Bibliography

Linguistic alignment in text-based communication means that people tend to adjust their language use to one another both in terms of word choice and sentence structure. Previous studies about linguistic alignment suggested that these two forms of adaptation are correlated with each other‚ and that they build up to alignment at a higher representational level‚ such as pragmatic alignment for support functions. Two types of social support have been identified as important for online health communities (OHCs): emotional and informational support between support seekers and support providers. Do the two lower-level alignment measures (lexical and syntactic) relate to these two types of social support in the same way or‚ are they different? Our hypothesis was that they are similar‚ due to their correlation relationship. However‚ we found that‚ based on an analysis of a 10-year online forum for cancer survivors‚ the lower-level alignment measures have distinct relationships to the two higher-level support functions. In this paper‚ we describe this finding and its implications regarding potential refinement of the Inter- active Alignment Model.

We propose a perspective on dialogue that focuses on relative information contributions of conversation partners as a key to successful communication. We predict the success of collaborative task in English and Danish corpora of task-oriented dialogue. Two features are extracted from the frequency domain representations of the lexical entropy series of each interlocutor‚ power spectrum overlap (PSO) and relative phase (RP). We find that PSO is a negative predictor of task success‚ while RP is a positive one. An SVM with these features significantly improved on previous task success prediction models. Our findings suggest that the strategic distribution of information density between interlocutors is relevant to task success.

Computational models of distributional semantics can analyze a corpus to derive representations of word meanings in terms of each word’s relationship to all other words in the corpus. While these models are sensitive to topic (e.g.‚ tiger and stripes) and synonymy (e.g.‚ soar and fly)‚ the models have limited sensitivity to part of speech (e.g.‚ book and shirt are both nouns). By augmenting a holographic model of semantic memory with additional levels of representations‚ we present evidence that sensitivity to syntax is supported by exploiting associations between words at varying degrees of separation. We find that sensitivity to associations at three degrees of separation reinforces the relationships between words that share part-of-speech and improves the ability of the model to construct grammatical sentences. Our model provides evidence that semantics and syntax exist on a continuum and emerge from a unitary cognitive system.

This paper investigates how cognition facilitates the adoption of new words through a study of the large-scale Reddit corpus‚ which contains written‚ threaded conversations conducted over the internet. Parameters for the cognitive architecture are estimated. Using ACT-R’s account of declarative memory‚ the activation of memory chunks representing words is traced and compared to usage statistics sampled from a year of data. Potential values for decay and retrieval threshold are identified according to model fit and growth rates of word adoption. The resulting estimate for the decay parameter‚ d‚ is 0.22‚ and the estimate for the retrieval threshold parameter‚ rt‚ lies between 3.4 and 4.5.

We examine working memory use and incrementality using a cognitive model of grammatical encoding. Our model com- bines an empirically validated framework‚ ACT-R‚ with a lin- guistic theory‚ Combinatory Categorial Grammar‚ to target that phase of language production. By building the model with the Switchboard corpus‚ it can attempt to realize a larger set of sentences. With this methodology‚ different strategies may be compared according to the similarity of the model’s sentences to the test sentences. In this way‚ the model can still be evalu- ated by its fit to human data‚ without overfitting to individual experiments. The results show that while having more work- ing memory available improves performance‚ using less work- ing memory during realization is correlated with a closer fit‚ even after controlling for sentence complexity. Further‚ sen- tences realized with a more incremental strategy are also more similar to the corpus sentences as measured by edit distance. As high incrementality is correlated with low working mem- ory usage‚ this study offers a possible mechanism by which incrementality can be explained.

The applicability of entropy rate constancy to dialogue is examined on two spoken dialogue corpora. The principle is found to hold; however‚ new entropy change patterns within the topic episodes of dialogue are described‚ which are different from written text. Speaker’s dynamic roles as topic initiators and topic responders are associated with decreasing and increasing entropy‚ respectively‚ which results in local convergence between these speakers in each topic episode. This implies that the sentence entropy in dialogue is conditioned on different contexts determined by the speaker’s roles. Explanations from the perspectives of grounding theory and interactive alignment are discussed‚ resulting in a novel‚ unified information-theoretic approach of dialogue.

Using corpus data of spoken dialogue‚ we examine the convergence of syntactic complexity levels between interlocutors in natural conversations‚ as it occurs within spans of topic episodes. The findings of general convergence in the Switchboard and BNC corpora are compatible with an information-theoretic model of dialogue and with Interactive Alignment Theory.

We present a novel fine-tuning algorithm in a deep hybrid architecture for semi-supervised text classification. During each increment of the online learning process‚ the fine-tuning algorithm serves as a top-down mechanism for pseudo-jointly modifying model parameters following a bottom-up generative learning pass. The resulting model‚ trained under what we call the Bottom-Up-Top-Down learning algorithm‚ is shown to outperform a variety of competitive models and baselines trained across a wide range of splits between supervised and unsupervised training data.

Linguistic alignment‚ such as lexical and syntactic alignment‚ is a universal phenomenon influencing dialogue participants in online conversations. Besides linguistic alignment‚ social support‚ such as emotional support and informational support‚ also provides important benefits to their members. More recently‚ identifying and analyzing social support facilitated insights in the construction of support oriented communities. In this paper‚ we study whether members show pragmatic alignment on social support. While adaptation can occur at lexical‚ syntactic and pragmatic levels‚ relationships between alignments at multiple levels are neither theoretically nor empirically well understood. Our results indicate pragmatic alignment of forum members along the axis of support type. We also find that lexical alignment is correlated with emotional support‚ and that the amount of lexical alignment is also correlated with the amount of pragmatic alignment. This finding can contribute to improving our understanding about the linguistic signature of different types of support‚ and enhancing theoretical model of the Interactive Alignment Model in a multi-party peer support conversation context.

Linguistic alignment has emerged as an important property of conversational language and a driver of mutual understanding in dialogue. While various computational measures of linguistic alignment in corpus and experimental data have been devised‚ a systematic evaluation of them is missing. In this study‚ we first evaluate the sensitivity and distributional properties of three measures‚ indiscriminate local linguistic alignment (LLA)‚ Spearman’s correlation coefficient (SCC)‚ and repetition decay (RepDecay). Then we apply them in a study of interactive alignment and individual differences to see how well they conform to the Interactive Alignment Model (IAM)‚ and how well they can reveal the individual differences in alignment propensity. Our results suggest that LLA has the overall best performance.

Task-solving in dialogue depends on the convergence of the situation models held by the dialogue partners. The Interactive Alignment Model (Pickering and Garrod‚ 2004) suggests that this convergence is the result of an interactive alignment process‚ which is based on mechanistic repetition at a number of linguistic levels. In this paper‚ we develop two predictions arising from the theory‚ along with two methods to quantify the known structural priming effects in the full inventory of syntactic choices found in text and speech corpora. (a) Under a rational perspective‚ we expect increased repetition in task-oriented dialogue compared to spontaneous conversation. We find within- and between-speaker priming in a corpus of spontaneous conversations‚ but stronger priming in task-oriented dialogue. (b) The Interactive Alignment Model predicts linguistic adaptation to be correlated with task success. We show this effect in a corpus of task-oriented dialogue‚ where we find a positive correlation of long-term adaptation and a quantifiable task success measure. We argue that the repetition tendency relevant for the high-level alignment of situation models is based on slow adaptation rather than short-term priming. We demonstrate that lexical and syntactic repetition are reliable and computationally exploitable predictors of task success.

Previous studies of alignment have focused on two-party conversations. We study multi-party conversation in online health communities‚ which have shown benefits for their members from forum conversations. So far‚ our understanding of the relationship between alignment in such multi-party conversations and its possible connection to member benefits has been limited. This paper quantifies linguistic alignment in the oldest and the largest cancer online forum. Alignment at lexical and syntactic levels‚ as well as decay of alignment was observed in forum threads‚ although the decay was slower than commonly found in psycholinguistic studies. The different pattern of adaptation to the initial post on a thread suggests that specific roles in the online forum (e.g.‚ seeking support from the community) can potentially be revealed through alignment theory and its extensions.

The development and refinement of natural-language communication systems among networked individuals is not well understood and difficult to study. This paper uses a task providing a controlled environment for the goal-oriented‚ collaborative exchange of short‚ natural-language messages between experimental participants (20 per group) in order to demonstrate lexical convergence. A technique for illustrating convergence based on graph layout by multidimensional scaling is described. While reliable convergence is shown‚ it is limited to the collaborative or communicative situation: participants did not adopt group terms in a separately administered post-test questionnaire.

We simulate the evolution of a domain vocabulary in small communities. Empirical data show that human communicators can evolve graphical languages quickly in a constrained task (Pictionary)‚ and that communities converge towards a common language. We propose that simulations of such cultural evolution incorporate properties of human memory (cue-based retrieval‚ learning‚ decay). A cognitive model is described that encodes abstract concepts with small sets of concrete‚ related concepts (directing)‚ and that also decodes such signs (matching). Learning captures conventionalized signs. Relatedness of concepts is characterized by a mixture of shared and individual knowledge‚ which we sample from a text corpus. Simulations show vocabulary convergence of agent communities of varied structure‚ but idiosyncrasy in vocabularies of each dyad of models. Convergence is weakened when agents do not alternate between encoding and decoding‚ predicting the necessity of bi-directional communication. Convergence is improved by explicit feedback about communicative success. We hypothesize that humans seek out subtle clues to gauge success in order to guide their vocabulary acquisition.

