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been shown to increase exploration of alternative response strategies even when exploration is suboptimal. In the present study, promotion- and prevention-focused subjects performed a choice task that required them to choose from one of two decks of cards on each trial. They either gained or lost points with each draw. In Experiment 1, optimal performance required an exploratory response pattern that entailed sampling from a deck that initially appeared disadvantageous but ultimately became advantageous. In Experiment 2, optimal performance required an exploitative response pattern. A softmax action selection model that includes an exploitation parameter was applied to each subject’s data from both experiments and revealed greater exploration of alternative strategies for people with a regulatory fit. This response strategy was optimal in Experiment 1 and led to superior performance, but was suboptimal in Experiment 2 and led to inferior performance. Motivation is central to action. The motivation literature makes a distinction between approach goals—positive states that one wants to achieve—and avoidance goals— negative states that one wants to avoid (see, e.g., Carver & Scheier, 1998). Higgins (1987) proposed regulatory focus theory, which argues for psychological states of readiness

Abstract: A wealth of research focuses on the decision-making processes that animals and humans employ when selecting actions in the face of reward and punishment. Initially such work stemmed from psychological investigations of conditioned behavior, and explanations of these in terms of computational models. Increasingly, analysis at the computational level has drawn on ideas from reinforcement learning, which provide a normative framework within which decision-making can be analyzed. More recently, the fruits of these extensive lines of research have made contact with investigations into the neural basis of decision making. Converging evidence now links reinforcement learning to specific neural substrates, assigning them precise computational roles. Specifically, electrophysiological recordings in behaving animals and functional imaging of human decision-making have revealed in the brain the existence of a key reinforcement learning signal, the temporal difference reward prediction error. Here, we first introduce the formal reinforcement learning framework. We then review the multiple lines of evidence linking reinforcement learning to the function of dopaminergic neurons in the mammalian midbrain and

The bandit problem is a dynamic decision-making task that is simply described, well-suited to controlled laboratory study, and representative of a broad class of real-world problems. In bandit problems, people must choose between a set of alternatives, each with different unknown reward rates, to maximize the total reward they receive over a fixed number of trials. A key feature of the task is that it challenges people to balance the exploration of unfamiliar choices with the exploitation of familiar ones. We use a Bayesian model of optimal decision-making on the task, in which how people balance exploration with exploitation depends on their assumptions about the distribution of reward rates. We also use Bayesian model selection measures that assesses how well people adhere to an optimal decision process, compared to simpler heuristic decision strategies. Using these models, we make inferences about the decision-making of 451 participants who completed a

Abstract. There are many examples of intelligent and learning systems that are based either on the connectionist or the symbolic approach. Although the latter can be successfully combined with statistical learning to create a hybrid system, it is not so clear how symbolic processing can emerge from a connectionst system. Human mind is a living proof that such a transition must be possible. Inspired by biological cognition, our project explores the ways symbolic processing can emerge in a system of neural cell–assemblies (CAs). Here, we present the meta–process that regulates learning of associations between the CAs. The process is compared with the stochastic learning theory, and its outcome is a set of optimal rules. The paper concludes by an example of a working system and the discussion of it biological plausibility. 1

This paper studies how automated agents can persuade humans to behave in certain ways. The motivation behind such agent’s behavior resides in the utility function that the agent’s designer wants to maximize and which may be different from the user’s utility function. Specifically, in the strategic settings studied, the agent provides correct yet partial information about a state of the world that is unknown to the user but relevant to his decision. Persuasion games were designed to study interactions between automated players where one player sends state information to the other to persuade it to behave in a certain way. We show that this game theory based model is not sufficient to model human-agent interactions, since people tend to deviate from the rational choice. We use machine learning to model such deviation in people from this game theory based model. The agent generates a probabilistic description of the world state that maximizes its benefit and presents it to the users. The proposed model was evaluated in an extensive empirical study involving road selection tasks that differ in length, costs and congestion. Results showed that people’s behavior indeed deviated significantly from the behavior predicted by the game theory based model. Moreover, the agent developed in our model performed better than an agent that followed the behavior dictated by the game-theoretical models.

Bayesian treatments of animal conditioning start from a generative model that specifies precisely a set of assumptions about the structure of the learning task. Optimal rules for learning are direct mathematical consequences of these assumptions. In terms of Marr’s (1982) levels of analyses, the main task at the computational level

Donald Hebb proposed a hypothesis that specialised groups of neurons, called cell-assemblies (CAs), form the basis for neural encoding of symbols in human mind. It is not clear, however, how CAs can be re-used and combined to form new representations as in classical symbolic systems. We demonstrate that Hebbian learning of synaptic weights alone is not adequate for the task, and that additional meta-control process should be involved. We describe a proposed earlier architecture implementing such a process, and then evaluate it by modelling the probability matching phenomenon in a classical twochoice task. The model and its results are discussed in view of mathematical theory of learning, existing cognitive architectures as well as some hypotheses about neural functioning in the brain.

Decision making lies at the very heart of many psychiatric diseases. It is also a central theoretical concern in a wide variety of fields and has undergone detailed, in-depth, analyses. We take as an example Major Depressive Disorder (MDD), applying insights from a Bayesian reinforcement learning framework. We focus on anhedonia and helplessness. Helplessness—a core element in the conceptualizations of MDD that has lead to major advances in its treatment, pharmacological and neurobiological understanding—is formalized as a simple prior over the outcome entropy of actions in uncertain environments. Anhedonia, which is an equally fundamental aspect of the disease, is related to the effective reward size. These formulations allow for the design of specific tasks to measure anhedonia and helplessness behaviorally. We show that these behavioral measures capture explicit, questionnaire-based cognitions. We also provide evidence that these tasks may allow classification of subjects into healthy and MDD groups based purely on a behavioural measure and avoiding any verbal reports.

In numerous and high-profile studies, researchers have recently begun to integrate computational models