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Journal articles on the topic 'Computational making'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Knight, Terry, and Theodora Vardouli. "Computational making." Design Studies 41 (November 2015): 1–7. http://dx.doi.org/10.1016/j.destud.2015.09.003.

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Mujica-Parodi, Lilianne R., and Helmut H. Strey. "Making Sense of Computational Psychiatry." International Journal of Neuropsychopharmacology 23, no. 5 (March 27, 2020): 339–47. http://dx.doi.org/10.1093/ijnp/pyaa013.

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Abstract In psychiatry we often speak of constructing “models.” Here we try to make sense of what such a claim might mean, starting with the most fundamental question: “What is (and isn’t) a model?” We then discuss, in a concrete measurable sense, what it means for a model to be useful. In so doing, we first identify the added value that a computational model can provide in the context of accuracy and power. We then present limitations of standard statistical methods and provide suggestions for how we can expand the explanatory power of our analyses by reconceptualizing statistical models as dynamical systems. Finally, we address the problem of model building—suggesting ways in which computational psychiatry can escape the potential for cognitive biases imposed by classical hypothesis-driven research, exploiting deep systems-level information contained within neuroimaging data to advance our understanding of psychiatric neuroscience.
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Li, Tianrui, Pawan Lingras, Yuefeng Li, and Joseph Herbert. "Computational Intelligence in Decision Making." International Journal of Computational Intelligence Systems 4, no. 1 (February 2011): i—iv. http://dx.doi.org/10.1080/18756891.2011.9727758.

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Espinilla, Macarena, Javier Montero, and J. Tinguaro Rodríguez. "Computational intelligence in decision making." International Journal of Computational Intelligence Systems 7, sup1 (October 11, 2013): 1–5. http://dx.doi.org/10.1080/18756891.2014.853925.

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5

Bankes, Steven, Robert Lempert, and Steven Popper. "Making Computational Social Science Effective." Social Science Computer Review 20, no. 4 (November 2002): 377–88. http://dx.doi.org/10.1177/089443902237317.

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Gottwald, Sebastian, and Daniel Braun. "Bounded Rational Decision-Making from Elementary Computations That Reduce Uncertainty." Entropy 21, no. 4 (April 6, 2019): 375. http://dx.doi.org/10.3390/e21040375.

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In its most basic form, decision-making can be viewed as a computational process that progressively eliminates alternatives, thereby reducing uncertainty. Such processes are generally costly, meaning that the amount of uncertainty that can be reduced is limited by the amount of available computational resources. Here, we introduce the notion of elementary computation based on a fundamental principle for probability transfers that reduce uncertainty. Elementary computations can be considered as the inverse of Pigou–Dalton transfers applied to probability distributions, closely related to the concepts of majorization, T-transforms, and generalized entropies that induce a preorder on the space of probability distributions. Consequently, we can define resource cost functions that are order-preserving and therefore monotonic with respect to the uncertainty reduction. This leads to a comprehensive notion of decision-making processes with limited resources. Along the way, we prove several new results on majorization theory, as well as on entropy and divergence measures.
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French, Robert M. "The computational modeling of analogy-making." Trends in Cognitive Sciences 6, no. 5 (May 2002): 200–205. http://dx.doi.org/10.1016/s1364-6613(02)01882-x.

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Bossaerts, Peter, and Carsten Murawski. "Computational Complexity and Human Decision-Making." Trends in Cognitive Sciences 21, no. 12 (December 2017): 917–29. http://dx.doi.org/10.1016/j.tics.2017.09.005.

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9

Giles, Jim. "Computational social science: Making the links." Nature 488, no. 7412 (August 2012): 448–50. http://dx.doi.org/10.1038/488448a.

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Pal, Nikhil R., and Rajani K. Mudi. "Computational intelligence for decision-making systems." International Journal of Intelligent Systems 18, no. 5 (April 22, 2003): 483–86. http://dx.doi.org/10.1002/int.10098.

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Dennis, Louise A. "Computational Goals, Values and Decision-Making." Science and Engineering Ethics 26, no. 5 (August 4, 2020): 2487–95. http://dx.doi.org/10.1007/s11948-020-00244-y.