The psycholinguistic literature has identified two syntactic adaptation effects in language production: rapidly decaying short-term priming and long-lasting adaptation. To explain both effects‚ we present an ACT-R model of syntactic priming based on a wide-coverage‚ lexicalized syntactic theory that explains priming as facilitation of lexical access. In this model‚ two well-established ACT-R mechanisms‚ base-level learning and spreading activation‚ account for long-term adaptation and short-term priming‚ respectively. Our model simulates incremental language production and in a series of modeling studies we show that it accounts for (a) the inverse frequency interaction; (b) the absence of a decay in long-term priming; and (c) the cumulativity of long-term adaptation. The model also explains the lexical boost effect and the fact that it only applies to short-term priming. We also present corpus data that verifies a prediction of the model‚ i.e.‚ that the lexical boost affects all lexical material‚ rather than just heads.

In an experiment involving teams of humans playing a cooperative game‚ we study the effect of local communication policies on the efficiency and the performance of teams and of individuals in different positions within a network. This design provides an experimental model of human communities‚ where information may spread from peer to peer by word of mouth. With this model‚ we explain the realistic tradeoff between liberal information dumping and targeted information sharing by human peers. Human subjects exchanged natural-language messages with relevance to a task‚ thereby sharing knowledge across a community. Communication took place along the edges of a small-world graph. Cooperation and individual efforts were incentivized. In one condition‚ participants were asked to request specific information and only supply information that they knew was needed. In another condition‚ they were asked to supply and forward as much information as possible. We found that a targeted communication policy was successfully implemented by the participants‚ increased task success‚ shortened the time it took to get answers to questions‚ increased efficiency (task success per communication bandwidth)‚ and may have done so selectively for nodes with fewer connections.

We describe results from analyzing a corpus of human-human tutorial dialogue‚ which are aimed at identifying‚ formalizing and automating natural tutoring in the domain of calculus‚ in particular symbolic differentiation. We analyzed a corpus of human-human tutoring dialogues‚ focusing on three areas important to system development: interleaved symbolic and natural language‚ domain modeling‚ and tutorial dialogue strategies. We provide empirical verification of previous results on interleaving natural and symbolic language‚ and show that the properties of interleaving are highly dependent on the input modality. We describe a task model for our domain‚ and provide corpus data to show that the model must cover basic algebra skills as well‚ which are involved in differentiation. We verify the applicability of an existing annotation scheme for tutoring algebra in our domain‚ and propose that it be extended to cover student initiative.

In the context of the FASiL project‚ we have studied natural language interactions in a unimodal (speech only) and multimodal (speech and graphics) interface to a personal information management database. We collected multilingual corpora to investigate these interactions in three languages: Portuguese‚ English and Swedish. The corpora are used to train language models‚ to update acoustic models‚ to study semantic concepts‚ multimodal interactions‚ and dialogue management strategies. The corpora are annotated in a uniform way‚ with timings‚ transcriptions‚ and semantics. In this paper‚ we report on the structure and design of the corpora which are now available via ELRA.

Countdowns and progress bars provide computer users with estimates of remaining wait times. These types of feedback are intended to manage their expectations and allow users to direct attention elsewhere. We suggest that they also moderate user’s impatience‚ which affects decision-making in the subsequent task. In an experiment with 421 participants‚ impatience in a timing decision task was effectively and systematically manip- ulated through a countdown‚ as it affected timing and performance of the user’s actions in the task. The effect persisted even after users gained task experience. More rapid countdowns reduced impatience. Post-hoc analysis also showed increased task satisfaction with rising countdown speed and suggested greater task satisfaction with a rapid countdown than with no waiting period at all.

Decisions over the timing of actions are critical in several safety‚ security and healthcare scenarios. These decisions‚ sim- ilar to discrete decisions‚ can be influenced by biases and individual traits. In this paper‚ a bias of impatience is studied in an experiment with 626 participants‚ with a focus on gender differences. Impatience was moderated with a manipula- tion of a variable-speed countdown. Men and women differed in how they expressed impatience. While men systematically and irrationally act earlier when become impatient following the slower countdowns‚ women react by irrationally requesting earlier information about the outcome of each trial‚ and impulsively pressing an inactive key.

We study personal and general biases in timing decisions‚ which concern practically common continuous decisions about when to act when facing external uncertainty and dynamic risk. In a series of experiments using a two-player game called FlipIt‚ we studied the influence of individual difference variables of Risk Propensity (RP) and Need for Cognition (NfC) and of manipulations that induce impatience. Participants make several timing decisions per round‚ which aim to counteract the hidden actions made by an opponent. The opponent’s actions become visible only after the participant’s choices have been made. Earlier actions are riskier‚ but a rational time point‚ maximizing utility‚ can be defined. Two experiments were run with a total of 450 participants from a general population that were compensated to incentivize maximizing payoff. RP and NfC were obtained with standardized instruments. Participants with RP around the population average benefitted from higher NfC levels (p<0.01). We also found a general impatience bias that advanced timing independent of RP. With increasing task experience‚ models of linear impatience become better predictors of timing bias than risk-dependent ones. In a further experiment with 422 participants‚ impatience was manipulated by showing slow or fast-changing countdowns during a constant delay period. Timing (vs. rational strategy) was retarded or advanced‚ with fast countdowns reducing impatience (p<0.01) and accelerating countdowns yielding closer-to-rational performance even than the control (no delay at all). We conclude that timing decisions are affected by interacting risk and deep-processing preferences as well as situational impatience biases.

This paper addresses security and safety choices that involve a decision on the timing of an action. Examples of such decisions include when to check log files for intruders and when to monitor financial accounts for fraud or errors. To better understand how performance in timing-related security situations is shaped by individuals’ cognitive predispositions‚ we effectively combine survey measures with economic experiments. Two behavioral experiments are presented in which the timing of online security actions is the critical decision-making factor. The feedback modality in the decision-environment is varied between visual feedback with history (Experiment 1)‚ and temporal feedback without history (Experiment 2). Using psychometric scales‚ we study the role of individual difference variables‚ specifically risk propensity and need for cognition. The analysis is based on the data from over 450 participants. We find that risk propensity is not a hindrance in timing tasks. Participants of average risk propensity generally benefit from a reflective disposition (high need for cognition)‚ particularly when visual feedback is given. Overall‚ participants benefit from need for cognition; however‚ in the more difficult‚ temporal-estimation task‚ this requires familiarity with the task.

Games of timing reflect dynamic decision-making under uncertainty‚ as it takes place in many real-world situations‚ including health care‚ safety and security. Rather than making discrete decisions‚ participants choose one or more points in time that determine the outcome. We study individual’s biases and characteristics in such games of timing. We examine risk propensity as a personal preference affecting timing decisions and document a bias‚ impatience. Experiment 1 analyzes people’s strategy in timing games in relation to a rational model. Contrasting two cognitive models suggests that individuals apply risk propensity to the probability distributions underlying short games and when unfamiliar with the situation‚ but that‚ over time‚ impatience takes over as a linear adjustment. In Experiment 2‚ impatient participants risk their incentive payment in order to play early‚ even if they receive no advantage from doing so.

Continuous games of timing represent an ecologically relevant case of decision-making under uncertainty with many applications to tactical security scenarios. We present two experiments collecting data using the two-player‚ 20-second FlipIt game‚ in which a player has to react to an estimated‚ externally controlled quantity in light of a continuous payoff distribution. The quantity is represented once as time interval‚ and once as distance in time and space. Risk-taking propensity is an individual parameter obtained via a standardized survey instrument. We find that risk propensity affects participants’ decisions in early and late rounds of the game and show that risk-seeking individuals fail to play well initially in the difficult‚ time-interval estimation task‚ but that they can be trained to make rational choices. Participants’ biases are gaussian-distributed. They do not reflect the distribution of payoffs or expected utility. We present an ACT-R model using established error distributions for temporal and visual perception and instance-based learning.

The cognitive modeling and artificial general intelligence research communities may reap greater scientific return on research investments – may achieve an improved understanding of architectures and models – if there is more emphasis on systematic sensitivity and necessity analyses during model development‚ evaluation‚ and comparison. We demonstrate this methodological prescription with two of the models submitted for the Dynamic Stocks and Flows (DSF) Model Comparison Challenge‚ exploring the complex interactions among architectural mechanisms‚ knowledge-level strategy variants‚ and task conditions. To cope with the computational demands of these analyses we use a predictive analytics approach similar to regression trees‚ combined with parallelization on high performance computing clusters‚ to enable large scale‚ simultaneous search and exploration.

A discriminative model is presented for crowd-sourcing the annotation of news stories to produce a structured dataset about incidents involving militarized disputes between nation-states. We used a question tree to gather partially redundant data from each crowd worker. A lattice of Bayesian Networks was then applied to error correct the individual worker annotations‚ the results of which were then aggregated via majority voting. The resulting hybrid model outperformed comparable‚ state-of-the-art aggregation models in both accuracy and computational scalability.

