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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 Luces 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 β 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.

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).

Whether estimating the size of a crowd or rating a restaurant on a five-star scale, humans frequently navigate between subjective sensory experiences and shared formal systems. Here we ask how people manage this in the case of estimating number. We present participants with arrays of dots and ask them to report how many dots there are. Our results produce two novel findings. First, people's estimates are best fit by a bilinear function in log space, rather than the traditional power law described in previous literature. Second, we find that people's estimates do not have a stable coefficient of variation at higher magnitudes, and that the likely cause of this is a "drift" in people's estimate calibration over many trials which has not previously been identified. Building on these results, we present a model of the mapping function from subjective numerosity to symbolic number that relies primarily on a constrained set of previous estimates and familiar numerosities, rather than the robust internal scale used in existing models. Our model is able to generate an accurate mapping with limited data and reproduce notable aspects of estimation seen in our experimental results. This suggests that human number estimation, and perhaps other domains in which we must navigate between subjective representations and formal systems, is governed by a relatively simple decision process that primarily seeks to maintain consistency with previous estimates. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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.

In simple dyadic games such as rock, paper, scissors (RPS), people exhibit peculiar sequential dependencies across repeated interactions with a stable opponent. These regularities seem to arise from a mutually adversarial process of trying to outwit their opponent. What underlies this process, and what are its limits? Here, we offer a novel framework for formally describing and quantifying human adversarial reasoning in the rock, paper, scissors game. We first show that this framework enables a precise characterization of the complexity of patterned behaviors that people exhibit themselves, and appear to exploit in others. This combination allows for a quantitative understanding of human opponent modeling abilities. We apply these tools to an experiment in which people played 300 rounds of RPS in stable dyads. We find that although people exhibit very complex move dependencies, they cannot exploit these dependencies in their opponents, indicating a fundamental limitation in people’s capacity for adversarial reasoning. Taken together, the results presented here show how the rock, paper, scissors game allows for precise formalization of human adaptive reasoning abilities.

Is cognitive science interdisciplinary or multidisciplinary? Wecontribute to this debate by examining the authorship struc-ture and topic similarity of contributions to the Cognitive Sci-ence Society from 2000 to 2019. We compare findings fromCogSci to abstracts from the Vision Science Society over thesame time frame. Our analysis focuses on graph theoretic fea-tures of the co-authorship network—edge density, transitivity,and maximum subgraph size—as well as clustering within thetopic space of CogSci contributions. We also combine struc-tural and semantic information with an analysis of homophily.We validate this approach by predicting new collaborations inthis year’s CogSci proceedings. Our results suggest that cog-nitive science has become increasingly interdisciplinary in thelast 19 years. More broadly, we argue that a formal quantita-tive approach which combines structural co-authorship infor-mation and semantic topic analysis provides inroads to ques-tions about the level of interdisciplinary collaboration in thecognitive science community.

In this study, we investigate people’s ability to predict andadapt to the behavior of others in order to make plans of theirown, a cornerstone of cooperative and competitive behavior.Participants played 300 rounds of rock, paper, scissors againstanother human player. We investigate the degree to which par-ticipants are able to identify patterns in their opponent’s be-havior in order to exploit them in subsequent rounds. We findstrong evidence that participants exploit their opponents overthe course of 300 rounds, suggesting that people identify de-pendencies in their opponent’s move choices during the game.Nonetheless, analysis of dependencies across participant movechoices reveals that people exhibit a number of regularities intheir own moves. Based on these dependencies, we argue thatparticipants are far from optimal in their exploiting, suggestingthat there are substantial constraints on people’s ability to iden-tify and adapt to patterned opponent behavior across repeatedinteractions.

Research has suggested that social impressions of faces madeby Western and Eastern people have different underlying di-mensionalities. However, the individual level consistency, thegroup-level agreement of rater groups, and the interactionsbetween face ethnicity, rater ethnicity, and social impressiontraits remain largely unknown. In this paper, we perform alarge-scale data-driven cross-cultural study of facial impres-sions, and illustrate the idiosyncrasies and similarities behindCaucasian and Asian participants in their social impressions offaces from both ethnicity groups. Our study illustrates multi-ple interesting findings: (1) Asians rate faces lower on mostpositive traits, compared with Caucasian raters, and they havemore diverse opinions than Caucasians. (2) Caucasian faces re-ceive higher average ratings on social impression traits relatedto warmth due to the preponderance of smiles in Caucasianimages, but similar mean scores on traits related to capability,compared to Asian faces. (3) Caucasians and Asians disagreemost on capability related traits, especially on “responsible”and “successful.” Opinions on these two traits diverge moreon Asian than on Caucasian faces. Our findings provide newinsights on the nuances of cross-cultural differences in socialimpressions of faces.