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Abstract Considering the popular framing of an artificial intelligence as a rational agent that always seeks to maximise its expected utility, referred to as its goal, one of the features attributed to such rational agents is that they will never select an action which will change their goal. Therefore, if such an agent is to be friendly towards humanity, one argument goes, we must understand how to specify this friendliness in terms of a utility function. Wolfhart Totschnig (Fully Autonomous AI, Science and Engineering Ethics, 2020), argues in contrast that a fully autonomous agent will have the ability to change its utility function and will do so guided by its values. This commentary examines computational accounts of goals, values and decision-making. It rejects the idea that a rational agent will never select an action that changes its goal but also argues that an artificial intelligence is unlikely to be purely rational in terms of always acting to maximise a utility function. It nevertheless also challenges the idea that an agent which does not change its goal cannot be considered fully autonomous. It does agree that values are an important component of decision-making and explores a number of reasons why.
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12

McCormick, S. Thomas. "Making sparse matrices sparser: Computational results." Mathematical Programming 49, no. 1-3 (November 1990): 91–111. http://dx.doi.org/10.1007/bf01588780.

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Zeng, Shouzhen. "Uncertain Intelligent Computational Decision-Making Methods." Recent Advances in Computer Science and Communications 14, no. 8 (February 19, 2021): 2465–66. http://dx.doi.org/10.2174/266625581408210219152347.

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Kool, Wouter, Samuel J. Gershman, and Fiery A. Cushman. "Planning Complexity Registers as a Cost in Metacontrol." Journal of Cognitive Neuroscience 30, no. 10 (October 2018): 1391–404. http://dx.doi.org/10.1162/jocn_a_01263.

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Decision-making algorithms face a basic tradeoff between accuracy and effort (i.e., computational demands). It is widely agreed that humans can choose between multiple decision-making processes that embody different solutions to this tradeoff: Some are computationally cheap but inaccurate, whereas others are computationally expensive but accurate. Recent progress in understanding this tradeoff has been catalyzed by formalizing it in terms of model-free (i.e., habitual) versus model-based (i.e., planning) approaches to reinforcement learning. Intuitively, if two tasks offer the same rewards for accuracy but one of them is much more demanding, we might expect people to rely on habit more in the difficult task: Devoting significant computation to achieve slight marginal accuracy gains would not be “worth it.” We test and verify this prediction in a sequential reinforcement learning task. Because our paradigm is amenable to formal analysis, it contributes to the development of a computational model of how people balance the costs and benefits of different decision-making processes in a task-specific manner; in other words, how we decide when hard thinking is worth it.
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Oba, Takeyuki, Kentaro Katahira, and Hideki Ohira. "Computational properties of decision-making in psychopathy." Proceedings of the Annual Convention of the Japanese Psychological Association 81 (September 20, 2017): 1D—004–1D—004. http://dx.doi.org/10.4992/pacjpa.81.0_1d-004.

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Chawla, Manisha, and Krishna P. Miyapuram. "Context-Sensitive Computational Mechanisms of Decision Making." Journal of Experimental Neuroscience 12 (January 2018): 117906951880905. http://dx.doi.org/10.1177/1179069518809057.

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Spinney, Sean, Hanie Edalati, and Patricia Conrod. "Computational Modelling of Developmental Adolescent Decision Making." Biological Psychiatry 87, no. 9 (May 2020): S376. http://dx.doi.org/10.1016/j.biopsych.2020.02.963.

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18

Kirsch, Alexandra. "A unifying computational model of decision making." Cognitive Processing 20, no. 2 (January 30, 2019): 243–59. http://dx.doi.org/10.1007/s10339-019-00904-3.

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19

Figg, Candace, Anjali Khirwadkar, and Shannon Welbourn. "Making ‘Math Making’ Virtual." Brock Education Journal 29, no. 2 (September 4, 2020): 30. http://dx.doi.org/10.26522/brocked.v29i2.836.

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Due to the COVID-19 pandemic, university professors are challenged to re-envision mathematics learning environments for virtual delivery. Those of us teaching in elementary teacher preparation programs are exploring different learning environments that not only promote meaningful learning but also foster positive attitudes about mathematics teaching. One learning environment that has been shown to be effective for introducing preservice teachers to the creative side of mathematics—the mathematics makerspace—promotes computational thinking and pedagogical understandings about teaching mathematics, but the collaborative, hands-on nature of such a learning environment is difficult to simulate in virtual delivery. This article describes the research-based design decisions for the re-envisioned virtual mathematics makerspace.
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20

Mazzone, Marian. "Andy Warhol: Computational Thinking, Computational Process." Leonardo 53, no. 2 (April 2020): 179–82. http://dx.doi.org/10.1162/leon_a_01574.

