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Mouse tracking, a new action-based measure of behavior, has advanced theories of decision making with the notion that cognitive and social decision making is fundamentally dynamic. Implicit in this theory is that people's decision strategies, such as discounting delayed rewards, are stable over task design and that mouse trajectory features correspond to specific segments of decision making. By applying the hierarchical drift diffusion model and the Bayesian delay discounting model, we tested these assumptions. Specifically, we investigated the extent to which the "mouse-tracking" design of decision-making tasks (delay discounting task, DDT and stop-signal task, SST) deviate from the standard "keypress" design of decision making tasks. We found remarkable agreement in delay discounting rates (intertemporal impatience) obtained in the keypress and mouse-tracking versions of DDT (ρ = 0.90) even though these tasks were given about 1 week apart. Rates of evidence accumulation converged well in the two versions (DDT, ρ = .86; SST, ρ = .55). Omission/commission error in SST showed high agreement (ρ = .42, ρ = .53). Mouse-motion features such as maximum velocity and AUC (area under the curve) correlated well with nondecision time (ρ = -.42) and boundary separation (ρ = .44)-the amount of information needed to accumulate prior to making a response. These results indicate that the response time (RT) and motion-based decision tasks converge well at a fundamental level, and that mouse-tracking features such as AUC and maximum velocity do indicate the degree of decision conflict and impulsivity., (© 2021 Cognitive Science Society LLC.)

We demonstrate that the key components of cognitive architectures (declarative and procedural memory) and their key capabilities (learning, memory retrieval, probability judgment, and utility estimation) can be implemented as algebraic operations on vectors and tensors in a high-dimensional space using a distributional semantics model. High-dimensional vector spaces underlie the success of modern machine learning techniques based on deep learning. However, while neural networks have an impressive ability to process data to find patterns, they do not typically model high-level cognition, and it is often unclear how they work. Symbolic cognitive architectures can capture the complexities of high-level cognition and provide human-readable, explainable models, but scale poorly to naturalistic, non-symbolic, or big data. Vector-symbolic architectures, where symbols are represented as vectors, bridge the gap between the two approaches. We posit that cognitive architectures, if implemented in a vector-space model, represent a useful, explanatory model of the internal representations of otherwise opaque neural architectures. Our proposed model, Holographic Declarative Memory (HDM), is a vector-space model based on distributional semantics. HDM accounts for primacy and recency effects in free recall, the fan effect in recognition, probability judgments, and human performance on an iterated decision task. HDM provides a flexible, scalable alternative to symbolic cognitive architectures at a level of description that bridges symbolic, quantum, and neural models of cognition., (© 2020 Cognitive Science Society, Inc.)

According to logical theories of meaning, a meaning of an expression can be formalized and encoded in truth conditions. Vagueness of the language and individual differences between people are a challenge to incorporate into the meaning representations. In this paper, we propose a new approach to study truth‐conditional representations of vague concepts. For a case study, we selected two natural language quantifiers most and more than half. We conducted two online experiments, each with 90 native English speakers. In the first experiment, we tested between‐subjects variability in meaning representations. In the second experiment, we tested the stability of meaning representations over time by testing the same group of participants in two experimental sessions. In both experiments, participants performed the verification task. They verified a sentence with a quantifier (e.g., "Most of the gleerbs are feezda.") based on the numerical information provided in the second sentence, (e.g., "60% of the gleerbs are feezda"). To investigate between‐subject and within‐subject differences in meaning representations, we proposed an extended version of the Diffusion Decision Model with two parameters capturing truth conditions and vagueness. We fit the model to responses and reaction times data. In the first experiment, we found substantial between‐subject differences in representations of most as reflected by the variability in the truth conditions. Moreover, we found that the verification of most is proportion‐dependent as reflected in the reaction time effect and model parameter. In the second experiment, we showed that quantifier representations are stable over time as reflected in stable model parameters across two experimental sessions. These findings challenge semantic theories that assume the truth‐conditional equivalence of most and more than half and contribute to the representational theory of vague concepts. The current study presents a promising approach to study semantic representations, which can have a wide application in experimental linguistics. [ABSTRACT FROM AUTHOR]