Existing knowledge shapes and distorts our memories, serv-ing as a prior for newly encoded information. Here, we in-vestigate the role of stable long-term priors (e.g. categoricalknowledge) in conjunction with priors arising from recentlyencountered information (e.g. ’serial dependence’) in visualworking memory for color. We use an iterated reproductionparadigm to allow a model-free assessment of the role of suchpriors. In Experiment 1, we find that participants’ reports re-liably converge to certain areas of color space, but that thisconvergence is largely distinct for different individuals, sug-gesting responses are biased by more than just shared categoryknowledge. In Experiment 2, we explicitly manipulate trialn-1 and find recent history plays a major role in participants’reports. Thus, we find that both global prior knowledge and re-cent trial information have biasing influences on visual work-ing memory, demonstrating an important role for both short-and long-term priors in actively maintained information.

The relationships between word frequency and various perceptual features have been used to study the cognitive processes involved in word production and recognition, as well as patterns in language use over time. However, little work has been done comparing spoken and written frequencies against each other, which leaves open the question of whether there are modality-specific relationships between perceptual features and frequency. Words have different frequencies in speech and written texts, with some words occurring disproportionately more often in one modality than the other. In the present study, we investigated whether perceptual features predict this frequency asymmetry across modalities. Our results suggest that perceptual features such as length, neighborhood density, and positional probability differentially affect speech and writing, which reveals different online processing constraints and considerations for communicative efficiency across the two modalities. These modality-specific effects exist above and beyond formality differences. This work provides arguments against theories that assume that words differing in frequency are perceptually equivalent, as well as models that predict little to no influence of perceptual features on top-down processes of word selection.

Accounts of human physical reasoning based on simulationfrom a noisy physics engine have enjoyed considerable suc-cess in recent years. However, simulating complex physicaldynamics can be a computationally expensive process, and itis possible that people use faster, cheaper shortcuts to makepredictions and inferences in complicated physical scenarios.Here we asked people to predict the eventual destination of aball on a 2D bumper table (in the style of Smith, de Peres, Vul,and Tenenbaum (2017)). We designed scenarios that we ex-pected would modulate the use of heuristics and simulation:the bumper table provided varying degrees of containment toconstrain future outcomes and to make a containment heuris-tic more useful, and could have more or less internal struc-ture to vary the reliability of noisy simulation. As the con-tainment heuristic becomes more useful, and as simulation be-comes more expensive, we expected that people would switchfrom using simulation to rely more on rapid heuristic-basedpredictions and therefore respond faster. Instead, we foundthat even when containment was very predictive, people wereprogressively slower and less accurate as simulation complex-ity increased, indicating that they persisted in using simulationrather than containment heuristics.

Modern society frequently requires that we express our sub-jective senses in objective, shared formal systems; this en-tails mapping multiple internal variables onto a common scale.Here we ask whether we accomplish this feat in the case ofestimating number by learning a single mapping between ex-plicit numbers and one integrated subjective estimate of nu-merosity, or if we separately map different perceptual featuresonto numbers. We present people with arrays of dots and askthem to report how many dots there are; we rely on the sys-tematic under/overestimation of number at higher quantities toestimate error in the mapping function. By comparing how thiserror changes over time, as the mapping fluctuates for differentvisual cues to numerosity, we can evaluate whether these cuesshare a single mapping, or are mapped onto number individu-ally. We find that area, size, and density all share a commonmapping, indicating that people obtain a unified subjective es-timate of numerosity before mapping it onto the formal numberline.

The human ability to deceive others and detect deception haslong been tied to theory of mind. We make a stronger argu-ment: in order to be adept liars – to balance gain (i.e. maxi-mizing their own reward) and plausibility (i.e. maintaining arealistic lie) – humans calibrate their lies under the assumptionthat their partner is a rational, utility-maximizing agent. Wedevelop an adversarial recursive Bayesian model that aims toformalize the behaviors of liars and lie detectors. We comparethis model to (1) a model that does not perform theory of mindcomputations and (2) a model that has perfect knowledge ofthe opponent’s behavior. To test these models, we introduce anovel dyadic, stochastic game, allowing for quantitative mea-sures of lies and lie detection. In a second experiment, we varythe ground truth probability. We find that our rational modelsqualitatively predict human lying and lie detecting behaviorbetter than the non-rational model. Our findings suggest thathumans control for the extremeness of their lies in a mannerreflective of rational social inference. These findings provide anew paradigm and formal framework for nuanced quantitativeanalysis of the role of rationality and theory of mind in lyingand lie detecting behavior.

