Visual fixations and the computation and comparison of value in simple choice

Biasing moral decisions by exploiting the dynamics of eye gaze

Eye gaze is a window onto cognitive processing in tasks such as spatial memory, linguistic processing, and decision making. We present evidence that information derived from eye gaze can be used to change the course of individuals' decisions, even when they are reasoning about high-level, moral issues. Previous studies have shown that when an experimenter actively controls what an individual sees the experimenter can affect simple decisions with alternatives of almost equal valence. Here we show that if an experimenter passively knows when individuals move their eyes the experimenter can change complex moral decisions. This causal effect is achieved by simply adjusting the timing of the decisions. We monitored participants' eye movements during a two-alternative forced-choice task with moral questions. One option was randomly predetermined as a target. At the moment participants had fixated the target option for a set amount of time we terminated their deliberation and prompted them to choose between the two alternatives. Although participants were unaware of this gaze-contingent manipulation, their choices were systematically biased toward the target option. We conclude that even abstract moral cognition is partly constituted by interactions with the immediate environment and is likely supported by gaze-dependent decision processes. By tracking the interplay between individuals, their sensorimotor systems, and the environment, we can influence the outcome of a decision without directly manipulating the content of the information available to them.

Revisiting the evidence for collapsing boundaries and urgency signals in perceptual decision-making

For nearly 50 years, the dominant account of decision-making holds that noisy information is accumulated until a fixed threshold is crossed. This account has been tested extensively against behavioral and neurophysiological data for decisions about consumer goods, perceptual stimuli, eyewitness testimony, memories, and dozens of other paradigms, with no systematic misfit between model and data. Recently, the standard model has been challenged by alternative accounts that assume that less evidence is required to trigger a decision as time passes. Such "collapsing boundaries" or "urgency signals" have gained popularity in some theoretical accounts of neurophysiology. Nevertheless, evidence in favor of these models is mixed, with support coming from only a narrow range of decision paradigms compared with a long history of support from dozens of paradigms for the standard theory. We conducted the first large-scale analysis of data from humans and nonhuman primates across three distinct paradigms using powerful model-selection methods to compare evidence for fixed versus collapsing bounds. Overall, we identified evidence in favor of the standard model with fixed decision boundaries. We further found that evidence for static or dynamic response boundaries may depend on specific paradigms or procedures, such as the extent of task practice. We conclude that the difficulty of selecting between collapsing and fixed bounds models has received insufficient attention in previous research, calling into question some previous results.

Prioritising the relevant information for learning and decision making within orbital and ventromedial prefrontal cortex

Our environment and internal states are frequently complex, ambiguous and dynamic, meaning we need to have selection mechanisms to ensure we are basing our decisions on currently relevant information. Here, we review evidence that orbitofrontal (OFC) and ventromedial prefrontal cortex (VMPFC) play conserved, critical but distinct roles in this process. While OFC may use specific sensory associations to enhance task-relevant information, particularly in the context of learning, VMPFC plays a role in ensuring irrelevant information does not impinge on the decision in hand.

Automatic versus Choice-Dependent Value Representations in the Human Brain

The subjective values of choice options can impact on behavior in two fundamentally different types of situations: first, when people explicitly base their actions on such values, and second, when values attract attention despite being irrelevant for current behavior. Here we show with functional magnetic resonance imaging (fMRI) that these two behavioral functions of values are encoded in distinct regions of the human brain. In the medial prefrontal cortex, value-related activity is enhanced when subjective value becomes choice-relevant, and the magnitude of this increase relates directly to the outcome and reliability of the value-based choice. In contrast, activity in the posterior cingulate cortex represents values similarly when they are relevant or irrelevant for the present choice, and the strength of this representation predicts attentional capture by choice-irrelevant values. Our results suggest that distinct components of the brain's valuation network encode value in context-dependent manners that serve fundamentally different behavioral aims.

Eye Movements in Risky Choice

We asked participants to make simple risky choices while we recorded their eye movements. We built a complete statistical model of the eye movements and found very little systematic variation in eye movements over the time course of a choice or across the different choices. The only exceptions were finding more (of the same) eye movements when choice options were similar, and an emerging gaze bias in which people looked more at the gamble they ultimately chose. These findings are inconsistent with prospect theory, the priority heuristic, or decision field theory. However, the eye movements made during a choice have a large relationship with the final choice, and this is mostly independent from the contribution of the actual attribute values in the choice options. That is, eye movements tell us not just about the processing of attribute values but also are independently associated with choice. The pattern is simple-people choose the gamble they look at more often, independently of the actual numbers they see-and this pattern is simpler than predicted by decision field theory, decision by sampling, and the parallel constraint satisfaction model. © 2015 The Authors. Journal of Behavioral Decision Making published by John Wiley & Sons Ltd.