When human adults make decisions (e.g., wearing a seat belt), we often consider the negative consequences that would ensue if our actions were to fail, even if we have never experienced such a failure. Do the same considerations guide our understanding of other people's decisions? In this paper, we investigated whether adults, who have many years of experience making such decisions, and 6‐ and 7‐year‐old children, who have less experience and are demonstrably worse at judging the consequences of their own actions, conceive others' actions as motivated both by reward (how good reaching one's intended goal would be), and by what we call "danger" (how badly one's action could end). In two pre‐registered experiments, we tested whether adults and 6‐ and 7‐year‐old children tailor their predictions and explanations of an agent's action choices to the specific degree of danger and reward entailed by each action. Across four different tasks, we found that children and adults expected others to negatively appraise dangerous situations and minimize the danger of their actions. Children's and adults' judgments varied systematically in accord with both the degree of danger the agent faced and the value the agent placed on the goal state it aimed to achieve. However, children did not calibrate their inferences about how much an agent valued the goal state of a successful action in accord with the degree of danger the action entailed, and adults calibrated these inferences more weakly than inferences concerning the agent's future action choices. These results suggest that from childhood, people use a degree of danger and reward to make quantitative, fine‐grained explanations and predictions about other people's behavior, consistent with computational models on theory of mind that contain continuous representations of other agents' action plans. [ABSTRACT FROM AUTHOR]

A number of important decision domains, including decisions about hiring, global warming, and weather hazards, are characterized by a global-local incompatibility. These domains involve variables that cannot be observed by a single decision maker (DM) and require the integration of observations from locally available information cues. This paper presents a new bifocal lens model that describes how the structure of the environment can lead to a unique form of overconfidence when generalizing the reliability of the local environment to a global scale. When the local environment does not reliably reflect the global environment, they are incompatible. While local perspectives vary across DMs, global-local incompatibility can be understood using the structure of classical test theory as the difference between (a) perceived estimates of the reliability derived from the local environment and (b) the true reliability of the local environment. I model global-local incompatibility as the difference between the true and estimated reliability when the assumptions of classical test theory are violated. Using a series of case studies and an empirical study, I demonstrate the widespread utility of this framework, and I conclude by discussing implications for cognitive-ecological theory, risk communication, and overconfidence., (© 2020 Cognitive Science Society, Inc.)

Abstract: Veksler and Gunzelmann (2018) argue that the vigilance decrement and the deleterious effects of sleep loss reflect functionally equivalent degradations in cognitive processing and performance. Our account is implemented in a cognitive architecture, where these factors produce breakdowns in goal‐directed cognitive processing that we refer to as microlapses. Altmann (2018) raises a number of challenges to microlapses as a unified account of these deficits. Under scrutiny, however, the challenges do little to discredit the theory or conclusions in the original paper. In our response, we address the most serious challenges. In so doing, we provide additional support for the theory and mechanisms, and we highlight opportunities for extending their explanatory breadth. [ABSTRACT FROM AUTHOR]

Abstract: Several attempts have been made previously to provide a biological grounding for cognitive architectures by relating their components to the computations of specific brain circuits. Often, the architecture's action selection system is identified with the basal ganglia. However, this identification overlooks one of the most important features of the basal ganglia—the existence of a direct and an indirect pathway that compete against each other. This characteristic has important consequences in decision‐making tasks, which are brought to light by Parkinson's disease as well as genetic differences in dopamine receptors. This paper shows that a standard model of action selection in a cognitive architecture (ACT‐R) cannot replicate any of these findings, details an alternative solution that reconciles action selection in the architecture with the physiology of the basal ganglia, and extends the domain of application of cognitive architectures. The implication of this solution for other architectures and existing models are discussed. [ABSTRACT FROM AUTHOR]

Blindfold chess is a special type of chess game where both the board and pieces are not visible to its players. This paper aims to determine whether the quality of the game played blindfolded is lower than when played under normal conditions. The best chess program was used to analyze games played by the world's top Grandmasters under both conditions. We have analyzed the Monaco 1993–1998 data set introduced by Chabris and Hearst (2003) . The results showed that although a larger number of mistakes occurred while playing blindfolded, no significant statistical difference between the rapid and blindfold games has been found. Nevertheless, by applying the same methodology to the Monaco 2002–2007 data set a substantial difference between the blindfold and the rapid chess game was noticed. In this paper, we have addressed the possible improvement of the chess game quality and the advances in chess programs that may be responsible for detecting more blunders. [ABSTRACT FROM AUTHOR]

Ranking theory is a formal epistemology that has been developed in over 600 pages in Spohn's recent book The Laws of Belief, which aims to provide a normative account of the dynamics of beliefs that presents an alternative to current probabilistic approaches. It has long been received in the AI community, but it has not yet found application in experimental psychology. The purpose of this paper is to derive clear, quantitative predictions by exploiting a parallel between ranking theory and a statistical model called logistic regression. This approach is illustrated by the development of a model for the conditional inference task using Spohn's (2013) ranking theoretic approach to conditionals., (Copyright © 2015 Cognitive Science Society, Inc.)

