Visual attention modulates the integration of goal-relevant evidence and not value

The role of visual attention in opportunity cost neglect and consideration

Choices necessitate opportunity costs: choosing one option means foregoing another. Despite their critical role in decision making, people often neglect opportunity costs and are less likely to make purchases when reminded of them. Here, we seek to understand whether and how opportunity-cost neglect can be explained by attention, a relationship that has been proposed but not explicitly tested. Participants made eye-tracked, incentivized purchase decisions in two conditions: one with implicit opportunity costs (e.g., "Buy" vs. "Do Not Buy") and one with explicit opportunity costs (e.g., "Buy" vs. "Keep Money"). Across two studies (approximately 30,000 choices), we find lower purchase rates when opportunity costs are explicit. More importantly, we show that the relationship between attention and opportunity cost considerations is two-fold. First, the amount of attention to the outside option is greater when opportunity costs are explicit, which partly accounts for the effect of opportunity cost salience on choice. Second, for some framings, the predictive power of attention to opportunity costs is greater when opportunity costs are explicit. Using the attentional drift-diffusion model, we model the effect of opportunity cost salience on choice via attention. These findings help explain why people are more likely to purchase when explicit opportunity cost reminders are absent.

Emotion-Guided Attention Impacts Deliberate Multi-Evidence Emotion-Related Perceptual Decision-Making

Emotion-guided endogenous attention (e.g., attending to fear) may play a crucial role in determining how humans integrate emotional evidence from various sources when assessing the general emotional tenor of the environment. For instance, what emotion a presenter focuses on can shape their perception of the overall emotion of the room. While there is an increasing interest in understanding how endogenous attention affects emotion perception, existing studies have largely focused on single-stimulus perception. There is limited understanding of how endogenous attention influences emotion evidence integration across multiple sources. To investigate this question, human participants (N = 40) were invited to judge the average emotion across an array of faces ranging from fearful to happy. Endogenous attention was manipulated by instructing participants to decide whether the face array was "fearful or not" (fear attention), "happy or not" (happy attention). Eye movement results revealed an endogenous attention-induced sampling bias such that participants paid more attention to extreme emotional evidence congruent with the target emotion. Computational modeling revealed that endogenous attention shifted the decision criterion to be more conservative, leading to reduced target-category decisions. These findings unraveled the cognitive and computational mechanisms of how endogenous attention impacts the way we gather emotional evidence and make integrative decisions, shedding light on emotion-related decision-making.

Confidence bias prescribes the neurocomputational mechanism of decision-making

A couple of decision-making models with different ingredients have successfully interpreted choices, confidence, and related neural activities. However, empirical and theoretical evidence is currently lacking to distinguish these models clearly. Here, we investigated the decision-congruent confidence bias, where confidence favors evidence from the chosen option, yet the choice and its correctness or optimality are determined equally by alternative evidence strengths across both perceptual and value-based decision-making tasks. This confidence bias is manifested by confidence-coding neural activities, particularly in the dorsal anterior cingulate cortex. Further analyses on an array of neurocomputational models show that only the decision-making model equipped with mutual inhibition and an urgency signal can produce such a selective bias across almost all parameter regimes. These findings suggest that mutual inhibition and an urgency signal are two indispensable features embedded in the decision-making process, while confidence bias might be its consequence.

Mutual inclusivity improves decision-making by smoothing out choice's competitive edge

Decisions form a central bottleneck to most tasks, one that people often experience as costly. Previous work proposes mitigating those costs by lowering one's threshold for deciding. Here we test an alternative solution, one that targets the basis of most choice costs: the idea that choosing one option sacrifices others (mutual exclusivity). Across 6 studies (N = 565), we test whether this tension can be relieved by framing choices as inclusive (allowing selection of more than 1 option, as in buffets). We find that inclusivity makes choices more efficient by selectively reducing competition between potential responses as participants accumulate information for each of their options. Inclusivity also made participants feel less conflicted, especially when they could not decide which good option to keep or which bad option to get rid of. These inclusivity benefits were also distinguishable from the effects of manipulating decision threshold (increased urgency), which improved choices but not experiences thereof.

The impact of inequality on social value orientation: an eye-tracking study

Introduction

Researchers have developed the social value orientation (SVO) framework to describe prosocial tendencies. However, existing tools for measuring SVO lack sufficient attention to the effect of option inequality, driven by the inequality-aversion motive. In this research, we conducted an eye-tracking experiment to compare the traditional SVO measure with the inequality-controlled condition, investigating how it influences estimated SVO values and underlying process mechanisms.

Methods

A within-subjects eye-tracking experiment was conducted with 65 university students recruited from a university's human subjects pool. Participants received 20 yuan (RMB; approximately US $2.9) in cash for their participation.

Results

SVOs were lower in the inequality-controlled condition than in the traditional SVO measure. Information processing, including complexity, depth, and direction, differed when fairness was controlled. The predictive effect of relative time advantage was also enhanced under controlled inequality conditions. In addition, the predictive effect of relative time advantage was stronger when controlling for option inequality, suggesting that controlling for option inequality enhances bottom-up information processing.

Discussion

These findings suggest that traditional SVO measures may overestimate prosocial tendencies due to a lack of inequality control. The study highlights the role of fairness evaluation in SVO assessments and provides insights into the cognitive mechanisms underlying prosocial decision-making, offering guidance for future SVO measurements.

