and analyst’s actions) are likely to influence decision-making during cyber-attacks. In an experiment, motivations of humans acting as hackers and analysts were manipulated across three between-subjects conditions in a repeated game: Equal-Payoff (Control; N = 25 pairs), Rewarding-Hacker (for successful attacks; N = 25 pairs) and Rewarding-Analyst (for correctly detecting cyber-attacks; N = 25 pairs). Hackers and analysts simultaneously decided in order to maximize their payoffs. Results revealed that the proportion of attacks was higher for Rewarding-Hacker condition and lower for Rewarding-Analyst condition compared to the Equal-Payoff condition. The proportion of defend actions was higher in Rewarding-Hacker condition and same in Rewarding-Analyst condition compared to the Equal-Payoff condition. We highlight the relevance of our results to cyber-attacks in the real world.