In this article, we describe the most extensive set of word associations collected to date. The database contains over 12,000 cue words for which more than 70,000 participants generated three ...responses in a multiple-response free association task. The goal of this study was (1) to create a semantic network that covers a large part of the human lexicon, (2) to investigate the implications of a multiple-response procedure by deriving a weighted directed network, and (3) to show how measures of centrality and relatedness derived from this network predict both lexical access in a lexical decision task and semantic relatedness in similarity judgment tasks. First, our results show that the multiple-response procedure results in a more heterogeneous set of responses, which lead to better predictions of lexical access and semantic relatedness than do single-response procedures. Second, the directed nature of the network leads to a decomposition of centrality that primarily depends on the number of incoming links or in-degree of each node, rather than its set size or number of outgoing links. Both studies indicate that adequate representation formats and sufficiently rich data derived from word associations represent a valuable type of information in both lexical and semantic processing.
Rational Approximations to Rational Models Sanborn, Adam N; Griffiths, Thomas L; Navarro, Daniel J
Psychological review,
10/2010, Letnik:
117, Številka:
4
Journal Article
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Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from ...more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models is thus explaining how optimal solutions can be approximated by psychological processes. We outline a general strategy for answering this question, namely to explore the psychological plausibility of approximation algorithms developed in computer science and statistics. In particular, we argue that Monte Carlo methods provide a source of
rational process models
that connect optimal solutions to psychological processes. We support this argument through a detailed example, applying this approach to
Anderson's (1990
,
1991
) rational model of categorization (RMC), which involves a particularly challenging computational problem. Drawing on a connection between the RMC and ideas from nonparametric Bayesian statistics, we propose 2 alternative algorithms for approximate inference in this model. The algorithms we consider include Gibbs sampling, a procedure appropriate when all stimuli are presented simultaneously, and particle filters, which sequentially approximate the posterior distribution with a small number of samples that are updated as new data become available. Applying these algorithms to several existing datasets shows that a particle filter with a single particle provides a good description of human inferences.
We present a lab-field experiment designed to systematically assess the external validity of social preferences elicited in a variety of experimental games. We do this by comparing behavior in the ...different games with several behaviors elicited in the field and with self-reported behaviors exhibited in the past, using the same sample of participants. Our results show that the experimental social preference games do a poor job explaining both social behaviors in the field and social behaviors from the past. We also include a systematic review and meta-analysis of previous literature on the external validity of social preference games.
Data are available at
https://doi.org/10.1287/mnsc.2017.2908
.
This paper was accepted by John List, behavioral economics.
Recent debates in the psychological literature have raised questions about the assumptions that underpin Bayesian models of cognition and what inferences they license about human cognition. In this ...paper we revisit this topic, arguing that there are 2 qualitatively different ways in which a Bayesian model could be constructed. The most common approach uses a Bayesian model as a normative standard upon which to license a claim about optimality. In the alternative approach, a descriptive Bayesian model need not correspond to any claim that the underlying cognition is optimal or rational, and is used solely as a tool for instantiating a substantive psychological theory. We present 3 case studies in which these 2 perspectives lead to different computational models and license different conclusions about human cognition. We demonstrate how the descriptive Bayesian approach can be used to answer different sorts of questions than the optimal approach, especially when combined with principled tools for model evaluation and model selection. More generally we argue for the importance of making a clear distinction between the 2 perspectives. Considerable confusion results when descriptive models and optimal models are conflated, and if Bayesians are to avoid contributing to this confusion it is important to avoid making normative claims when none are intended.
•How do people trade off information seeking and reward taking?•We investigate explore–exploit dilemmas in static and dynamic environments.•We present a rational analysis of the decision making ...problem.•Two experiments showing when and in what respects humans make normative choices.•Four process models used to fit the data at a trial by trial level.
