A critical aspect of human cognition is the ability to actively query the environment for information. One important (but often overlooked) factor in the decision to gather information is the cost associated with accessing different sources of information. Using a simple sequential information search task, we explore the degree to which human learners are sensitive to variations in the amount of utility related to different potential observations. Across two experiments we find greater support for the idea that people gather information to reduce their uncer- tainty about the current state of the environment (a "disinterested", or cost-insenstive, sampling strategy). Implications for theories of rational information collection are discussed.

People can test hypotheses through either selection or reception. In a selection task, the learner actively chooses observations to test their beliefs, while in reception tasks data is passively encountered. People routinely use both forms of testing in everyday life, but the critical psychological differences between selection and reception learning remain poorly understood. One hypothesis is that selection learning improves learning performance by enhancing generic cognitive processes related to motivation, attention, and engagement. Alternatively, we suggest that differences between these two learning modes derives from a hypothesis-dependent sampling bias that is introduced when a person collects data to test their own individual hypothesis. Drawing on influential models of sequential hypothesis testing behavior, we show that such a bias 1) can lead to the collection of data that facilitates learning compared to reception learning, and 2) can be more effective than observing the selections of another person. We then report a novel experiment based on a popular category learning paradigm that compares reception and selection learning. We additionally compare selection learners to a set of ``yoked" participants who viewed the exact same sequence of observations under reception conditions. The results revealed systematic differences in performance that depended on the learner's role in collecting information and the abstract structure of the problem.

We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the ?rst, learning is based on the gradual accumulation of direct associations between events based on simple conditioning principles. The other view describes learning as the process of inducing the transformational structure that de?nes the material. Each of these learning mechanisms predict di?erences in the rate of acquisition for di?erently organized sequences. Across a set of empirical studies, we compare the predictions of each class of model with the behavior of human sub jects. We ?nd that learning mechanisms based on transformations of an internal state, such as recurrent network architectures (e.g., Elman, 1990), have di?culty accounting for the pattern of human results relative to a simpler (but more limited) learning mechanism based on learning direct associations. Our results suggest new constraints on the cognitive mechanisms supporting sequential learning behavior.

A widely advocated idea in education is that people learn better when the flow of experience is under their control (i.e., learning is self-directed). However, the reasons why volitional control might result in superior acquisition, and the limits to such advantages, remain poorly understood. We review this issue from both a cognitive and computational perspective. On the cognitive side, self-directed learning allows individuals to focus effort on useful information they do not yet possess, can expose information that is inaccessible via passive observation, and may enhance the encoding and retention of materials. On the computational side, "active learning" algorithms that optimize learning by selecting their own learning experiences is an emerging research topic in machine learning. This review argues that recent advances in these related fields may offer a fresh theoretical perspective on how people gather information to support their own learning.

Studies of human category learning typically focus on situations where explicit category labels accompany each example (supervised learning) or on situations were people must infer category structure entirely from the distribution of unlabeled examples (unsupervised learning). However, real-world category learning likely involves a mixture of both types of learning (semi-supervised learning). Surprisingly, a number of recent findings suggest that people have difficulty learning in semi-supervised tasks. To further explore this issue, we devised a category learning task in which the distribution of labeled and unlabeled items suggested alternative organizations of a category. This design allowed us to determine whether learners combined information from both types of episodes via their patterns of generalization at test. In contrast with the prediction of many models, we find little evidence that unlabeled items influenced categorization behavior when labeled items were also present.

A series of experiments are presented that explore the effect that learning internally structured categories has on the ability to visually discriminate category members. The results demonstrate the classic categorical perception (CP) effect whereby discrimination of stimuli that belong to different categories is improved following supervised category training, while the ability to discriminate stimuli belonging to the same category is reduced. However, we also find evidence of a within-category perceptual effect whereby category members that share the same category label but fall into different "sub-clusters" within the category are better discriminated than items that share the same category and cluster. The results show that learners are sensitive to multiple sources of structure when learning beyond simply the labels provided during supervised training including the distribution of items within categories. We compare these results to the predictions of a variety of models of CP including those based on error-driven learning processes and selective attention to diagnostic information. The results appear most consistent with an account whereby multiple levels of encoding (i.e., at the item-, cluster-, and category- level) may simultaneously, and competitively, contribute to perceptual processing.