How is emotional evidence from multiple sources used in perceptual decision making?

Judging the emotional nature of a scene requires us to deliberately integrate pieces of evidence with varying intensities of emotion. Our existing knowledge about emotion-related perceptual decision-making is largely based on paradigms using single stimulus and, when involving multiple stimuli, rapid decisions. Consequently, it remains unclear how we sample and integrate multiple pieces of emotional evidence deliberately to form an overall judgment. Findings from non-emotion rapid decision-making studies show humans down-sample and downweight extreme evidence. However, deliberate decision-making may rely on a different attention mode than in rapid decision-making; and extreme emotional stimuli are inherently salient. Given these critical differences, it is imperative to directly examine the deliberate decision-making process about multiple emotional stimuli. In the current study, human participants (N = 33) viewed arrays of faces with expressions ranging from extremely fearful to extremely happy freely with their eye movement tracked. They then decided whether the faces were more fearful or happier on average. In contrast to conclusions drawn from non-emotion and rapid decision-making studies, eye movement measures revealed that participants attentionally sampled extreme emotional evidence more than less extreme evidence. Computational modeling results indicated that even though participants exhibited biased attention distribution, they weighted various emotional evidence equally. These findings provide novel insights into how people sample and integrate multiple pieces of emotional evidence, contribute to a more comprehensive understanding of emotion-related decision-making, and shed light on the mechanisms of pathological affective decisions.

A Population Representation of the Confidence in a Decision in the Parietal Cortex

Confidence in a decision is the belief, prior to feedback, that one's choice is correct. In the brain, many decisions are implemented as a race between competing evidence-accumulation processes. We ask whether the neurons that represent evidence accumulation also carry information about whether the choice is correct (i.e., confidence). Monkeys performed a reaction time version of the random dot motion task. Neuropixels probes were used to record from neurons in the lateral intraparietal (LIP) area. LIP neurons with response fields that overlap the choice-target contralateral to the recording site (T in neurons) represent the accumulation of evidence in favor of contralateral target selection. We demonstrate that shortly before a contralateral choice is reported, the population ofT in neurons contains information about the accuracy of the choice (i.e., whether the choice is correct or incorrect). This finding is unexpected because, on average,T in neurons exhibit a level of activity before the report that is independent of reaction time and evidence strength-both strong predictors of accuracy. This apparent contradiction is resolved by examining the variability in neuronal responses across the population ofT in neurons. While on average,T in neurons exhibit a stereotyped level of activity before a contralateral choice, many neurons depart from this average in a consistent manner. From these neurons, the accuracy of the choice can be predicted using a simple logistic decoder. The accuracy of the choice predicted from neural activity reproduces the hallmarks of confidence identified in human behavioral experiments. Therefore, neurons that represent evidence accumulation can also inform the monkey's confidence.

Value construction through sequential sampling explains serial dependencies in decision making

Deciding between a pair of familiar items is thought to rely on a comparison of their subjective values. When the values are similar, decisions take longer, and the choice may be inconsistent with stated value. These regularities are thought to be explained by the same mechanism of noisy evidence accumulation that leads to perceptual errors under conditions of low signal to noise. However, unlike perceptual decisions, subjective values may vary with internal states (e.g. desires, priorities) that change over time. This raises the possibility that the apparent stochasticity of choice reflects changes in value rather than mere noise. We hypothesized that these changes would manifest in serial dependencies across decision sequences. We analyzed data from a task in which participants chose between snack items. We developed an algorithm, Reval, that revealed significant fluctuations of the subjective values of items within an experimental session. The dynamic values predicted choices and response times more accurately than stated values. The dynamic values also furnished a superior account of the BOLD signal in ventromedial prefrontal cortex. A novel bounded-evidence accumulation model with temporally correlated evidence samples supports the idea that revaluation reflects the dynamic construction of subjective value during deliberation, which in turn influences subsequent decisions.

Context changes retrieval of prospective outcomes during decision deliberation

Foreseeing the future outcomes is the art of decision-making. Substantial evidence shows that, during choice deliberation, the brain can retrieve prospective decision outcomes. However, decisions are seldom made in a vacuum. Context carries information that can radically affect the outcomes of a choice. Nevertheless, most investigations of retrieval processes examined decisions in isolation, disregarding the context in which they occur. Here, we studied how context shapes prospective outcome retrieval during deliberation. We designed a decision-making task where participants were presented with object-context pairs and made decisions which led to a certain outcome. We show during deliberation, likely outcomes were retrieved in transient patterns of neural activity, as early as 3 s before participants decided. The strength of prospective outcome retrieval explains participants' behavioral efficiency, but only when context affects the decision outcome. Our results suggest context imparts strong constraints on retrieval processes and how neural representations are shaped during decision-making.

Common neural choice signals can emerge artefactually amid multiple distinct value signals

Previous work has identified characteristic neural signatures of value-based decision-making, including neural dynamics that closely resemble the ramping evidence accumulation process believed to underpin choice. Here we test whether these signatures of the choice process can be temporally dissociated from additional, choice-'independent' value signals. Indeed, EEG activity during value-based choice revealed distinct spatiotemporal clusters, with a stimulus-locked cluster reflecting affective reactions to choice sets and a response-locked cluster reflecting choice difficulty. Surprisingly, 'neither' of these clusters met the criteria for an evidence accumulation signal. Instead, we found that stimulus-locked activity can 'mimic' an evidence accumulation process when aligned to the response. Re-analysing four previous studies, including three perceptual decision-making studies, we show that response-locked signatures of evidence accumulation disappear when stimulus-locked and response-locked activity are modelled jointly. Collectively, our findings show that neural signatures of value can reflect choice-independent processes and look deceptively like evidence accumulation.

