Repeated rock, paper, scissors play reveals limits in adaptive sequential behavior.

How do people adapt to others in adversarial settings? Prior work has shown that people often violate rational models of adversarial decision-making in repeated interactions. In particular, in mixed strategy equilibrium (MSE) games, where optimal action selection entails choosing moves randomly, people often do not play randomly, but instead try to outwit their opponents. However, little is known about the adaptive reasoning that underlies these deviations from random behavior. Here, we examine strategic decision-making across repeated rounds of rock, paper, scissors, a well-known MSE game. In experiment 1, participants were paired with bot opponents that exhibited distinct stable move patterns, allowing us to identify the bounds of the complexity of opponent behavior that people can detect and adapt to. In experiment 2, bot opponents instead exploited stable patterns in the human participants' moves, providing a symmetrical bound on the complexity of patterns people can revise in their own behavior. Across both experiments, people exhibited a robust and flexible attention to transition patterns from one move to the next, exploiting these patterns in opponents and modifying them strategically in their own moves. However, their adaptive reasoning showed strong limitations with respect to more sophisticated patterns. Together, results provide a precise and consistent account of the surprisingly limited scope of people's adaptive decision-making in this setting.

Local but not global graph theoretic measures of semantic networks generalize across tasks.

"Dogs" are connected to "cats" in our minds, and "backyard" to "outdoors." Does the structure of this semantic knowledge differ across people? Network-based approaches are a popular representational scheme for thinking about how relations between different concepts are organized. Recent research uses graph theoretic analyses to examine individual differences in semantic networks for simple concepts and how they relate to other higher-level cognitive processes, such as creativity. However, it remains ambiguous whether individual differences captured via network analyses reflect true differences in measures of the structure of semantic knowledge, or differences in how people strategically approach semantic relatedness tasks. To test this, we examine the reliability of local and global metrics of semantic networks for simple concepts across different semantic relatedness tasks. In four experiments, we find that both weighted and unweighted graph theoretic representations reliably capture individual differences in local measures of semantic networks (e.g., how related pot is to pan versus lion). In contrast, we find that metrics of global structural properties of semantic networks, such as the average clustering coefficient and shortest path length, are less robust across tasks and may not provide reliable individual difference measures of how people represent simple concepts. We discuss the implications of these results and offer recommendations for researchers who seek to apply graph theoretic analyses in the study of individual differences in semantic memory.

Scanning the horizon: towards transparent and reproducible neuroimaging research.

Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions.

Triadic conflict "primitives" can be reduced to welfare trade-off ratios.

Pietraszewski proposes four triadic "primitives" for representing social groups. We argue that, despite surface differences, these triads can all be reduced to similar underlying welfare trade-off ratios, which are a better candidate for social group primitives. Welfare trade-off ratios also have limitations, however, and we suggest there are multiple computational strategies by which people recognize and reason about social groups.

The role of kinematic properties in multiple object tracking.

People commonly track objects moving in complex natural displays and their performance in the multiple object tracking paradigm has been used to study such visual attention for more than three decades. Given the theoretical and practical importance of object tracking, it is critical to understand how people solve the correspondence problem to track objects; however, it remains unclear what information people use to achieve this feat. In particular, although people can track multiple moving objects based on their positions, there is ambiguity about whether people can track objects via higher order kinematic information, such as velocity. We designed a paradigm in which position was rendered uninformative to directly examine whether people could use higher order kinematic information to track multiple objects. We find that people can track via velocity, but not acceleration, even though observers can reliably detect the acceleration cue that they cannot use for tracking. Furthermore, we show a capacity constraint on using higher order kinematic information-people perform worse when required to use velocity to resolve correspondence for multiple object pairs simultaneously. Together, our results suggest that, although people can use higher order kinematic information for object tracking, precise higher order kinematic information is not freely available from the early visual system.

Models of lineup memory.