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instance-based learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision-making process: instance-based knowledge, recognition-based retrieval, adaptive strategies, necessity-based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision-making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, adapt their judgment strategies from heuristic-based to instance-based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT’s learning mechanisms have been implemented in an ACT-R cognitive model. Through a series of experiments, this paper shows how the IBLT’s learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments. [Copyright &y& Elsevier]

I Cognitive Science i , I 47 i (4), e13259. https://doi.org/10.1111/cogs.13259 S. Musslick & J. Masís contributed equally to this letter. The correct reference is: Masís, J., Chapman, T., Rhee, J.Y., Cox, D.D., & Saxe, A.M. (2023). Musslick, S., & Masís, J. (2023). [Extracted from the article]

Capacities for abstract thinking and problem‐solving are central to human cognition. Processes of abstraction allow the transfer of experiences and knowledge between contexts helping us make informed decisions in new or changing contexts. While we are often inclined to relate such reasoning capacities to individual minds and brains, they may in fact be contingent on human‐specific modes of collaboration, dialogue, and shared attention. In an experimental study, we test the hypothesis that social interaction enhances cognitive processes of rule‐induction, which in turn improves problem‐solving performance. Through three sessions of increasing complexity, individuals and groups were presented with a problem‐solving task requiring them to categorize a set of visual stimuli. To assess the character of participants' problem representations, after each training session, they were presented with a transfer task involving stimuli that differed in appearance, but shared relations among features with the training set. Besides, we compared participants' categorization behaviors to simulated agents relying on exemplar learning. We found that groups performed superior to individuals and agents in the training sessions and were more likely to correctly generalize their observations in the transfer phase, especially in the high complexity session, suggesting that groups more effectively induced underlying categorization rules from the stimuli than individuals and agents. Crucially, variation in performance among groups was predicted by semantic diversity in members' dialogical contributions, suggesting a link between social interaction, cognitive diversity, and abstraction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Similarity is one of the most important relations humans perceive, arguably subserving category learning and categorization, generalization and discrimination, judgment and decision making, and other cognitive functions. Researchers have proposed a wide range of representations and metrics that could be at play in similarity judgment, yet have not comprehensively compared the power of these representations and metrics for predicting similarity within and across different semantic categories. We performed such a comparison by pairing nine prominent vector semantic representations with seven established similarity metrics that could operate on these representations, as well as supervised methods for dimensional weighting in the similarity function. This approach yields a factorial model structure with 126 distinct representation‐metric pairs, which we tested on a novel dataset of similarity judgments between pairs of cohyponymic words in eight categories. We found that cosine similarity and Pearson correlation were the overall best performing unweighted similarity functions, and that word vectors derived from free association norms often outperformed word vectors derived from text (including those specialized for similarity). Importantly, models that used human similarity judgments to learn category‐specific weights on dimensions yielded substantially better predictions than all unweighted approaches across all types of similarity functions and representations, although dimension weights did not generalize well across semantic categories, suggesting strong category context effects in similarity judgment. We discuss implications of these results for cognitive modeling and natural language processing, as well as for theories of the representations and metrics involved in similarity. [ABSTRACT FROM AUTHOR]

This work is an initial step toward developing a cognitive theory of cyber deception. While widely studied, the psychology of deception has largely focused on physical cues of deception. Given that present‐day communication among humans is largely electronic, we focus on the cyber domain where physical cues are unavailable and for which there is less psychological research. To improve cyber defense, researchers have used signaling theory to extended algorithms developed for the optimal allocation of limited defense resources by using deceptive signals to trick the human mind. However, the algorithms are designed to protect against adversaries that make perfectly rational decisions. In behavioral experiments using an abstract cybersecurity game (i.e., Insider Attack Game), we examined human decision‐making when paired against the defense algorithm. We developed an instance‐based learning (IBL) model of an attacker using the Adaptive Control of Thought‐Rational (ACT‐R) cognitive architecture to investigate how humans make decisions under deception in cyber‐attack scenarios. Our results show that the defense algorithm is more effective at reducing the probability of attack and protecting assets when using deceptive signaling, compared to no signaling, but is less effective than predicted against a perfectly rational adversary. Also, the IBL model replicates human attack decisions accurately. The IBL model shows how human decisions arise from experience, and how memory retrieval dynamics can give rise to cognitive biases, such as confirmation bias. The implications of these findings are discussed in the perspective of informing theories of deception and designing more effective signaling schemes that consider human bounded rationality. [ABSTRACT FROM AUTHOR]

