Mankind’s research on marketing has a history of hundreds of years, and it has been fruitful in continuous summary and research. Now the theory of marketing has gradually penetrated into the minds of every company and even individual. A successful marketing strategy is the inevitable result of scientific planning and effective implementation. However, the current marketing strategy has gradually failed to meet the needs of corporates. In order to find the best solution for corporate marketing strategy, we built a (...) class='Hi'>simulation environment based on mobile computing and machine learning and compared the differences by simulating several companies of the same size in this city. The results of the study found that intelligent marketing based on machine learning is more suitable for enterprises than general marketing strategies. The efficiency of enterprises has increased by about 20%, and the income of enterprises has increased by more than 30% compared with traditional marketing strategies. This shows that the intelligent marketing strategy based on mobile computing and machine learning to build a simulated environment plays an extremely important role in the peculiarities. (shrink)

Mental simulation — such as imagining tilting a glass to figure out the angle at which water would spill — can be a way of coming to know the answer to an internally or externally posed query. Is this form of learning a species of inference or a form of observation? We argue that it is neither: learning through simulation is a genuinely distinct form of learning. On our account, simulation can provide knowledge of the answer to (...) a query even when the basis for that answer is opaque to the learner. Moreover, through repeated simulation, the learner can reduce this opacity, supporting self-training and the acquisition of more accurate models of the world. Simulation is thus an essential part of the story of how creatures like us become effective learners and knowers. (shrink)

In this paper, we pursue three general aims: (I) We will define a notion of fundamental opacity and ask whether it can be found in High Energy Physics (HEP), given the involvement of machine learning (ML) and computer simulations (CS) therein. (II) We identify two kinds of non-fundamental, contingent opacity associated with CS and ML in HEP respectively, and ask whether, and if so how, they may be overcome. (III) We address the question of whether any kind of opacity, contingent (...) or fundamental, is unique to ML or CS, or whether they stand in continuity to kinds of opacity associated with other scientific research. (shrink)

This research explores the application of an innovative learning approach by using trading simulation tutorials to reinforce the conventional learning styles in a corporate finance subject at postgraduate level. The majority of surveyed students perceive that their learning experience has been significantly enhanced through simulated trading tutorials. The post-trading survey shows students also indicate feeling more confident to self-monitor their learning. Furthermore, themajority of students feel able to recognise ethical issues in relation to trading in securities. This research highlights (...) some potentially negative effects if trading simulations were to be used in isolation without providing students with a solid background in ethics. This issue is even more relevant in the post Global Financial Crisis environment. If carefully designed and implemented, trading simulations have a potential to mitigate some potentially negative effects of the use of technology and be an effective and inclusive teaching tool. (shrink)

David Lewis (1969) introduced sender-receiver games as a way of investigating how meaningful language might evolve from initially random signals. In this report I investigate the conditions under which Lewis signaling games evolve to perfect signaling systems under various learning dynamics. While the 2-state/2- term Lewis signaling game with basic urn learning always approaches a signaling system, I will show that with more than two states suboptimal pooling equilibria can evolve. Inhomogeneous state distributions increase the likelihood of pooling equilibria, but (...) learning strategies with negative reinforcement or certain sorts of mutation can decrease the likelihood of, and even eliminate, pooling equilibria. Both Moran and APR learning strategies (Bereby-Meyer and Erev 1998) are shown to promote successful convergence to signaling systems. A model is presented that illustrates how a language that codes state-act pairs in an order-based grammar might evolve in the context of a Lewis signaling game. The terms, grammar, and the corresponding partitions of the state space co-evolve to generate a language whose structure appears to reflect canonical natural kinds. The evolution of these apparent natural kinds, however, is entirely in service of the rewards that accompany successful distinctions between the sender and receiver. Any metaphysics grounded on the structure of a natural language that evolved in this way would track arbitrary, but pragmatically useful distinctions. (shrink)