Self-directed learning is often associated with better long-term memory retention, however, the mechanisms that underly this advantage remain poorly understood. This series of experiments was designed to ?deconstruct" the notion of self-directed learning in order to better identify the factors most responsible for these improvements to memory. In particular, we isolate the memory advantage that comes from controlling the content of study episodes from the advantage that comes from controlling the timing of those episodes. Across four experiments, self-directed learning significantly enhanced recognition memory relative to passive observation, replicating an earlier report [Voss, et al., 2011b. Hippocampal brain-network coordination during volitional exploratory behavior enhances learning. Nature Neuroscience, 14 (1), 115-120.]. However, the advantage for self-directed learning was found to be present even under extremely minimal conditions of volitional control (simply pressing a button when ready to advance to the next item). Our results suggest that improvements to memory following self-directed encoding may be related to the ability to coordinate stimulus presentation with the learner?s current preparatory or attentional state, and highlight the need to consider the range of cognitive control processes involved in and influenced by self-directed study.

Theories of optimal information sampling assert that the decision to gather information should take into account accrual costs such as time, energy, and money. Here we explore how effective people are in trading off the monetary costs and benefits of gathering information using an intuitive visuomotor estimation task. Subjects were rewarded for touching a hidden circular target based on imperfect visual cues to the target's location. Each cue provided additional information about the target's location. Subjects could request as many cues as they wished, but the potential reward for touching the target decreased by a fixed amount for each additional cue requested. We found that while subjects correctly adapted to changes in the cost structure of the task, almost all of them systematically sampled more cues than dictated by the optimal rule (i.e., over-sampling). We evaluate this result in the context of recent arguments in the literature that people typically gather less information than they should (i.e., under-sampling). In addition, we explore why subjects mistakenly vary the number of cues that they sample from trial to trial, given that optimally they should be sampling a fixed number of cues on each trial.

Studies of human category learning typically focus on situations where explicit category labels accompany each example (supervised learning) or in situations were learners must infer category structure entirely from the distribution of unlabeled examples (unsupervised learning). However, real-world category learning likely involves a mixture of both types of learning (semi-supervised learning). Recent empirical studies examining human semi-supervised learning often have provided contradictory results. In the present paper, we describe a set of theoretical predictions for when semi-supervised learning should or should not occur. Our predictions are derived directly from Anderson?s (1991) rational model of categorization (RMC), which makes a variety of yet-untested predictions for human performance in these situations. Across three experiments we find that participants show behavior qualitatively consistent with the model?s a priori predictions, although there are also important deviations. A novel extension to the RMC is proposed to explain the discrepancy between the predicted and observed patterns of behavior.

People can test hypotheses through either selection or reception. In a selection task, the learner actively chooses observations to test their beliefs, while in reception tasks data is passively encountered. People routinely use both forms of testing in everyday life, but the critical psychological differences between selection and reception learning remain poorly understood. One hypothesis is that selection learning improves learning performance by enhancing generic cognitive processes related to motivation, attention, and engagement. Alternatively, we suggest that differences between these two learning modes derives from a hypothesis-dependent sampling bias that is introduced when a person collects data to test their own individual hypothesis. Drawing on influential models of sequential hypothesis testing behavior, we show that such a bias 1) can lead to the collection of data that facilitates learning compared to reception learning, and 2) can be more effective than observing the selections of another person. We then report a novel experiment based on a popular category learning paradigm that compares reception and selection learning. We additionally compare selection learners to a set of ``yoked" participants who viewed the exact same sequence of observations under reception conditions. The results revealed systematic differences in performance that depended on the learner's role in collecting information and the abstract structure of the problem.

What effect does labeling an object as a member of a familiar category have on memory for that object? Recent studies suggest that recognition memory can be negatively impacted by categorizing objects during encoding. This paper examines the "representational shift hypothesis" which argues that categorizing an object impairs recognition memory by altering the trace of the encoded memory to be more similar to the category prototype. Previous evidence for this idea comes from experiments in which a basic-level category labeling task was compared to a non-category labeling incidental encoding task, usually a preference judgment (e.g., "Do you like this item?"). In two experiments, we examine alternative tasks that attempt to control for processing demands and the degree to which category information is explicitly recruited at the time of study. Contrary to the predictions of the representational shift hypothesis, we find no evidence that memory is selectively impaired by category labeling. Overall, the pattern of results across both studies appears consistent with well-established variables known to influence memory such as encoding specificity and distinctiveness effects.