Intervening on causal systems can illuminate their underlying structures. Past work has shown that, relative to adults, young children often make intervention decisions that appear to confirm a single hypothesis rather than those that optimally discriminate alternative hypotheses. Here, we investigated how the ability to make informative causal interventions changes across development. Ninety participants between the ages of 7 and 25 completed 40 different puzzles in which they had to intervene on various causal systems to determine their underlying structures. Each puzzle comprised a three‐ or four‐node computer chip with hidden wires. On each trial, participants viewed two possible arrangements of the chip's hidden wires and had to select a single node to activate. After observing the outcome of their intervention, participants selected a wire configuration and rated their confidence in their selection. We characterized participant choices with a Bayesian measurement model that indexed the extent to which participants selected nodes that would best disambiguate the two possible causal structures versus those that had high causal centrality in one of the two causal hypotheses but did not necessarily discriminate between them. Our model estimates revealed that the use of a discriminatory strategy increased through early adolescence. Further, developmental improvements in intervention strategy were related to changes in the ability to accurately judge the strength of evidence that interventions revealed, as indexed by participants' confidence in their selections. Our results suggest that improvements in causal information‐seeking extend into adolescence and may be driven by metacognitive sensitivity to the efficacy of previous interventions in discriminating competing ideas. [ABSTRACT FROM AUTHOR]

Research has documented that individuals consider outcomes, intentions, and transgressor negligence when making morally relevant judgments (Nobes, Panagiotaki, & Engelhardt, 2017). However, less is known about whether individuals attend to both victim and transgressor negligence in their evaluations. The current study measured 3‐ to 6‐year‐olds (N = 70), 7‐ to 12‐year‐olds (N = 54), and adults' (N = 97, ages 18–25 years) moral judgments about scenarios in which an accidental transgression occurred involving property damage or physical harm. Participants were either assigned to conditions where the victim or the transgressor was negligent. Results revealed attention to negligence among all participants across a range of different moral judgment measures (including acceptability judgments, punishment judgments, and attributions of blame), with age‐related increases in attention to negligence evident. Results provide novel evidence that children and adults consider not just outcomes and intentions, but also the role of negligence in both victims and transgressors, when making social decisions. [ABSTRACT FROM AUTHOR]

How, and how well, do people switch between exploration and exploitation to search for and accumulate resources? We study the decision processes underlying such exploration/exploitation trade‐offs using a novel card selection task that captures the common situation of searching among multiple resources (e.g., jobs) that can be exploited without depleting. With experience, participants learn to switch appropriately between exploration and exploitation and approach optimal performance. We model participants' behavior on this task with random, threshold, and sampling strategies, and find that a linear decreasing threshold rule best fits participants' results. Further evidence that participants use decreasing threshold‐based strategies comes from reaction time differences between exploration and exploitation; however, participants themselves report non‐decreasing thresholds. Decreasing threshold strategies that "front‐load" exploration and switch quickly to exploitation are particularly effective in resource accumulation tasks, in contrast to optimal stopping problems like the Secretary Problem requiring longer exploration. [ABSTRACT FROM AUTHOR]

Abstract: Situation awareness is a key construct in human factors and arises from a process of situation assessment (SA). SA comprises the perception of information, its integration with existing knowledge, the search for new information, and the prediction of the future state of the world, including the consequences of planned actions. Causal models implemented as Bayesian networks (BNs) are attractive for modeling all of these processes within a single, unified framework. We elicited declarative knowledge from two Royal Australian Air Force (RAAF) fighter pilots about the information sources used in the identification (ID) of airborne entities and the causal relationships between these sources. This knowledge was represented in a BN (the declarative model) that was evaluated against the performance of 19 RAAF fighter pilots in a low‐fidelity simulation. Pilot behavior was well predicted by a simple associative model (the behavioral model) with only three attributes of ID. Search for information by pilots was largely compensatory and was near‐optimal with respect to the behavioral model. The average revision of beliefs in response to evidence was close to Bayesian, but there was substantial variability. Together, these results demonstrate the value of BNs for modeling human SA. [ABSTRACT FROM AUTHOR]

Human behavior unfolds primarily in built environments, where the arrangement of objects is a result of ongoing human decisions and actions, yet these organizational decisions have received limited experimental study. In two experiments, we introduce a novel paradigm designed to explore how individuals organize task-relevant objects in space. Participants completed goals by locating and accessing sequences of objects in a computer-based task, and they were free to rearrange the positions of objects at any time. We measure a variety of organization changes and evaluate how these measures relate to individual differences in performance. In Experiment 1, we show that with weak structure in task demands, changes in object positions that arise through performance of the task lead to improved order, characterized predominantly by a centralization of frequently used items and a peripheralization of infrequently used objects. In Experiment 2, with increased task structure, we observe more refined organizational tendencies, with selective contraction and clustering of interrelated task-relevant objects. We further demonstrate that these more selective organization behaviors are reliably associated with individual differences in task performance. Collectively, these two studies reveal properties of space and of task demands that support and facilitate effective organization of the environment in support of ongoing behavior. [ABSTRACT FROM AUTHOR]

