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.

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).

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).

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).

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.

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.

Ongoing dynamic calibration produces unstable number estimates.

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).

Eyewitness Identification Is a Visual Search Task.

The simultaneous six-pack photo lineup is a standard eyewitness identification procedure, consisting of one police suspect plus five physically similar fillers. The photo lineup is either a target-present array (the suspect is guilty) or a target-absent array (the suspect is innocent). The eyewitness is asked to search the six photos in the array with respect to a target template stored in memory (namely, the memory of the perpetrator's face). If the witness determines that the perpetrator is in fact in the lineup (detection), then the next step is to specify the position of the perpetrator's face in the lineup (localization). The witness may also determine that the perpetrator is not present and reject the lineup. In other words, a police lineup is a detection-plus-localization visual search task. Signal detection concepts that have long guided thinking about visual search have recently had a significant impact on our understanding of police lineups.

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.

Integration by Parts: Collaboration and Topic Structure in the CogSci Community.

Is cognitive science interdisciplinary or multidisciplinary? We contribute to this debate by examining the authorship structure and topic similarity of contributions to the Cognitive Science Society from 2000 to 2019. Our analysis focuses on graph theoretic features of the co-authorship network-edge density, transitivity, and maximum subgraph size-as well as clustering within the space of scientific topics. We also combine structural and semantic information with an analysis of how authors choose their collaborators based on their interests and prior collaborations. We compare findings from CogSci to abstracts from the Vision Science Society over the same time frame and validate our approach by predicting new collaborations in the 2020 CogSci proceedings. Our results suggest that collaboration across authors and topics within cognitive science has become increasingly integrated in the last 19 years. More broadly, we argue that a formal quantitative approach which combines structural co-authorship information and semantic topic analysis provides inroads to questions about the level of interdisciplinary collaboration in a scientific community.

The structure of illusory conjunctions reveals hierarchical binding of multipart objects.

The world around us is filled with complex objects, full of color, motion, shape, and texture, and these features seem to be represented separately in the early visual system. Anne Treisman pointed out that binding these separate features together into coherent conscious percepts is a serious challenge, and she argued that selective attention plays a critical role in this process. Treisman also showed that, consistent with this view, outside the focus of attention we suffer from illusory conjunctions: misperceived pairings of features into objects. Here we used Treisman's logic to study the structure of pre-attentive representations of multipart, multicolor objects, by exploring the patterns of illusory conjunctions that arise outside the focus of attention. We found consistent evidence of some pre-attentive binding of colors to their parts, and weaker evidence of binding multiple colors of the same object. The extent to which such hierarchical binding occurs seems to depend on the geometric structure of multipart objects: Objects whose parts are easier to separate seem to exhibit greater pre-attentive binding. Together, these results suggest that representations outside the focus of attention are not entirely a "shapeless bundles of features," but preserve some meaningful object structure.

Perceptual features predict word frequency asymmetry across modalities.

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.

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.

How effective is incidental learning of the shape of probability distributions?

The idea that people learn detailed probabilistic generative models of the environments they interact with is intuitively appealing, and has received support from recent studies of implicit knowledge acquired in daily life. The goal of this study was to see whether people efficiently induce a probability distribution based upon incidental exposure to an unknown generative process. Subjects played a 'whack-a-mole' game in which they attempted to click on objects appearing briefly, one at a time on the screen. Horizontal positions of the objects were generated from a bimodal distribution. After 180 plays of the game, subjects were unexpectedly asked to generate another 180 target positions of their own from the same distribution. Their responses did not even show a bimodal distribution, much less an accurate one (Experiment 1). The same was true for a pre-announced test (Experiment 2). On the other hand, a more extreme bimodality with zero density in a middle region did produce some distributional learning (Experiment 3), perhaps reflecting conscious hypothesis testing. We discuss the challenge this poses to the idea of efficient accurate distributional learning.

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.

Fragile associations coexist with robust memories for precise details in long-term memory.

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.

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.