This article uses simulation as an empirical method for identifying process models of strategy effects in a category-learning task. A general set of learning assumptions defined a symbolic learning framework in which alternative simulation models were defined and tested. The goal was to identify process models that could account for previously reported data on the interaction between how a learner encounters category variance across a series of training samples and whether the task instructions suggested an active, hypothesis-testing approach, (...) or a more passive learning mode. Descriptive characterizations of active and passive learning were mapped into complementary settings of parameters operating with the general learning framework. Alternative models, defined by different configurations of these parameters, were evaluated on their goodness of fit to the observed data. The signature differences between models that best fit the passive learning data and models that best fit the active learning data concerned a delayed versus immediate learning parameter and a degree-of-match parameter that determined which patterns were retrieved to make category decisions. A functional account of these parameters is given by considering the learning task as a search process and the role of these parameters in localizing the impact of learning mechanisms in certain areas of the search space. Issues related to simulation as an empirical method for identifying candidate process models are discussed. (shrink)

Using four examples of models and computer simulations from the history of psychology, I discuss some of the methodological aspects involved in their construction and use, and I illustrate how the existence of a model can demonstrate the viability of a hypothesis that had previously been deemed impossible on a priori grounds. This shows a new way in which scientists can learn from models that extends the analysis of Morgan (1999), who has identified the construction and manipulation of models as (...) those phases in which learning from models takes place. (shrink)

This article makes a dual contribution to scholarship in science and technology studies on simulation-building. It both documents a specific simulation-building project, and demonstrates a concrete contribution of STS insights to interdisciplinary work. The article analyses the struggles that arise in the course of determining what counts as theory, as model and even as a simulation. Such debates are especially decisive when working across disciplinary boundaries, and their resolution is an important part of the work involved in (...) building simulations. In particular, we show how ontological arguments about the value of simulations tend to determine the direction of simulation-building. This dynamic makes it difficult to maintain an interest in the heterogeneity of simulations and a view of simulations as unfolding scientific objects. As an outcome of our analysis of the process and reflections about interdisciplinary work around simulations, we propose a chart, as a tool to facilitate discussions about simulations. This chart can be a means to create common ground among actors in a simulation-building project, and a support for discussions that address other features of simulations besides their ontological status. Rather than foregrounding the chart’s classificatory potential, we stress its role in discussing and reflecting on simulation-building as interdisciplinary endeavor. This chart is a concrete instance of the kinds of contributions that STS can make to better, more reflexive practice of simulation-building. (shrink)

Margaret Morrison, (2015) Reconstructing Reality: Models, Mathematics, and Simulations. Oxford University Press, New York. -/- Scientific models, mathematical equations, and computer simulations are indispensable to scientific practice. Through the use of models, scientists are able to effectively learn about how the world works, and to discover new information. However, there is a challenge in understanding how scientists can generate knowledge from their use, stemming from the fact that models and computer simulations are necessarily incomplete representations, and partial descriptions, of their (...) target systems. In order to construct a model, one must make idealizations, approximations, and abstractions. Given these constraints in constructing models, there is a question of whether they can provide new insight into how real systems actually work. So, how is it that highly abstract models inform us about the nature of the world, and more specifically, how do they provide explanatory knowledge? In Reconstructing Reality: Models, Mathematics, and Simulations, philosopher Margaret Morrison undertakes this task of ... (shrink)

In four studies, we investigated the role of remembering, reflecting on, and mutating personal past moral transgressions to learn from those moral mistakes and to form intentions for moral improvement. Participants reported having ruminated on their past wrongdoings, particularly their more severe transgressions, and they reported having frequently thought about morally better ways in which they could have acted instead (i.e., morally upward counterfactuals; Studies 1–3). The more that participants reported having mentally simulated morally better ways in which they could (...) have acted, the stronger their intentions were to improve in the future (Studies 2 and 3). Implementing an experimental manipulation, we then found that making accessible a morally upward counterfactual after committing a moral transgression strengthened reported intentions for moral improvement—relative to resimulating the remembered event and considering morally worse ways in which they could have acted instead (Study 4). We discuss the implications of these results for competing theoretical views on the relationship between memory and morality and for functional theories of counterfactual thinking. (shrink)