fMRI and EEG predictors of dynamic decision parameters during human reinforcement learning

What are the neural dynamics of choice processes during reinforcement learning? Two largely separate literatures have examined dynamics of reinforcement learning (RL) as a function of experience but assuming a static choice process, or conversely, the dynamics of choice processes in decision making but based on static decision values. Here we show that human choice processes during RL are well described by a drift diffusion model (DDM) of decision making in which the learned trial-by-trial reward values are sequentially sampled, with a choice made when the value signal crosses a decision threshold. Moreover, simultaneous fMRI and EEG recordings revealed that this decision threshold is not fixed across trials but varies as a function of activity in the subthalamic nucleus (STN) and is further modulated by trial-by-trial measures of decision conflict and activity in the dorsomedial frontal cortex (pre-SMA BOLD and mediofrontal theta in EEG). These findings provide converging multimodal evidence for a model in which decision threshold in reward-based tasks is adjusted as a function of communication from pre-SMA to STN when choices differ subtly in reward values, allowing more time to choose the statistically more rewarding option.

Modeling Eye Movements and Response Times in Consumer Choice

Peoples’ choices are not instantaneous, nor are they perfectly self consistent. While these two facts may at first seem unrelated, they are in fact inextricably linked. Decision scientists are accustomed to using logit and probit models to account for “noise” in their choice data. But what is the driving force behind these behavioral inconsistencies? Random utility theory (RUT) provides little guidance in this respect. While providing a mathematical basis for dealing with stochastic choice, RUT is agnostic about whether the noise is due to unobserved characteristics of the decision maker and/or the choice environment, or due to actual “mistakes.” The distinction is important because the former implies that from the point of view of the decision maker, her choices are perfectly consistent, while the latter implies that the decision maker herself may be surprised by her set of choices. Here we argue that non-choice (“process”) data strongly favors the latter explanation. Rather than thinking of choice as an instantaneous realization of stored preferences, we instead conceptualize choice as a dynamical process of information accumulation and comparison. Adapting “sequential sampling models” from cognitive psychology to economic choice, we illustrate the surprisingly complex relationship between choice and response-time data. Finally, we review recent data demonstrating how other process measures such as eye-tracking and neural recordings can be incorporated into this modeling approach, yielding further insights into the choice process.

How the Eyes Tell Lies: Social Gaze During a Preference Task

Social attention is thought to require detecting the eyes of others and following their gaze. To be effective, observers must also be able to infer the person's thoughts and feelings about what he or she is looking at, but this has only rarely been investigated in laboratory studies. In this study, participants' eye movements were recorded while they chose which of four patterns they preferred. New observers were subsequently able to reliably guess the preference response by watching a replay of the fixations. Moreover, when asked to mislead the person guessing, participants changed their looking behavior and guessing success was reduced. In a second experiment, naïve participants could also guess the preference of the original observers but were unable to identify trials which were lies. These results confirm that people can spontaneously use the gaze of others to infer their judgments, but also that these inferences are open to deception.

Taken last, selected first: the sampling bias is also present in the haptic domain

When people are presented with a pair of images and asked to identify which one is more attractive, their eye gaze shifts gradually toward the image that they eventually choose. This study examined whether this sampling bias also occurs in other sensory modalities by observing participants' behavior in a haptic preference task. The results indicated that the participants tended to sample the chosen item just prior to making their decision when they were instructed to identify their most preferred item (i.e., the "like" task), but not when they were instructed to identify their least preferred item (i.e., the "dislike" task). This indicates that the sampling bias is a general phenomenon regardless of sensory modality. In addition, the sampling bias in the like task was larger when the difference in preference ratings between the paired items was smaller. However, the sampling bias decreased when the two items were given equal preference ratings, despite there being a longer decision time on those trials. This suggests that the sampling bias is not simply related to task difficulty, but is also related to preference formation and/or selective encoding of task-relevant information.

Searching for the highest number

When viewing a collection of products how does a consumer decide which one to buy? To do this task, the consumer not only needs to evaluate the desirability of the products, taking into account factors such as quality and price, but also needs to search through the products to find the most desirable one. We studied the search process using an abstraction of a common consumer choice task. In our task, observers searched an array of numbers for the highest. Crucially, the observers did not know in advance what this number would be, which made it difficult to know when the search should be terminated. In this way, our search task mimicked a problem often faced by consumers in a supermarket setting where they also may not know in advance what the most desirable product will be. We compared several computational models. We found that our data was best described by a process that assumes that observers terminate their search when they find a number that exceeds an internal threshold. Depending on the observer and the circumstances, this threshold appeared either to be fixed or to decrease over the course of the trial. This threshold can explain why in some situations the observers terminate the search without inspecting all the numbers in the display, whereas in other situations observers act in a seemingly irrational manner, continuing the search even after inspecting all the numbers.