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This article positions Andy Warhol as a model for computational thinking and art-making, linking him to concepts in new media art. Warhol's work is analyzed for its variability in form generation and output, both in painting and on the early Amiga computer. His work becomes a simulation of the abstraction of process and methods of production familiar to us in electronic computational art of today. Rather than seen as banal mass production on the modern assembly line, Warhol's work can be seen as inspiration for new media arts practitioners.
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21

Taylor, Charles A., Mary T. Draney, Joy P. Ku, David Parker, Brooke N. Steele, Ken Wang, and Christopher K. Zarins. "Predictive Medicine: Computational Techniques in Therapeutic Decision-Making." Computer Aided Surgery 4, no. 5 (January 1999): 231–47. http://dx.doi.org/10.3109/10929089909148176.

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Schnecke, Volker, and Jonas Boström. "Computational chemistry-driven decision making in lead generation." Drug Discovery Today 11, no. 1-2 (January 2006): 43–50. http://dx.doi.org/10.1016/s1359-6446(05)03703-7.

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23

Bishop, Sonia J., and Christopher Gagne. "Anxiety, Depression, and Decision Making: A Computational Perspective." Annual Review of Neuroscience 41, no. 1 (July 8, 2018): 371–88. http://dx.doi.org/10.1146/annurev-neuro-080317-062007.

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In everyday life, the outcomes of our actions are rarely certain. Further, we often lack the information needed to precisely estimate the probability and value of potential outcomes as well as how much effort will be required by the courses of action under consideration. Under such conditions of uncertainty, individual differences in the estimation and weighting of these variables, and in reliance on model-free versus model-based decision making, have the potential to strongly influence our behavior. Both anxiety and depression are associated with difficulties in decision making. Further, anxiety is linked to increased engagement in threat-avoidance behaviors and depression is linked to reduced engagement in reward-seeking behaviors. The precise deficits, or biases, in decision making associated with these common forms of psychopathology remain to be fully specified. In this article, we review evidence for which of the computations supporting decision making are altered in anxiety and depression and consider the potential consequences for action selection. In addition, we provide a schematic framework that integrates the findings reviewed and will hopefully be of value to future studies.
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24

GOLAB, J. T. "ChemInform Abstract: Making Industrial Decisions with Computational Chemistry." ChemInform 29, no. 29 (June 20, 2010): no. http://dx.doi.org/10.1002/chin.199829346.

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Taylor, Charles A., Mary T. Draney, Joy P. Ku, David Parker, Brooke N. Steele, Ken Wang, and Christopher K. Zarins. "Predictive medicine: Computational techniques in therapeutic decision‐making." Computer Aided Surgery 4, no. 5 (1999): 231–47. http://dx.doi.org/10.1002/(sici)1097-0150(1999)4:5<231::aid-igs1>3.3.co;2-q.

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26

Martin, F., B. Mikhak, and B. Silverman. "MetaCricket: A designer's kit for making computational devices." IBM Systems Journal 39, no. 3.4 (2000): 795–815. http://dx.doi.org/10.1147/sj.393.0795.

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27

Grigoryan, Gevorg. "Making Picky Proteins: Computational Design of Interaction Specificity." Biophysical Journal 102, no. 3 (January 2012): 438a—439a. http://dx.doi.org/10.1016/j.bpj.2011.11.2401.

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28

Thaduri, Adithya, Uday Kumar, and Ajit Kumar Verma. "Computational intelligence framework for context-aware decision making." International Journal of System Assurance Engineering and Management 8, S4 (December 4, 2014): 2146–57. http://dx.doi.org/10.1007/s13198-014-0320-8.

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29

Godzik, A., M. Jambon, and I. Friedberg. "Computational protein function prediction: Are we making progress?" Cellular and Molecular Life Sciences 64, no. 19-20 (July 5, 2007): 2505–11. http://dx.doi.org/10.1007/s00018-007-7211-y.

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30

Sadiku, Matthew N. O., Justin Foreman, and Sarhan M. Musa. "Computational Intelligence." European Scientific Journal, ESJ 14, no. 21 (July 31, 2018): 56. http://dx.doi.org/10.19044/esj.2018.v14n21p56.