A hybrid architecture is presented capable of online learning from both labeled and unlabeled samples. It combines both generative and discriminative objectives to derive a new variant of the Deep Belief Network‚ i.e.‚ the Stacked Boltzmann Experts Network model. The model’s training algorithm is built on principles developed from hybrid discriminative Boltzmann machines and composes deep architectures in a greedy fashion. It makes use of its inherent “layer-wise ensemble" nature to perform useful classification work. We (1) compare this architecture against a hybrid Denoising Auto-encoder version of itself as well as several other models and (2) investigate training in the context of an incremental learning procedure. The best-performing hybrid model‚ the Stacked Boltzmann Experts Network‚ consistently outperforms all others.

In a congestion game‚ individuals exhaust a common resource out of selfish behavior. In this scenario‚ drivers create traffic jams by choosing the shortest route according to their individual knowledge. They can avoid them by communicating their belief states about the traffic situation in real-time through a peer-to-peer network‚ assuming unlimited bandwidth. We study two potential‚ cognitively inspired models of human behavior: 1) categorization (quantized memorization and communication)‚ which dampens communication and belief adoption‚ but leads to undesired oscillations and lower performance. 2) Instance-based blending with memory decay‚ which achieves good dynamics and near-optimal performance without the same bandwidth needs. We argue that this supports our hypothesis of co-adaptation of cognitive function and communicating communities.

Two information decay methods are examined that help multi-agent systems cope with dynamic environments. The agents in this simulation have human-like memory and a mechanism to moderate their communications: they forget internally stored information via temporal decay‚ and they forget distributed information by filtering it as it passes through a communication network. The agents play a foraging game‚ in which performance depends on communicating facts and requests and on storing facts in internal memory. Parameters of the game and agent models are tuned to human data. Agent groups with moderated communication in small-world networks achieve optimal performance for typical human memory decay values‚ while non-adaptive agents benefit from stronger memory decay. The decay and filtering strategies interact with the properties of the network graph in ways suggestive of an evolutionary co-optimization between the human cognitive system and an external social structure.

This study examines how novel words originate in and disperse through online communities. It asks whether larger numbers of people or closer social ties are better environments to foster the adoption of new words. The data stem from Reddit‚ a large sample of web-mediated‚ asynchronous conversations. Reddit communities are divided by size in this study: larger communities are based on discussing general topics and have weak social ties‚ whereas small communities are based on discussing specific topics and have strong social ties. The analysis shows that the majority of new words are created / first adopted in larger communities.

Much of the data used to measure conflict is extracted from news reports. This is typically accomplished using either expert coders to quantify the relevant information or machine coders to automatically extract data from documents. Although expert coding is costly‚ it produces quality data. Machine coding is fast and inexpensive‚ but the data are noisy. To diminish the severity of this tradeoff‚ we introduce a method for analyzing news documents that uses crowdsourcing‚ supplemented with computational approaches. The new method is tested on documents about Militarized Interstate Disputes‚ and its accuracy ranges between about 68 and 76 percent. This is shown to be a considerable improvement over automated coding‚ and to cost less and be much faster than expert coding.

Armed conflicts around the world produce displacement‚ in- jury‚ and death. This study examines how anonymous and pseudony- mous Internet commenters discuss such conflicts. Specifically‚ we ask how permissible it is to express positive or negative sentiments about these conflicts as a function of variables including region‚ conflict nature‚ and severity. Data from the Armed Conflicts Database is aggregated to identify a number of potential factors that may influence views on ac- ceptable sentiments. A large-scale sample of the Reddit comment forum was coded for positive and negative sentiments using sentiment analy- sis techniques. Permissibility is judged using the Reddit voting features. Regressions reveal that positive sentiments are found not permissible for higher numbers of fatalities‚ and that negative sentiments are found permissible for certain regions‚ more permissible for older conflicts‚ and less permissible for territorial conflicts. A number of alternative‚ non- correlated predictors are presented.

In a congestion game‚ individuals exhaust a common resource out of selfish behavior. In this scenario‚ drivers create traffic jams by choosing the shortest route according to their individual knowledge. They can avoid them by communicating their belief states about the traffic situation in real-time through a peer-to-peer network‚ assuming unlimited bandwidth. We study two potential‚ cognitively inspired models of human behavior: 1) categorization (quantized memorization and communication)‚ which dampens communication and belief adoption‚ but leads to undesired oscillations and lower performance. 2) Instance-based blending with memory decay‚ which achieves good dynamics and near-optimal performance without the same bandwidth needs. We argue that this supports our hypothesis of co-adaptation of cognitive function and communicating communities.

The development and refinement of natural-language communication systems among networked individuals is not well understood and difficult to study. This paper uses a task providing a controlled environment for the goal-oriented‚ collaborative exchange of short‚ natural-language messages between experimental participants (20 per group) in order to demonstrate lexical convergence. A technique for illustrating convergence based on graph layout by multidimensional scaling is described. While reliable convergence is shown‚ it is limited to the collaborative or communicative situation: participants did not adopt group terms in a separately administered post-test questionnaire.

We simulate the evolution of a domain vocabulary in small communities. Empirical data show that human communicators can evolve graphical languages quickly in a constrained task (Pictionary)‚ and that communities converge towards a common language. We propose that simulations of such cultural evolution incorporate properties of human memory (cue-based retrieval‚ learning‚ decay). A cognitive model is described that encodes abstract concepts with small sets of concrete‚ related concepts (directing)‚ and that also decodes such signs (matching). Learning captures conventionalized signs. Relatedness of concepts is characterized by a mixture of shared and individual knowledge‚ which we sample from a text corpus. Simulations show vocabulary convergence of agent communities of varied structure‚ but idiosyncrasy in vocabularies of each dyad of models. Convergence is weakened when agents do not alternate between encoding and decoding‚ predicting the necessity of bi-directional communication. Convergence is improved by explicit feedback about communicative success. We hypothesize that humans seek out subtle clues to gauge success in order to guide their vocabulary acquisition.

In an experiment involving teams of humans playing a cooperative game‚ we study the effect of local communication policies on the efficiency and the performance of teams and of individuals in different positions within a network. This design provides an experimental model of human communities‚ where information may spread from peer to peer by word of mouth. With this model‚ we explain the realistic tradeoff between liberal information dumping and targeted information sharing by human peers. Human subjects exchanged natural-language messages with relevance to a task‚ thereby sharing knowledge across a community. Communication took place along the edges of a small-world graph. Cooperation and individual efforts were incentivized. In one condition‚ participants were asked to request specific information and only supply information that they knew was needed. In another condition‚ they were asked to supply and forward as much information as possible. We found that a targeted communication policy was successfully implemented by the participants‚ increased task success‚ shortened the time it took to get answers to questions‚ increased efficiency (task success per communication bandwidth)‚ and may have done so selectively for nodes with fewer connections.

Computational models of memory presented in this issue reflect varied empirical data and levels of representation. From mathematical models to neural and cognitive architectures‚ all aim to converge on a unified theory of the mind.

Most literature on symmetry perception has focused on bilateral reflection symmetry‚ with some suggesting that it is the only type of symmetry humans can perceive (Wilson&Wilkinson‚ Vis. Res. 42(5)‚ 2002). Using image-stimuli generated from mathematically well-defined 17 wallpaper groups (Kohler et al.‚ J. Neurosci.‚ in press) and timed trials‚ we seek to demonstrate that humans can discriminate various symmetries found in 2D wallpaper patterns (Liu‚ et al Found.&Trends in Comp.Graph.&Vis.‚ 2010). Furthermore‚ we examine which features play an essential role in wallpaper pattern perception. The features include: reflection‚ maximum order of rotation‚ glide reflection‚ tile shape and subgroup distance‚ referring to the shortest path between the two wallpaper groups in the group hierarchy. We recruited 106 individuals (Amazon Mechanical Turks) to compare three images (target‚ probe‚ probe) and to choose the probe most similar to the target within five seconds. Each participant performed 272 (=17x16) trials. Participants compare among all possible wallpaper group pairs. Every group comparison except one is distinguishable (p< 0.05); all are likely distinguishable (p< 0.10). After modeling every defining feature of symmetry groups using linear mixed-effects regression (GLMM)‚ we used AIC selection to compare models. Our selected model includs subgroup distance‚ 3-fold rotation‚ 4-fold rotation‚ and the T1 and D1 axes. Even in a model with all other features‚ subgroup distance is a significant additional predictor (p< 0.0001). This distance measure has shown to be a better proxy describing human perceptual processes than individual features. This suggests that the mathematically motivated symmetry group hierarchy may be a valid model of pattern perception.

We report a way to build a series of GOMS-like cognitive user models representing a range of performance at different stages of learning. We use a spreadsheet task across multiple sessions as an example task; it takes about 20–30 min. to perform. The models were created in ACT-R using a compiler. The novice model has 29 rules and 1‚152 declarative memory task elements (chunks)—it learns to create procedural knowledge to perform the task. The expert model has 617 rules and 614 task chunks (that it does not use) and 538 command string chunks—it gets slightly faster through limited declarative learning of the command strings and some further production compilation; there are a range of intermediate models. These models were tested against aggregate and individual human learning data‚ confirming the models’ predictions. This work suggests that user models can be created that learn like users while doing the task.