Recent laboratory experiments have shown that both infant and adult learners can acquire word‐referent mappings using cross‐situational statistics. The vast majority of the work on this topic has used unfamiliar objects presented on neutral backgrounds as the visual contexts for word learning. However, these laboratory contexts are much different than the real‐world contexts in which learning occurs. Thus, the feasibility of generalizing cross‐situational learning beyond the laboratory is in question. Adapting the Human Simulation Paradigm, we conducted a series of (...) experiments examining cross‐situational learning from children's egocentric videos captured during naturalistic play. Focusing on individually ambiguous naming moments that naturally occur during toy play, we asked how statistical learning unfolds in real time through accumulating cross‐situational statistics in naturalistic contexts. We found that even when learning situations were individually ambiguous, learners’ performance gradually improved over time. This improvement was driven in part by learners’ use of partial knowledge acquired from previous learning situations, even when they had not yet discovered correct word‐object mappings. These results suggest that word learning is a continuous process by means of real‐time information integration. (shrink)

In a small course on technology and society, students participated in an extended role-play simulation for two weeks. Each student played a different adult character in a fictional community, which faces technological decisions in three scenarios set in the near future. The three scenarios involved stem cell research, nanotechnology, and privacy. Each student had an active role in two scenarios and served as an observer for the third. At the beginning, students were apprehensive, excited, and uncertain. During the first (...) and second sessions, they experienced some frustration, but by the end, they were generally satisfied with the outcomes. Over the two weeks, students changed their definitions of success: Initially they tried to convince others to agree with their positions; at the end, they felt that a consensus represented success. In their final reflective essays, students reported that the role-play experience helped them learn to understand the perspectives of others. (shrink)

Students in an undergraduate legal and ethical issues course continually told the authors that they did not have time to study for the course because they were busy studying for their clinical courses. Faculty became concerned that students were failing to realize the value of legal and ethical concepts as applicable to clinical practice. This led the authors to implement a transformational learning experience in which students applied legal and ethical course content in a high-fidelity human simulation (HFHS) scenario. (...) A preliminary evaluation compared the new HFHS experience with in-person and online student groups using the same case. Based on both student and faculty perceptions, the HFHS was identified as the best of the three approaches for providing a transformational learning experience regarding legal and ethical content. (shrink)

Using a multiple-trial stock market decision paradigm, the possibility that counterfactual thinking can be dysfunctional for learning and performance by distorting the processing of outcome information was examined. Correlational (Study 1) and experimental (Study 2) evidence suggested that counterfactuals are associated with a decrease in experiential learning. When counterfactuals were made salient, participants displayed significantly poorer performance compared to their counterparts for whom counterfactuals were relatively less salient. A counterfactual salience ? need for cognition (NFC) interaction qualified these findings. High (...) NFC participants outperformed their counterparts when counterfactuals were not salient. Evidence for a memory-based mechanism was also supported. (shrink)

The words in children's language learning environments are strongly predictive of cognitive development and school achievement. But how do we measure language environments and do so at the scale of the many words that children hear day in, day out? The quantity and quality of words in a child's input are typically measured in terms of total amount of talk and the lexical diversity in that talk. There are disagreements in the literature whether amount or diversity is the more critical (...) measure of the input. Here we analyze the properties of a large corpus of speech to children and simulate learning environments that differ in amount of talk per unit time, lexical diversity, and the contexts of talk. The central conclusion is that what researchers need to theoretically understand, measure, and change is not the total amount of words, or the diversity of words, but the function that relates total words to the diversity of words, and how that function changes across different contexts of talk. (shrink)

Cognitive Science, Volume 46, Issue 4, April 2022.

Scientificity is essentially methodology. The use of information technology as methodological instruments in science has been increasing for decades, this raises the question: Does this transform science? This question is the subject of the Special Issue in Minds and Machines “The epistemological significance of methods in computer simulation and machine learning”. We show that there is a technological change in this area that has three methodological and epistemic consequences: methodological opacity, reproducibility issues, and altered forms of justification.