Amazon Mechanical Turk (AMT) is an online crowdsourcing service where anonymous online workers complete web-based tasks for small sums of money. The service has attracted attention from experimental psychologists interested in gathering human subject data more efficiently. However, relative to traditional laboratory studies, many aspects of the testing environment are not under the experimenter's control. In this paper, we attempt to empirically evaluate the fidelity of the AMT system for use in cognitive behavioral experiments. These types of experiment differ from simple surveys in that they require multiple trials, sustained attention from participants, and millisecond accuracy for response recording and stimulus presentation. We replicate a diverse body of tasks from experimental psychology including the Stroop, Switching, Flanker, Simon, Posner Cuing, attentional blink, subliminal priming, and category learning tasks using participants recruited using AMT. While most of replications were qualitatively successful and validated the approach of collecting data anonymously online using a web-browser, for others the alignment between laboratory results and online results showed more of a disparity. A number of important lessons were encountered in the process of conducting these replications that should be of value to other researchers.

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A critical aspect of human cognition is the ability to actively query the environment for information. One important (but often overlooked) factor in the decision to gather information is the cost associated with accessing different sources of information. Using a simple sequential information search task, we explore the degree to which human learners are sensitive to variations in the amount of utility related to different potential observations. Across two experiments we find greater support for the idea that people gather information to reduce their uncer- tainty about the current state of the environment (a "disinterested", or cost-insenstive, sampling strategy). Implications for theories of rational information collection are discussed.

A widely advocated idea in education is that people learn better when the flow of experience is under their control (i.e., learning is self-directed). However, the reasons why volitional control might result in superior acquisition, and the limits to such advantages, remain poorly understood. We review this issue from both a cognitive and computational perspective. On the cognitive side, self-directed learning allows individuals to focus effort on useful information they do not yet possess, can expose information that is inaccessible via passive observation, and may enhance the encoding and retention of materials. On the computational side, "active learning" algorithms that optimize learning by selecting their own learning experiences is an emerging research topic in machine learning. This review argues that recent advances in these related fields may offer a fresh theoretical perspective on how people gather information to support their own learning.

Studies of human category learning typically focus on situations where explicit category labels accompany each example (supervised learning) or on situations were people must infer category structure entirely from the distribution of unlabeled examples (unsupervised learning). However, real-world category learning likely involves a mixture of both types of learning (semi-supervised learning). Surprisingly, a number of recent findings suggest that people have difficulty learning in semi-supervised tasks. To further explore this issue, we devised a category learning task in which the distribution of labeled and unlabeled items suggested alternative organizations of a category. This design allowed us to determine whether learners combined information from both types of episodes via their patterns of generalization at test. In contrast with the prediction of many models, we find little evidence that unlabeled items influenced categorization behavior when labeled items were also present.

Self-directed information sampling - the ability to collect information that one expects to be useful - has been shown to improve the efficiency of concept acquisition for both human and machine learners. However, little is known about how people decide which information is worth learning more about. In this study, we examine self-directed learning in a relatively complex rule learning task that gave participants the ability to "design and test" stimuli they wanted to learn about. On a subset of trials we recorded participants' uncertainty about how to classify the item they had just designed. Analyses of these uncertainty judgments show that people prefer gathering information about items that help refine one rule at a time (i.e., those that fall close to a pairwise category "margin") rather than items that have the highest overall uncertainty across all relevant hypotheses or rules. Our results give new insight into how people gather information to test currently entertained hypotheses in complex problem solving tasks.

This study examines how people deal with inherently stochastic cues when estimating a latent environmental property. Seven cues to a hidden location were presented one at a time in rapid succession. The seven cues were sampled from seven different Gaussian distributions that shared a common mean but differed in precision (the reciprocal of variance). The experimental task was to estimate the common mean of the Gaussians from which the cues were drawn. Observers ran in two conditions on separate days. In the "decreasing precision" condition the seven cues were ordered from most precise to least precise. In the "increasing precision" condition this ordering was reversed. For each condition, we estimated the weight that each cue in the sequence had on observers' estimates and compared human performance to that of an ideal observer who maximizes expected gain. We found that observers integrated information from more than one cue, and that they adaptively gave more weight to more precise cues and less weight to less precise cues. However, they did not assign weights that would maximize their expected gain, even over the course of several hundred trials with corrective feedback. The cost to observers of their sub-optimal performance was on average 16% of their maximum possible winnings.

How do people manipulate their environment when balancing trade-offs between probability of success and payoff? Individuals in a city park played a simple lottery using a small set of marbles placed in an urn. Participants had the ability to actively improve their chances of winning but only by reducing the amount of money that they could possibly win. Hence, participants controlled the lottery?s intuitive trade-off between probability of success and potential payout. Across four dif- ferent lottery structures, participants, on average, behaved systematically safer than the optimal strategy that maximizes expected gain. We explore two different accounts of this sub-optimal choice behavior: probability distortion, and intrinsic utility of winning.

