Oversampling of minority categories drives misperceptions of group compositions.

The ability to estimate proportions informs our immediate impressions of social environments (e.g., of the diversity of races or genders within a crowded room). This study examines how the distribution of attention during brief glances shapes estimates of group gender proportions. Performance-wise, subjects exhibit a canonical pattern of judgment errors: small proportions are overestimated while large values are underestimated. Subjects' eye movements at sub-second timescales reveal that these biases follow from a tendency to visually oversample members of the gender minority. Rates of oversampling dovetail with average levels of error magnitudes, response variability, and response times. Visual biases are thus associated with the inherent difficulty in estimating particular proportions. All results are replicated at a within-subjects level with non-human ensembles using natural scene stimuli; the observed attentional patterns and judgment biases are thus not exclusively guided by face-specific visual properties. Our results reveal the biased distribution of attention underlying typical judgment errors of group proportions.

Individual differences in the perception of probability.

In recent studies of humans estimating non-stationary probabilities, estimates appear to be unbiased on average, across the full range of probability values to be estimated. This finding is surprising given that experiments measuring probability estimation in other contexts have often identified conservatism: individuals tend to overestimate low probability events and underestimate high probability events. In other contexts, repulsive biases have also been documented, with individuals producing judgments that tend toward extreme values instead. Using extensive data from a probability estimation task that produces unbiased performance on average, we find substantial biases at the individual level; we document the coexistence of both conservative and repulsive biases in the same experimental context. Individual biases persist despite extensive experience with the task, and are also correlated with other behavioral differences, such as individual variation in response speed and adjustment rates. We conclude that the rich computational demands of our task give rise to a variety of behavioral patterns, and that the apparent unbiasedness of the pooled data is an artifact of the aggregation of heterogeneous biases.

Uncertainty-based overestimation in the perception of group actions.

Individuals are adept at estimating average properties of group visual stimuli, even following brief presentations. In estimating the directional heading of walking human figures, judgments are biased in a peculiar manner: groups facing intermediate directions are perceived to be more leftward- or rightward-facing than actual averages. This effect was previously explained as a repulsive bias away from a central category boundary; groups along this boundary (directly facing the observer) are estimated with lower variability and with relatively greater accuracy. Here we show that: (i) the original effect replicates and is constant over time in a novel estimation task with persistent directional states; and, (ii) novel patterns of response variability and durations align with the entire range of overestimation. A simple model of additive errors proportional to viewer uncertainty matches the observed bias magnitudes. We furthermore show that the bias generalizes beyond approaching walkers with the use of rearward-facing walkers presented at a nonparallel angle. Overall, the recurring relation between bias and uncertainty is also consistent with top-down and post-perceptual causes of misestimation.

Speed of person perception affects immediate and ongoing aesthetic evaluation.

Recent studies have shed light on how aesthetic judgments are formed following presentations lasting less than a second. Meanwhile, dedicated neural mechanisms are understood to enable the rapid detection of human faces, bodies, and actions. On the basis of cognitive studies of: (i) the speed and acuity of person perception, and (ii) preferential attention given to human imagery (e.g., faces and bodies), we hypothesize that the visual detection of humans in portraits increases the magnitude and stability (i.e., similarity to later responses) of aesthetic ratings. Ease of person perception is also expected to elicit longer durations of preferential viewing time, a surplus measure of viewing behavior that should be positively related to subsequent ratings. To test these ideas, we use a set of cubist portraits previously established to be more or less categorizable in terms of the aggregate time required to perceive the depicted person. Using these images, we track aesthetic judgments made following short and unconstrained presentations; in an intervening task, we measure viewing behavior when subjects are able to selectively reveal regions of these images. We find that highly categorizable artworks (those that require less time to identify the figure as human) elicit higher and more predictive aesthetic ratings following 30 ms presentations while also eliciting longer viewing durations. Changes in ratings throughout the task are positively correlated with cumulative viewing time; critically, an image's categorizability level further moderates the strength of this relationship. These results demonstrate that a particular kind of visual object recognition - the recognition of human forms - modulates aesthetic preferences at a glance, subsequent viewing patterns, as well as rating changes over time.

Temporal Discounting and Search Habits: Evidence for a Task-Dependent Relationship.

Recent experiments suggest that search direction causally affects the discounted valuation of delayed payoffs. Comparisons between options can increase individuals' patience toward future payoff options, while searching within options instead promotes impatient choices. We further test the robustness and specificity of this relationship using a novel choice task. Here individuals choose between pairs of delayed payoffs instead of single delayed outcomes. We observe a relationship between search styles and temporal discounting that are the opposite of those previously reported. Integrators-those who tend to compare attributes within alternatives-discount and choose more slowly than comparators-those who are more likely to compare between alternatives. This finding supports and augments the view that individuals' search strategy is predictive of subsequent discount rates. In particular, the direction of this relationship is further modifiable based on the spatial layout and varying information within an individual's decision-making environment.

Continuous aesthetic judgment of image sequences.