Attention, reward, and information seeking

Decision making is thought to be guided by the values of alternative options and involve the accumulation of evidence to an internal bound. However, in natural behavior, evidence accumulation is an active process whereby subjects decide when and which sensory stimulus to sample. These sampling decisions are naturally served by attention and rapid eye movements (saccades), but little is known about how saccades are controlled to guide future actions. Here we review evidence that was discussed at a recent symposium, which suggests that information selection involves basal ganglia and cortical mechanisms and that, across different contexts, it is guided by two central factors: the gains in reward and gains in information (uncertainty reduction) associated with sensory cues.

Debiasing the disposition effect by reducing the saliency of information about a stock's purchase price

The disposition effect refers to the empirical fact that investors have a higher propensity to sell risky assets with capital gains compared to risky assets with capital losses, and it has been associated with low trading performance. We use a stock trading laboratory experiment to investigate if it is possible to reduce subjects' tendency to exhibit a disposition effect by making information about a stock's purchase price, and thus about capital gains and losses, less salient. We compare two experimental conditions: a high-saliency condition in which the purchase price of a stock is prominently displayed by the trading software, and a low-saliency condition in which it is not displayed at all. We find that individuals exhibit a disposition effect in the high-saliency condition, and that the effect is 25% smaller in the low-saliency condition. This suggests that it is possible to debias the disposition effect by reducing the saliency with which information about a stock's purchase price is displayed on financial statements and online trading platforms.

A free-choice premium in the basal ganglia

Apparently, the act of free choice confers value: when selecting between an item that you had previously chosen and an identical item that you had been forced to take, the former is often preferred. What could be the neural underpinnings of this free-choice bias in decision making? An elegant study recently published in Neuron suggests that enhanced reward learning in the basal ganglia may be the culprit.

Attention in risky choice

Previous research on the processes involved in risky decisions has rarely linked process data to choice directly. We used a simple measure based on the relative amount of attentional deployment to different components (gains/losses and their probabilities) of a risky gamble during the choice process, and we related this measure to the actual choice. In an experiment we recorded the decisions, decision times, and eye movements of 80 participants who made decisions on 11 choice problems. We used the number of eye fixations and fixation transitions to trace the deployment of attention during the choice process and obtained the following main results. First, different components of a gamble attracted different amounts of attention depending on participants' actual choice. This was reflected in both the number of fixations and the fixation transitions. Second, the last-fixated gamble but not the last-fixated reason predicted participants' choices. Third, a comparison of data obtained with eye tracking and data obtained with verbal protocols from a previous study showed a large degree of convergence regarding the process of risky choice. Together these findings tend to support dimensional decision strategies such as the priority heuristic.

The angular gyrus and visuospatial attention in decision-making under risk

Recent neuroimaging studies on decision-making under risk indicate that the angular gyrus (AG) is sensitive to the probability and variance of outcomes during choice. A separate body of research has established the AG as a key area in visual attention. The current study used repetitive transcranial magnetic stimulation (rTMS) in healthy volunteers to test whether the causal contribution of the AG to decision-making is independent of or linked to the guidance of visuospatial attention. A within-subject design compared decision making on a laboratory gambling task under three conditions: following rTMS to the AG, following rTMS to the premotor cortex (PMC, as an active control condition) and without TMS. The task presented two different trial types, 'visual' and 'auditory' trials, which entailed a high versus minimal demand for visuospatial attention, respectively. Our results showed a systematic effect of rTMS to the AG upon decision-making behavior in visual trials. Without TMS and following rTMS to the control region, decision latencies reflected the odds of winning; this relationship was disrupted by rTMS to the AG. In contrast, no significant effects of rTMS to the AG (or to the PMC) upon choice behavior in auditory trials were found. Thus, rTMS to the AG affected decision-making only in the task condition requiring visuospatial attention. The current findings suggest that the AG contributes to decision-making by guiding attention to relevant information about reward and punishment in the visual environment.