Recent functional magnetic resonance imaging (fMRI) studies have found that distinct neural systems may mediate perceptual category learning under incidental and intentional learning conditions. The present study was designed to replicate and extend previous investigations of these effects using an event-related design. In addition, the design is aimed at decoupling the influence of stimulus-encoding processes from the contribution of implicit and explicit learning on the recruitment of alternative neural systems. Consistent with previous reports, following incidental learning in a dot-pattern classification task, participants show decreased neural activity in occipital visual cortex (extrastriate region V3, BA 19) in response to novel exemplars of a studied category compared to members of a foil category, but do not show this decreased neural activity following explicit learning. Crucially, however, our results show that this pattern can be modulated by aspects of the stimulus-encoding instructions provided at the time of study. In particular, when participants in an implicit learning condition were encouraged to evaluate the overall shape and configuration of the stimuli during study, we failed to find the pattern of brain activity that has been taken to be a signature of implicit learning, suggesting that activity in this area does not uniquely reflect implicit memory for perceptual categories.

Numerous proposal have been put forward concerning the nature of human category representations, ranging from rules to exemplars to prototypes. However, it is unlikely that a single, ?xed form of representation is suf?cient to account for the ?exibility of human categories. In this chapter, we describe an alternative to these ?xed-representation accounts based on the principle of adaptive clustering. The speci?c model we consider, SUSTAIN, represents categories in terms of feature bundles called clusters which are adaptively recruited in response to task demands. In some cases, SUSTAIN acts like an exemplar model, storing each category instance, while in others it appears more like a prototype model extracting only the central tendency of a number of items. In addition, selective attention in the model allows it to mimic many of the behaviors associated with rule-based systems. We review a variety of evidence in support of the clustering principle, including studies of the relationship between categorization and recognition memory, changes in unsupervised category learning abilities across development, and the in?uence of category learning on perceptual discrimination. In each case, we show how the nature of human category representations are best accounted for using an adaptive clustering scheme. SUSTAIN is just one example of a system that casts category learning in terms of adaptive clustering and future directions for the approach are discussed.

Discovering the latent structure underlying sequential data is a central part of human learning in domains ranging from language to perception. We develop a rational framework based on Bayesian induction of hidden markov models (HMM) for understanding the principles guiding human sequence learning. The model uses nonparametric Bayesian methods to infer a distribution over possible hidden markov model (HMM) structures that are consistent with a particular training sequence. We successfully apply the model to a number of well known findings in artificial grammar learning (AGL) tasks (e.g., Gomez, 2002). In contrast to accounts based on associative learning principles (e.g., Simple Recurrent Networks), we argue that human sequence learning is better explained as a structured inference process that reflects the uncertainty over candidate structures.

In this paper, we evaluate the "representational shift" hypothesis which argues that the act of explicitly labeling an object as a member of a familiar semantic category alters the trace of the encoded memory in the direction of the category prototype. The typical procedure for such experiments has been to compare category labeling to a non-categorization encoding task such as a preference judgement. In a series of experiments, we examine alternative comparison tasks that attempt to control the depth of encoding and the degree to which category information is explicitly recruited at the time of study. The results appear most consistent with a depth of processing (Craik & Lockhart, 1972) (Exp. 1) or distinctiveness (Exp. 2) explanation for the pattern of memory effects found in previous studies.

We investigated how subjects sample information in order to improve performance in a visuomotor estimation task. Subjects were rewarded for touching a hidden circular target based on visual cues to the target?s location. The cues were 'dots' drawn from a Gaussian distribution centered on the middle of the target. Subjects could sample as many cues as they wished, but the potential reward for hitting the target decreased by a fixed amount for each additional cue requested. The subjects' objective was to balance the benefits of increased information against the costs incurred in acquiring it. We compared human performance to ideal and found that subjects sampled more cues than dictated by the optimal stopping rule that tries to maximize expected gain. We contrast our results with recent reports in the literature that subjects typically under-sample.

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We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the ?rst, learning is based on the gradual accumulation of direct associations between events based on simple conditioning principles. The other view describes learning as the process of inducing the transformational structure that de?nes the material. Each of these learning mechanisms predict di?erences in the rate of acquisition for di?erently organized sequences. Across a set of empirical studies, we compare the predictions of each class of model with the behavior of human sub jects. We ?nd that learning mechanisms based on transformations of an internal state, such as recurrent network architectures (e.g., Elman, 1990), have di?culty accounting for the pattern of human results relative to a simpler (but more limited) learning mechanism based on learning direct associations. Our results suggest new constraints on the cognitive mechanisms supporting sequential learning behavior.