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Computational intelligence (CI) refers to recreating human-like intelligence in a computing machine. It consists of a set of computing systems with the ability to learn and deal with new situations such that the systems are perceived to have some attributes of intelligence. It is efficient in solving realworld problems which require reasoning and decision-making. It produces more robust, simpler, and tractable solutions than the traditional techniques. This paper provides a brief introduction to computational intelligence.
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31

Meena, V., Obulaporam Gireesha, Kannan Krithivasan, and V. S. Shankar Sriram. "Fuzzy simplified swarm optimization for multisite computational offloading in mobile cloud computing." Journal of Intelligent & Fuzzy Systems 39, no. 6 (December 4, 2020): 8285–97. http://dx.doi.org/10.3233/jifs-189148.

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Mobile Cloud Computing (MCC)’s rapid technological advancements facilitate various computational-intensive applications on smart mobile devices. However, such applications are constrained by limited processing power, energy consumption, and storage capacity of smart mobile devices. To mitigate these issues, computational offloading is found to be the one of the promising techniques as it offloads the execution of computation-intensive applications to cloud resources. In addition, various kinds of cloud services and resourceful servers are available to offload computationally intensive tasks. However, their processing speeds, access delays, computation capability, residual memory and service charges are different which retards their usage, as it becomes time-consuming and ambiguous for making decisions. To address the aforementioned issues, this paper presents a Fuzzy Simplified Swarm Optimization based cloud Computational Offloading (FSSOCO) algorithm to achieve optimum multisite offloading. Fuzzy logic and simplified swarm optimization are employed for the identification of high powerful nodes and task decomposition respectively. The overall performance of FSSOCO is validated using the Specjvm benchmark suite and compared with the state-of-the-art offloading techniques in terms of the weighted total cost, energy consumption, and processing time.
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Cervantes, José-Antonio, Luis-Felipe Rodríguez, Sonia López, Félix Ramos, and Francisco Robles. "Cognitive Process of Moral Decision-Making for Autonomous Agents." International Journal of Software Science and Computational Intelligence 5, no. 4 (October 2013): 61–76. http://dx.doi.org/10.4018/ijssci.2013100105.

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There are a great variety of theoretical models of cognition whose main purpose is to explain the inner workings of the human brain. Researchers from areas such as neuroscience, psychology, and physiology have proposed these models. Nevertheless, most of these models are based on empirical studies and on experiments with humans, primates, and rodents. In fields such as cognitive informatics and artificial intelligence, these cognitive models may be translated into computational implementations and incorporated into the architectures of intelligent autonomous agents (AAs). Thus, the main assumption in this work is that knowledge in those fields can be used as a design approach contributing to the development of intelligent systems capable of displaying very believable and human-like behaviors. Decision-Making (DM) is one of the most investigated and computationally implemented functions. The literature reports several computational models that enable AAs to make decisions that help achieve their personal goals and needs. However, most models disregard crucial aspects of human decision-making such as other agents' needs, ethical values, and social norms. In this paper, the authors present a set of criteria and mechanisms proposed to develop a biologically inspired computational model of Moral Decision-Making (MDM). To achieve a process of moral decision-making believable, the authors propose a cognitive function to determine the importance of each criterion based on the mood and emotional state of AAs, the main objective the model is to enable AAs to make decisions based on ethical and moral judgment.
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Andrienko, Natalia, and Gennady Andrienko. "Informed Spatial Decisions Through Coordinated Views." Information Visualization 2, no. 4 (December 2003): 270–85. http://dx.doi.org/10.1057/palgrave.ivs.9500058.

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According to a commonly accepted view, the process of decision making comprises three major phases: intelligence (situation analysis and problem recognition), design (finding possible variants of problem solution), and choice (evaluation of the options and selection of the most appropriate ones). It is widely recognised that exploratory data visualisation is very helpful during the first phase of the decision-making process, while the other phases require different software tools. In particular, the choice phase is typically supported by various computational methods that find appropriate trade-offs among multiple conflicting criteria taking into account user-specified priorities. Visualisation plays a limited role: in the best case, it is used to represent the final results of the computations. We argue that conscious, well-substantiated choice requires a more extensive use of exploratory visualisation facilities, which need to be properly coordinated with the computational multi-criteria decision support methods. Extremely important is a high degree of user interactivity, which allows the user to probe the robustness and quality of computationally derived solutions. We suggest several mechanisms for linking and coordinating visual exploratory tools with two types of computational methods differing in the sort of output they produce. We demonstrate the use of this dynamic link with an example of a real spatially related decision problem.
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M. LONGA, VíCTOR. "Making Prehistoric Lines Speak: Inferring Language and Mental Computations from ‘Natural’ Lines of Parietal Art1." Philology 4, no. 2018 (January 1, 2019): 243–78. http://dx.doi.org/10.3726/phil042019.7.