How do people solve the explore–exploit trade-off in a changing environment? In this paper we present experimental evidence from an “observe or bet” task, in which people have to determine when to engage in information-seeking behavior and when to switch to reward-taking actions. In particular we focus on the comparison between people’s behavior in a changing environment and their behavior in an unchanging one. Our experimental work is motivated by rational analysis of the problem that makes strong predictions about information search and reward seeking in static and changeable environments. Our results show a striking agreement between human behavior and the optimal policy, but also highlight a number of systematic differences. In particular, we find that while people often employ suboptimal strategies the first time they encounter the learning problem, most people are able to approximate the correct strategy after minimal experience. In order to describe both the manner in which people’s choices are similar to but slightly different from an optimal standard, we introduce four process models for the observe or bet task and evaluate them as potential theories of human behavior.
Similarity plays an important role in organizing the semantic system. However, given that similarity cannot be defined on purely logical grounds, it is important to understand how people perceive ...similarities between different entities. Despite this, the vast majority of studies focus on measuring similarity between very closely related items. When considering concepts that are very weakly related, little is known. In this article, we present 4 experiments showing that there are reliable and systematic patterns in how people evaluate the similarities between very dissimilar entities. We present a semantic network account of these similarities showing that a spreading activation mechanism defined over a word association network naturally makes correct predictions about weak similarities, whereas, though simpler, models based on direct neighbors between word pairs derived using the same network cannot.
En el presente documento se analiza el proceso evolutivo que se ha producido en el ámbito jurídico civil español en lo que se refiere a las crisis de pareja y las implicaciones respecto de los ...animales de compañía. En consecuencia, se examina la evolución que se ha producido en España respecto a la descosificación de los animales. A este respecto, se analiza parte de la jurisprudencia menor previa a la Ley 17/2021, de 15 de diciembre, de modificación del Código Civil, la Ley Hipotecaria y la Ley de Enjuiciamiento Civil, sobre el régimen jurídico de los animales y, concretamente, la Sentencia de fecha 7 de octubre de 2021 del Juzgado de Primera Instancia número 11 de Madrid que ha sido una de las últimas dictadas en este sentido. Cabe subrayar, la importancia de la cuestión interpretativa en este tipo de sentencias, que ofrecieron soluciones a las problemáticas que se suscitaban mediante argumentos jurídicos derivados de una interpretación extensiva de la normativa, lo que genera, indefectiblemente, la necesidad de un debate sosegado, sobre la conceptualización del derecho y su aplicabilidad por los tribunales. Del mismo modo, se estudiarán las implicaciones en materia de descosificación animal derivadas de la citada Ley 17/2021, así como el cambio que se está produciendo en los procesos judiciales motivados por rupturas de parejas.
A core assumption of many theories of development is that children can learn indirectly from other people. However, indirect experience (or testimony) is not constrained to provide veridical ...information. As a result, if children are to capitalize on this source of knowledge, they must be able to infer who is trustworthy and who is not. How might a learner make such inferences while at the same time learning about the world? What biases, if any, might children bring to this problem? We address these questions with a computational model of epistemic trust in which learners reason about the helpfulness and knowledgeability of an informant. We show that the model captures the competencies shown by young children in four areas: (1) using informants’ accuracy to infer how much to trust them; (2) using informants’ recent accuracy to overcome effects of familiarity; (3) inferring trust based on consensus among informants; and (4) using information about mal‐intent to decide not to trust. The model also explains developmental changes in performance between 3 and 4 years of age as a result of changing default assumptions about the helpfulness of other people.
A core assumption of many theories of development is that children can learn indirectly from other people. However, indirect experience (or testimony) is not constrained to provide veridical information. As a result, if children are to capitalize on this source of knowledge, they must be able to infer who is trustworthy and who is not. How might a learner make such inferences while at the same time learning about the world? What biases, if any, might children bring to this problem?
Every time we encounter a new object, action, or event, there is some chance that we will need to assign it to a novel category. We describe and evaluate a class of probabilistic models that detect ...when an object belongs to a category that has not previously been encountered. The models incorporate a prior distribution that is influenced by the distribution of previous objects among categories, and we present 2 experiments that demonstrate that people are also sensitive to this distributional information. Two additional experiments confirm that distributional information is combined with similarity when both sources of information are available. We compare our approach to previous models of unsupervised categorization and to several heuristic-based models, and find that a hierarchical Bayesian approach provides the best account of our data.
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