In multimodal human computer interaction users can often select between specific input modalities. Modality choice is influenced by various factors including user attributes‚ system attributes‚ the task and the environment (e.g. Lemmelä et al.‚ 2008). Here‚ we describe on-going research into cognitive models of input modality selection.

In social tasks‚ communication and task execution are often co-dependent‚ yet they represent a tradeoff. We present a cognitive model of an experimental task consisting of a collaborative and competitive game played by groups of human participants organized in a smallworld graph. In an experiment involving teams of humans playing a cooperative game‚ the effect of local communication policies on the efficiency and the performance of networked participants was observed. The model follows the ACT-R theory and provides a formalization of the decision-making processes and tradeoffs involved. A simulation of the hypothetical case of unnatural memory decay shows decreased performance and supports a prediction of the thesis that memory limitations have co-evolved with social structure.

The Wolves and Sheep game is an educational‚ multi-agent computer simulation for students of programming classes. Players move about in a two- dimensional landscape‚ aiming to reach targets or catch other players. Students write programs that control individual players in the game. The programs then participate in a tournament. Students practice basic programming and algorithmic thinking‚ object-orientation and the role of interfaces‚ and are even exposed to basic Artificial Intelligence. Gamification helps motivate students and creates positive‚ but challenging learning experiences.

We present a novel lifelong neural architecture‚ the Temporal Neural Coding Network (TNCN)‚ and its learning algorithm‚ for uncovering multiple levels of distributed representations for language data streams. The TNCN model adapts its parameters iteratively as new samples are observed without resorting to the popular‚ but expensive back-propagation through time procedure needed to calculate gradients for recurrent neural networks‚ notably requiring no unrolling of internally defined recurrence relations. Furthermore‚ we integrate concepts from variational encoder-decoder models‚ such as those in [1]‚ to easily demonstrate how uncertainty can be characterized in the model’s next step predictions. We discuss how the proposed TNCN works specifically on language-based tasks formulated in the streaming setting‚ compared to various neural models‚ as well as how the TNCN may be easily adapted to other real-time tasks‚ such as video sequence modeling‚ yielding a natural framework for online multi-modal and online semi-supervised learning.

This is a multi-agent‚ bio-inspired simulation; it is a competitive game and a tournament written for 5-50 beginner programmers. It is designed as educational tool: Students of an intermediate programming class taught in Java write code that controls a single player in this simulation. Players are pitched against each other; typically 5 at a time. A tournament runs many combinations of players in many adjustable scenarios. The tournament is set up to run regularly on a server‚ taking .jar files provided by students. It uses standard visibility and sandboxing features of Java to provide a basic level of security.

This tool explores new ways to make bibliographies interactive‚ searchable and discoverable. Zot_bib_web generates HTML bibliographies based on one or more collections in a Zotero repository. These collections can be maintained by groups of people‚ using Zotero’s web interface or their desktop applications.

Much of the data used to measure conflict is extracted from news reports. This is typically accomplished using either expert coders to quantify the relevant information or machine coders to automatically extract data from documents. Although expert coding is costly‚ it produces quality data. Machine coding is fast and inexpensive‚ but the data are noisy. To diminish the severity of this tradeoff‚ we introduce a method for analyzing news documents that uses crowdsourcing‚ supplemented with computational approaches. The new method is tested on documents about Militarized Interstate Disputes‚ and its accuracy ranges between about 68 and 76 percent. This is shown to be a considerable improvement over automated coding‚ and to cost less and be much faster than expert coding.

The applicability of entropy rate constancy to dialogue is examined on two spoken dialogue corpora. The principle is found to hold; however‚ new entropy change patterns within the topic episodes of dialogue are described‚ which are different from written text. Speaker’s dynamic roles as topic initiators and topic responders are associated with decreasing and increasing entropy‚ respectively‚ which results in local convergence between these speakers in each topic episode. This implies that the sentence entropy in dialogue is conditioned on different contexts determined by the speaker’s roles. Explanations from the perspectives of grounding theory and interactive alignment are discussed‚ resulting in a novel‚ unified information-theoretic approach of dialogue.

A discriminative model is presented for crowd-sourcing the annotation of news stories to produce a structured dataset about incidents involving militarized disputes between nation-states. We used a question tree to gather partially redundant data from each crowd worker. A lattice of Bayesian Networks was then applied to error correct the individual worker annotations‚ the results of which were then aggregated via majority voting. The resulting hybrid model outperformed comparable‚ state-of-the-art aggregation models in both accuracy and computational scalability.

Task-solving in dialogue depends on the convergence of the situation models held by the dialogue partners. The Interactive Alignment Model (Pickering and Garrod‚ 2004) suggests that this convergence is the result of an interactive alignment process‚ which is based on mechanistic repetition at a number of linguistic levels. In this paper‚ we develop two predictions arising from the theory‚ along with two methods to quantify the known structural priming effects in the full inventory of syntactic choices found in text and speech corpora. (a) Under a rational perspective‚ we expect increased repetition in task-oriented dialogue compared to spontaneous conversation. We find within- and between-speaker priming in a corpus of spontaneous conversations‚ but stronger priming in task-oriented dialogue. (b) The Interactive Alignment Model predicts linguistic adaptation to be correlated with task success. We show this effect in a corpus of task-oriented dialogue‚ where we find a positive correlation of long-term adaptation and a quantifiable task success measure. We argue that the repetition tendency relevant for the high-level alignment of situation models is based on slow adaptation rather than short-term priming. We demonstrate that lexical and syntactic repetition are reliable and computationally exploitable predictors of task success.

This paper addresses security and safety choices that involve a decision on the timing of an action. Examples of such decisions include when to check log files for intruders and when to monitor financial accounts for fraud or errors. To better understand how performance in timing-related security situations is shaped by individuals’ cognitive predispositions‚ we effectively combine survey measures with economic experiments. Two behavioral experiments are presented in which the timing of online security actions is the critical decision-making factor. The feedback modality in the decision-environment is varied between visual feedback with history (Experiment 1)‚ and temporal feedback without history (Experiment 2). Using psychometric scales‚ we study the role of individual difference variables‚ specifically risk propensity and need for cognition. The analysis is based on the data from over 450 participants. We find that risk propensity is not a hindrance in timing tasks. Participants of average risk propensity generally benefit from a reflective disposition (high need for cognition)‚ particularly when visual feedback is given. Overall‚ participants benefit from need for cognition; however‚ in the more difficult‚ temporal-estimation task‚ this requires familiarity with the task.

Previous studies of alignment have focused on two-party conversations. We study multi-party conversation in online health communities‚ which have shown benefits for their members from forum conversations. So far‚ our understanding of the relationship between alignment in such multi-party conversations and its possible connection to member benefits has been limited. This paper quantifies linguistic alignment in the oldest and the largest cancer online forum. Alignment at lexical and syntactic levels‚ as well as decay of alignment was observed in forum threads‚ although the decay was slower than commonly found in psycholinguistic studies. The different pattern of adaptation to the initial post on a thread suggests that specific roles in the online forum (e.g.‚ seeking support from the community) can potentially be revealed through alignment theory and its extensions.

Games of timing reflect dynamic decision-making under uncertainty‚ as it takes place in many real-world situations‚ including health care‚ safety and security. Rather than making discrete decisions‚ participants choose one or more points in time that determine the outcome. We study individual’s biases and characteristics in such games of timing. We examine risk propensity as a personal preference affecting timing decisions and document a bias‚ impatience. Experiment 1 analyzes people’s strategy in timing games in relation to a rational model. Contrasting two cognitive models suggests that individuals apply risk propensity to the probability distributions underlying short games and when unfamiliar with the situation‚ but that‚ over time‚ impatience takes over as a linear adjustment. In Experiment 2‚ impatient participants risk their incentive payment in order to play early‚ even if they receive no advantage from doing so.

The Wolves and Sheep game is an educational‚ multi-agent computer simulation for students of programming classes. Players move about in a two- dimensional landscape‚ aiming to reach targets or catch other players. Students write programs that control individual players in the game. The programs then participate in a tournament. Students practice basic programming and algorithmic thinking‚ object-orientation and the role of interfaces‚ and are even exposed to basic Artificial Intelligence. Gamification helps motivate students and creates positive‚ but challenging learning experiences.

Continuous games of timing represent an ecologically relevant case of decision-making under uncertainty with many applications to tactical security scenarios. We present two experiments collecting data using the two-player‚ 20-second FlipIt game‚ in which a player has to react to an estimated‚ externally controlled quantity in light of a continuous payoff distribution. The quantity is represented once as time interval‚ and once as distance in time and space. Risk-taking propensity is an individual parameter obtained via a standardized survey instrument. We find that risk propensity affects participants’ decisions in early and late rounds of the game and show that risk-seeking individuals fail to play well initially in the difficult‚ time-interval estimation task‚ but that they can be trained to make rational choices. Participants’ biases are gaussian-distributed. They do not reflect the distribution of payoffs or expected utility. We present an ACT-R model using established error distributions for temporal and visual perception and instance-based learning.