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Abstract According to many archaeologists and paleoanthropologists, the presence of symbolism in the prehistoric archaeological record indicates complex language. Therefore, archaeological remains have usually been analyzed from the perspective of the behavior (in this case, symbolic) they could be associated with. This paper proposes a very different approach, arising from formal linguistics and mathematical theory of computation: to analyze archaeological remains from the perspective of the computational processes and capabilities required for their production. This approach is not concerned with the ‘semantics’ of the pieces (symbolism, etc.), but with the analysis of purely formal features revealing a language-like computational complexity. I will exemplify this approach through the computational analysis of representations of Upper Palaeolithic parietal art, concentrating on the use of ‘natural’ lines, i.e. lines preexisting in the rocks (cracks, fissures, etc.), which were used as anchorage points for many representations. This use of natural lines will be shown to reveal a high computational complexity.
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Aikhuele, Daniel O., and Faiz Mohd Turan. "An exponential-related function for decision-making in engineering and management." Open Engineering 7, no. 1 (June 9, 2017): 153–60. http://dx.doi.org/10.1515/eng-2017-0022.

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AbstractAn intuitionistic fuzzy TOPSIS model, which is based on an exponential-related function (IF-TOPSIS) and a fuzzy entropy method, has been proposed in this study. The exponential-related function, which represents the aggregated effect of positive and negative evaluations in the performance ratings of the alternatives, based on the intuitionistic fuzzy set (IFS) data. Serves, as a computational tool for measuring the separation distance of decision alternatives from the intuitionistic fuzzy positive and negative ideal solution to determine the relative closeness coefficient. The main advantage of this new approach is that (1) it uses a subjective and objective based approach for the computation of the criteria weight and (2) its simplicity both in its concept and computational procedures. The proposed method has successfully been implemented for the evaluation of some engineering designs related problems including the selection of a preferred floppy disk from a group of design alternatives, the selection of the best concept design for a new air-conditions system and finally, the selection of a preferred mouse from a group of alternatives as a reference for a new design. Also, for each of the three case studies, the method has been compared with some similar computational approaches.
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Mitchell, Melanie. "Theories of structure versus theories of change." Behavioral and Brain Sciences 21, no. 5 (October 1998): 645–46. http://dx.doi.org/10.1017/s0140525x98421733.

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The dynamics/computation debate recalls a similar debate in the evolutionary biology community concerning the relative primacy of theories of structure versus theories of change. A full account of cognition will require a rapprochement between such theories and will include both computational and dynamical notions. The key to making computation relevant to cognition is not making it analog, but rather understanding how functional information-processing structures can emerge in complex dynamical systems.
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Krause, Andreas, Daniel Golovin, and Sarah Converse. "Sequential Decision Making in Computational Sustainability via Adaptive Submodularity." AI Magazine 35, no. 2 (June 19, 2014): 8–18. http://dx.doi.org/10.1609/aimag.v35i2.2526.

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Many problems in computational sustainability require making a sequence of decisions in complex, uncertain environments. Such problems are generally notoriously difficult. In this article, we review the recently discovered notion of adaptive submodularity, an intuitive diminishing returns condition that generalizes the classical notion of submodular set functions to sequential decision problems. Problems exhibiting the adaptive submodularity property can be efficiently and provably near-optimally solved using simple myopic policies. We illustrate this concept in several case studies of interest in computational sustainability: First, we demonstrate how it can be used to efficiently plan for resolving uncertainty in adaptive management scenarios. Secondly, we show how it applies to dynamic conservation planning for protecting endangered species, a case study carried out in collaboration with the US Geological Survey and the US Fish and Wildlife Service.
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Carbonaro, Michael. "Making a Connection between Computational Modeling and Educational Research." Journal of Educational Computing Research 28, no. 1 (January 2003): 63–81. http://dx.doi.org/10.2190/l1th-3v6m-2w5q-8ltj.

