Most organisms facing a choice between multiple stimuli will look repeatedly at them, presumably implementing a comparison process between the items' values. Little is known about the nature of the comparison process in value-based decision-making or about the role of visual fixations in this process. We created a computational model of value-based binary choice in which fixations guide the comparison process and tested it on humans using eye-tracking. We found that the model can quantitatively explain complex relationships between fixation patterns and choices, as well as several fixation-driven decision biases.

How do we make decisions when confronted with several alternatives (e.g., on a supermarket shelf)? Previous work has shown that accumulator models, such as the drift-diffusion model, can provide accurate descriptions of the psychometric data for binary value-based choices, and that the choice process is guided by visual attention. However, the computational processes used to make choices in more complicated situations involving three or more options are unknown. We propose a model of trinary value-based choice that generalizes what is known about binary choice, and test it using an eye-tracking experiment. We find that the model provides a quantitatively accurate description of the relationship between choice, reaction time, and visual fixation data using the same parameters that were estimated in previous work on binary choice. Our findings suggest that the brain uses similar computational processes to make binary and trinary choices.A basic goal of decision neuroscience is to characterize the computational processes used by individuals to make different types of decisions, as well as the neurobiological substrates of such computations (1-5). A significant amount of effort has been devoted to characterizing these processes in the realm of perceptual decision making involving two-alternative forced choices (2, 6-8). However, many important decisions do not fit this framework: they involve choices among multiple alternatives (n > 2) associated with different reward values (e.g., which food to select from a buffet table). Here we investigate these types of decisions.The standard drift-diffusion model (DDM), as well as closely related versions, such as the leaky competitive accumulator (LCA) model (3, 4, 9), have been highly successful in providing quantitative explanations of the psychometrics, chronometrics, and neurometrics of binary perceptual choice (2, 10-16), and more recently in binary value-based choice (17-20). These models assume that decisions are made by accumulating stochastic information over time until the net evidence in favor of one option exceeds a prespecified threshold. The size of the threshold can be chosen to optimally balance the benefit of accumulating more information with the cost of taking more time to reach a decision (21). Consider, for example, the canonical dot-motion task that has been widely used to study perceptual decision making. Here the stimulus itself is stochastic and each instant is thought to provide noisy but informative evidence for the net direction of movement in the display. Thus, as the individual accumulates more evidence, his knowledge about the true net direction of movement increases (2).The DDM has also been shown to provide highly accurate descriptions of accuracy and response times in domains such as memory retrieval and decision-making, where the stimuli are not explicitly stochastic (17,19,20,(22)(23)(24)(25)(26)(27)(28); this suggests that these decisions might be made using a similar process of random information accumulation and integration. To see why, ...

Curiosity has been described as the "wick in the candle of learning" but its underlying mechanisms are not well-understood. We scanned subjects with fMRI while they read trivia questions. The level of curiosity when reading questions is correlated with activity in caudate regions previously suggested to be involved in anticipated reward or encoding prediction error. This finding led to a behavioral study showing that subjects spend more scarce resources (either limited tokens, or waiting time) to find out answers when they are more curious. The fMRI also showed that curiosity increases activity in memory areas when subjects guess incorrectly, which suggests that curiosity may enhance memory for surprising new information. This prediction about memory enhancement is confirmed in a behavioral study-higher curiosity in the initial session is correlated with better recall of surprising answers 10 days later.Keywords: Neuroimaging, Memory, Learning, Brain 2 Curiosity is the complex feeling and cognition accompanying the desire to learn what is unknown. Curiosity can be both helpful and dangerous. It plays a critical role in motivating learning and discovery, especially by creative professionals, increasing the world's store of knowledge. Einstein, for example, once said, "I have no special talents. I am only passionately curious (Hoffmann, 1972)." The dangerous side of curiosity is its association with exploratory behaviors with harmful consequences. An ancient example is the mythical Pandora, who opened a box that unleashed misfortunes on the world. In modern times, technology such as the Internet augments both good and bad effects of curiosity, by putting both enormous amounts of information and potentially dangerous social encounters a mouse click away.Despite its importance, the psychological and neural underpinnings of human curiosity remain poorly understood. Philosophers and psychologists have described curiosity as an appetite for knowledge, a drive like hunger and thirst (Loewenstein, 1994), the hunger pang of an 'info-vore' (Biederman & Vessel, 2006), and "the wick in the candle of learning" (William Arthur Ward). In reinforcement learning a "novelty bonus" is used to motivate the choice of unexplored strategies (Kakade & Dayan, 2002).Curiosity can be thought of as the psychological manifestation of such a novelty bonus.A theory guiding our research holds that curiosity arises from an incongruity or 'information gap'-a discrepancy between what one knows and what one wants to know (Loewenstein, 1994). The theory assumes that the aspired level of knowledge increases sharply with a small increase in knowledge, so that the information gap grows with initial learning. When one is sufficiently knowledgeable, however, the gap shrinks and curiosity falls. If curiosity is like a hunger for knowledge, then a small "priming dose" of information increases the hunger, and the decrease in curiosity from knowing a lot is like being satiated by information.In the information-gap theory, the object of curiosity is a...

Curiosity has been described as the "wick in the candle of learning" but its underlying mechanisms are not well-understood. We scanned subjects with fMRI while they read trivia questions. The level of curiosity when reading questions is correlated with activity in caudate regions previously suggested to be involved in anticipated reward or encoding prediction error. This finding led to a behavioral study showing that subjects spend more scarce resources (either limited tokens, or waiting time) to find out answers when they are more curious. The fMRI also showed that curiosity increases activity in memory areas when subjects guess incorrectly, which suggests that curiosity may enhance memory for surprising new information. This prediction about memory enhancement is confirmed in a behavioral study-higher curiosity in the initial session is correlated with better recall of surprising answers 10 days later.

How do we make simple purchasing decisions (e.g., whether or not to buy a product at a given price)? Previous work has shown that the attentional drift-diffusion model (aDDM) can provide accurate quantitative descriptions of the psychometric data for binary and trinary value-based choices, and of how the choice process is guided by visual attention. Here we extend the aDDM to the case of purchasing decisions, and test it using an eye-tracking experiment. We find that the model also provides a reasonably accurate quantitative description of the relationship between choice, reaction time, and visual fixations using parameters that are very similar to those that best fit the previous data. The only critical difference is that the choice biases induced by the fixations are about half as big in purchasing decisions as in binary choices. This suggests that a similar computational process is used to make binary choices, trinary choices, and simple purchasing decisions.