Symmetrical choices and biased confidence during uncertain personality trait judgments

While great methodological strides have been made in the area of decision making research, decisions that rely on subjective stimuli, such as personality traits, still pose a challenge for researchers, partly because it is difficult to define a standard of accuracy for such choices-they lack a "ground truth". In studies on value-based decisions, this same problem has been circumvented by comparing uncertain subjective decisions against a separately assessed judgment of value-a "standard". Here we apply this method in a task of verbal personality trait judgment, and show how a separately assessed standard judgment can be used to precisely control stimulus presentation and analyze subjective personality choices via the method of reverse correlation. Per trial, a series of quasi-randomly sampled adjectives was shown, which participants categorized as more descriptive of either themselves of another person well known to them. Participants also indicated their confidence in the response. Each trial's difficulty was controlled based on the previously assessed standard judgment. Analysis of the behavioral data shows several decision-general properties during these personality judgments, such as symmetrical choices, steeper choice functions for confident trials, and a positive evidence bias during confidence judgment. We discuss how these findings can shed light on the cognitive processes involved in personality perception. The task and results together may help bridge the gap between research on complex, social forms of judgment and findings on more basic decision processes.

Dynamics of visual attention in exploration and exploitation for reward-guided adjustment tasks

The learning process encompasses exploration and exploitation phases. While reinforcement learning models have revealed functional and neuroscientific distinctions between these phases, knowledge regarding how they affect visual attention while observing the external environment is limited. This study sought to elucidate the interplay between these learning phases and visual attention allocation using visual adjustment tasks combined with a two-armed bandit problem tailored to detect serial effects only when attention is dispersed across both arms. Per our findings, human participants exhibited a distinct serial effect only during the exploration phase, suggesting enhanced attention to the visual stimulus associated with the non-target arm. Remarkably, although rewards did not motivate attention dispersion in our task, during the exploration phase, individuals engaged in active observation and searched for targets to observe. This behavior highlights a unique information-seeking process in exploration that is distinct from exploitation.

Goal commitment is supported by vmPFC through selective attention

When striking a balance between commitment to a goal and flexibility in the face of better options, people often demonstrate strong goal perseveration. Here, using functional MRI (n = 30) and lesion patient (n = 26) studies, we argue that the ventromedial prefrontal cortex (vmPFC) drives goal commitment linked to changes in goal-directed selective attention. Participants performed an incremental goal pursuit task involving sequential decisions between persisting with a goal versus abandoning progress for better alternative options. Individuals with stronger goal perseveration showed higher goal-directed attention in an interleaved attention task. Increasing goal-directed attention also affected abandonment decisions: while pursuing a goal, people lost their sensitivity to valuable alternative goals while remaining more sensitive to changes in the current goal. In a healthy population, individual differences in both commitment biases and goal-oriented attention were predicted by baseline goal-related activity in the vmPFC. Among lesion patients, vmPFC damage reduced goal commitment, leading to a performance benefit.

Attention biases the process of risky decision-making: Evidence from eye-tracking

Attention determines what kind of option information is processed during risky choices owing to the limitation of visual attention. This paper reviews research on the relationship between higher-complexity risky decision-making and attention as illustrated by eye-tracking to explain the process of risky decision-making by the effect of attention. We demonstrate this process from three stages: the pre-phase guidance of options on attention, the process of attention being biased, and the impact of attention on final risk preference. We conclude that exogenous information can capture attention directly to salient options, thereby altering evidence accumulation. In particular, for multi-attribute risky decision-making, attentional advantages increase the weight of specific attributes, thus biasing risk preference in different directions. We highlight the significance of understanding how people use available information to weigh risks from an information-processing perspective via process data.

A confidence framing effect: Flexible use of evidence in metacognitive monitoring

Human behavior is flexibly regulated by specific goals of cognitive tasks. One notable example is goal-directed modulation of metacognitive behavior, where logically equivalent decision-making problems can yield different patterns of introspective confidence depending on the frame in which they are presented. While this observation highlights the important heuristic nature of metacognitive monitoring, computational mechanisms underlying this phenomenon remain elusive. We confirmed the confidence framing effect in two-alternative dot-number discrimination and in previously published preference-choice data, demonstrating distinctive confidence patterns between "choose more" or "choose less" frames. Formal model comparisons revealed a simple confidence heuristic behind this phenomenon, which assigns greater weight to chosen than unchosen stimulus evidence. This computation appears to be based on internal evidence constituted under specific task demands rather than physical stimulus intensity itself, a view justified in terms of ecological rationality. These results shed light on the adaptive nature of human decision-making and metacognitive monitoring.

Metacognition and Confidence: A Review and Synthesis

Determining the psychological, computational, and neural bases of confidence and uncertainty holds promise for understanding foundational aspects of human metacognition. While a neuroscience of confidence has focused on the mechanisms underpinning subpersonal phenomena such as representations of uncertainty in the visual or motor system, metacognition research has been concerned with personal-level beliefs and knowledge about self-performance. I provide a road map for bridging this divide by focusing on a particular class of confidence computation: propositional confidence in one's own (hypothetical) decisions or actions. Propositional confidence is informed by the observer's models of the world and their cognitive system, which may be more or less accurate-thus explaining why metacognitive judgments are inferential and sometimes diverge from task performance. Disparate findings on the neural basis of uncertainty and performance monitoring are integrated into a common framework, and a new understanding of the locus of action of metacognitive interventions is developed.