Anterior cingulate engagement in a foraging context reflects choice difficulty, not foraging value

Previous theories predict that human dorsal anterior cingulate (dACC) should respond to decision difficulty. An alternative theory has been recently advanced that proposes that dACC evolved to represent the value of 'non-default', foraging behavior, calling into question its role in choice difficulty. However, this new theory does not take into account that choosing whether or not to pursue foraging-like behavior can also be more difficult than simply resorting to a default. The results of two neuroimaging experiments show that dACC is only associated with foraging value when foraging value is confounded with choice difficulty; when the two are dissociated, dACC engagement is only explained by choice difficulty, and not the value of foraging. In addition to refuting this new theory, our studies help to formalize a fundamental connection between choice difficulty and foraging-like decisions, while also prescribing a solution for a common pitfall in studies of reward-based decision making.

How active perception and attractor dynamics shape perceptual categorization: a computational model

We propose a computational model of perceptual categorization that fuses elements of grounded and sensorimotor theories of cognition with dynamic models of decision-making. We assume that category information consists in anticipated patterns of agent-environment interactions that can be elicited through overt or covert (simulated) eye movements, object manipulation, etc. This information is firstly encoded when category information is acquired, and then re-enacted during perceptual categorization. The perceptual categorization consists in a dynamic competition between attractors that encode the sensorimotor patterns typical of each category; action prediction success counts as "evidence" for a given category and contributes to falling into the corresponding attractor. The evidence accumulation process is guided by an active perception loop, and the active exploration of objects (e.g., visual exploration) aims at eliciting expected sensorimotor patterns that count as evidence for the object category. We present a computational model incorporating these elements and describing action prediction, active perception, and attractor dynamics as key elements of perceptual categorizations. We test the model in three simulated perceptual categorization tasks, and we discuss its relevance for grounded and sensorimotor theories of cognition.

Decision making: the neuroethological turn

Neuroeconomics applies models from economics and psychology to inform neurobiological studies of choice. This approach has revealed neural signatures of concepts like value, risk, and ambiguity, which are known to influence decision making. Such observations have led theorists to hypothesize a single, unified decision process that mediates choice behavior via a common neural currency for outcomes like food, money, or social praise. In parallel, recent neuroethological studies of decision making have focused on natural behaviors like foraging, mate choice, and social interactions. These decisions strongly impact evolutionary fitness and thus are likely to have played a key role in shaping the neural circuits that mediate decision making. This approach has revealed a suite of computational motifs that appear to be shared across a wide variety of organisms. We argue that the existence of deep homologies in the neural circuits mediating choice may have profound implications for understanding human decision making in health and disease.

Eye tracking and pupillometry are indicators of dissociable latent decision processes

Can you predict what people are going to do just by watching them? This is certainly difficult: it would require a clear mapping between observable indicators and unobservable cognitive states. In this report, we demonstrate how this is possible by monitoring eye gaze and pupil dilation, which predict dissociable biases during decision making. We quantified decision making using the drift diffusion model (DDM), which provides an algorithmic account of how evidence accumulation and response caution contribute to decisions through separate latent parameters of drift rate and decision threshold, respectively. We used a hierarchical Bayesian estimation approach to assess the single trial influence of observable physiological signals on these latent DDM parameters. Increased eye gaze dwell time specifically predicted an increased drift rate toward the fixated option, irrespective of the value of the option. In contrast, greater pupil dilation specifically predicted an increase in decision threshold during difficult decisions. These findings suggest that eye tracking and pupillometry reflect the operations of dissociated latent decision processes.

A probabilistic, dynamic, and attribute-wise model of intertemporal choice

Most theoretical and empirical research on intertemporal choice assumes a deterministic and static perspective, leading to the widely adopted delay discounting models. As a form of preferential choice, however, intertemporal choice may be generated by a stochastic process that requires some deliberation time to reach a decision. We conducted 3 experiments to investigate how choice and decision time varied as a function of manipulations designed to examine the delay duration effect, the common difference effect, and the magnitude effect in intertemporal choice. The results, especially those associated with the delay duration effect, challenged the traditional deterministic and static view and called for alternative approaches. Consequently, various static or dynamic stochastic choice models were explored and fit to the choice data, including alternative-wise models derived from the traditional exponential or hyperbolic discount function and attribute-wise models built upon comparisons of direct or relative differences in money and delay. Furthermore, for the first time, dynamic diffusion models, such as those based on decision field theory, were also fit to the choice and response time data simultaneously. The results revealed that the attribute-wise diffusion model with direct differences, power transformations of objective value and time, and varied diffusion parameter performed the best and could account for all 3 intertemporal effects. In addition, the empirical relationship between choice proportions and response times was consistent with the prediction of diffusion models and thus favored a stochastic choice process for intertemporal choice that requires some deliberation time to make a decision.