Face recognition memory is often tested by the police using a photo lineup, which consists of one suspect, who is either innocent or guilty, and five or more physically similar fillers, all of whom are known to be innocent. For many years, lineups were investigated in lab studies without guidance from standard models of recognition memory. More recently, signal detection theory has been used to conceptualize lineup memory and to motivate receiver operating characteristic (ROC) analysis of lineup performance. Here, we describe three competing signal-detection models of lineup memory, derive their likelihood functions, and fit them to empirical ROC data. We also introduce the notion that memory signals generated by the faces in a lineup are likely to be correlated because, by design, those faces share features. The models we investigate differ in their predictions about the effect that correlated memory signals should have on the ability to discriminate innocent from guilty suspects. A popular compound signal detection model known as the Integration model predicts that correlated memory signals should impair discriminability. Empirically, this model performed so poorly that, going forward, it should probably be abandoned. The best-fitting model incorporates a principle known as "ensemble coding," which predicts that correlated memory signals should enhance discriminability. The ensemble model aligns with a previously proposed theory of eyewitness identification according to which the simultaneous presentation of faces in a lineup enhances discriminability compared to when faces are presented in isolation because it permits eyewitnesses to detect and discount non-diagnostic facial features.

Ensemble clustering in visual working memory biases location memories and reduces the Weber noise of relative positions.

People seem to compute the ensemble statistics of objects and use this information to support the recall of individual objects in visual working memory. However, there are many different ways that hierarchical structure might be encoded. We examined the format of structured memories by asking subjects to recall the locations of objects arranged in different spatial clustering structures. Consistent with previous investigations of structured visual memory, subjects recalled objects biased toward the center of their clusters. Subjects also recalled locations more accurately when they were arranged in fewer clusters containing more objects, suggesting that subjects used the clustering structure of objects to aid recall. Furthermore, subjects had more difficulty recalling larger relative distances, consistent with subjects encoding the positions of objects relative to clusters and recalling them with magnitude-proportional (Weber) noise. Our results suggest that clustering improved the fidelity of recall by biasing the recall of locations toward cluster centers to compensate for uncertainty and by reducing the magnitude of encoded relative distances.

Hierarchical encoding makes individuals in a group seem more attractive.

In the research reported here, we found evidence of the cheerleader effect-people seem more attractive in a group than in isolation. We propose that this effect arises via an interplay of three cognitive phenomena: (a) The visual system automatically computes ensemble representations of faces presented in a group, (b) individual members of the group are biased toward this ensemble average, and (c) average faces are attractive. Taken together, these phenomena suggest that individual faces will seem more attractive when presented in a group because they will appear more similar to the average group face, which is more attractive than group members' individual faces. We tested this hypothesis in five experiments in which subjects rated the attractiveness of faces presented either alone or in a group with the same gender. Our results were consistent with the cheerleader effect.

Quantifying error distributions in crowding.

When multiple objects are in close proximity, observers have difficulty identifying them individually. Two classes of theories aim to account for this crowding phenomenon: spatial pooling and spatial substitution. Variations of these accounts predict different patterns of errors in crowded displays. Here we aim to characterize the kinds of errors that people make during crowding by comparing a number of error models across three experiments in which we manipulate flanker spacing, display eccentricity, and precueing duration. We find that both spatial intrusions and individual letter confusions play a considerable role in errors. Moreover, we find no evidence that a naïve pooling model that predicts errors based on a nonadditive combination of target and flankers explains errors better than an independent intrusion model (indeed, in our data, an independent intrusion model is slightly, but significantly, better). Finally, we find that manipulating trial difficulty in any way (spacing, eccentricity, or precueing) produces homogenous changes in error distributions. Together, these results provide quantitative baselines for predictive models of crowding errors, suggest that pooling and spatial substitution models are difficult to tease apart, and imply that manipulations of crowding all influence a common mechanism that impacts subject performance.

Designing and detecting lies by reasoning about other agents.

How do people detect lies from the content of messages, and design lies that go undetected? Lying requires strategic reasoning about how others think and respond. We propose a unified framework underlying lie design and detection, formalized as recursive social reasoning. Senders design lies by inferring the likelihood the receiver detects potential lies; receivers detect lies by inferring if and how the sender would lie. Under this framework, we can predict the rate and content of lies people produce, and which lies are detected. In Experiment 1, we show that people calibrate the extremeness of their lies and what lies they detect to beliefs about goals and the statistics of the world. In Experiment 2, we present stronger diagnostic evidence for the function of social reasoning in lying: people cater their lies to their audience, even when their audience's beliefs differ from their own. We conclude that recursive and rational social reasoning is a key cognitive process underlying how people communicate in adversarial settings. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Structured visuospatial representations revealed through serial reproduction.