A goal-centric outlook on learning

Goals play a central role in human cognition. However, computational theories of learning and decision-making often take goals as given. Here, we review key empirical findings showing that goals shape the representations of inputs, responses, and outcomes, such that setting a goal crucially influences the central aspects of any learning process: states, actions, and rewards. We thus argue that studying goal selection is essential to advance our understanding of learning. By following existing literature in framing goal selection within a hierarchy of decision-making problems, we synthesize important findings on the principles underlying goal value attribution and exploration strategies. Ultimately, we propose that a goal-centric perspective will help develop more complete accounts of learning in both biological and artificial agents.

Risky decisions are influenced by individual attributes as a function of risk preference

It has long been assumed in economic theory that multi-attribute decisions involving several attributes or dimensions - such as probabilities and amounts of money to be earned during risky choices - are resolved by first combining the attributes of each option to form an overall expected value and then comparing the expected values of the alternative options, using a unique evidence accumulation process. A plausible alternative would be performing independent comparisons between the individual attributes and then integrating the results of the comparisons afterwards. Here, we devise a novel method to disambiguate between these types of models, by orthogonally manipulating the expected value of choice options and the relative salience of their attributes. Our results, based on behavioral measures and drift-diffusion models, provide evidence in favor of the framework where information about individual attributes independently impacts deliberation. This suggests that risky decisions are resolved by running in parallel multiple comparisons between the separate attributes - possibly alongside an additional comparison of expected value. This result stands in contrast with the assumption of standard economic theory that choices require a unique comparison of expected values and suggests that at the cognitive level, decision processes might be more distributed than commonly assumed. Beyond our planned analyses, we also discovered that attribute salience affects people of different risk preference type in different ways: risk-averse participants seem to focus more on probability, except when monetary amount is particularly high; risk-neutral/seeking participants, in contrast, seem to focus more on monetary amount, except when probability is particularly low.

Judging the difficulty of perceptual decisions

Deciding how difficult it is going to be to perform a task allows us to choose between tasks, allocate appropriate resources, and predict future performance. To be useful for planning, difficulty judgments should not require completion of the task. Here, we examine the processes underlying difficulty judgments in a perceptual decision-making task. Participants viewed two patches of dynamic random dots, which were colored blue or yellow stochastically on each appearance. Stimulus coherence (the probability, pblue, of a dot being blue) varied across trials and patches thus establishing difficulty, |pblue -0.5|. Participants were asked to indicate for which patch it would be easier to decide the dominant color. Accuracy in difficulty decisions improved with the difference in the stimulus difficulties, whereas the reaction times were not determined solely by this quantity. For example, when the patches shared the same difficulty, reaction times were shorter for easier stimuli. A comparison of several models of difficulty judgment suggested that participants compare the absolute accumulated evidence from each stimulus and terminate their decision when they differed by a set amount. The model predicts that when the dominant color of each stimulus is known, reaction times should depend only on the difference in difficulty, which we confirm empirically. We also show that this model is preferred to one that compares the confidence one would have in making each decision. The results extend evidence accumulation models, used to explain choice, reaction time, and confidence to prospective judgments of difficulty.

Neural and computational underpinnings of biased confidence in human reinforcement learning

While navigating a fundamentally uncertain world, humans and animals constantly evaluate the probability of their decisions, actions or statements being correct. When explicitly elicited, these confidence estimates typically correlates positively with neural activity in a ventromedial-prefrontal (VMPFC) network and negatively in a dorsolateral and dorsomedial prefrontal network. Here, combining fMRI with a reinforcement-learning paradigm, we leverage the fact that humans are more confident in their choices when seeking gains than avoiding losses to reveal a functional dissociation: whereas the dorsal prefrontal network correlates negatively with a condition-specific confidence signal, the VMPFC network positively encodes task-wide confidence signal incorporating the valence-induced bias. Challenging dominant neuro-computational models, we found that decision-related VMPFC activity better correlates with confidence than with option-values inferred from reinforcement-learning models. Altogether, these results identify the VMPFC as a key node in the neuro-computational architecture that builds global feeling-of-confidence signals from latent decision variables and contextual biases during reinforcement-learning.

Paradoxical evidence weighting in confidence judgments for detection and discrimination

When making discrimination decisions between two stimulus categories, subjective confidence judgments are more positively affected by evidence in support of a decision than negatively affected by evidence against it. Recent theoretical proposals suggest that this "positive evidence bias" may be due to observers adopting a detection-like strategy when rating their confidence-one that has functional benefits for metacognition in real-world settings where detectability and discriminability often go hand in hand. However, it is unknown whether, or how, this evidence-weighting asymmetry affects detection decisions about the presence or absence of a stimulus. In four experiments, we first successfully replicate a positive evidence bias in discrimination confidence. We then show that detection decisions and confidence ratings paradoxically suffer from an opposite "negative evidence bias" to negatively weigh evidence even when it is optimal to assign it a positive weight. We show that the two effects are uncorrelated and discuss our findings in relation to models that account for a positive evidence bias as emerging from a confidence-specific heuristic, and alternative models where decision and confidence are generated by the same, Bayes-rational process.