Behavioral and neurophysiological correlates of regret in rat decision-making on a neuroeconomic task

Disappointment entails the recognition that one did not get the value expected. In contrast, regret entails recognition that an alternative (counterfactual) action would have produced a more valued outcome. In humans, the orbitofrontal cortex is active during expressions of regret, and humans with damage to the orbitofrontal cortex do not express regret. In rats and nonhuman primates, both the orbitofrontal cortex and the ventral striatum have been implicated in reward computations. We recorded neural ensembles from orbitofrontal cortex and ventral striatum in rats encountering wait or skip choices for delayed delivery of different flavors using an economic framework. Economically, encountering a high-cost choice after skipping a low-cost choice should induce regret. In these situations, rats looked backwards toward the lost option, cells within orbitofrontal cortex and ventral striatum represented the missed action, rats were more likely to wait for the long delay, and rats rushed through eating the food after that delay.

Dissociable contributions of ventromedial prefrontal and posterior parietal cortex to value-guided choice

Two long-standing traditions have highlighted cortical decision mechanisms in the parietal and prefrontal cortices of primates, but it has not been clear how these processes differ, or when each cortical region may influence behaviour. Recent data from ventromedial prefrontal cortex (vmPFC) and posterior parietal cortex (PPC) have suggested one possible axis on which the two decision processes might be delineated. Fast decisions may be resolved primarily by parietal mechanisms, whereas decisions made without time pressure may rely on prefrontal mechanisms. Here, we report direct evidence for such dissociation. During decisions under time pressure, a value comparison process was evident in PPC, but not in vmPFC. Value-related activity was still found in vmPFC under time pressure. However, vmPFC represented overall input value rather than compared output value. In contrast, when decisions were made without time pressure, vmPFC transitioned to encode a value comparison while value-related parameters were entirely absent from PPC. Furthermore, under time pressure, decision performance was primarily governed by PPC, while it was dominated by vmPFC at longer decision times. These data demonstrate that parallel cortical mechanisms may resolve the same choices in differing circumstances, and offer an explanation of the diverse neural signals reported in vmPFC and PPC during value-guided choice.

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Value parameter represented in ventromedial PFC depends on available decision time.

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Under time pressure, vmPFC activity represents overall input value.

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Without time pressure, vmPFC transitions to encode a value comparison.

The speed-accuracy tradeoff: history, physiology, methodology, and behavior

There are few behavioral effects as ubiquitous as the speed-accuracy tradeoff (SAT). From insects to rodents to primates, the tendency for decision speed to covary with decision accuracy seems an inescapable property of choice behavior. Recently, the SAT has received renewed interest, as neuroscience approaches begin to uncover its neural underpinnings and computational models are compelled to incorporate it as a necessary benchmark. The present work provides a comprehensive overview of SAT. First, I trace its history as a tractable behavioral phenomenon and the role it has played in shaping mathematical descriptions of the decision process. Second, I present a "users guide" of SAT methodology, including a critical review of common experimental manipulations and analysis techniques and a treatment of the typical behavioral patterns that emerge when SAT is manipulated directly. Finally, I review applications of this methodology in several domains.

How the brain decides when to work and when to rest: dissociation of implicit-reactive from explicit-predictive computational processes

A pervasive case of cost-benefit problem is how to allocate effort over time, i.e. deciding when to work and when to rest. An economic decision perspective would suggest that duration of effort is determined beforehand, depending on expected costs and benefits. However, the literature on exercise performance emphasizes that decisions are made on the fly, depending on physiological variables. Here, we propose and validate a general model of effort allocation that integrates these two views. In this model, a single variable, termed cost evidence, accumulates during effort and dissipates during rest, triggering effort cessation and resumption when reaching bounds. We assumed that such a basic mechanism could explain implicit adaptation, whereas the latent parameters (slopes and bounds) could be amenable to explicit anticipation. A series of behavioral experiments manipulating effort duration and difficulty was conducted in a total of 121 healthy humans to dissociate implicit-reactive from explicit-predictive computations. Results show 1) that effort and rest durations are adapted on the fly to variations in cost-evidence level, 2) that the cost-evidence fluctuations driving the behavior do not match explicit ratings of exhaustion, and 3) that actual difficulty impacts effort duration whereas expected difficulty impacts rest duration. Taken together, our findings suggest that cost evidence is implicitly monitored online, with an accumulation rate proportional to actual task difficulty. In contrast, cost-evidence bounds and dissipation rate might be adjusted in anticipation, depending on explicit task difficulty.