The development and refinement of natural-language communication systems among networked individuals is not well understood and difficult to study. This paper uses a task providing a controlled environment for the goal-oriented‚ collaborative exchange of short‚ natural-language messages between experimental participants (20 per group) in order to demonstrate lexical convergence. A technique for illustrating convergence based on graph layout by multidimensional scaling is described. While reliable convergence is shown‚ it is limited to the collaborative or communicative situation: participants did not adopt group terms in a separately administered post-test questionnaire.

In multimodal human computer interaction users can often select between specific input modalities. Modality choice is influenced by various factors including user attributes‚ system attributes‚ the task and the environment (e.g. Lemmelä et al.‚ 2008). Here‚ we describe on-going research into cognitive models of input modality selection.

We simulate the evolution of a domain vocabulary in small communities. Empirical data show that human communicators can evolve graphical languages quickly in a constrained task (Pictionary)‚ and that communities converge towards a common language. We propose that simulations of such cultural evolution incorporate properties of human memory (cue-based retrieval‚ learning‚ decay). A cognitive model is described that encodes abstract concepts with small sets of concrete‚ related concepts (directing)‚ and that also decodes such signs (matching). Learning captures conventionalized signs. Relatedness of concepts is characterized by a mixture of shared and individual knowledge‚ which we sample from a text corpus. Simulations show vocabulary convergence of agent communities of varied structure‚ but idiosyncrasy in vocabularies of each dyad of models. Convergence is weakened when agents do not alternate between encoding and decoding‚ predicting the necessity of bi-directional communication. Convergence is improved by explicit feedback about communicative success. We hypothesize that humans seek out subtle clues to gauge success in order to guide their vocabulary acquisition.

The psycholinguistic literature has identified two syntactic adaptation effects in language production: rapidly decaying short-term priming and long-lasting adaptation. To explain both effects‚ we present an ACT-R model of syntactic priming based on a wide-coverage‚ lexicalized syntactic theory that explains priming as facilitation of lexical access. In this model‚ two well-established ACT-R mechanisms‚ base-level learning and spreading activation‚ account for long-term adaptation and short-term priming‚ respectively. Our model simulates incremental language production and in a series of modeling studies we show that it accounts for (a) the inverse frequency interaction; (b) the absence of a decay in long-term priming; and (c) the cumulativity of long-term adaptation. The model also explains the lexical boost effect and the fact that it only applies to short-term priming. We also present corpus data that verifies a prediction of the model‚ i.e.‚ that the lexical boost affects all lexical material‚ rather than just heads.

In an experiment involving teams of humans playing a cooperative game‚ we study the effect of local communication policies on the efficiency and the performance of teams and of individuals in different positions within a network. This design provides an experimental model of human communities‚ where information may spread from peer to peer by word of mouth. With this model‚ we explain the realistic tradeoff between liberal information dumping and targeted information sharing by human peers. Human subjects exchanged natural-language messages with relevance to a task‚ thereby sharing knowledge across a community. Communication took place along the edges of a small-world graph. Cooperation and individual efforts were incentivized. In one condition‚ participants were asked to request specific information and only supply information that they knew was needed. In another condition‚ they were asked to supply and forward as much information as possible. We found that a targeted communication policy was successfully implemented by the participants‚ increased task success‚ shortened the time it took to get answers to questions‚ increased efficiency (task success per communication bandwidth)‚ and may have done so selectively for nodes with fewer connections.

The cognitive modeling and artificial general intelligence research communities may reap greater scientific return on research investments – may achieve an improved understanding of architectures and models – if there is more emphasis on systematic sensitivity and necessity analyses during model development‚ evaluation‚ and comparison. We demonstrate this methodological prescription with two of the models submitted for the Dynamic Stocks and Flows (DSF) Model Comparison Challenge‚ exploring the complex interactions among architectural mechanisms‚ knowledge-level strategy variants‚ and task conditions. To cope with the computational demands of these analyses we use a predictive analytics approach similar to regression trees‚ combined with parallelization on high performance computing clusters‚ to enable large scale‚ simultaneous search and exploration.

We describe results from analyzing a corpus of human-human tutorial dialogue‚ which are aimed at identifying‚ formalizing and automating natural tutoring in the domain of calculus‚ in particular symbolic differentiation. We analyzed a corpus of human-human tutoring dialogues‚ focusing on three areas important to system development: interleaved symbolic and natural language‚ domain modeling‚ and tutorial dialogue strategies. We provide empirical verification of previous results on interleaving natural and symbolic language‚ and show that the properties of interleaving are highly dependent on the input modality. We describe a task model for our domain‚ and provide corpus data to show that the model must cover basic algebra skills as well‚ which are involved in differentiation. We verify the applicability of an existing annotation scheme for tutoring algebra in our domain‚ and propose that it be extended to cover student initiative.

We report a way to build a series of GOMS-like cognitive user models representing a range of performance at different stages of learning. We use a spreadsheet task across multiple sessions as an example task; it takes about 20–30 min. to perform. The models were created in ACT-R using a compiler. The novice model has 29 rules and 1‚152 declarative memory task elements (chunks)—it learns to create procedural knowledge to perform the task. The expert model has 617 rules and 614 task chunks (that it does not use) and 538 command string chunks—it gets slightly faster through limited declarative learning of the command strings and some further production compilation; there are a range of intermediate models. These models were tested against aggregate and individual human learning data‚ confirming the models’ predictions. This work suggests that user models can be created that learn like users while doing the task.

Abstract Current computational cognitive architectures lack a comprehensive representation of human physiology. Connecting a more realistic model of physiology to a cognitive architecture allows one to develop more realistic computational process models that include an account for the effect of physiology on cognition. It is also useful to include some theory of affect and emotion to act as a functional layer between some representations of physiology and cognition. We chose to extend the ACT-R (adaptive control of thought – rational) cognitive architecture with physiology and affect to make the ACT-RΦ hybrid architecture. We connected the HumMod physiological simulation system with the ACT-R architecture; ACT-RΦ has a module that communicates between ACT-R and HumMod. A new affect system was also developed using an existing theoretical framework to serve as a functional layer between some of the physiological and cognitive representations. With this new architecture one can model a wide range of human behavior (e.g.‚ the effects of homeostasis on cognition) in a more tractable fashion.

The  ability  to  process  and  use  spatial  knowledge  is  a  basic  cognitive  ability.  Two  human  navigation  strategy  types  (map-­based  and  route-­based)  relying  on  two  different  knowledge  representations  have  been  frequently  observed.  These  studies  suggest  that  the  first  strategy  uses  a  sequential  representation  and  the  second  uses  a  hierarchical  cluster-­based  representation.  These  studies  also  suggest  that  humans  also  routinely  use  hybrid  strategies‚  and  that  the  ratio  between  cognitive  load  and  relative  utility  mediated  by  situational  factors  influences‚  and  when  modeled‚  could  successfully  predict  strategy  choice.  We  created  an  ACT-­R  model  to  test  these  hypotheses  by  simulating  navigation  strategies‚  strategy  choices‚  and  strategy  switches.  This  model  deepens  the  empirical  findings  by  defining  more  clearly  the  memory  mechanisms  involved  in  generating  the  basic  representation  types‚  and  by  positing  a  theory  of  interaction  between  these  types  based  on  ACT-­We  believe  that  such  a  work  provides  a  concrete  example  on  principles  of  these  biological  theories  can  be  implemented  and  used  in  cognitive  architectures.

Continuous games of timing represent an ecologically relevant case of decision-making under uncertainty with many applications to tactical security scenarios. We present two experiments collecting data using the two-player‚ 20-second FlipIt game‚ in which a player has to react to an estimated‚ externally controlled quantity in light of a continuous payoff distribution. The quantity is represented once as time interval‚ and once as distance in time and space. Risk-taking propensity is an individual parameter obtained via a standardized survey instrument. We find that risk propensity affects participants’ decisions in early and late rounds of the game and show that risk-seeking individuals fail to play well initially in the difficult‚ time-interval estimation task‚ but that they can be trained to make rational choices. Participants’ biases are gaussian-distributed. They do not reflect the distribution of payoffs or expected utility. We present an ACT-R model using established error distributions for temporal and visual perception and instance-based learning.

The psycholinguistic literature has identified two syntactic adaptation effects in language production: rapidly decaying short-term priming and long-lasting adaptation. To explain both effects‚ we present an ACT-R model of syntactic priming based on a wide-coverage‚ lexicalized syntactic theory that explains priming as facilitation of lexical access. In this model‚ two well-established ACT-R mechanisms‚ base-level learning and spreading activation‚ account for long-term adaptation and short-term priming‚ respectively. Our model simulates incremental language production and in a series of modeling studies we show that it accounts for (a) the inverse frequency interaction; (b) the absence of a decay in long-term priming; and (c) the cumulativity of long-term adaptation. The model also explains the lexical boost effect and the fact that it only applies to short-term priming. We also present corpus data that verifies a prediction of the model‚ i.e.‚ that the lexical boost affects all lexical material‚ rather than just heads.

Computational models of memory presented in this issue reflect varied empirical data and levels of representation. From mathematical models to neural and cognitive architectures‚ all aim to converge on a unified theory of the mind.