Theories of the mental processes people use to perform physi-cal reasoning often differ on whether they are based on simu-lation or on logical reasoning. Here we test how these differentprocesses might combine in a motion-prediction task that canbe solved either by simulation or by reasoning about the topol-ogy of the scene. Participants were asked to predict which oftwo goals a computerized ball would reach first, but in someof these scenes the ball was ‘contained’ in the same space asone goal but was topologically separated from the other. Evenin these contained scenes, participants responded faster whenthey received motion information that would speed up simu-lation but not affect topological parsing. This suggests thatsimulation contributes to predicting short-range motion, evenwhen alternate strategies are available.

Rational accounts of decision-making are incompatible withthe prevalence and success of ubiquitous marketing strategies.In this paper, we demonstrate, using computational experi-ments, how an ideal Bayesian observer model of preferencelearning is compatible with the manipulation of purchasingdecisions via a number of well-known marketing techniques.The ability of this model to predict the effects of both famil-iar and novel marketing interventions suggests it as a plausiblecandidate theory of consumer marketing. Simultaneously, byclarifying the logic underneath the interplay between environ-mental exposure and preference distortions seen in economicdecisions, this model rationalizes the seemingly irrational sus-ceptibility of consumers to marketing.

What happens to memories as we forget? They might gradually lose fidelity, lose their associations (and thus be retrieved in response to the incorrect cues), or be completely lost. Typical long-term memory studies assess memory as a binary outcome (correct/incorrect), and cannot distinguish these different kinds of forgetting. Here we assess long-term memory for scalar information, thus allowing us to quantify how different sources of error diminish as we learn, and accumulate as we forget. We trained subjects on visual and verbal continuous quantities (the locations of objects and the distances between major cities, respectively), tested subjects after extended delays, and estimated whether recall errors arose due to imprecise estimates, misassociations, or complete forgetting. Although subjects quickly formed precise memories and retained them for a long time, they were slow to learn correct associations and quick to forget them. These results suggest that long-term recall is especially limited in its ability to form and retain associations.

How much do individuals, compared to the population, knowabout the distribution of values in the world? Participantsreported the prices of consumer goods such as watches andbelts and we compared how accurately individuals vs. theoverall population knew the mean and dispersion of prices.Although individuals and the population both knew objects’average prices and relative standard deviations, the populationwas more sensitive to the absolute standard deviation ofprices. In a second experiment, we examined whetherindividuals’ impoverished distribution knowledge impairstheir ability to interpret advertisements. Consistent withpeople using Bayesian inference, the higher an object’s actualprice dispersion, the more participants relied onadvertisements; however, this effect is considerably smallerthan a simple proportional offset, suggesting again thatindividuals underestimate dispersion. Thus, despite having asense of the distribution of real world quantities, individualstend to know only a fraction of the world distribution.

Cognitive models of choice almost universally implicate se-quential evidence accumulation as a fundamental element ofthe mechanism by which preferences are formed. When to stop evidence accumulation is an important question that suchmodels do not currently try to answer. We present the first cog-nitive model that accurately predicts stopping decisions in in-dividual economic decisions-from-experience trials, using anonline learning model. Analysis of stopping decisions acrossthree different datasets reveals three useful predictors of sam-pling duration - relative evidence strength, how long it takesparticipants to see all rewards, and a novel indicator of con-vergence of an underlying learning process, which we call pre-dictive volatility. We quantify the relative strengths of thesefactors in predicting observers’ stopping points, finding thatpredictive volatility consistently dominates relative evidencestrength in stopping decisions.

In this paper, we investigate how people use mental simulations:do people vary the number of simulations that they runin order to optimally balance speed and accuracy? We combineda model of noisy physical simulation with a decisionmaking strategy called the sequential probability ratio test, orSPRT (Wald, 1947). Our model predicted that people shoulduse more samples when it is harder to make an accurate predictiondue to higher simulation uncertainty. We tested thisthrough a task in which people had to judge whether a ballbouncing in a box would go through a hole or not. We variedthe uncertainty across trials by changing the size of theholes and the margin by which the ball went through or missedthe hole. Both people’s judgments and response times werewell-predicted by our model, demonstrating that people have asystematic strategy to allocate resources for mental simulation