Behavioral evidence for the COVIS dual‐process model of category learning has been widely reported in over a hundred publications (Ashby & Valentin, ). It is generally accepted that the validity of such evidence depends on the accurate identification of individual participants' categorization strategies, a task that usually falls to Decision Bound analysis (Maddox & Ashby, ). Here, we examine the accuracy of this analysis in a series of model‐recovery simulations. In Simulation 1, over a third of simulated participants using (...) an Explicit (conjunctive) strategy were misidentified as using a Procedural strategy. In Simulation 2, nearly all simulated participants using a Procedural strategy were misidentified as using an Explicit strategy. In Simulation 3, we re‐examined a recently reported COVIS‐supporting dissociation (Smith et al., ) and found that these misidentification errors permit an alternative, single‐process, explanation of the results. Implications for due process in the future evaluation of dual‐process theories, including recommendations for future practice, are discussed. (shrink)

This paper proposes a feature extraction algorithm based on the maximum entropy phrase reordering model in statistical machine translation in language learning machines. The algorithm can extract more accurate phrase reordering information, especially the feature information of reversed phrases, which solves the problem of imbalance of feature data during maximum entropy training in the original algorithm, and improves the accuracy of phrase reordering in translation. In the experiment, they were combined with linguistic features such as parts of speech, words, and (...) syntactic features extracted by using the syntax analyzer, and the maximum entropy classifier was used to predict translation errors, and the experimental verification was performed on the Chinese-English translation data set and compared. The experimental results show that different word posterior probabilities have a significant impact on the classification error rate, and the combination of linguistic features based on the word posterior probability can significantly reduce the classification error rate and improve the translation error prediction performance. (shrink)

With the emergence of the COVID-19 pandemic, virtual simulation games have provided an effective teaching method for online entrepreneurship education. By exploring the mechanisms that influence student engagement and learning outcomes from different perspectives, such as game design, team and individual perspectives, numerous scholars have demonstrated that such a teaching method can effectively improve students’ engagement and learning performance. However, the existing studies are relatively scattered, and there is a scarcity of studies in which the effects of said factors (...) are considered. Based on the learning process 3P model proposed by Biggs, students’ perceived experience of game design, teamwork and self-efficacy were taken as variables in the early learning stage in the present study, and the influence mechanism of virtual simulation game learning experience on students’ engagement and entrepreneurial skill development was explored, so as to close the gap in existing research. In the present study, 177 college students from Chinese universities were surveyed and the data were surveyed using AMOS 23.0 software. Although the empirical results show that students’ “goal and feedback” and “alternative” experience of game design did not have a significant positive impact on students’ engagement, there was a direct and significant effect the development of entrepreneurial skills. Students’ experience of teamwork and general self-efficacy could not only directly and significantly affect the development of entrepreneurial skills, but also indirectly affect the development of entrepreneurial skills through learning engagement. The research results are practically significant for teachers in the selection and development of virtual simulation games, can be effectively applied in teaching process management, and can improve students’ engagement and learning performance. (shrink)

Some empirical evidence in the artificial language acquisition literature has been taken to suggest that statistical learning mechanisms are insufficient for extracting structural information from an artificial language. According to the more than one mechanism (MOM) hypothesis, at least two mechanisms are required in order to acquire language from speech: (a) a statistical mechanism for speech segmentation; and (b) an additional rule-following mechanism in order to induce grammatical regularities. In this article, we present a set of neural network studies demonstrating (...) that a single statistical mechanism can mimic the apparent discovery of structural regularities, beyond the segmentation of speech. We argue that our results undermine one argument for the MOM hypothesis. (shrink)

A significant part of everyday learning occurs incidentally — a process typically described as implicit learning. A central issue in this and germane domains such as language acquisition is the extent to which performance depends on the acquisition and deployment of abstract rules. In an attempt to address this question, we show that the apparent use of such rules in a simple categorisation task of artificial grammar strings, as reported by Shanks, Johnstone, and Staggs (1997), can be simulated by means (...) of a simple recurrent network, and may thus turn out not be incompatible with the acquisition of statistical regularities rooted in the processing of exemplars of the presented material. (shrink)

Prior research has used an Interactive Landslide Simulator tool to investigate human decision making against landslide risks. It has been found that repeated feedback in the ILS tool about damages due to landslides causes an improvement in human decisions against landslide risks. However, little is known on how theories of learning from feedback would account for human decisions in the ILS tool. The primary goal of this paper is to account for human decisions in the ILS tool via computational models (...) based upon reinforcement learning and to explore the model mechanisms involved when people make decisions in the ILS tool. Four different reinforcement-learning models were developed and evaluated in their ability to capture human decisions in an experiment involving two conditions in the ILS tool. The parameters of an Expectancy-Valence model, two Prospect-Valence-Learning models, a combination EV-PU model, and a random model were calibrated to human decisions in the ILS tool across the two conditions. Later, different models with their calibrated parameters were generalized to data collected in an experiment involving a new condition in ILS. When generalized to this new condition, the PVL-2 model’s parameters of both damage-feedback conditions outperformed all other RL models. We highlight the implications of our results for decision making against landslide risks. (shrink)