We investigated how people design interventions to affect the outcomes of causal systems. We propose that the abstract structural properties of a causal system, in addition to people's content and mechanism knowledge, influence decisions about how to intervene. In Experiment 1, participants preferred to intervene at specific locations (immediate causes, root causes) in a causal chain regardless of which content variables occupied those positions. In Experiment 2, participants were more likely to intervene on root causes versus immediate causes when they were presented with a long-term goal versus a short-term goal. These results show that the structural properties of a causal system can guide the design of interventions. [ABSTRACT FROM AUTHOR]

In most decision-making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. How do decision makers determine in what sequence to collect information and when to stop? In two experiments, we administered a version of the German cities task developed by Gigerenzer and Goldstein (1996), in which participants had to decide which of two cities had the larger population. Decision makers were not provided with the names of the cities, but they were able to collect different kinds of cues for both response alternatives (e.g., "Does this city have a university?") before making a decision. Our experiments differed in whether participants were free to determine the number of cues they examined. We demonstrate that a novel model, using hierarchical latent mixtures and Bayesian inference (Lee & Newell, 2011) provides a more complete description of the data from both experiments than simple conventional strategies, such as the take-the-best or the Weighted Additive heuristics. [ABSTRACT FROM AUTHOR]

Discrete choice experiments-selecting the best and/or worst from a set of options-are increasingly used to provide more efficient and valid measurement of attitudes or preferences than conventional methods such as Likert scales. Discrete choice data have traditionally been analyzed with random utility models that have good measurement properties but provide limited insight into cognitive processes. We extend a well-established cognitive model, which has successfully explained both choices and response times for simple decision tasks, to complex, multi-attribute discrete choice data. The fits, and parameters, of the extended model for two sets of choice data (involving patient preferences for dermatology appointments, and consumer attitudes toward mobile phones) agree with those of standard choice models. The extended model also accounts for choice and response time data in a perceptual judgment task designed in a manner analogous to best-worst discrete choice experiments. We conclude that several research fields might benefit from discrete choice experiments, and that the particular accumulator-based models of decision making used in response time research can also provide process-level instantiations for random utility models. [ABSTRACT FROM AUTHOR]

Successfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the integrated model with standalone AL and RL models in three simulation studies. First, a synthetic grid-navigation task is employed to highlight performance advantages for the integrated model in an environment where the reward structure is both diverse and dynamic. The second and third simulations contrast the performances of the three models in behavioral experiments, demonstrating advantages for the integrated model in accounting for behavioral data. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) models of decision-making cannot account for human decisions in the absence of prior reward or punishment. We propose a mechanism for choosing among available options based on goal-option association strengths, where association strengths between objects represent previously experienced object proximity. The proposed mechanism, Goal-Proximity Decision-making (GPD), is implemented within the ACT-R cognitive framework. GPD is found to be more efficient than RL in three maze-navigation simulations. GPD advantages over RL seem to grow as task difficulty is increased. An experiment is presented where participants are asked to make choices in the absence of prior reward. GPD captures human performance in this experiment better than RL. [ABSTRACT FROM AUTHOR]

This response discusses the experiment reported in Krahmer et al.'s Letter to the Editor of Cognitive Science. We observe that their results do not tell us whether the Incremental Algorithm is better or worse than its competitors, and we speculate about implications for reference in complex domains, and for learning from ''normal' (i.e., non-semantically-balanced) corpora. [ABSTRACT FROM AUTHOR]

Inductive generalization, where people go beyond the data provided, is a basic cognitive capability, and it underpins theoretical accounts of learning, categorization, and decision making. To complete the inductive leap needed for generalization, people must make a key ''sampling'' assumption about how the available data were generated. Previous models have considered two extreme possibilities, known as strong and weak sampling. In strong sampling, data are assumed to have been deliberately generated as positive examples of a concept, whereas in weak sampling, data are assumed to have been generated without any restrictions. We develop a more general account of sampling that allows for an intermediate mixture of these two extremes, and we test its usefulness. In two experiments, we show that most people complete simple one-dimensional generalization tasks in a way that is consistent with their believing in some mixture of strong and weak sampling, but that there are large individual differences in the relative emphasis different people give to each type of sampling. We also show experimentally that the relative emphasis of the mixture is influenced by the structure of the available information. We discuss the psychological meaning of mixing strong and weak sampling, and possible extensions of our modeling approach to richer problems of inductive generalization. [ABSTRACT FROM AUTHOR]