What hierarchical structures do people use to encode visualdisplays? We examined visual working memory’s priors forlocations by asking participants to recall the locations ofobjects in an iterated learning task. We designed a nonparametricclustering algorithm that infers the clusteringstructure of objects and encodes individual items within thisstructure. Over many iterations, participants recalled objectswith more similar displacement errors, especially for objectsour clustering algorithm grouped together, suggesting thatsubjects grouped objects in memory. Additionally,participants increasingly remembered objects as lines withsimilar orientations and lengths, consistent with the Gestaltgrouping principles of continuity and similarity. Furthermore,the increasing tendency of participants to remember objects ascomponents of hierarchically organized lines rather thanindividual objects or clusters suggests that these priors aid theperception of higher-level structures from ensemble statistics

Recent research has suggested that people make physicalpredictions based on extrapolation from a noisy representationof the world, which gives rise to a probabilistic distributionover possible future worlds. But can people use theuncertainty of their predictions to inform their decisions, orcan people access only a single possible future? Here wedemonstrate that confidence-sensitive decisions about thefuture track the amount of uncertainty expected fromprobabilistic forward extrapolations. Participants were askedto make predictions about where a ball would go and indicatean expected range around that prediction. This range was wellcorrelated with two measures of uncertainty: variability inpredictions across participants and the amount of uncertaintyexpected by a model of physical prediction. This suggests thatpeople form a probabilistic distribution over possible futuresin the course of physical prediction and base their decisionsabout the future on this range.

This paper examines the psychological differences betweenhedonic and utilitarian patterns of preference behavior. Insteadof using latent variables like self-control and emotionto explain these differences, we show that they emerge as naturalconsequences of solving two different, but related problemswithin an inductive framework of preference learning.We show that hedonic decisions involve tracking the variabilityof a binary variable, whereas utilitarian decisions requirethe maintenance of a distribution over a vector of object labels.Computational experiments show that this difference in cognitiverepresentation ensures that hedonic decisions have a lowercognitive sampling cost, which makes them less effortful. Furtherexperiments reveal differences in error rates as a functionof deliberative effort between the two paradigms. Deliberativeeffort benefits utilitarian choices, but not hedonic ones. Overall,our work demonstrates the critical role of cognitive representationsin extracting strikingly different behavior patternsfrom simple models of information processing.

We present a computational model of multiple object trackingthat makes trial-level predictions about the allocation ofvisual attention and the resulting performance. This modelfollows the intuition of allocated resources modulating spatialresolution, but it implements it in a specific way that leads toaccurate predictions in multiple task manipulations. Experimentson human subjects, guided by the model’s predictions,demonstrate that observers tracking multiple objects use lowlevelcomputations of target confusability to adjust the spatialresolution at which the target needs to be tracked, and that theresulting allocation closely approximates the rational solution.Whereas earlier models of multiple object tracking have predictedthe big picture relationship between stimulus complexityand response accuracy, our approach makes accurate predictionsof both the aggregate effect of target number and velocityand of the variations in difficulty across individual trialsand targets arising from the idiosyncratic within-trial interactionsof targets and distractors.

Others’ internal qualities (e.g. dispositions, attitudes) are not directly observable so we must infer them from behavior. Classic attribution theories agree that we consider both internal qualities and situational pressure when making these judgments. However, one of the most well known ideas in psychology is that social judgments are biased, and we tend to underestimate the pressure that situations exert and overestimate the influence of disposition (known as the Fundamental Attribution Error). We propose that the social judgments made in classic studies of attribution have been interpreted as biased only because they have been compared to an inappropriate benchmark of rationality predicated on the assumption of deterministic dispositions and situations. We show that these results are actually consistent with the behavior of a simple ideal Bayesian observer who must reason about uncertain and probabilistic influences of situations and dispositions.

Functional Magnetic Resonance Imaging studies of emotion, personality, and social cognition have drawn much attention in recent years, with high-profile studies frequently reporting extremely high (e.g., >.8) correlations between behavioral and self-report measures of personality or emotion and measures of brain activation. We show that these correlations often exceed what is statistically possible assuming the (evidently rather limited) reliability of both fMRI and personality/emotion measures. The implausibly high correlations are all the more puzzling because method sections rarely contain sufficient detail to ascertain how these correlations were obtained. We surveyed authors of 54 articles that reported findings of this kind to determine a few details on how these correlations were computed. More than half acknowledged using a strategy that computes separate correlations for individual voxels, and reports means of just the subset of voxels exceeding chosen thresholds. We show how this non-independent analysis grossly inflates correlations, while yielding reassuring-looking scattergrams. This analysis technique was used to obtain the vast majority of the implausibly high correlations in our survey sample. In addition, we argue that other analysis problems likely created entirely spurious correlations in some cases. We outline how the data from these studies could be reanalyzed with unbiased methods to provide the field with accurate estimates of the correlations in question. We urge authors to perform such reanalyses and to correct the scientific record.