Most literature on symmetry perception has focused on bilateral reflection symmetry‚ with some suggesting that it is the only type of symmetry humans can perceive (Wilson&Wilkinson‚ Vis. Res. 42(5)‚ 2002). Using image-stimuli generated from mathematically well-defined 17 wallpaper groups (Kohler et al.‚ J. Neurosci.‚ in press) and timed trials‚ we seek to demonstrate that humans can discriminate various symmetries found in 2D wallpaper patterns (Liu‚ et al Found.&Trends in Comp.Graph.&Vis.‚ 2010). Furthermore‚ we examine which features play an essential role in wallpaper pattern perception. The features include: reflection‚ maximum order of rotation‚ glide reflection‚ tile shape and subgroup distance‚ referring to the shortest path between the two wallpaper groups in the group hierarchy. We recruited 106 individuals (Amazon Mechanical Turks) to compare three images (target‚ probe‚ probe) and to choose the probe most similar to the target within five seconds. Each participant performed 272 (=17x16) trials. Participants compare among all possible wallpaper group pairs. Every group comparison except one is distinguishable (p< 0.05); all are likely distinguishable (p< 0.10). After modeling every defining feature of symmetry groups using linear mixed-effects regression (GLMM)‚ we used AIC selection to compare models. Our selected model includs subgroup distance‚ 3-fold rotation‚ 4-fold rotation‚ and the T1 and D1 axes. Even in a model with all other features‚ subgroup distance is a significant additional predictor (p< 0.0001). This distance measure has shown to be a better proxy describing human perceptual processes than individual features. This suggests that the mathematically motivated symmetry group hierarchy may be a valid model of pattern perception.

We report a way to build a series of GOMS-like cognitive user models representing a range of performance at different stages of learning. We use a spreadsheet task across multiple sessions as an example task; it takes about 20–30 min. to perform. The models were created in ACT-R using a compiler. The novice model has 29 rules and 1‚152 declarative memory task elements (chunks)—it learns to create procedural knowledge to perform the task. The expert model has 617 rules and 614 task chunks (that it does not use) and 538 command string chunks—it gets slightly faster through limited declarative learning of the command strings and some further production compilation; there are a range of intermediate models. These models were tested against aggregate and individual human learning data‚ confirming the models’ predictions. This work suggests that user models can be created that learn like users while doing the task.

We present a hybrid architecture capable of online learning from both labeled and unlabeled samples. We combine both generative and discriminative objectives to derive a new variant of the Deep Belief Network‚ the Stacked Boltzmann Experts Network model. Our model’s training algorithm builds on principles developed from hybrid discriminative Boltzmann machines and composing deep architectures in a greedy fashion. It makes use of its inherent "layer-wise ensemble" nature to perform useful classification work. We (1) compare this architecture against a hybrid Denoising Auto-encoder version of itself as well as several shallow models and (2) investigate the effects of training the model in two different ways in the context of an incremental learning procedure. We find that in all settings explored in this paper‚ our best-performing hybrid model‚ the Stacked Boltzmann Experts Network‚ consistently outperforms all others.

This paper presents a cognitive model that sim- ulates how the reliance on automation in a sim- ple tracking task‚ which represents vehicle operation‚ changes as the success probabilities of automatic and manual mode vary. The model was developed by us- ing ACT-R‚ and we also introduce three methods of reinforcement learning: the synchronization of utili- ties in the same mode‚ the summation of rewards over time‚ and the prediction from the past. The model performs this task through productions that manage perception and motor control. The utility values of these productions are updated based on rewards in every perception-action cycle. A run of this model simulated the overall trends of the behavioral data‚ suggesting some validity of the assumptions made in our model.

Our behavior is influenced all the time by social norms‚ what is expected (i.e.‚ customs)‚ what is required (i.e.‚ laws)‚ and what is moral (i.e.‚ what is ethical). Other names for norms are standard operating procedures‚ regulations‚ standing orders‚ rules of engagement‚ and customs. All of the data that has been collected to develop and test theories of cognition has been collected where norms actively influenced the behavior observed. However‚ the effect of norms‚ how other agents’ expectations and behavior influence behavior‚ has not been very often addressed by cognitive models‚ architectures‚ or agent-­‐based research. As cognitive models become more social and social models become more cognitive‚ the role of norms in modeling behavior will become increasingly important. Cognitive modelers who read this book by Conte and her colleagues can learn about norms and how to include them in models. Researchers who study norms can better understand the role of simulation and norms. This book presents a theory of how norms‚ arising through interactions between individuals‚ are implemented in and by those individuals. A series of simulations using a new architecture‚ EMIL‚ help explain how bottom-­‐up and top-­‐down processes can produce emergent behavior‚ and provide lessons for work on norms (e.g.‚ how norms can arise through small interactions)‚ and for other architectures (e.g.‚ the many ways norms influence cognition). Through a thoughtful series of models‚ capabilities are added building up to a system demonstrating necessary and sufficient capabilities for the emergence and function of norms. While the models are not complete and may at times appear far from complete—for some even too incomplete to trust—more complete models will only arise through work like this and the insights it provides. Currently‚ most models just act. This work suggests that our models need to have more social cognition‚ metacognition‚ implicit reasoning‚ and reflection to filter actions making them more accurate‚ knowledge-­‐rich‚ reflective‚ and slower. Is the processing of norms done with fixed capabilities that everyone has or is it knowledge? If it is a fixed capability it belongs in the architecture‚ but untangling this capability that might have components of both types will surely be interesting‚ productive‚ and also enlightening about how humans are both cognitive

Most tasks get faster with practice. This holds across task size and task type. Learning curves plot time to complete a task as a function of practice. Such curves generally follow what is called a power law‚ thus‚ they are often said to conform to ‘the power law of practice.’ Cognitive psychology has shown that the power law of practice is ubiquitous‚ and cognitive modeling has explained both the general speedup and some of the variability in performance‚ which previously was taken to be completely noise. Research is ongoing to find out why it is ubiquitous and where it does not apply.

Decisions over the timing of actions are critical in several safety‚ security and healthcare scenarios. These decisions‚ sim- ilar to discrete decisions‚ can be influenced by biases and individual traits. In this paper‚ a bias of impatience is studied in an experiment with 626 participants‚ with a focus on gender differences. Impatience was moderated with a manipula- tion of a variable-speed countdown. Men and women differed in how they expressed impatience. While men systematically and irrationally act earlier when become impatient following the slower countdowns‚ women react by irrationally requesting earlier information about the outcome of each trial‚ and impulsively pressing an inactive key.

Countdowns and progress bars provide computer users with estimates of remaining wait times. These types of feedback are intended to manage their expectations and allow users to direct attention elsewhere. We suggest that they also moderate user’s impatience‚ which affects decision-making in the subsequent task. In an experiment with 421 participants‚ impatience in a timing decision task was effectively and systematically manip- ulated through a countdown‚ as it affected timing and performance of the user’s actions in the task. The effect persisted even after users gained task experience. More rapid countdowns reduced impatience. Post-hoc analysis also showed increased task satisfaction with rising countdown speed and suggested greater task satisfaction with a rapid countdown than with no waiting period at all.

We study personal and general biases in timing decisions‚ which concern practically common continuous decisions about when to act when facing external uncertainty and dynamic risk. In a series of experiments using a two-player game called FlipIt‚ we studied the influence of individual difference variables of Risk Propensity (RP) and Need for Cognition (NfC) and of manipulations that induce impatience. Participants make several timing decisions per round‚ which aim to counteract the hidden actions made by an opponent. The opponent’s actions become visible only after the participant’s choices have been made. Earlier actions are riskier‚ but a rational time point‚ maximizing utility‚ can be defined. Two experiments were run with a total of 450 participants from a general population that were compensated to incentivize maximizing payoff. RP and NfC were obtained with standardized instruments. Participants with RP around the population average benefitted from higher NfC levels (p<0.01). We also found a general impatience bias that advanced timing independent of RP. With increasing task experience‚ models of linear impatience become better predictors of timing bias than risk-dependent ones. In a further experiment with 422 participants‚ impatience was manipulated by showing slow or fast-changing countdowns during a constant delay period. Timing (vs. rational strategy) was retarded or advanced‚ with fast countdowns reducing impatience (p<0.01) and accelerating countdowns yielding closer-to-rational performance even than the control (no delay at all). We conclude that timing decisions are affected by interacting risk and deep-processing preferences as well as situational impatience biases.

This paper addresses security and safety choices that involve a decision on the timing of an action. Examples of such decisions include when to check log files for intruders and when to monitor financial accounts for fraud or errors. To better understand how performance in timing-related security situations is shaped by individuals’ cognitive predispositions‚ we effectively combine survey measures with economic experiments. Two behavioral experiments are presented in which the timing of online security actions is the critical decision-making factor. The feedback modality in the decision-environment is varied between visual feedback with history (Experiment 1)‚ and temporal feedback without history (Experiment 2). Using psychometric scales‚ we study the role of individual difference variables‚ specifically risk propensity and need for cognition. The analysis is based on the data from over 450 participants. We find that risk propensity is not a hindrance in timing tasks. Participants of average risk propensity generally benefit from a reflective disposition (high need for cognition)‚ particularly when visual feedback is given. Overall‚ participants benefit from need for cognition; however‚ in the more difficult‚ temporal-estimation task‚ this requires familiarity with the task.