Exploring how people represent natural categories is a key step toward developing a better understanding of how people learn, form memories, and make decisions. Much research on categorization has focused on artificial categories that are created in the laboratory, since studying natural categories defined on high-dimensional stimuli such as images is methodologically challenging. Recent work has produced methods for identifying these representations from observed behavior, such as reverse correlation (RC). We compare RC against an alternative method for inferring the structure of natural categories called Markov chain Monte Carlo with People (MCMCP). Based on an algorithm used in computer science and statistics, MCMCP provides a way to sample from the set of stimuli associated with a natural category. We apply MCMCP and RC to the problem of recovering natural categories that correspond to two kinds of facial affect (happy and sad) from realistic images of faces. Our results show that MCMCP requires fewer trials to obtain a higher quality estimate of people's mental representations of these two categories. [ABSTRACT FROM AUTHOR]

One of the most influential studies in all expertise research is study of chess players, which suggested that pattern recognition, rather than search, was the key determinant of expertise. Many changes have occurred in the chess world since de Groot's study, leading some authors to argue that the cognitive mechanisms underlying expertise have also changed. We decided to replicate de Groot's study to empirically test these claims and to examine whether the trends in the data have changed over time. Six Grandmasters, five International Masters, six Experts, and five Class A players completed the think-aloud procedure for two chess positions. Findings indicate that Grandmasters and International Masters search more quickly than Experts and Class A players, and that both groups today search substantially faster than players in previous studies. The findings, however, support de Groot's overall conclusions and are consistent with predictions made by pattern recognition models. [ABSTRACT FROM AUTHOR]

The tendency to test outcomes that are predicted by our current theory (the confirmation bias) is one of the best-known biases of human decision making. We prove that the confirmation bias is an optimal strategy for testing hypotheses when those hypotheses are deterministic, each making a single prediction about the next event in a sequence. Our proof applies for two normative standards commonly used for evaluating hypothesis testing: maximizing expected information gain and maximizing the probability of falsifying the current hypothesis. This analysis rests on two assumptions: (a) that people predict the next event in a sequence in a way that is consistent with Bayesian inference; and (b) when testing hypotheses, people test the hypothesis to which they assign highest posterior probability. We present four behavioral experiments that support these assumptions, showing that a simple Bayesian model can capture people's predictions about numerical sequences (Experiments 1 and 2), and that we can alter the hypotheses that people choose to test by manipulating the prior probability of those hypotheses (Experiments 3 and 4). [ABSTRACT FROM AUTHOR]

Two experiments examined the typicality structure of contrasting political categories. In Experiment 1, two separate groups of participants rated the typicality of 15 individuals, including political figures and media personalities, with respect to the categories Democrat or Republican. The relation between the two sets of ratings was negative, linear, and extremely strong, r = −.9957. Essentially, one category was treated as a mirror image of the other. Experiment 2 replicated this result, showing some boundary conditions, and extending the result to liberal and conservative categories. The same method was applied to two other pairs of contrasting categories, healthy and junk foods, and male and female jobs. For those categories, the relation between contrasting pairs was weaker and there was less of a direct trade-off between typicality in one category versus typicality in its opposite. The results are discussed in terms of implications for political decision making and reasoning, and conceptual representation. [ABSTRACT FROM AUTHOR]

Argumentation is a very fertile area of research in Artificial Intelligence, and various semantics have been developed to predict when an argument can be accepted, depending on the abstract structure of its defeaters and defenders. When these semantics make conflicting predictions, theoretical arbitration typically relies on ad hoc examples and normative intuition about what prediction ought to be the correct one. We advocate a complementary, descriptive-experimental method, based on the collection of behavioral data about the way human reasoners handle these critical cases. We report two studies applying this method to the case of reinstatement (both in its simple and floating forms). Results speak for the cognitive plausibility of reinstatement and yet show that it does not yield the full expected recovery of the attacked argument. Furthermore, results show that floating reinstatement yields comparable effects to that of simple reinstatement, thus arguing in favor of preferred argumentation semantics, rather than grounded argumentation semantics. Besides their theoretical value for validating and inspiring argumentation semantics, these results have applied value for developing artificial agents meant to argue with human users. [ABSTRACT FROM AUTHOR]

We created paired moral dilemmas with minimal contrasts in wording, a research strategy that has been advocated as a way to empirically establish principles operative in a domain-specific moral psychology. However, the candidate “principles” we tested were not derived from work in moral philosophy, but rather from work in the areas of consumer choice and risk perception. Participants were paradoxically less likely to choose an action that sacrifices one life to save others when they were asked to provide more reasons for doing so (Experiment 1), and their willingness to sacrifice lives depended not only on how many lives would be saved, but on the number of lives at risk (Experiment 2). The latter effect was also found in a within-subjects design (Experiment 3). These findings suggest caution in the use of artificial dilemmas as a key testbed for revealing principled bases for moral judgment. [ABSTRACT FROM AUTHOR]