Linguistic category learning has been shown to be highly sensitive to linear order, and depending on the task, differentially sensitive to the information provided by preceding category markers (premarkers, e.g., gendered articles) or succeeding category markers (postmarkers, e.g., gendered suffixes). Given that numerous systems for marking grammatical categories exist in natural languages, it follows that a better understanding of these findings can shed light on the factors underlying this diversity. In two discriminative learning simulations and an artificial language learning experiment, (...) we identify two factors that modulate linear order effects in linguistic category learning: category structure and the level of abstraction in a category hierarchy. Regarding category structure, we find that postmarking brings an advantage for learning category diagnostic stimulus dimensions, an effect not present when categories are non‐confusable. Regarding levels of abstraction, we find that premarking of super‐ordinate categories (e.g., noun class) facilitates learning of subordinate categories (e.g., nouns). We present detailed simulations using a plausible candidate mechanism for the observed effects, along with a comprehensive analysis of linear order effects within an expectation‐based account of learning. Our findings indicate that linguistic category learning is differentially guided by pre‐ and postmarking, and that the influence of each is modulated by the specific characteristics of a given category system. (shrink)

Simulation as an epistemic tool between theory and practice: A Comparison of the Relationship between Theory and Simulation in Science and in Folk Psychology In this paper I explore the concept of simulation that is employed by proponents of the so-called simulation theory within the debate about the nature and scientific status of folk psychology. According to simulation theory, folk psychology is not a sort of theory that postulates theoretical entities (mental states and processes) and (...) general laws, but a practice whereby we put ourselves into others’ shoes and simulate their situation from our own perspective. On the basis of this sort of simulation, we supposedly know how we would act or think or feel, and then expect the same of others. A closer look at the concept of simulation reveals some problems with this view, but also helps to clarify the insight motivating simulation theory. Specifically, I defend the thesis that the analogy to simulations in science shows us how theoretical elements in folk psychology can be complemented by (i.e. not replaced by) the central idea of simulation theory – namely that our own cognitive habits and dispositions provide us with a resource that is distinct from propositional knowledge in folk psychology. I also discuss the idea that our use of simulations during cognitive development enables us to imitate the people around us and thereby to become more similar to them, which in turn makes simulation an increasingly effective epistemic strategy. Insofar as theoretical elements – such as the distinctions, relations, and entities referred to in folk psychological discourse – play a role in imitative learning, they are causally embedded in our cognitive development, so we have good reason to regard them as being among the real causes of our behavior. (shrink)

This paper deals with EMIL-S, a software tool box which was designed during the EMIL project for the simulation of processes during which norms emerged in an agent society. This tool box implements the cognitive architecture of normative agents which was designed during the EMIL project which is also discussed in other papers in this issue. This implementation is described in necessary detail, and two examples of its application to several different scenarios are given, namely a scenario in which (...) persons involved in micro finance are simulated and learn how to sanction free riders and how to learn from these sanctions, and a scenario in which simulated persons move through a simulated airport where they more often than not have to wait in queues and learn how to behave properly in queues. (shrink)

Random simulation of complex dynamical systems is generally used in order to obtain information about their asymptotic behaviour (i.e., when time or size of the system tends towards infinity). A fortunate and welcome circumstance in most of the systems studied by physicists, biologists, and economists is the existence of an invariant measure in the state space allowing determination of the frequency with which observation of asymptotic states is possible. Regions found between contour lines of the surface density of this (...) invariant measure are called confiners. An example of such confiners is given for a formal neural network capable of learning. Finally, an application of this methodology is proposed in studying dependency of the network's invariant measure with regard to: 1) the mode of neurone updating (parallel or sequential), and 2) boundary conditions of the network (searching for phase transitions). (shrink)