This work explores the influence of motivation on choice behavior in a dynamic decision-making environment, where the payoffs from each choice depend on one?s recent choice history. Previous research reveals that participants in a regulatory fit exhibit increased levels of exploratory choice and flexible use of multiple strategies over the course of an experiment. The present study placed promotion and prevention-focused participants in a dynamic environment for which optimal performance is facilitated by systematic exploration of the decision space. These participants either gained or lost points with each choice. Our experiment revealed that participants in a regulatory fit were more likely to engage in systematic exploration of the task environment than participants in a regulatory mismatch and performed more optimally as a result. Implications for contemporary models of human reinforcement-learning are discussed.

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Laboratory studies of human category learning tend to emphasize passive learning situations by limiting participants' control over what information they experience on every trial. In this paper, we explore the impact that active data selection has on category learning. In our experiment, participants attempted to learn standard rule-based (RB) and information-integration (II) categories under either entirely passive (observational) conditions, or by actively selecting and querying the labels associated with particular stimuli. Our primary aim was to characterize the information sampling strategy that participants adopted in the task and to examine how the passive/active learning distinction interacted with the structure of the categories. We found that participants acquired categories faster when they were able to select and query category members on their own. Furthermore, this advantage depended on learners actually making the decisions about which stimuli to query themselves rather than simply the statistics of the experienced exemplars. Model based analyses explain this effect in terms of the number of active hypotheses under consideration which is assumed to be higher in the active learning condition due to the greater engagement of the learner in the task

Perceptual aliasing arises in situations where multiple, distinct states of the world give rise to the same percept. In this study, we examine how the degree of perceptual aliasing in a task impacts the ability of human agents to learn reward-maximizing decision strategies. Previous work has shown that the presence of perceptual cues that help signal distinct states of the environment can improve the ability of learners to adopt an optimal decision strategy in sequential decision making tasks (Gureckis & Love, 2009). In our experiments, we parametrically manipulated the degree of perceptual aliasing afforded by certain perceptual cues in a similar task. Our empirical results and simulations show how the ability of the learner improves as relevant states in the world uniquely map to differentiated percepts. The results provide further support for the model of sequential decision making proposed by Gureckis & Love (2009) and highlight the important role that state representations may have on behavior in dynamic decision making and learning tasks.

Recent research has argued that categorization is strongly tied to language processing. For example, language (in the form of verbal category labels) has been shown to influence the perceptual discriminations of color (Winawer et al., 2007). However, does this imply that categorical perception is essentially verbally-mediated perception? The present studies extend recent findings in our lab showing that categorical perception can occur even in the absence of overt labels. In particular, we evaluate the degree to which certain interference task (verbal, spatial) reduce the effect of learned categorical perception for complex visual stimuli (faces). Contrary to previous findings with color categories, our results show that a verbal interference task does not disrupt learned categorical perception effects for faces. Our results are interpreted in light of the ongoing debate about the role of language in categorization. In particular, we suggest that at least a sub-set of categorical perception effect may be effectively "language-free."

Successful investors seeking returns, animals foraging for food, and pilots controlling aircraft all must take into account how their current decisions will impact their future standing. One challenge facing decision makers is that options that appear attractive in the short-term may not turn out best in the long run. In this paper, we explore human learning in a dynamic control task which places short- and long-term rewards in con?ict. Our goal in these studies was to evaluate how people?s mental representation of a task a?ects their ability to discover an optimal decision strategy. We ?nd that perceptual cues that readily align with the underlying state of the task environment help people overcome the impulsive appeal of short term rewards. Our experimental manipulations, predictions, and analyses are motivated by current work in reinforcement learning which details how learners discount future rewards, the importance of ?state? representations, and the role that exploration and exploitation play in e?ective learning.

Research on dynamic decision-making tasks, in which the payoffs associated with each choice vary with participants recent choice history, finds that humans have difficulty making long-term optimal choices in the presence of attractive immediate rewards. However, a number of recent studies have shown that simple cues providing information about the underlying state of the task environment may facilitate optimal responding. This study examines the mechanism by which this state knowledge influences choice behavior. We examine the possibility that participants use state information in conjunction with changing payoffs to extrapolate payoffs in future states. We find support for this hypothesis in a study where generalizations based on this state information work to the benefit or detriment of task performance, depending on the task's payoff structure.