Working memory is a reconstructive process that requires integrating multiple hierarchical representations of objects. This hierarchical reconstruction allows us to overcome perceptual uncertainty and limited cognitive capacity but yields systematic biases in working memory as individual items are influenced by the ensemble statistics of the scene, or of their particular group. Given the importance of the hierarchical encoding of a display, we aim to characterize what structures people use to encode visual scenes using a nonparametric data-driven approach. In Experiment 1, we examine visuospatial memory for locations by asking participants to recall the locations of objects in a serial reproduction task. We show that people report items in a more compact structure than they initially were and organize them into clustered spatial groups. In Experiment 2, we explicitly introduce discrete color groups, allowing us to test whether the color feature governs the spatial grouping. We find that the spatial structures were color-contingent. By analyzing color groups, we circumvent the grouping uncertainty in Experiment 1 and further reveal that people compress color groups into collinear structures with similar orientations and equidistant spacing. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

How do people build up visual memory representations from sensory evidence? Revisiting two classic models of choice.

In many decision tasks, we have a set of alternative choices and are faced with the problem of how to use our latent beliefs and preferences about each alternative to make a single choice. Cognitive and decision models typically presume that beliefs and preferences are distilled to a scalar latent strength for each alternative, but it is also critical to model how people use these latent strengths to choose a single alternative. Most models follow one of two traditions to establish this link. Modern psychophysics and memory researchers make use of signal detection theory, assuming that latent strengths are perturbed by noise, and the highest resulting signal is selected. By contrast, many modern decision theoretic modeling and machine learning approaches use the softmax function (which is based on Luce's choice axiom; Luce, 1959) to give some weight to non-maximal-strength alternatives. Despite the prominence of these two theories of choice, current approaches rarely address the connection between them, and the choice of one or the other appears more motivated by the tradition in the relevant literature than by theoretical or empirical reasons to prefer one theory to the other. The goal of the current work is to revisit this topic by elucidating which of these two models provides a better characterization of latent processes in m -alternative decision tasks, with a particular focus on memory tasks. In a set of visual memory experiments, we show that, within the same experimental design, the softmax parameter ß varies across m -alternatives, whereas the parameter d ' of the signal-detection model is stable. Together, our findings indicate that replacing softmax with signal-detection link models would yield more generalizable predictions across changes in task structure. More ambitiously, the invariance of signal detection model parameters across different tasks suggests that the parametric assumptions of these models may be more than just a mathematical convenience, but reflect something real about human decision-making.

Modeling face similarity in police lineups.

Police investigators worldwide use lineups to test an eyewitness's memory of a perpetrator. A typical lineup consists of one suspect (who is innocent or guilty) plus five or more fillers who resemble the suspect and who are known to be innocent. Although eyewitness identification decisions were once biased by police pressure and poorly constructed lineups, decades of social science research led to the development of reformed lineup procedures that provide a more objective test memory. Under these improved testing conditions, cognitive models of memory can be used to better understand and ideally enhance eyewitness identification performance. In this regard, one question that has bedeviled the field for decades is how similar the lineup fillers should be to the suspect to optimize performance. Here, we model the effects of manipulating filler similarity to better understand why such manipulations have the intriguing effects they do. Our findings suggest that witnesses rely on a decision variable consisting of the degree to which the memory signal for a particular face in the lineup stands out relative to the crowd of memory signals generated by the set of faces in the lineup. The use of that decision variable helps to explain why discriminability is maximized by choosing fillers that match the suspect on basic facial features typically described by the eyewitness (e.g., age, race, gender) but who otherwise are maximally dissimilar to the suspect. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Voodoo and circularity errors.

We briefly describe the circularity/non-independence problem, and our perception of the impact the ensuing discussion has had on fMRI research.

Search for patterns of functional specificity in the brain: a nonparametric hierarchical Bayesian model for group fMRI data.