Games of timing reflect dynamic decision-making under uncertainty‚ as it takes place in many real-world situations‚ including health care‚ safety and security. Rather than making discrete decisions‚ participants choose one or more points in time that determine the outcome. We study individual’s biases and characteristics in such games of timing. We examine risk propensity as a personal preference affecting timing decisions and document a bias‚ impatience. Experiment 1 analyzes people’s strategy in timing games in relation to a rational model. Contrasting two cognitive models suggests that individuals apply risk propensity to the probability distributions underlying short games and when unfamiliar with the situation‚ but that‚ over time‚ impatience takes over as a linear adjustment. In Experiment 2‚ impatient participants risk their incentive payment in order to play early‚ even if they receive no advantage from doing so.

Continuous games of timing represent an ecologically relevant case of decision-making under uncertainty with many applications to tactical security scenarios. We present two experiments collecting data using the two-player‚ 20-second FlipIt game‚ in which a player has to react to an estimated‚ externally controlled quantity in light of a continuous payoff distribution. The quantity is represented once as time interval‚ and once as distance in time and space. Risk-taking propensity is an individual parameter obtained via a standardized survey instrument. We find that risk propensity affects participants’ decisions in early and late rounds of the game and show that risk-seeking individuals fail to play well initially in the difficult‚ time-interval estimation task‚ but that they can be trained to make rational choices. Participants’ biases are gaussian-distributed. They do not reflect the distribution of payoffs or expected utility. We present an ACT-R model using established error distributions for temporal and visual perception and instance-based learning.

We report a way to build a series of GOMS-like cognitive user models representing a range of performance at different stages of learning. We use a spreadsheet task across multiple sessions as an example task; it takes about 20–30 min. to perform. The models were created in ACT-R using a compiler. The novice model has 29 rules and 1‚152 declarative memory task elements (chunks)—it learns to create procedural knowledge to perform the task. The expert model has 617 rules and 614 task chunks (that it does not use) and 538 command string chunks—it gets slightly faster through limited declarative learning of the command strings and some further production compilation; there are a range of intermediate models. These models were tested against aggregate and individual human learning data‚ confirming the models’ predictions. This work suggests that user models can be created that learn like users while doing the task.

This paper investigates performance change arising through learning‚ forgetting‚ and relearning. Participants learned a spreadsheet task with either keystroke-driven (keyboard‚ n = 30) or mousebased menu-driven (mouse‚ n = 30) commands. Their performance confirmed the power law of practice. The keyboard users learned to complete the task faster than the mouse users on the last learning session (Day 4). At a 6-day retention interval‚ the mouse users were observed to forget more—they took more time to complete the task than the keyboard users. Interestingly‚ the participants in the two modality groups showed no significant differences in their forgetting under the retention of 12 and 18 days. With additional practice‚ the mouse group users with the 6-day retention relearned more—they significantly reduced the time to complete the task in comparison to the keyboard group. The experiment with these two modality groups helps understand why people may choose to use a mouse-driven graphical user interface (GUI) rather than a keystroke-driven interface—people choosing to use a mouse-based menu-driven interface may not need to use a knowledge-in-the-head strategy but knowledge-in-the-world‚ and may be doing so because this strategy provides better relearning‚ rather than because it is faster or easier initially or because it is better for learning or forgetting. This helps provide a richer explanation of why menu-driven interfaces (knowledge-in-the-world) are more ubiquitous‚ and suggests when they can be replaced‚ e.g.‚ where use is infrequent but often enough that forgetting does not occur. Our results provide preliminary suggestions for choosing optimal training strategies and supporting these strategies in terms of the three stages of learning and forgetting.

Student projects at all levels can have impact. I’ll present an approach for using class projects in Junior level and above human-computer interaction (HCI) classes to have a social and economic impact. This can make your teaching improve by being more motivating to students‚ to be more interesting to you‚ and to have more impact. I’ll provide an example project description used at Penn State’s College of IST since 2000‚ and example projects that have suggested useful interface changes to the web sites of a variety of government‚ non-profit‚ university‚ and small and medium-sized businesses. These reports fulfill a pedagogical goal by having students demonstrate mastery of the material‚ particularly evidence- and theory-based suggested changes to improve the usability of web sites. The reports provide (in the best cases) strong‚ correct suggestions for improving the usability of these web sites. The use of these reports also helps reduce plagiarism because the sites and thus work are unique.

We have created a tutoring architecture‚ D2P (Declarative to Procedural)‚ based on ACT-R’s theory of knowledge and learning for declarative and procedural memory. D2P also draws on general theories of learning and a recent review by Kim‚ Ritter‚ and Koubek (in press). D2P provides multimedia instruction pages followed by questions‚ video‚ and/or simulations to test and proceduralise the declarative knowledge. It is implemented in XML‚ XML-based tools‚ Java‚ and COTS tools. We have created two D2P tutors. We have evaluated one‚ and it has a 1.48 effect size on learning how much to lead. We have found that modern tools help create a tutoring system; creating a tutoring architecture requires more than just the system (such as manuals and example systems); and the next steps are to test the theory of learning and use a cognitive architecture-based knowledge model to make the system adaptive.

This   paper   describes   a   case   study   focused   on   identifying   and   modeling   opportunities   to   support   emergency   medical   services   (EMS)   with   next-­generation   interactive   technologies.   The   method   used   is   a   novel   integration   of   different   approaches   including   scenario-­based   design‚   task   analysis‚   and   utility-­based   interaction   modeling.   The   goal   of   the   project   is   to   develop  a  structured  model  of  EMS  activity  and  to  identify  where   and   how   this   activity   can   be   supported   with   current   and   emerging   information   technologies.   Of   particular   importance   is   identifying  tasks  with  the  highest  potential  for  meaningful  use  of   these  technologies.  The  SUMMIT  method  and  tool  set  is  being   used   to   model   meaningful   use   as   a   function   of   the   overall   utility   (benefits‚   costs‚   and   risks)   potentially   derived   from   technology   support.   The   paper   provides   a   description   and   rationale   for   the   approach   and   gives   lessons   learned   from   the   early   phases   of   the   project.   The   SUMMIT   approach   may   be   generally   useful   for   organizations   that   need   to   understand   how   new   interactive   technologies   might   provide   meaningful   support   for   healthcare   operations.   These   models   may   also   be   an   effective   resource   for   promoting  continuous  learning  and  reflection  on  EMS  skills.

Adding a physiological representation to cognitive architectures offers an attractive approach to modeling the effects of stress on cognition. We introduce ACT-RΦ‚ an extended version of the ACT-R cognitive architecture that includes an integrative physiological model. The extension allows the representation of non-linear physiological effects on cognition‚ and cognitive effects on physiology. This additional physiological substrate was used to represent potential effects of a startle response during a mental arithmetic (subtraction) task. We compare predic- tions from models loaded into the new hybrid architecture to models previously developed with- in the ACT-R architecture; the ACT-R models use parameter value sets to represent the effects of task-based stress on cognition during a subtraction task. General behavior differed between models in that the new models run in ACT-RΦ exhibited dynamic declarative memory noise over the course of the task‚ i.e. memory noise changed across time. The models run in ACT-RΦ also exhibited a lower percentage of subtraction problems answered correctly during the task as well as a higher number of responses; this more closely matched human performance than the previous ACT-R models. Using these models within ACT-RΦ allows a more tractable integration of current physiological and cognitive perspectives on stress. ACT-RΦ also permits further exploration of the interaction between cognition and physiology‚ and the emergent effects on behavior caused by the interaction among physiological subsystems. This extension is useful for anyone exploring how the human mind can occur in the physical universe.

We compare how the same cognitive model completes a task within two alternative modifications to a cognitive architecture to represent sleep deprivation. One modification (ACT-R/F) has a module that uses a biomathematical model of the effects of sleep deprivation on performance to drive parameter changes in the architecture that impact behavior and performance. The second‚ new‚ modification (ACT-R/Phi represents the effects of sleep deprivation on physiological systems and has these systems modulate cognition. The model completes the psychomotor vigilance task (PVT) within both ACT-R/Phi and ACT-R/F. We found that the two implementations produced similar response times (means) in simulated days one and two. However‚ the distribution of the response times across the two days of sleep deprivation varied between models. The ACT-Phi model shows a wider distribution in both days 1 and 2 due to an increased and modulating production utility noise that affects its ability to select the correct rules consistently. Though they represent sleep deprivation in different ways‚ and on different levels‚ both of these implementations lead us towards a more unified understanding of how sleep deprivation affects our bodies‚ how we think and behave over time‚ and how to represent these effects.

We report a new mobile application‚ HHeal‚ that inte- grates individuals’ flu risk information and flu-preventive behaviors. The app provides a personal flu risk bar that rises when the user is near someone with flu-like symp- toms and drops when the user finishes one of the sug- gested flu-preventive behaviors. Preliminary results show that participants favored the personal flu risk bar design. Participants had positive experiences when their personal risk bars dropped. They were motivated to initiate flu-preventive behaviors when their personal risk bars rose. Our next step includes studying reward strat- egies‚ users’ motivations to share personal health information‚ and building a more accurate model of flu risk.