We examine the interdependence between individual and group behavior surrounding a somewhat arbitrary, real world decision: selecting a name for one's child. Using a historical database of the names given to children over the last century in the United States, we find that naming choices are influuenced by both the frequency of a name in the general population, and by its "momentum" in the recent past in the sense that names which are growing in popularity are preferentially chosen. This bias toward rising names is a recent phenomena: in the early part of the 20th century, increasing popularity of a name from one time period to the next was correlated with a decrease in future popularity. However, more recently this trend has reversed. We evaluate a number of formal models that detail how individual decision making strategies, played out in a large population of interacting agents, can explain these empirical observations. We argue that cognitive capacities for change detection, the encoding of frequency in memory, and biases towards novel or incongruous stimuli may interact with the behavior of other decision makers to determine the distribution and dynamics of cultural tokens such as names.

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The resurgence of interest in collective behavior is in large part due to tools recently made available for conducting laboratory experiments on groups, statistical methods for analyzing large data sets reflecting social interactions, the rapid growth of a diverse variety of on-line self-organized collectives, and computational modeling methods for understanding both universal and scenario-specific social patterns. We consider case studies of collective behavior along four attributes: the primary motivation of individuals within the group, kinds of interactions among individuals, typical dynamics that result from these interactions, and characteristic outcomes at the group level. With this framework, we compare the collective patterns of non-interacting decision makers, bee swarms, groups forming paths in physical and abstract spaces, sports teams, cooperation and competition for resource usage, and the spread and extension of innovations in an on-line community. Some critical issues surrounding collective behavior are then reviewed, including the questions of "Does group behavior always reduce to individual behavior?," "Is `group cognition` possible?", and "What is the value of formal modeling for understanding group behavior"

In engineering systems, noise is a curse, obscuring important signals and increasing the uncertainty associated with measurement. However, the negative effects of noise are not universal. In this paper, we examine how people learn sequential control strategies given different sources and amounts of feedback variability. In particular, we consider people's behavior in a task where short- and long-term rewards are placed in conflict (i.e., the best option in the short-term is worst in the long-term). Consistent with a model based on reinforcement learning principles (Gureckis & Love, 2009), we find that learners differentially weight information predictive of the current task state. In particular, when cues that signal state are noisy, we find that participants' ability to identify an optimal strategy is strongly impaired relative to equivalent amounts of variability that obscure the rewards/valuations of those states. In other situations, we find that noise and variability in reward signals may paradoxically improve performance by encouraging exploration. Our results demonstrate how experimentally-manipulated task variability can be used to test predictions about the mechanisms that learners engage in dynamic decision making tasks.

Effective learning often involves actively querying the environment for information that disambiguates potential hypotheses. However, the space of observations available in any situation can vary greatly in potential "informativeness." In this report, we study participants' ability to gauge the information value of potential observations in a cognitive search task based on the children's game Battleship. Participants selected observations to disambiguate between a large number of potential game configurations subject to information-collection costs and penalties for making errors in a test phase. An "ideal-learner" model is developed to quantify the utility of possible observations in terms of the expected gain in points from knowing the outcome of that observation. The model was used as a tool for measuring search efficiency, and for classifying various types of information collection decisions. We find that participants are generally effective at maximizing gain relative to their current state of knowledge and the constraints of the task. In addition, search behavior shifts between an slower, but more efficient "exploitive" mode of local search and a faster, less efficient pattern of "exploration."

This work explores the influence of motivation on choice behavior in a dynamic decision-making environment, where the payoff from each choice depend on one's recent choice history. Previous research reveals increased levels of exploratory choice among participants in a regulatory fit. The present study placed promotion and prevention-focused participants in a dynamic environment for which optimal performance requires that participants sustain a single choice strategy in the face of temporary payoff decreases. These participants either gained or lost points with each choice. Our behavioral results and a model-based analysis, using an average-reward reinforcement learning framework, revealed differential levels of reactivity to local changes in payoffs - specifically, participants in a regulatory fit were less reactive to local perturbations in payoffs than participants in a regulatory mismatch and performed more optimally as a result.

Our laboratory has been studying the emergence of collective search behavior from a complex systems perspective. We have developed an Internet-based experimental platform that allows groups of people to interact with each other in real-time on networked computers. The experiments implement virtual environments where participants can see the moment-to-moment actions of their peers and imme- diately respond to their environment. Agent-based computational models are used as accounts of the experimental results. We describe two paradigms for collective search: one in physical space and the other in an abstract problem space. The phys- ical search situation concerns competitive foraging for resources by individuals in- habiting an environment consisting largely of other individuals foraging for the same resources. The abstract search concerns the dissemination of innovations in social networks. Across both scenarios, the group-level behavior that emerges reveals in- fluences of exploration and exploitation, bandwagon effects, population waves, and compromises between individuals using their own information and information ob- tained from their peers.