Imagine that ahead of you is a long time of work: when will you take a break? This sort of issue – how to allocate effort over time – has been addressed by distinct theoretical fields, with different emphasis on reactive and predictive processes. An intuitive view is that you start working, stop when you are tired, and start again when fatigue goes away. Biologically, this means that decisions are taken when some physiological variable reaches a given bound on the risk of homeostatic failure. In a more economic perspective, fatigue translates into effort cost, which must be anticipated and compared to expected benefit before engaging an action. We proposed a computational model that bridges these perspectives from sport physiology and decision theory. Decisions are made in reaction to bounds being reached by an implicit cost variable that accumulates during effort, at a rate proportional to task difficulty, and dissipates during rest. However, some latent parameters (bounds and dissipation rate) are adjusted in anticipation, depending on explicit costs and benefits. This model was supported by behavioral data obtained using a paradigm where participants squeeze a handgrip to win a monetary payoff proportional to effort duration.

Intertemporal choice as discounted value accumulation

Two separate cognitive processes are involved in choosing between rewards available at different points in time. The first is temporal discounting, which consists of combining information about the size and delay of prospective rewards to represent subjective values. The second involves a comparison of available rewards to enable an eventual choice on the basis of these subjective values. While several mathematical models of temporal discounting have been developed, the reward selection process has been largely unexplored. To address this limitation, we evaluated the applicability of the Linear Ballistic Accumulator (LBA) model as a theory of the selection process in intertemporal choice. The LBA model formalizes the selection process as a sequential sampling algorithm in which information about different choice options is integrated until a decision criterion is reached. We compared several versions of the LBA model to demonstrate that choice outcomes and response times in intertemporal choice are well captured by the LBA process. The relationship between choice outcomes and response times that derives from the LBA model cannot be explained by temporal discounting alone. Moreover, the drift rates that drive evidence accumulation in the best-fitting LBA model are related to independently estimated subjective values derived from various temporal discounting models. These findings provide a quantitative framework for predicting dynamics of choice-related activity during the reward selection process in intertemporal choice and link intertemporal choice to other classes of decisions in which the LBA model has been applied.

Neurophysiology of performance monitoring and adaptive behavior

Successful goal-directed behavior requires not only correct action selection, planning, and execution but also the ability to flexibly adapt behavior when performance problems occur or the environment changes. A prerequisite for determining the necessity, type, and magnitude of adjustments is to continuously monitor the course and outcome of one's actions. Feedback-control loops correcting deviations from intended states constitute a basic functional principle of adaptation at all levels of the nervous system. Here, we review the neurophysiology of evaluating action course and outcome with respect to their valence, i.e., reward and punishment, and initiating short- and long-term adaptations, learning, and decisions. Based on studies in humans and other mammals, we outline the physiological principles of performance monitoring and subsequent cognitive, motivational, autonomic, and behavioral adaptation and link them to the underlying neuroanatomy, neurochemistry, psychological theories, and computational models. We provide an overview of invasive and noninvasive systemic measures, such as electrophysiological, neuroimaging, and lesion data. We describe how a wide network of brain areas encompassing frontal cortices, basal ganglia, thalamus, and monoaminergic brain stem nuclei detects and evaluates deviations of actual from predicted states indicating changed action costs or outcomes. This information is used to learn and update stimulus and action values, guide action selection, and recruit adaptive mechanisms that compensate errors and optimize goal achievement.

Rapid decisions from experience

In many everyday decisions, people quickly integrate noisy samples of information to form a preference among alternatives that offer uncertain rewards. Here, we investigated this decision process using the Flash Gambling Task (FGT), in which participants made a series of choices between a certain payoff and an uncertain alternative that produced a normal distribution of payoffs. For each choice, participants experienced the distribution of payoffs via rapid samples updated every 50ms. We show that people can make these rapid decisions from experience and that the decision process is consistent with a sequential sampling process. Results also reveal a dissociation between these preferential decisions and equivalent perceptual decisions where participants had to determine which alternatives contained more dots on average. To account for this dissociation, we developed a sequential sampling rank-dependent utility model, which showed that participants in the FGT attended more to larger potential payoffs than participants in the perceptual task despite being given equivalent information. We discuss the implications of these findings in terms of computational models of preferential choice and a more complete understanding of experience-based decision making.