Expert chess players, specialized in different openings, recalled positions and solved problems within and outside their area of specialization. While their general expertise was at a similar level, players performed better with stimuli from their area of specialization. The effect of specialization on both recall and problem solving was strong enough to override general expertise—players remembering positions and solving problems from their area of specialization performed at around the level of players 1 standard deviation (SD) above them in general skill. Their problem-solving strategy also changed depending on whether the problem was within their area of specialization. When it was, they searched more in depth and less in breadth; with problems outside their area of specialization, the reverse. The knowledge that comes from familiarity with a problem area is more important than general purpose strategies in determining how an expert will tackle it. These results demonstrate the link in experts between problem solving and memory of specific experiences and indicate that the search for context-independent general purpose problem-solving strategies to teach to future experts is unlikely to be successful. [ABSTRACT FROM AUTHOR]

Determining the knowledge that guides human judgments is fundamental to understanding how people reason, make decisions, and form predictions. We use an experimental procedure called ‘‘iterated learning,’’ in which the responses that people give on one trial are used to generate the data they see on the next, to pinpoint the knowledge that informs people's predictions about everyday events (e.g., predicting the total box office gross of a movie from its current take). In particular, we use this method to discriminate between two models of human judgments: a simple Bayesian model ( Griffiths & Tenenbaum, 2006 ) and a recently proposed alternative model that assumes people store only a few instances of each type of event in memory (Min K; Mozer, Pashler, & Homaei, 2008 ). Although testing these models using standard experimental procedures is difficult due to differences in the number of free parameters and the need to make assumptions about the knowledge of individual learners, we show that the two models make very different predictions about the outcome of iterated learning. The results of an experiment using this methodology provide a rich picture of how much people know about the distributions of everyday quantities, and they are inconsistent with the predictions of the Min K model. The results suggest that accurate predictions about everyday events reflect relatively sophisticated knowledge on the part of individuals. [ABSTRACT FROM AUTHOR]

When two agents of unequal strength compete, the stronger one is expected to always win the competition. This expectation is based on the assumption that evaluation of performance is complete, hence flawless. If, however, the agents are evaluated on the basis of only a small sample of their performance, the weaker agent still stands a chance of winning occasionally. A theoretical analysis indicates that, to increase the chance of this happening the weaker agent ought to give up on enough occasions so that he or she can match the stronger agent on the remaining ones. We model such a competition in a game, present its game-theoretic solution, and report an experiment, involving 144 individuals, in which we tested whether players (both weak and strong) are actually sensitive to their relative strengths and know how to allocate their resources accordingly. Our results indicate that they do. [ABSTRACT FROM AUTHOR]

Experts in all fields are able to see what is invisible to others. Experts are also able to see what is visible to all—and this is explored by Bilalić and Gobet. We question the method of normalizing all subjects in an experimental condition, and asking experts to behave as if they were novices. We claim that method leads Bilalić and Gobet to a nonsequitur. [ABSTRACT FROM AUTHOR]

Animals depleting one patch of resources must decide when to leave and switch to a fresh patch. Foraging theory has predicted various decision mechanisms; which is best depends on environmental variation in patch quality. Previously we tested whether these mechanisms underlie human decision making when foraging for external resources; here we test whether humans behave similarly in a cognitive task seeking internally generated solutions. Subjects searched for meaningful words made from random letter sequences, and as their success rate declined, they could opt to switch to a fresh sequence. As in the external foraging context, time since the previous success and the interval preceding it had a major influence on when subjects switched. Subjects also used the commonness of sequence letters as a proximal cue to patch quality that influenced when to switch. Contrary to optimality predictions, switching decisions were independent of whether sequences differed little or widely in quality. [ABSTRACT FROM AUTHOR]

Participants were asked to draw inferences about correlation from single x,y observations. In Experiment 1 statistically sophisticated participants were given the univariate characteristics of distributions of x and y and asked to infer whether a single x, y observation came from a correlated or an uncorrelated population. In Experiment 2, students with a variety of statistical backgrounds assigned posterior probabilities to five possible populations based on single x, y observations, again given knowledge of the univariate statistics. In Experiment 3, statistically naïve participants were given a problem analogous to that given in Experiment 1, framed verbally. Experiment 4 replicated Experiment 3 but added an “impossible to determine” response option. Models that rely on computing sample correlations make no predictions about these investigations. From a Bayesian perspective, participants’ inferences in all four experiments tended to make probabilistically valid inferences as long as the single datum was directional. The results are discussed in light of the Brunswikian notion of vicarious functioning. [ABSTRACT FROM AUTHOR]