Just as neurons interconnect in networks that create structured thoughts beyond the ken of any individual neuron, so people spontaneously organize themselves into groups to create emergent organizations that no individual may intend, comprehend, or even perceive. Recent technological advances have provided us with unprecedented opportunities for conducting controlled laboratory experiments on human collective behavior. We describe two experimental paradigms in which we attempt to build predictive bridges between the beliefs, goals, and cognitive capacities of individuals and patterns of behavior at the group level, showing how the members of a group dynamically allocate themselves to resources and how innovations diffuse through a social network. Agent-based computational models have provided useful explanatory and predictive accounts. Together, the models and experiments point to tradeoffs between exploration and exploitation?that is, compromises between individuals using their own innovations and using innovations obtained from their peers and the emergence of group-level organizations such as population waves, bandwagon effects, and spontaneous specialization.

We live in a world consisting of concrete experiences, yet we appear to form abstractions that transcend the details of our experiences. In this contribution, we argue that the abstract nature of our thought is overstated and that our representations are inherently bound to the examples we experience during learning. We present three lines of related research to support this general point. The ?rst line of research suggests that there are no separate learning systems for acquiring mental rules and storing exceptions to these rules. Instead, both items types share a common representational substrate that is grounded in experienced training examples. The second line of research suggests that representations of abstract concepts, such as same and different that can range over an unbounded set of stimulus properties, are rooted in experienced examples coupled with analogical processes. Finally, we consider how people perform in dynamic decision tasks in which short? and long?term rewards are in opposition. Rather than invoking explicit reasoning processes and planning, people's performance is best explained by reinforcement learning procedures that update estimates of action values in a reactive, trial?by?trial fashion. All three lines of research implicate mechanisms of thought that are capable of broad generalization, yet inherently local in terms of the procedures used for updating mental representations and planning future actions. We end by considering the bene?ts of designing systems that operate according to these principles.

We asked 169 participants to spontaneously categorize nine sets of items. A category structure was assumed to be more intuitive if a large number of participants consistently produced the same classification. Our results provide a rich empirical framework for examining models of unsupervised categorization?and illustrate the corresponding profound modeling challenge. We provide a preliminary examination comparing two models of unsupervised categorization: SUSTAIN (Love, Medin, & Gureckis, 2004) and the simplicity model (Pothos & Chater, 2002).

A novel study is presented that explores the effect that learning internally organized categories has on the ability to subsequently discriminate category members. The results demonstrate the classic categorical perception effect whereby discrimination of stimuli that belong to different categories is improved following training, while the ability to discriminate stimuli belonging to the same category is reduced. We further report a new within-category perceptual effect whereby category members that share the same category label but fall into different sub-clusters within that category are better discriminated than items that share the same category and cluster. The results show that learners are sensitive to multiple sources structure beyond simply the labels provided during supervised training. A computational model is presented to account for the results whereby multiple levels of encoding (i.e., at the item-, cluster-, and category- level) may simultaneously contribute to perception.

Mental localization efforts tend to stress the where more than the what. We argue that the proper targets for localization are well-specified cognitive models. We make this case by relating an existing cognitive model of category learning to a learning circuit involving the hippocampus, perirhinal, and prefrontal cortices. Results from groups varying in function along this circuit (e.g., infants, amnesics, and older adults) are successfully simulated by reducing the model's ability to form new clusters in response to surprising events, such as an error in supervised learning or an unfamiliar stimulus in unsupervised learning. Clusters in the model are akin to conjunctive codes that are rooted in an episodic experience (the surprising event) yet can develop to resemble abstract codes as they are updated by subsequent experiences. Thus, the model holds that the line separating episodic and semantic information can become blurred. Dissociations (categorization vs. recognition) are explained in terms of cluster recruitment demands.

In recent years, statistical learning (SL) has emerged as a compelling alternative to nativist theories of language acquisition (i.e., Chomsky, 1980). However, in many ways the framework of statistical learning echoes aspects of classic behaviorism by stressing the role of associative learning processes and the environment in shaping behavior. How far backwards has the needle swung? In this paper, we show how a subset of behaviors studied under the rubric of SL are in fact entirely consistent with a simple form of conditioned priming inspired by models from the behaviorist tradition (i.e. a variant of Rescola-Wagner which learns associative relationships through time).

Is cognition an exclusive property of the individual or can groups have a mind of their own? We explore this question from the perspective of complex adaptive systems. One of the principle insights from this line of work is that rules that govern behavior at one level of analysis (the individual) can cause qualitatively different behavior at higher levels (the group). We review a number of behavioral studies from our lab that demonstrate how groups of people interacting in real-time can self-organize into adaptive, problem-solving group structures. A number of principles are derived concerning the critical features of such "distributed" information processing systems. We suggest that while cognitive science has traditionally focused on the individual, cognitive processes may manifest at many levels including the emergent group-level behavior that results from the interaction of multiple agents and their environment.