Neuronal origins of choice variability in economic decisions

To investigate the mechanisms through which economic decisions are formed, I examined the activity of neurons in the orbitofrontal cortex while monkeys chose between different juice types. Different classes of cells encoded the value of individual offers (offer value), the value of the chosen option (chosen value), or the identity of the chosen juice (chosen juice). Choice variability was partly explained by the tendency to repeat choices (choice hysteresis). Surprisingly, near-indifference decisions did not reflect fluctuations in the activity of offer value cells. In contrast, near-indifference decisions correlated with fluctuations in the preoffer activity of chosen juice cells. After the offer, the activity of chosen juice cells reflected the decision difficulty but did not resemble a race-to-threshold. Finally, chosen value cells presented an "activity overshooting" closely related to the decision difficulty and possibly due to fluctuations in the relative value of the juices. This overshooting was independent of choice hysteresis.

An evolutionary perspective on the behavioral consequences of exogenous oxytocin application

Oxytocin (OT) is released in response to social signals, particularly positive ones like eye contact, social touch, sexual behavior, and affiliative vocalizations. Conversely, exogenous delivery of OT has diverse behavioral effects, sometimes promoting affiliative and prosocial behaviors, but sometimes suppressing them. Here, we argue that one unifying interpretation of these diverse effects is to view OT as an evolutionarily conserved physiological signal indicating affiliative interactions and predicting their behavioral consequences. In this model, OT regulates the way information about the social environment accesses the neural circuitry responsible for social behavior, thereby shaping it in sometimes counter intuitive but adaptive ways. Notably, prosociality is not always the most adaptive response to an affiliative signal from another individual. In many circumstances, an asocial or even antisocial response may confer greater fitness benefits. We argue that the behavioral effects of exogenous OT delivery not only parallel the behavioral effects of affiliative interactions, but are themselves adaptive responses to affiliative interactions. In support of this idea, we review recent evidence that OT does not unilaterally enhance social attention, as previously thought, but rather can reduce the typical prioritization of social information at the expense of other information or goals. Such diminished social vigilance may be an adaptive response to affiliative social interactions because it frees attentional resources for the pursuit of other goals. Finally, we predict that OT may mediate other behavioral consequences of social interactions, such as reduced predator vigilance, and argue that this is a rich avenue for future behavioral and neurobiological study.

A review of the findings and theories on surface size effects on visual attention

That surface size has an impact on attention has been well-known in advertising research for almost a century; however, theoretical accounts of this effect have been sparse. To address this issue, we review studies on surface size effects on eye movements in this paper. While most studies find that large objects are more likely to be fixated, receive more fixations, and are fixated faster than small objects, a comprehensive explanation of this effect is still lacking. To bridge the theoretical gap, we relate the findings from this review to three theories of surface size effects suggested in the literature: a linear model based on the assumption of random fixations (Lohse, 1997), a theory of surface size as visual saliency (Pieters etal., 2007), and a theory based on competition for attention (CA; Janiszewski, 1998). We furthermore suggest a fourth model - demand for attention - which we derive from the theory of CA by revising the underlying model assumptions. In order to test the models against each other, we reanalyze data from an eye tracking study investigating surface size and saliency effects on attention. The reanalysis revealed little support for the first three theories while the demand for attention model showed a much better alignment with the data. We conclude that surface size effects may best be explained as an increase in object signal strength which depends on object size, number of objects in the visual scene, and object distance to the center of the scene. Our findings suggest that advertisers should take into account how objects in the visual scene interact in order to optimize attention to, for instance, brands and logos.

Adaptive sampling of information in perceptual decision-making

In many perceptual and cognitive decision-making problems, humans sample multiple noisy information sources serially, and integrate the sampled information to make an overall decision. We derive the optimal decision procedure for two-alternative choice tasks in which the different options are sampled one at a time, sources vary in the quality of the information they provide, and the available time is fixed. To maximize accuracy, the optimal observer allocates time to sampling different information sources in proportion to their noise levels. We tested human observers in a corresponding perceptual decision-making task. Observers compared the direction of two random dot motion patterns that were triggered only when fixated. Observers allocated more time to the noisier pattern, in a manner that correlated with their sensory uncertainty about the direction of the patterns. There were several differences between the optimal observer predictions and human behaviour. These differences point to a number of other factors, beyond the quality of the currently available sources of information, that influences the sampling strategy.