The systemic complexity of sustainable development imposes a major cognitive challenge to students’ learning. Faculty can explore new approaches in the classroom to teach the topic successfully, including the use of technology. We conducted an experiment to compare the effectiveness of a simulation vis-à-vis a case-based method to teach sustainable development. We found that both pedagogical methods are effective for teaching this concept, although our results support the idea that simulations are slightly more effective than case studies, particularly to (...) teach its multidimensional and inter-temporal nature. Therefore, our findings suggest the use of both simulations and case studies as pedagogical tools to convey the main ideas associated with sustainable development. (shrink)

Clinical simulation is used in nursing education and in other health professional programs to prepare students for future clinical practice. Simulation can be used to teach students communication skills and how to deliver bad news to patients and families. However, skilled communication in clinical practice requires students to move beyond simply learning superficial communication techniques and behaviors. This article presents an unexplored concept in the simulation literature: the exercise of moral imagination by the health professional student. Drawing (...) from the works of Hume, Aristotle and Gadamer, a conceptualization of moral imagination is first provided. Next, this article argues that students must exercise moral imagination on two levels: towards the direct communication exchange before them; and to the representative nature of simulation encounters. Last, the limits of moral imagination in simulation-based education are discussed. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Simulation, Subjective Knowledge, and the Cognitive Value of Literary NarrativeScott R. Stroud (bio)IntroductionLiterary narrative holds the power to move individuals to thought, reflection, action, and belief. According to a longstanding view of literature, it is this impact on the reader that leads to literary narrative being valued so highly in our culture and in others. What exactly is the value of literature? Humanists such as Peter Lamarque and (...) Stein Olsen1 argue for this value in terms of perennial themes that literature allows one to "grapple with," as opposed to cognitivist accounts that discuss the value of literary narrative in terms of conveyed truths or knowledge. One particularly important position that explains the cognitive value of literature can be labeled the "Subjective Knowledge Theory" (SKT). Such an account of literature places its value in the subjective perspectives or experiences the narrative opens up to the reader. Thus, one can gain knowledge of "what it is like" to be caught in the throes of jealousy, endangered by an opposing army, or captivated by the lure of magical prowess. Lamarque and Olsen, however, argue that SKT is fundamentally flawed in its analysis of knowledge derivable from the literary narrative and in terms of its account of literary practice.I wish to defend a version of SKT in this article. In order to do so, I make and defend the following claims. First, Lamarque and Olsen's critique of SKT relies on a conflation of two distinct types of knowledge—the knowledge provided by the simulated subjective experience and its appropriation/ generalization to forms useful for the reader's real life activities. I will argue that the former of these outcomes is due strictly to the aesthetic features of the literary narrative, whereas the second involves a non-necessary psychological reaction to the text (that is, judgments of the veracity or usefulness of the text for one's own purposes). By conflating both of these into the cognitive knowledge derivable from the text, Lamarque and Olsen's critique can [End Page 19] gain its purchase against SKT. If these two processes and the resultant sorts of knowledge are separated, then the SKT account does seem to show how literature holds value in itself and in relation to the reader's moral activities/ judgments. Second, the process of simulation is the primary means by which one can gain any type of subjective knowledge from a literary narrative. This type of knowledge will be gained by virtue of the literary narrative's aesthetic qualities, which result in a certain type of activity in the reader. Third, I will argue that the second sort of knowledge—that derived from the simulated subjective experience—results from the process of reader identification with the values, beliefs, and/or behaviors of the simulated agent. This sense of identification involves selective appropriation and generalization and will be shown not to be subject to the critiques of simple identification offered by Noël Carroll.2 It is at this stage that individuals learn what they believe they ought to value, how they ought to act, and what they should believe from their experience of simulation in reading the literary narrative. Notice that this knowledge is separate from the first sort of knowledge, which is knowledge of what it is like to experience x through y's eyes.As I elucidate and defend these claims, I hope it will become clear that this is an account that does justice to the value resident in literature and in the activities readers do after they partake in the aesthetic object of the literary narrative. Thus, I will conclude by showing how this account does not fall prey to the conflation that both critics and supporters of SKT commit in muddling the different types of knowledge available from and in relation to a literary narrative.Before I proceed, it is important that some basic concepts are clearly set forth. A narrative can be defined as any representation of two or more events over time. The number of events and the relationship among them can be as simple or complex as one likes, but the crucial aspect is that the representation supplies the... (shrink)