Peripheral Visual Information Halves Attentional Choice Biases

A growing body of research has shown that simple choices involve the construction and comparison of values at the time of decision. These processes are modulated by attention in a way that leaves decision makers susceptible to attentional biases. Here, we studied the role of peripheral visual information on the choice process and on attentional choice biases. We used an eye-tracking experiment in which participants (N = 50 adults) made binary choices between food items that were displayed in marked screen "shelves" in two conditions: (a) where both items were displayed, and (b) where items were displayed only when participants fixated within their shelves. We found that removing the nonfixated option approximately doubled the size of the attentional biases. The results show that peripheral visual information is crucial in facilitating good decisions and suggest that individuals might be influenceable by settings in which only one item is shown at a time, such as e-commerce.

Measuring utility with diffusion models

The drift diffusion model (DDM) is a prominent account of how people make decisions. Many of these decisions involve comparing two alternatives based on differences of perceived stimulus magnitudes, such as economic values. Here, we propose a consistent estimator for the parameters of a DDM in such cases. This estimator allows us to derive decision thresholds, drift rates, and subjective percepts (i.e., utilities in economic choice) directly from the experimental data. This eliminates the need to measure these values separately or to assume specific functional forms for them. Our method also allows one to predict drift rates for comparisons that did not occur in the dataset. We apply the method to two datasets, one comparing probabilities of earning a fixed reward and one comparing objects of variable reward value. Our analysis indicates that both datasets conform well to the DDM. We find that utilities are linear in probability and slightly convex in reward.

Information acquisition and decision strategies in intertemporal choice

Intertemporal decision models describe choices between outcomes with different delays. While these models mainly focus on predicting choices, they make implicit assumptions about how people acquire and process information. A link between information processing and choice model predictions is necessary for a complete mechanistic account of decision making. We establish this link by fitting 18 intertemporal choice models to experimental datasets with both choice and information acquisition data. First, we show that choice models have highly correlated fits: people that behave according to one model also behave according to other models that make similar information processing assumptions. Second, we develop and fit an attention model to information acquisition data. Critically, the attention model parameters predict which type of intertemporal choice models best describes a participant's choices. Overall, our results relate attentional processes to models of intertemporal choice, providing a stepping stone towards a complete mechanistic account of intertemporal decision making.

Experiential values are underweighted in decisions involving symbolic options

Standard models of decision-making assume each option is associated with subjective value, regardless of whether this value is inferred from experience (experiential) or explicitly instructed probabilistic outcomes (symbolic). In this study, we present results that challenge the assumption of unified representation of experiential and symbolic value. Across nine experiments, we presented participants with hybrid decisions between experiential and symbolic options. Participants' choices exhibited a pattern consistent with a systematic neglect of the experiential values. This normatively irrational decision strategy held after accounting for alternative explanations, and persisted even when it bore an economic cost. Overall, our results demonstrate that experiential and symbolic values are not symmetrically considered in hybrid decisions, suggesting they recruit different representational systems that may be assigned different priority levels in the decision process. These findings challenge the dominant models commonly used in value-based decision-making research.

Goals, usefulness and abstraction in value-based choice

Colombian drug lord Pablo Escobar, while on the run, purportedly burned two million dollars in banknotes to keep his daughter warm. A stark reminder that, in life, circumstances and goals can quickly change, forcing us to reassess and modify our values on-the-fly. Studies in decision-making and neuroeconomics have often implicitly equated value to reward, emphasising the hedonic and automatic aspect of the value computation, while overlooking its functional (concept-like) nature. Here we outline the computational and biological principles that enable the brain to compute the usefulness of an option or action by creating abstractions that flexibly adapt to changing goals. We present different algorithmic architectures, comparing ideas from artificial intelligence (AI) and cognitive neuroscience with psychological theories and, when possible, drawing parallels.

Preferences for seeking effort or reward information bias the willingness to work

Research suggests that the temporal order in which people receive information about costs and benefits whilst making decisions can influence their choices. But, do people have a preference for seeking information about costs or benefits when making effort-based decisions, and does this impact motivation? Here, participants made choices about whether to exert different levels of physical effort to obtain different magnitudes of reward, or rest for low reward. Prior to each effort-based choice, they also had to decide which information they wanted to see first: how much physical effort would be required, or how large the reward would be. We found no overall preference for seeking reward or effort information first, but motivation did change when people saw reward or effort information first. Seeking effort information first, both someone's average tendency to do so and their choice to see effort first on a given trial, was associated with reductions in the willingness to exert higher effort. Moreover, the tendency to prefer effort information first was associated with reduced vigorous exercise and higher levels of fatigue in everyday life. These findings highlight that preferences for seeking effort information may be a bias that reduces people's willingness to exert effort in the lab and in everyday life.

Value-directed information search in partner choice

It is a widely held view that people rely on incomplete information to find a relationship partner, resulting in non-compensatory choice heuristics. However, recent experimental work typically finds that partner choice follows compensatory choice strategies. To bridge this gap between theory and experimental evidence, we characterize the mate choice problem by distinguishing the information search process from the evaluation process. In an eye-tracking experiment and a MouseLab experiment, we show that people display strong value-directed search heuristics in response to all types of cues and that the magnitude of value-directed searches increases with cue primacy. Cue primacy also explains the interaction effect of cue type and participant sex on the extent of valued-directed search. We further argue that value-directed searching does not necessarily lead to non-compensatory choice rules but may serve compensatory decision-making. Our results demonstrate that people may adopt remarkably smart search heuristics to find an ideal partner efficiently.