Predeliberation activity in prefrontal cortex and striatum and the prediction of subsequent value judgment

Rational, value-based decision-making mandates selecting the option with highest subjective expected value after appropriate deliberation. We examined activity in the dorsolateral prefrontal cortex (DLPFC) and striatum of monkeys deciding between smaller, immediate rewards and larger, delayed ones. We previously found neurons that modulated their activity in this task according to the animal's choice, while it deliberated (choice neurons). Here we found neurons whose spiking activities were predictive of the spatial location of the selected target (spatial-bias neurons) or the size of the chosen reward (reward-bias neurons) before the onset of the cue presenting the decision-alternatives, and thus before rational deliberation could begin. Their predictive power increased as the values the animals associated with the two decision alternatives became more similar. The ventral striatum (VS) preferentially contained spatial-bias neurons; the caudate nucleus (CD) preferentially contained choice neurons. In contrast, the DLPFC contained significant numbers of all three neuron types, but choice neurons were not preferentially also bias neurons of either kind there, nor were spatial-bias neurons preferentially also choice neurons, and vice versa. We suggest a simple winner-take-all (WTA) circuit model to account for the dissociation of choice and bias neurons. The model reproduced our results and made additional predictions that were borne out empirically. Our data are compatible with the hypothesis that the DLPFC and striatum harbor dissociated neural populations that represent choices and predeliberation biases that are combined after cue onset; the bias neurons have a weaker effect on the ultimate decision than the choice neurons, so their influence is progressively apparent for trials where the values associated with the decision alternatives are increasingly similar.

The effect of visual salience on memory-based choices

Deciding whether a stimulus is the "same" or "different" from a previous presented one involves integrating among the incoming sensory information, working memory, and perceptual decision making. Visual selective attention plays a crucial role in selecting the relevant information that informs a subsequent course of action. Previous studies have mainly investigated the role of visual attention during the encoding phase of working memory tasks. In this study, we investigate whether manipulation of bottom-up attention by changing stimulus visual salience impacts on later stages of memory-based decisions. In two experiments, we asked subjects to identify whether a stimulus had either the same or a different feature to that of a memorized sample. We manipulated visual salience of the test stimuli by varying a task-irrelevant feature contrast. Subjects chose a visually salient item more often when they looked for matching features and less often so when they looked for a nonmatch. This pattern of results indicates that salient items are more likely to be identified as a match. We interpret the findings in terms of capacity limitations at a comparison stage where a visually salient item is more likely to exhaust resources leading it to be prematurely parsed as a match.

Attention as foraging for information and value

What is the purpose of attention? One avenue of research has led to the proposal that attention might be crucial for gathering information about the environment, while other lines of study have demonstrated how attention may play a role in guiding behavior to rewarded options. Many experiments that study attention require participants to make a decision based on information acquired discretely at one point in time. In real-world situations, however, we are usually not presented with information about which option to select in such a manner. Rather we must initially search for information, weighing up reward values of options before we commit to a decision. Here, we propose that attention plays a role in both foraging for information and foraging for value. When foraging for information, attention is guided toward the unknown. When foraging for reward, attention is guided toward high reward values, allowing decision-making to proceed by accept-or-reject decisions on the currently attended option. According to this account, attention can be regarded as a low-cost alternative to moving around and physically interacting with the environment—“teleforaging”—before a decision is made to interact physically with the world. To track the timecourse of attention, we asked participants to seek out and acquire information about two gambles by directing their gaze, before choosing one of them. Participants often made multiple refixations on items before making a decision. Their eye movements revealed that early in the trial, attention was guided toward information, i.e., toward locations that reduced uncertainty about value. In contrast, late in the trial, attention was guided by expected value of the options. At the end of the decision period, participants were generally attending to the item they eventually chose. We suggest that attentional foraging shifts from an uncertainty-driven to a reward-driven mode during the evolution of a decision, permitting decisions to be made by an engage-or-search strategy.

The response dynamics of preferential choice

The ubiquity of psychological process models requires an increased degree of sophistication in the methods and metrics that we use to evaluate them. We contribute to this venture by capitalizing on recent work in cognitive science analyzing response dynamics, which shows that the bearing information processing dynamics have on intended action is also revealed in the motor system. This decidedly "embodied" view suggests that researchers are missing out on potential dependent variables with which to evaluate their models-those associated with the motor response that produces a choice. The current work develops a method for collecting and analyzing such data in the domain of decision making. We first validate this method using widely normed stimuli from the International Affective Picture System (Experiment 1), and demonstrate that curvature in response trajectories provides a metric of the competition between choice options. We next extend the method to risky decision making (Experiment 2) and develop predictions for three popular classes of process model. The data provided by response dynamics demonstrate that choices contrary to the maxim of risk seeking in losses and risk aversion in gains may be the product of at least one "online" preference reversal, and can thus begin to discriminate amongst the candidate models. Finally, we incorporate attentional data collected via eye-tracking (Experiment 3) to develop a formal computational model of joint information sampling and preference accumulation. In sum, we validate response dynamics for use in preferential choice tasks and demonstrate the unique conclusions afforded by response dynamics over and above traditional methods.