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Bruner, Goodnow, and Austin's (1956) research on concept development is re-examined from a connectionist perspective. A neural network was constructed which associates positive and negative instances of a concept with their corresponding attribute values. Two methods were used to help preserve the ecological validity of the input: 1) closely mapping the input to the actual visual stimuli; and 2) structuring the output layer based on Gagne's (1962, 1985) work on human concept learning. This resulted in the addition of output units referred to as attribute context constraints. These units required the network to demonstrate the identification of attributes both relevant and irrelevant to the task of classification. Results suggest that the simultaneous learning of attributes guided the network in constructing a faster and more generalizable representation than when attribute constraints were absent. Results are discussed with respect to the advantages of computational approaches to studying learning.
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O’Connell, Redmond G., Michael N. Shadlen, KongFatt Wong-Lin, and Simon P. Kelly. "Bridging Neural and Computational Viewpoints on Perceptual Decision-Making." Trends in Neurosciences 41, no. 11 (November 2018): 838–52. http://dx.doi.org/10.1016/j.tins.2018.06.005.

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Knochel, Aaron D., and Ryan M. Patton. "IfArt EducationThenCritical Digital Making: Computational Thinking and Creative Code." Studies in Art Education 57, no. 1 (October 2015): 21–38. http://dx.doi.org/10.1080/00393541.2015.11666280.

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41

Summerfield, Christopher, and Floris P. de Lange. "Expectation in perceptual decision making: neural and computational mechanisms." Nature Reviews Neuroscience 15, no. 11 (October 15, 2014): 745–56. http://dx.doi.org/10.1038/nrn3838.

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42

Migliore, Michele, Thomas M. Morse, Andrew P. Davison, Luis Marenco, Gordon M. Shepherd, and Michael L. Hines. "ModelDB: Making Models Publicly Accessible to Support Computational Neuroscience." Neuroinformatics 1, no. 1 (2003): 135–40. http://dx.doi.org/10.1385/ni:1:1:135.

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Cipresso, Pietro, Daniela Villani, Claudia Repetto, Lucia Bosone, Anna Balgera, Maurizio Mauri, Marco Villamira, Alessandro Antonietti, and Giuseppe Riva. "Computational Psychometrics in Communication and Implications in Decision Making." Computational and Mathematical Methods in Medicine 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/985032.

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Recent investigations emphasized the role of communication features on behavioral trust and reciprocity in economic decision making but no studies have been focused on the effect of communication on affective states in such a context. Thanks to advanced methods of computational psychometrics, in this study, affective states were deeply examined using simultaneous and synchronized recordings of gazes and psychophysiological signals in 28 female students during an investment game. Results showed that participants experienced different affective states according to the type of communication (personal versus impersonal). In particular, participants involved in personal communication felt more relaxed than participants involved in impersonal communication. Moreover, personal communication influenced reciprocity and participants’ perceptions about trust and reciprocity. Findings were interpreted in the light of the Arousal/Valence Model and self-disclosure process.
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Mincheol Kang. "Team-Soar: a computational model for multilevel decision making." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 31, no. 6 (2001): 708–14. http://dx.doi.org/10.1109/3468.983426.

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Sandak, Billie, Shai Cohen, Avi Gilboa, and David Harel. "Computational elucidation of the effects induced by music making." PLOS ONE 14, no. 3 (March 7, 2019): e0213247. http://dx.doi.org/10.1371/journal.pone.0213247.

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Menges, Achim. "The New Cyber-Physical Making in Architecture: Computational Construction." Architectural Design 85, no. 5 (September 2015): 28–33. http://dx.doi.org/10.1002/ad.1950.

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Yazici, Sevil. "Rule-based rationalization of form: learning by computational making." International Journal of Technology and Design Education 30, no. 3 (March 8, 2019): 613–33. http://dx.doi.org/10.1007/s10798-019-09509-5.

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Hadad, Roxana, Kate Thomas, Mila Kachovska, and Yue Yin. "Practicing Formative Assessment for Computational Thinking in Making Environments." Journal of Science Education and Technology 29, no. 1 (November 5, 2019): 162–73. http://dx.doi.org/10.1007/s10956-019-09796-6.

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49

Cain, Nicholas, and Eric Shea-Brown. "Computational models of decision making: integration, stability, and noise." Current Opinion in Neurobiology 22, no. 6 (December 2012): 1047–53. http://dx.doi.org/10.1016/j.conb.2012.04.013.

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50

Carrillo, José Antonio, Stéphane Cordier, and Simona Mancini. "A decision-making Fokker–Planck model in computational neuroscience." Journal of Mathematical Biology 63, no. 5 (December 24, 2010): 801–30. http://dx.doi.org/10.1007/s00285-010-0391-3.

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