Perceptual judgments are said to be reference-dependent as they change on the basis of recent experiences. Here we quantify sequence effects within two types of aesthetic judgments: (i) individual ratings of single images (during self-paced trials) and (ii) continuous ratings of image sequences. As in the case of known contrast effects, trial-by-trial aesthetic responses are negatively correlated with judgments made toward the preceding image. During continuous judgment, a different type of bias is observed. The onset of change within a sequence introduces a persistent increase in ratings (relative to when the same images are judged in isolation). Furthermore, subjects indicate adjustment patterns and choices that selectively favor sequences that are rich in change. Sequence effects in aesthetic judgments thus differ greatly depending on the continuity and arrangement of presented stimuli. The effects highlighted here are important in understanding sustained aesthetic responses over time, such as those elicited during choreographic and musical arrangements. In contrast, standard measurements of aesthetic responses (over trials) may represent a series of distinct aesthetic experiences (e.g., viewing artworks in a museum).

Normalized value coding explains dynamic adaptation in the human valuation process.

The notion of subjective value is central to choice theories in ecology, economics, and psychology, serving as an integrated decision variable by which options are compared. Subjective value is often assumed to be an absolute quantity, determined in a static manner by the properties of an individual option. Recent neurobiological studies, however, have shown that neural value coding dynamically adapts to the statistics of the recent reward environment, introducing an intrinsic temporal context dependence into the neural representation of value. Whether valuation exhibits this kind of dynamic adaptation at the behavioral level is unknown. Here, we show that the valuation process in human subjects adapts to the history of previous values, with current valuations varying inversely with the average value of recently observed items. The dynamics of this adaptive valuation are captured by divisive normalization, linking these temporal context effects to spatial context effects in decision making as well as spatial and temporal context effects in perception. These findings suggest that adaptation is a universal feature of neural information processing and offer a unifying explanation for contextual phenomena in fields ranging from visual psychophysics to economic choice.

Forecasting the outcome of a time-varying Bernoulli process: Data from a laboratory experiment.

The data presented in this article are related to the research article entitled "Discrete Adjustment to a Changing Environment: Experimental Evidence" (Khaw et al., 2017) [1]. We present data from a laboratory experiment that asks subjects to forecast the outcome of a time-varying Bernoulli process. On a computer program, subjects draw rings with replacement from a virtual box containing green and red rings in an unknown proportion. Subjects provide their estimates of the probability of drawing a green ring. They are rewarded for their participation and for the accuracy of their estimates. The actual probability of drawing a green ring is initially drawn from a uniform distribution. It then changes intermittently throughout the session, and each subsequent probability is an independent draw from the uniform distribution. Each session involves 1000 ring draws. The dataset contains the values of the underlying probability, the sequence of ring draws that are realized, and the subjects' estimates and response times. The dataset contains the performance of 11 subjects who each completed 10 sessions over the course of several days.

Reminders of past choices bias decisions for reward in humans.

We provide evidence that decisions are made by consulting memories for individual past experiences, and that this process can be biased in favour of past choices using incidental reminders. First, in a standard rewarded choice task, we show that a model that estimates value at decision-time using individual samples of past outcomes fits choices and decision-related neural activity better than a canonical incremental learning model. In a second experiment, we bias this sampling process by incidentally reminding participants of individual past decisions. The next decision after a reminder shows a strong influence of the action taken and value received on the reminded trial. These results provide new empirical support for a decision architecture that relies on samples of individual past choice episodes rather than incrementally averaged rewards in evaluating options and has suggestive implications for the underlying cognitive and neural mechanisms.

The Measurement of Subjective Value and Its Relation to Contingent Valuation and Environmental Public Goods.

Environmental public goods--including national parks, clean air/water, and ecosystem services--provide substantial benefits on a global scale. These goods have unique characteristics in that they are typically "nonmarket" goods, with values from both use and passive use that accrue to a large number of individuals both in current and future generations. In this study, we test the hypothesis that neural signals in areas correlated with subjective valuations for essentially all other previously studied categories of goods (ventromedial prefrontal cortex and ventral striatum) also correlate with environmental valuations. We use contingent valuation (CV) as our behavioral tool for measuring valuations of environmental public goods. CV is a standard stated preference approach that presents survey respondents with information on an issue and asks questions that help policymakers determine how much citizens are willing to pay for a public good or policy. We scanned human subjects while they viewed environmental proposals, along with three other classes of goods. The presentation of all four classes of goods yielded robust and similar patterns of temporally synchronized brain activation within attentional networks. The activations associated with the traditional classes of goods replicate previous correlations between neural activity in valuation areas and behavioral preferences. In contrast, CV-elicited values for environmental proposals did not correlate with brain activity at either the individual or population level. For a sub-population of participants, CV-elicited values were correlated with activity within the dorsomedial prefrontal cortex, a region associated with cognitive control and shifting decision strategies. The results show that neural activity associated with the subjective valuation of environmental proposals differs profoundly from the neural activity associated with previously examined goods and preference measures.

Normalization is a general neural mechanism for context-dependent decision making.

Understanding the neural code is critical to linking brain and behavior. In sensory systems, divisive normalization seems to be a canonical neural computation, observed in areas ranging from retina to cortex and mediating processes including contrast adaptation, surround suppression, visual attention, and multisensory integration. Recent electrophysiological studies have extended these insights beyond the sensory domain, demonstrating an analogous algorithm for the value signals that guide decision making, but the effects of normalization on choice behavior are unknown. Here, we show that choice models using normalization generate significant (and classically irrational) choice phenomena driven by either the value or number of alternative options. In value-guided choice experiments, both monkey and human choosers show novel context-dependent behavior consistent with normalization. These findings suggest that the neural mechanism of value coding critically influences stochastic choice behavior and provide a generalizable quantitative framework for examining context effects in decision making.