1,721,066 research outputs found
Quantum models of cognition and decision
No full textMuch of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory. The first, "contextuality", is a way to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, "entanglement", allows cognitive phenomena to be modelled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light..
Decision making under time pressure: an independent test of sequential sampling models
Get PDFChoice probability and choice response time data from a risk-taking decision-making task were compared with predictions made by a sequential sampling model. The behavioral data, consistent with the model, showed that participants were less likely to take an action as risk levels increased, and that time pressure did not have a uniform effect on choice probability. Under time pressure, participants were more conservative at the lower risk levels but were more prone to take risks at the higher levels of risk. This crossover interaction reflected a reduction of the threshold within a single decision strategy rather than a switching of decision strategies. Response time data, as predicted by the model, showed that participants took more time to make decisions at the moderate risk levels and that time pressure reduced response time across all risk levels, but particularly at the those risk levels that took longer time with no pressure. Finally, response time data were used to rule out the hypothesis that time pressure effects could be explained by a fast-guess strategy
Adaptive learning strategies in a time-series prediction task
No full textA vast majority of past research on decision making under uncertainty has focused on static and single-trial decisions while ignoring an important issue of learning. Even the research in dynamic decision making quite often concentrated only on the subjects\u27 asymptote performance, thus leaving unexplored the question of how they achieved the asymptote level. Taking seriously the adaptive nature of decision making, the present study investigated adaptive learning strategies that people use in a stochastic environment in order to achieve their goal of optimizing an objective function. A time-series prediction learning task of the stock market in which subjects were required to predict stock prices based on outcome feedback was employed. In each experiment a crucial experimental factor which would disclose the nature of adaptive learning strategies was manipulated. Experiment 1 investigated the effects of the nonstationarity of environment where outcome probabilities change over time. In particular, time-invariance of the human learning system was tested by introducing nonstationary shifts in probability at different points of time during training. The results indicated that the predictions by the time-invariant model were clearly violated with the observation of a slow down tendency of the subjects\u27 adaptation to the nonstationary shift as the time of the shift was delayed. In Experiment 2, the effects of an objective function were explored using different shapes of symmetric and asymmetric loss functions. The subjects\u27 trial-by-trial changes in prediction as well as their asymptote performance were analyzed by qualitative and quantitative methods. The delta rule, the gradient learning model, and the hill-climbing learning model were tested. The major finding was a strong tendency for the subjects to use a modified form of the delta rule in which learning rate depends upon not only time but also performance level. A small but reliable evidence for the hill-climbing learning and no support for the gradient learning were obtained. A two-stage learning model that assumes the modified delta rule in initial training and the hill-climbing learning in later training was proposed and verified through computer simulations
USING TEST OF INTRANSITIVITY TO COMPARE COMPETING STATIC AND DYNAMIC MODELS OF INTERTEMPORAL CHOICE
Get PDFThesis (Ph.D.) - Indiana University, Psychological & Brain Sciences, 2014Most traditional research on intertemporal choice assumes a deterministic, static, and alternative-wise perspective, leading to the widely adopted delay discounting paradigm. Recently, however, Dai and Busemeyer (2014) demonstrated that intertemporal choice is probabilistic, dynamic, and attribute-wise in nature, and they developed an attribute-wise diffusion model to account for these properties. This dissertation advances the previous research. Specifically, two new experiments with different types of intertemporal choice questions and an even more extensive comparison of competing static and dynamic models were conducted to further examine the relevant properties and look for a more comprehensive cognitive model of intertemporal choice. The results of the first experiment indicated that the probabilistic, dynamic, and attribute-wise nature of intertemporal choice was supported under both conditions when the SS options occurred immediately or were delayed options. In addition, the results of the second experiment indicated that most participants showed transitive intertemporal preferences in terms of weak stochastic transitivity. The extensive model comparison led to an overall best model which was a generalization of the diffusion model with direct differences as advocated in Dai and Busemeyer. This model can account for all the effects and phenomena examined in this dissertation, including the delay duration effect, the common difference effect (and its reversal), the magnitude effect, and the potential intransitivity of intertemporal choice, as well as the marginal and conditional relationships between choice proportions and response times observed in individual data as a demonstration of the dynamic nature of intertemporal choice. Furthermore, this model can be conveniently extended to intertemporal choice between losses and account for the relevant gain-loss asymmetry. Consequently, it is recommended as a replacement for the existing models of intertemporal choice which assume a deterministic, static, and alternative-wise perspective on the topic
Evaluating categorization and connectionistic models of conceptual rule learning
No full textThree current categorization models, ALCOVE (Kruschke, 1992), the configural-cue model (Gluck & Bower, 1988b), and RULEX (Nosofsky, Palmeri, & McKinley, 1994) were rigorously tested on a rich data set collected in an experiment, which employed the conceptual rule learning paradigm. Subjects were given relevant attributes and dimensions and were asked to learn an unspecified rule that relates the attributes. The main results of this study were consistent with previous findings, the conjunctive the easiest and the biconditional the hardest; but allowed much finer tests of the models. ALCOVE was superior to the other models not only in quantitative fitting of learning curves of the eight fundamental rules but in qualitative predictions regarding learning curves of logical subgroups of stimuli. In qualitative predictions of transfer between rules, ALCOVE was as good as the configural-cue model, although ALCOVE provided the best quantitative fit for transfer performance. Possible reasons of the successes and failures of the models were examined
Interaction between prior knowledge and type of nonlinear relationship on function learning
No full textThe purpose of this thesis was to examine subjects\u27 prediction patterns during training and during interpolation and extrapolation when subjects learned a function with different degrees of correspondence to prior knowledge. Experiments 1a and 1b showed that linear functions were easier to learn than power and other monotonic nonlinear functions. Experiment 2 indicated that prior knowledge interacted with function form during training. Experiment 3 replicated Experiment 2 but with an extrapolation test. One striking finding was subjects\u27 tendency to fall back on prior knowledge when they made extrapolations even after they learned the training function very well. Finally, Experiment 4 examined whether or not subjects test hypotheses and found evidence for abrupt changes in hypotheses from trial to trial. None of the current major models accounts for the effects of prior knowledge on extrapolation adequately. An extension of the rule competition model (Busemeyer & Myung, 1992) seems to have potential to explain these results
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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