It is a hallmark of a good model to make accurate a priori predictions to new conditions (Busemeyer & Wang, 2000). This study compared 8 decision learning models with respect to their generalizability. Participants performed 2 tasks (the Iowa Gambling Task and the Soochow Gambling Task), and each model made a priori predictions by estimating the parameters for each participant from 1 task and using those same parameters to predict on the other task. Three methods were used to evaluate the models at the individual level of analysis. The first method used a post hoc fit criterion, the second method used a generalization criterion for short-term predictions, and the third method again used a generalization criterion for long-term predictions. The results suggest that the models with the prospect utility function can make generalizable predictions to new conditions, and different learning models are needed for making short-versus long-term predictions on simple gambling tasks. [ABSTRACT FROM AUTHOR]

This article explores the complementary strengths and weaknesses of grounded and abstract representations in the domain of early algebra. Abstract representations, such as algebraic symbols, are concise and easy to manipulate but are distanced from any physical referents. Grounded representations, such as verbal descriptions of situations, are more concrete and familiar, and they are more similar to physical objects and everyday experience. The complementary computational characteristics of grounded and abstract representations lead to trade-offs in problem-solving performance. In prior research with high school students solving relatively simple problems, Koedinger and Nathan (2004) demonstrated performance benefits of grounded representations over abstract representations - students were better at solving simple story problems than the analogous equations. This article extends this prior work to examine both simple and more complex problems in two samples of college students. On complex problems with two references to the unknown, a "symbolic advantage" emerged, such that students were better at solving equations than analogous story problems. Furthermore, the previously observed "verbal advantage" on simple problems was replicated. We thus provide empirical support for a trade-off between grounded, verbal representations, which show advantages on simpler problems, and abstract, symbolic representations, which show advantages on more complex problems. [ABSTRACT FROM AUTHOR]

We consider human performance on an optimal stopping problem where people are presented with a list of numbers independently chosen from a uniform distribution. People are told how many numbers are in the list, and how they were chosen. People are then shown the numbers one at a time, and are instructed to choose the maximum, subject to the constraint that they must choose a number at the time it is presented, and any choice below the maximum is incorrect. We present empirical evidence that suggests people use threshold-based models to make decisions, choosing the first currently maximal number that exceeds a fixed threshold for that position in the list. We then develop a hierarchical generative account of this model family, and use Bayesian methods to learn about the parameters of the generative process, making inferences about the threshold decision models people use. We discuss the interesting aspects of human performance on the task, including the lack of learning, and the presence of large individual differences, and consider the possibility of extending the modeling framework to account for individual differences. We also use the modeling results to discuss the merits of hierarchical, generative and Bayesian models of cognitive processes more generally. [ABSTRACT FROM AUTHOR]

Five studies examined how interacting with the physical environment can support the development of fraction concepts. Nine- and 10-year-old children worked on fraction problems they could not complete mentally. Experiments 1 and 2 showed that manipulating physical pieces facilitated children's ability to develop an interpretation of fractions. Experiment 3 demonstrated that when children understood a content area well, they used their interpretations to repurpose many environments to support problem solving, whereas when they needed to learn, they were prone to the structure of the environment. Experiments 4 and 5 examined transfer after children had learned by manipulating physical pieces. Children who learned by adapting relatively unstructured environments transferred to new materials better than children who learned with "well-structured" environments that did not require equivalent adaptation. Together, the findings reveal that during physically distributed learning, the opportunity to adapt an environment permits the development of new interpretations that can advance learning. [ABSTRACT FROM AUTHOR]

In his enormously influential The Modularity of Mind, Jerry Fodor (1983) proposed that the mind was divided into input modules and central processes. Much subsequent research focused on the modules and whether processes like speech perception or spatial vision are truly modular. Much less attention has been given to Fodor's writing on the central processes, what would today be called higher‐level cognition. In "Fodor's First Law of the Nonexistence of Cognitive Science," he argued that central processes are "bad candidates for scientific study" and would resist attempts at empirical analysis. This essay evaluates his argument for this remarkable claim, concluding that although central processes may well be "messier" than input modules, this does not mean that they cannot be studied and understood. The article briefly reviews the scientific progress made in understanding central processes in the 35 years since the book was published, showing that Fodor's prediction is clearly falsified by massive advances in topics like decision making and analogy. The essay concludes that Fodor's Law was not based on a clear argument for why the complexities of central systems could not be studied but was likely based on intuitions and preferences that were common in psychology at the time. [ABSTRACT FROM AUTHOR]