Two-Year-Olds' Eye Movements Reflect Confidence in Their Understanding of Words

We studied the fundamental issue of whether children evaluate the reliability of their language interpretation, that is, their confidence in understanding words. In two experiments, 2-year-olds (Experiment 1: N = 50; Experiment 2: N = 60) saw two objects and heard one of them being named; both objects were then hidden behind screens and children were asked to look toward the named object, which was eventually revealed. When children knew the label used, they showed increased postdecision persistence after a correct compared with an incorrect anticipatory look, a marker of confidence in word comprehension (Experiment 1). When interacting with an unreliable speaker, children showed accurate word comprehension but reduced confidence in the accuracy of their own choice, indicating that children's confidence estimates are influenced by social information (Experiment 2). Thus, by the age of 2 years, children can estimate their confidence during language comprehension, long before they can talk about their linguistic skills.

Optimal Allocation of Finite Sampling Capacity in Accumulator Models of Multialternative Decision Making

When facing many options, we narrow down our focus to very few of them. Although behaviors like this can be a sign of heuristics, they can actually be optimal under limited cognitive resources. Here, we study the problem of how to optimally allocate limited sampling time to multiple options, modeled as accumulators of noisy evidence, to determine the most profitable one. We show that the effective sampling capacity of an agent increases with both available time and the discriminability of the options, and optimal policies undergo a sharp transition as a function of it. For small capacity, it is best to allocate time evenly to exactly five options and to ignore all the others, regardless of the prior distribution of rewards. For large capacities, the optimal number of sampled accumulators grows sublinearly, closely following a power law as a function of capacity for a wide variety of priors. We find that allocating equal times to the sampled accumulators is better than using uneven time allocations. Our work highlights that multialternative decisions are endowed with breadth–depth tradeoffs, demonstrates how their optimal solutions depend on the amount of limited resources and the variability of the environment, and shows that narrowing down to a handful of options is always optimal for small capacities.

Humans actively sample evidence to support prior beliefs

No one likes to be wrong. Previous research has shown that participants may underweight information incompatible with previous choices, a phenomenon called confirmation bias. In this paper, we argue that a similar bias exists in the way information is actively sought. We investigate how choice influences information gathering using a perceptual choice task and find that participants sample more information from a previously chosen alternative. Furthermore, the higher the confidence in the initial choice, the more biased information sampling becomes. As a consequence, when faced with the possibility of revising an earlier decision, participants are more likely to stick with their original choice, even when incorrect. Critically, we show that agency controls this phenomenon. The effect disappears in a fixed sampling condition where presentation of evidence is controlled by the experimenter, suggesting that the way in which confirmatory evidence is acquired critically impacts the decision process. These results suggest active information acquisition plays a critical role in the propagation of strongly held beliefs over time.

Motivational signals disrupt metacognitive signals in the human ventromedial prefrontal cortex

A growing body of evidence suggests that, during decision-making, BOLD signal in the ventromedial prefrontal cortex (VMPFC) correlates both with motivational variables – such as incentives and expected values – and metacognitive variables – such as confidence judgments – which reflect the subjective probability of being correct. At the behavioral level, we recently demonstrated that the value of monetary stakes bias confidence judgments, with gain (respectively loss) prospects increasing (respectively decreasing) confidence judgments, even for similar levels of difficulty and performance. If and how this value-confidence interaction is reflected in the VMPFC remains unknown. Here, we used an incentivized perceptual decision-making fMRI task that dissociates key decision-making variables, thereby allowing to test several hypotheses about the role of the VMPFC in the value-confidence interaction. While our initial analyses seemingly indicate that the VMPFC combines incentives and confidence to form an expected value signal, we falsified this conclusion with a meticulous dissection of qualitative activation patterns. Rather, our results show that strong VMPFC confidence signals observed in trials with gain prospects are disrupted in trials with no – or negative (loss) – monetary prospects. Deciphering how decision variables are represented and interact at finer scales seems necessary to better understand biased (meta)cognition.

The human ventromedial prefrontal cortex helps to determine value and confidence in certain decisions, but only in situations when there is a potential for a (monetary) reward.

Filling the gaps: Cognitive control as a critical lens for understanding mechanisms of value-based decision-making

While often seeming to investigate rather different problems, research into value-based decision making and cognitive control have historically offered parallel insights into how people select thoughts and actions. While the former studies how people weigh costs and benefits to make a decision, the latter studies how they adjust information processing to achieve their goals. Recent work has highlighted ways in which decision-making research can inform our understanding of cognitive control. Here, we provide the complementary perspective: how cognitive control research has informed understanding of decision-making. We highlight three particular areas of research where this critical interchange has occurred: (1) how different types of goals shape the evaluation of choice options, (2) how people use control to adjust the ways they make their decisions, and (3) how people monitor decisions to inform adjustments to control at multiple levels and timescales. We show how adopting this alternate viewpoint offers new insight into the determinants of both decisions and control; provides alternative interpretations for common neuroeconomic findings; and generates fruitful directions for future research.