Functional MRI studies have uncovered a number of brain areas that demonstrate highly specific functional patterns. In the case of visual object recognition, small, focal regions have been characterized with selectivity for visual categories such as human faces. In this paper, we develop an algorithm that automatically learns patterns of functional specificity from fMRI data in a group of subjects. The method does not require spatial alignment of functional images from different subjects. The algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary activation variable. At the next level, we define a prior over sets of activation variables in all subjects. We use a Hierarchical Dirichlet Process as the prior in order to learn the patterns of functional specificity shared across the group, which we call functional systems, and estimate the number of these systems. Inference based on our model enables automatic discovery and characterization of dominant and consistent functional systems. We apply the method to data from a visual fMRI study comprised of 69 distinct stimulus images. The discovered system activation profiles correspond to selectivity for a number of image categories such as faces, bodies, and scenes. Among systems found by our method, we identify new areas that are deactivated by face stimuli. In empirical comparisons with previously proposed exploratory methods, our results appear superior in capturing the structure in the space of visual categories of stimuli.

Data-driven functional clustering reveals dominance of face, place, and body selectivity in the ventral visual pathway.

Regions selective for faces, places, and bodies feature prominently in the literature on the human ventral visual pathway. Are selectivities for these categories in fact the most robust response profiles in this pathway, or is their prominence an artifact of biased sampling of the hypothesis space in prior work? Here we use a data-driven structure discovery method that avoids the assumptions built into most prior work by 1) giving equal consideration to all possible response profiles over the conditions tested, 2) relaxing implicit anatomical constraints (that important functional profiles should manifest themselves in spatially contiguous voxels arising in similar locations across subjects), and 3) testing for dominant response profiles over images, rather than categories, thus enabling us to discover, rather than presume, the categories respected by the brain. Even with these assumptions relaxed, face, place, and body selectivity emerge as dominant in the ventral stream.

A bayesian optimal foraging model of human visual search.

Real-world visual searches often contain a variable and unknown number of targets. Such searches present difficult metacognitive challenges, as searchers must decide when to stop looking for additional targets, which results in high miss rates in multiple-target searches. In the study reported here, we quantified human strategies in multiple-target search via an ecological optimal foraging model and investigated whether searchers adapt their strategies to complex target-distribution statistics. Separate groups of individuals searched displays with the number of targets per trial sampled from different geometric distributions but with the same overall target prevalence. As predicted by optimal foraging theory, results showed that individuals searched longer when they expected more targets to be present and adjusted their expectations on-line during each search by taking into account the higher-order, across-trial target distributions. However, compared with modeled ideal observers, participants systematically responded as if the target distribution were more uniform than it was, which suggests that training could improve multiple-target search performance.

Multistability and perceptual inference.

Ambiguous images present a challenge to the visual system: How can uncertainty about the causes of visual inputs be represented when there are multiple equally plausible causes? A Bayesian ideal observer should represent uncertainty in the form of a posterior probability distribution over causes. However, in many real-world situations, computing this distribution is intractable and requires some form of approximation. We argue that the visual system approximates the posterior over underlying causes with a set of samples and that this approximation strategy produces perceptual multistability--stochastic alternation between percepts in consciousness. Under our analysis, multistability arises from a dynamic sample-generating process that explores the posterior through stochastic diffusion, implementing a rational form of approximate Bayesian inference known as Markov chain Monte Carlo (MCMC). We examine in detail the most extensively studied form of multistability, binocular rivalry, showing how a variety of experimental phenomena--gamma-like stochastic switching, patchy percepts, fusion, and traveling waves--can be understood in terms of MCMC sampling over simple graphical models of the underlying perceptual tasks. We conjecture that the stochastic nature of spiking neurons may lend itself to implementing sample-based posterior approximations in the brain.

Measuring the Development of Social Attention Using Free-Viewing.

How do young children direct their attention to other people in the natural world? Although many studies have examined the perception of faces and of goal-directed actions, relatively little work has focused on what children will look at in complex and unconstrained viewing environments. To address this question, we showed videos of objects, faces, children playing with toys, and complex social scenes to a large sample of infants and toddlers between 3 and 30 months old. We found systematic developmental changes in what children looked at. When viewing faces alone, younger children looked more at eyes and older children more at mouths, especially when the faces were making expressions or talking. In the more complex videos, older children looked more at hands than younger children, especially when the hands were performing actions. Our results suggest that as children develop they become better able to direct their attention to the parts of complex scenes that are most interesting socially.