Simulation software used for modeling has become as ubiquitous as computers themselves. Despite growing reliance on simulation in educational and workplace settings, users encounter frustration in using simulation software programs. The authors conducted a study with 26 engineering students and interviewed them about their experience learning the simulation software Arena for optimization modeling. These students experienced frustration with the process of learning to “think” like the simulation software. Students explained their difficulty with learning the software (...) in a way that implied human dialogue with a machine and negotiation with Arena in an attempt to overcome this frustration. Rather than being gender specific, frustrations were experienced by most male and female students equally, specifically those who held a perception of difference between human and machine “thinking.” The authors contend that communication difficulty with the embedded assumptions of the software is the source of frustration rather than deficiencies of users. (shrink)

This paper provides an epistemological and methodological analysis of the recent practice of using neural language models to simulate brain language processing. It is argued that, on the one hand, this practice can be understood as an instance of the traditional simulative method in artificial intelligence, following a mechanistic understanding of the mind; on the other hand, that it modifies the simulative method significantly. Firstly, neural language models are introduced; a study case showing how neural language models are being applied (...) in cognitive neuroscience for simulative purposes is then presented; after recalling the main epistemological features of the simulative method in artificial intelligence, it is finally highlighted how the epistemic opacity of neural language models is tackled by using the brain itself to simulate the neural language model and to test hypotheses about it, in what is called here a co-simulation. (shrink)

In simulation-based learning environments, 'unexpected' phenomena often work as counterexamples which promote a learner to reconsider the problem. It is important that counterexamples contain sufficient information which leads a learner to correct understanding. This paper proposes a method for creating such counterexamples. Error-Based Simulation (EBS) is used for this purpose, which simulates the erroneous motion in mechanics based on a learner's erroneous equation. Our framework is as follows: (1) to identify the cause of errors by comparing a learner's (...) answer with the problem-solver's correct one, (2) to visualize the cause of errors by the unnatural motions in EBS. To perform (1), misconceptions are classified based on problem-solving model, and related to their appearance on a learner's answers (error-identification rules). To perform (2), objects' motions in EBS are classified and related to their suggesting misconceptions (error-visualization rules). A prototype system is implemented and evaluated through a preliminary test, to confirm the usefulness of the framework. (shrink)

Linguistic category learning has been shown to be highly sensitive to linear order, and depending on the task, differentially sensitive to the information provided by preceding category markers (premarkers, e.g., gendered articles) or succeeding category markers (postmarkers, e.g., gendered suffixes). Given that numerous systems for marking grammatical categories exist in natural languages, it follows that a better understanding of these findings can shed light on the factors underlying this diversity. In two discriminative learning simulations and an artificial language learning experiment, (...) we identify two factors that modulate linear order effects in linguistic category learning: category structure and the level of abstraction in a category hierarchy. Regarding category structure, we find that postmarking brings an advantage for learning category diagnostic stimulus dimensions, an effect not present when categories are non‐confusable. Regarding levels of abstraction, we find that premarking of super‐ordinate categories (e.g., noun class) facilitates learning of subordinate categories (e.g., nouns). We present detailed simulations using a plausible candidate mechanism for the observed effects, along with a comprehensive analysis of linear order effects within an expectation‐based account of learning. Our findings indicate that linguistic category learning is differentially guided by pre‐ and postmarking, and that the influence of each is modulated by the specific characteristics of a given category system. (shrink)

According to an epistemically optimistic view of empathy – understood as the simulation of another person’s state – agents learn (1) in which state the target is and (2) what it is like for her to be in this state. Assuming the necessity of justification for knowledge, this view faces the challenge of how imagination can justify beliefs. Constraining simulation to match the target’s state seems to be a solution. Because of the abundance of plausible psychological reactions towards (...) a specific situation, however, we need to know how the target reacts in order to simulate the correct mental state. According to narrativists, we gain this knowledge by means of testimony, which makes simulation superfluous. For those who accept that (2) has epistemic value, the paper presents a solution to the problem of justification that assigns a central role to both testimony and imagination. (shrink)