Sources of confidence in value-based choice

Confidence, the subjective estimate of decision quality, is a cognitive process necessary for learning from mistakes and guiding future actions. The origins of confidence judgments resulting from economic decisions remain unclear. We devise a task and computational framework that allowed us to formally tease apart the impact of various sources of confidence in value-based decisions, such as uncertainty emerging from encoding and decoding operations, as well as the interplay between gaze-shift dynamics and attentional effort. In line with canonical decision theories, trial-to-trial fluctuations in the precision of value encoding impact economic choice consistency. However, this uncertainty has no influence on confidence reports. Instead, confidence is associated with endogenous attentional effort towards choice alternatives and down-stream noise in the comparison process. These findings provide an explanation for confidence (miss)attributions in value-guided behaviour, suggesting mechanistic influences of endogenous attentional states for guiding decisions and metacognitive awareness of choice certainty.

A causal role for the right frontal eye fields in value comparison

Recent studies have suggested close functional links between overt visual attention and decision making. This suggests that the corresponding mechanisms may interface in brain regions known to be crucial for guiding visual attention - such as the frontal eye field (FEF). Here, we combined brain stimulation, eye tracking, and computational approaches to explore this possibility. We show that inhibitory transcranial magnetic stimulation (TMS) over the right FEF has a causal impact on decision making, reducing the effect of gaze dwell time on choice while also increasing reaction times. We computationally characterize this putative mechanism by using the attentional drift diffusion model (aDDM), which reveals that FEF inhibition reduces the relative discounting of the non-fixated option in the comparison process. Our findings establish an important causal role of the right FEF in choice, elucidate the underlying mechanism, and provide support for one of the key causal hypotheses associated with the aDDM.

Magnitude-sensitivity: rethinking decision-making

Magnitude-sensitivity refers to the result that performance in decision-making, across domains and organisms, is affected by the total value of the possible alternatives. This simple result offers a window into fundamental issues in decision-making and has led to a reconsideration of ecological decision-making, prominent computational models of decision-making, and optimal decision-making. Moreover, magnitude-sensitivity has inspired the design of new robotic systems that exploit natural solutions and apply optimal decision-making policies. In this article, we review the key theoretical and empirical results about magnitude-sensitivity and highlight the importance that this phenomenon has for the understanding of decision-making. Furthermore, we discuss open questions and ideas for future research.

Webcam-based online eye-tracking for behavioral research

Experiments are increasingly moving online. This poses a major challenge for researchers who rely on in-lab techniques such as eye-tracking. Researchers in computer science have developed web-based eye-tracking applications (WebGazer; Papoutsaki et al., 2016) but they have yet to see them used in behavioral research. This is likely due to the extensive calibration and validation procedure, inconsistent temporal resolution (Semmelmann & Weigelt, 2018), and the challenge of integrating it into experimental software. Here, we incorporate WebGazer into a JavaScript library widely used by behavioral researchers (jsPsych) and adjust the procedure and code to reduce calibration/validation and improve the temporal resolution (from 100–1000 ms to 20–30 ms). We test this procedure with a decision-making study on Amazon MTurk, replicating previous in-lab findings on the relationship between gaze and choice, with little degradation in spatial or temporal resolution. This provides evidence that online web-based eye-tracking is feasible in behavioral research.

Salience effects in information acquisition: No evidence for a top-down coherence influence

The Integrated Coherence-Based Decision and Search (iCodes) model proposed by Jekel et al. (Psychological Review, 125 (5), 744-768, 2018) formalizes both decision making and pre-decisional information search as coherence-maximization processes in an interactive network. Next to bottom-up attribute influences, the coherence of option information exerts a top-down influence on the search processes in this model, predicting the tendency to continue information search with the currently most attractive option. This hallmark "attraction search effect" (ASE) has been demonstrated in several studies. In three experiments with 250 participants altogether, a more subtle prediction of an extended version of iCodes including exogenous influence factors was tested: The salience of information is assumed to have both a direct (bottom-up) and an indirect (top-down) effect on search, the latter driven by the match between information valence and option attractiveness. The results of the experiments largely agree in (1) showing a strong ASE, (2) demonstrating a bottom-up salience effect on search, but (3) suggesting the absence of the hypothesized indirect top-down salience effect. Hence, only two of three model predictions were confirmed. Implications for various implementations of exogenous factors in the iCodes model are discussed.

Medial Frontal Cortex Activity Predicts Information Sampling in Economic Choice

Decision-making not only requires agents to decide what to choose but also how much information to sample before committing to a choice. Previously established frameworks for economic choice argue for a deliberative process of evidence accumulation across time. These tacitly acknowledge a role of information sampling in that decisions are only made once sufficient evidence is acquired, yet few experiments have explicitly placed information sampling under the participant's control. Here, we use fMRI to investigate the neural basis of information sampling in economic choice by allowing participants (n = 30, sex not recorded) to actively sample information in a multistep decision task. We show that medial frontal cortex (MFC) activity is predictive of further information sampling before choice. Choice difficulty (inverse value difference, keeping sensory difficulty constant) was also encoded in MFC, but this effect was explained away by the inclusion of information sampling as a coregressor in the general linear model. A distributed network of regions across the prefrontal cortex encoded key features of the sampled information at the time it was presented. We propose that MFC is an important controller of the extent to which information is gathered before committing to an economic choice. This role may explain why MFC activity has been associated with evidence accumulation in previous studies in which information sampling was an implicit rather than explicit feature of the decision.SIGNIFICANCE STATEMENT The decisions we make are determined by the information we have sampled before committing to a choice. Accumulator frameworks of decision-making tacitly acknowledge the need to sample further information during the evidence accumulation process until a decision boundary is reached. However, relatively few studies explicitly place this decision to sample further information under the participant's control. In this fMRI study, we find that MFC activity is related to information sampling decisions in a multistep economic choice task. This suggests that an important role of evidence representations within MFC may be to guide adaptive sequential decisions to sample further information before committing to a final decision.