How do physiological and affective processes interact with cognitive processes to change the way we think? How can we better understand the processes that underlie decision-making and choice behavior? This dissertation presents a novel hybrid cognitive architecture‚ ACT-R/Φ‚ which extends the ACT-R cognitive architecture with an integrative model of physiology and a model of affect and emotion. Extending a cognitive architecture with representations of affect and physiology allows the straightforward development of more illustrative computational process models of human behavior that explain experimental results from physiology‚ neuroscience‚ and psychology. Computational models were developed that provide an account of how physiological change due to psychological stress or homeostasis can modulate cognitive processes. An experiment was also run to explore how subliminal visual emotional stimuli affect physiology and decision-making behavior during the Iowa Gambling Task. Results indicate that effects of subliminal affective stimuli were dependent on participant sex and personality differences. A computational process model was developed that performs the same task and behaves similarly to the participants. Physiological and affective states continually interact with cognitive processes‚ biasing memory and‚ consequently‚ decisions. Evolved adaptations that support physiological and affective change of behavior (e.g.‚ natural reactions to thirst or hunger) affect the way we learn and make choices. The ACT-R/Φ hybrid architecture‚ and the theoretical architectural models that define it‚ can be used to develop models of human behavior that include the necessary accounts of physiology and affect that describe what the body needs and how changes in behavior affects these needs. This improved understanding of the architecture that constrains our behavior gives us a better opportunity to comprehend why we make the decisions we do and how we can use this knowledge to make better decisions.

Linguistic alignment in text-based communication means that people tend to adjust their language use to one another both in terms of word choice and sentence structure. Previous studies about linguistic alignment suggested that these two forms of adaptation are correlated with each other‚ and that they build up to alignment at a higher representational level‚ such as pragmatic alignment for support functions. Two types of social support have been identified as important for online health communities (OHCs): emotional and informational support between support seekers and support providers. Do the two lower-level alignment measures (lexical and syntactic) relate to these two types of social support in the same way or‚ are they different? Our hypothesis was that they are similar‚ due to their correlation relationship. However‚ we found that‚ based on an analysis of a 10-year online forum for cancer survivors‚ the lower-level alignment measures have distinct relationships to the two higher-level support functions. In this paper‚ we describe this finding and its implications regarding potential refinement of the Inter- active Alignment Model.

This study examines how novel words originate in and disperse through online communities. It asks whether larger numbers of people or closer social ties are better environments to foster the adoption of new words. The data stem from Reddit‚ a large sample of web-mediated‚ asynchronous conversations. Reddit communities are divided by size in this study: larger communities are based on discussing general topics and have weak social ties‚ whereas small communities are based on discussing specific topics and have strong social ties. The analysis shows that the majority of new words are created / first adopted in larger communities.

Armed conflicts around the world produce displacement‚ in- jury‚ and death. This study examines how anonymous and pseudony- mous Internet commenters discuss such conflicts. Specifically‚ we ask how permissible it is to express positive or negative sentiments about these conflicts as a function of variables including region‚ conflict nature‚ and severity. Data from the Armed Conflicts Database is aggregated to identify a number of potential factors that may influence views on ac- ceptable sentiments. A large-scale sample of the Reddit comment forum was coded for positive and negative sentiments using sentiment analy- sis techniques. Permissibility is judged using the Reddit voting features. Regressions reveal that positive sentiments are found not permissible for higher numbers of fatalities‚ and that negative sentiments are found permissible for certain regions‚ more permissible for older conflicts‚ and less permissible for territorial conflicts. A number of alternative‚ non- correlated predictors are presented.

A discriminative model is presented for crowd-sourcing the annotation of news stories to produce a structured dataset about incidents involving militarized disputes between nation-states. We used a question tree to gather partially redundant data from each crowd worker. A lattice of Bayesian Networks was then applied to error correct the individual worker annotations‚ the results of which were then aggregated via majority voting. The resulting hybrid model outperformed comparable‚ state-of-the-art aggregation models in both accuracy and computational scalability.

Two information decay methods are examined that help multi-agent systems cope with dynamic environments. The agents in this simulation have human-like memory and a mechanism to moderate their communications: they forget internally stored information via temporal decay‚ and they forget distributed information by filtering it as it passes through a communication network. The agents play a foraging game‚ in which performance depends on communicating facts and requests and on storing facts in internal memory. Parameters of the game and agent models are tuned to human data. Agent groups with moderated communication in small-world networks achieve optimal performance for typical human memory decay values‚ while non-adaptive agents benefit from stronger memory decay. The decay and filtering strategies interact with the properties of the network graph in ways suggestive of an evolutionary co-optimization between the human cognitive system and an external social structure.

The development and refinement of natural-language communication systems among networked individuals is not well understood and difficult to study. This paper uses a task providing a controlled environment for the goal-oriented‚ collaborative exchange of short‚ natural-language messages between experimental participants (20 per group) in order to demonstrate lexical convergence. A technique for illustrating convergence based on graph layout by multidimensional scaling is described. While reliable convergence is shown‚ it is limited to the collaborative or communicative situation: participants did not adopt group terms in a separately administered post-test questionnaire.

We simulate the evolution of a domain vocabulary in small communities. Empirical data show that human communicators can evolve graphical languages quickly in a constrained task (Pictionary)‚ and that communities converge towards a common language. We propose that simulations of such cultural evolution incorporate properties of human memory (cue-based retrieval‚ learning‚ decay). A cognitive model is described that encodes abstract concepts with small sets of concrete‚ related concepts (directing)‚ and that also decodes such signs (matching). Learning captures conventionalized signs. Relatedness of concepts is characterized by a mixture of shared and individual knowledge‚ which we sample from a text corpus. Simulations show vocabulary convergence of agent communities of varied structure‚ but idiosyncrasy in vocabularies of each dyad of models. Convergence is weakened when agents do not alternate between encoding and decoding‚ predicting the necessity of bi-directional communication. Convergence is improved by explicit feedback about communicative success. We hypothesize that humans seek out subtle clues to gauge success in order to guide their vocabulary acquisition.

In an experiment involving teams of humans playing a cooperative game‚ we study the effect of local communication policies on the efficiency and the performance of teams and of individuals in different positions within a network. This design provides an experimental model of human communities‚ where information may spread from peer to peer by word of mouth. With this model‚ we explain the realistic tradeoff between liberal information dumping and targeted information sharing by human peers. Human subjects exchanged natural-language messages with relevance to a task‚ thereby sharing knowledge across a community. Communication took place along the edges of a small-world graph. Cooperation and individual efforts were incentivized. In one condition‚ participants were asked to request specific information and only supply information that they knew was needed. In another condition‚ they were asked to supply and forward as much information as possible. We found that a targeted communication policy was successfully implemented by the participants‚ increased task success‚ shortened the time it took to get answers to questions‚ increased efficiency (task success per communication bandwidth)‚ and may have done so selectively for nodes with fewer connections.

This paper investigates performance change arising through learning‚ forgetting‚ and relearning. Participants learned a spreadsheet task with either keystroke-driven (keyboard‚ n = 30) or mousebased menu-driven (mouse‚ n = 30) commands. Their performance confirmed the power law of practice. The keyboard users learned to complete the task faster than the mouse users on the last learning session (Day 4). At a 6-day retention interval‚ the mouse users were observed to forget more—they took more time to complete the task than the keyboard users. Interestingly‚ the participants in the two modality groups showed no significant differences in their forgetting under the retention of 12 and 18 days. With additional practice‚ the mouse group users with the 6-day retention relearned more—they significantly reduced the time to complete the task in comparison to the keyboard group. The experiment with these two modality groups helps understand why people may choose to use a mouse-driven graphical user interface (GUI) rather than a keystroke-driven interface—people choosing to use a mouse-based menu-driven interface may not need to use a knowledge-in-the-head strategy but knowledge-in-the-world‚ and may be doing so because this strategy provides better relearning‚ rather than because it is faster or easier initially or because it is better for learning or forgetting. This helps provide a richer explanation of why menu-driven interfaces (knowledge-in-the-world) are more ubiquitous‚ and suggests when they can be replaced‚ e.g.‚ where use is infrequent but often enough that forgetting does not occur. Our results provide preliminary suggestions for choosing optimal training strategies and supporting these strategies in terms of the three stages of learning and forgetting.

This paper examines how cognitive and environmental factors influence the formation of dyadic ties. We use agent models instantiated in ACT-R that interact in a social simulation‚ to illustrate the effect of memory constraints on networks. We also show that environmental factors are important including popu- lation size‚ running time‚ and map configuration. To examine these relationships‚ we ran simulations of networks using a factorial design. Our analyses suggest three interesting conclusions: first‚ the tie formation of these networks approximates a logistic growth model; second‚ that agent memory quality (i.e.‚ perfect or human- like) strongly alters the network’s density and structure; third‚ that the three environmental factors all influence both network density and some aspects of net- work structure. These findings suggest that meaningful variance of social network analysis measures occur in a narrow band of memory strength (the cognitive band); the threshold for defining tie criteria is important; and future simulations examining generative social networks should control and carefully report these environmental and cognitive factors.

Invited talk‚ plenary