Mental localization efforts tend to stress the where more than the what. We argue that the proper targets for localization are well-specified cognitive models. We make this case by relating an existing cognitive model of category learning to a learning circuit involving the hippocampus, perirhinal, and prefrontal cortex. Results from groups varying in function along this circuit (e.g., infants, amnesics, older adults) are successfully simulated by reducing the model's ability to form new clusters in response to surprising events, such as an error in supervised learning or an unfamiliar stimulus in unsupervised learning. Reported task dissociations (e.g., categorization vs. recognition) are explained in terms of cluster recruitment demands.

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In this report, a model of human sequence learning is developed called the linear associative shift register (LASR). LASR uses a simple error-driven associative learning rule to incrementally acquire information about the structure of event sequences. In contrast to recent modeling approaches, LASR describes learning as a simple and limited process. We argue that this simplicity is a virtue in that the complexity of the model is better matched to the demonstrated complexity of human processing. The model is applied in a variety of situations including implicit learning via the serial reaction time (SRT) task and statistical word learning. The results of these simulations highlight commonalities between different tasks and learning modalities which suggest similar underlying learning mechanisms. LASR provides a similar account of the type of processing which underlies performance in both kinds of tasks, suggesting that they may rely on similar underlying mechanisms.

Human behavior is intrinsically linked to the temporal and sequential characteristics of the environment. In this report, a model of human sequence learning is developed called the linear associative shift register (LASR). LASR uses a simple error-driven associative learning rule to incrementally acquire information about the structure of event sequences. The model is applied in a variety of situations including implicit learning via the serial reaction time (SRT) task and statistical word learning. The results of these simulations highlight commonalities between different tasks and learning modalities which suggests similar underlying learning mechanisms. Two novel experiments are reported which test unique predictions of the model and which differentiate between competing theories concerning the mechanisms underlying implicit sequence learning.

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes/attractors/rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.

Computational models of infant categorization often fail to elaborate the transitional mechanisms that allow infants to achieve adult performance. In this paper, we apply a successful connectionist model of adult category learning to developmental data. The Supervised and Unsupervised Stratified Adaptive Increment Network (SUSTAIN) model is able to account for the emergence of infants' sensitivity to correlated attributes (e.g., has wings and can fly). SUSTAIN offers two complimentary explanations of the developmental trend. One explanation centers on memory storage limitations, whereas the other focuses on limitations in perceptual systems. Both explanations parallel published findings concerning the cognitive and sensory limitations of infants. SUSTAIN's simulations suggest that conceptual development follows a continuous and smooth trajectory, despite qualitative changes in behavior, and that the mechanisms that underlie infant and adult categorization may not differ significantly.

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SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a network model of human category learning. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the suprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes/attractors/rules. SUSTAIN has expanded the scope of findings that models of human category learning can address. This paper extends SUSTAIN so that it can be used to account for both supervised and unsupervised learning data through a common mechanism. A modified recruitment mechanism is introduced that creates new conceptual clusters in response to surprising events during learning. The new formulation of the model is called uSUSTAIN for "unifed SUSTAIN." The implications of using a unified recruitment method for both supervised and unsupervised learning is discussed.

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a network model of human category learning. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes/attractors/rules. SUSTAIN has expanded the scope of findings that models of human category learning can address. This paper extends SUSTAIN to account for both supervised and unsupervised learning data through a common mechanism. The modi?ed model, uSUSTAIN (unified SUSTAIN), is successfully applied to human learning data drawn from Love (2002) that compares unsupervised and supervised learning performance.

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a network model of human category learning. This paper extends SUSTAIN so that it can be used to model unsupervised learning data. A modified recruitment mechanism is introduced that creates new conceptual clusters in response to surprising events during learning. Two seemingly contradictory unsupervised learning data sets are modeled using this new recruitment method. In addition, the feasibility of using a unified recruitment method for both supervised and unsupervised learning is discussed.

How do people learn and organize examples in the absence of a teacher? This paper explores this question through a examination of human data and computational modeling results. The SUSTAIN (Supervised and Unsupervised STratified Incremental Network) model successfully fits human learning data drawn from two published studies. The first study examines how correlations between features can facilitate unsupervised learning. The second set of studies examines the role that similarity and attention play in unsupervised category construction (i.e., sorting) tasks. Importantly, SUSTAIN suggests two novel behavioral predictions that are confirmed.