The elusiveness of context effects in decision making

Contextual features influence human and non-human decision making, giving rise to preference reversals. Decades of research have documented the species and situations in which these effects are observed. More recently, however, researchers have focused on boundary conditions, that is, settings in which established effects disappear or reverse. This work is scattered across academic disciplines and some results appear to contradict each other. We synthesize recent findings and resolve apparent contradictions by considering them in terms of three core categories of decision context: spatial arrangement, attribute concreteness, and deliberation time. We suggest that these categories could be understood using theories of choice representation, which specify how context shapes the information over which deliberation processes operate.

The influence of visual attention on memory-based preferential choice

Many decisions rely on past experiences. Recent research indicates that people's choices are biased towards choosing better-remembered options, even if these options are comparatively unattractive (i.e., a memory bias). In the current study, we used eye tracking to compare the influence of visual attention on preferential choice between memory-based and non-memory-based decisions. Participants completed the remember-and-decide task. In this task, they first learned associations between screen locations and snack items. Then, they made binary choices between snack items. These snacks were either hidden and required recall (memory-based decisions), or they were visible (non-memory-based decisions). Remarkably, choices were more strongly influenced by attention in memory-based compared to non-memory-based decisions. However, visual attention did not mediate the memory bias on preferential choices. Finally, we adopt and expand a recently proposed computational model to provide a comprehensive description of the role of attention in memory-based decisions. In sum, the present work elucidates how visual attention interacts with episodic memory and preference formation in memory-based decisions.

Object Detection Combining CNN and Adaptive Color Prior Features

When compared with the traditional manual design method, the convolutional neural network has the advantages of strong expressive ability and it is insensitive to scale, light, and deformation, so it has become the mainstream method in the object detection field. In order to further improve the accuracy of existing object detection methods based on convolutional neural networks, this paper draws on the characteristics of the attention mechanism to model color priors. Firstly, it proposes a cognitive-driven color prior model to obtain the color prior features for the known types of target samples and the overall scene, respectively. Subsequently, the acquired color prior features and test image color features are adaptively weighted and competed to obtain prior-based saliency images. Finally, the obtained saliency images are treated as features maps and they are further fused with those extracted by the convolutional neural network to complete the subsequent object detection task. The proposed algorithm does not need training parameters, has strong generalization ability, and it is directly fused with convolutional neural network features at the feature extraction stage, thus has strong versatility. Experiments on the VOC2007 and VOC2012 benchmark data sets show that the utilization of cognitive-drive color priors can further improve the performance of existing object detection algorithms.

Uncovering the computational mechanisms underlying many-alternative choice

How do we choose when confronted with many alternatives? There is surprisingly little decision modelling work with large choice sets, despite their prevalence in everyday life. Even further, there is an apparent disconnect between research in small choice sets, supporting a process of gaze-driven evidence accumulation, and research in larger choice sets, arguing for models of optimal choice, satisficing, and hybrids of the two. Here, we bridge this divide by developing and comparing different versions of these models in a many-alternative value-based choice experiment with 9, 16, 25, or 36 alternatives. We find that human choices are best explained by models incorporating an active effect of gaze on subjective value. A gaze-driven, probabilistic version of satisficing generally provides slightly better fits to choices and response times, while the gaze-driven evidence accumulation and comparison model provides the best overall account of the data when also considering the empirical relation between gaze allocation and choice.

Optimal policy for attention-modulated decisions explains human fixation behavior

Traditional accumulation-to-bound decision-making models assume that all choice options are processed with equal attention. In real life decisions, however, humans alternate their visual fixation between individual items to efficiently gather relevant information (Yang et al., 2016). These fixations also causally affect one's choices, biasing them toward the longer-fixated item (Krajbich et al., 2010). We derive a normative decision-making model in which attention enhances the reliability of information, consistent with neurophysiological findings (Cohen and Maunsell, 2009). Furthermore, our model actively controls fixation changes to optimize information gathering. We show that the optimal model reproduces fixation-related choice biases seen in humans and provides a Bayesian computational rationale for this phenomenon. This insight led to additional predictions that we could confirm in human data. Finally, by varying the relative cognitive advantage conferred by attention, we show that decision performance is benefited by a balanced spread of resources between the attended and unattended items.

Fixation patterns in simple choice reflect optimal information sampling

Simple choices (e.g., eating an apple vs. an orange) are made by integrating noisy evidence that is sampled over time and influenced by visual attention; as a result, fluctuations in visual attention can affect choices. But what determines what is fixated and when? To address this question, we model the decision process for simple choice as an information sampling problem, and approximate the optimal sampling policy. We find that it is optimal to sample from options whose value estimates are both high and uncertain. Furthermore, the optimal policy provides a reasonable account of fixations and choices in binary and trinary simple choice, as well as the differences between the two cases. Overall, the results show that the fixation process during simple choice is influenced dynamically by the value estimates computed during the decision process, in a manner consistent with optimal information sampling.