Word associations have been used widely in psychology, but the validity of their application strongly depends on the number of cues included in the study and the extent to which they probe all ...associations known by an individual. In this work, we address both issues by introducing a new English word association dataset. We describe the collection of word associations for over 12,000 cue words, currently the largest such English-language resource in the world. Our procedure allowed subjects to provide multiple responses for each cue, which permits us to measure weak associations. We evaluate the utility of the dataset in several different contexts, including lexical decision and semantic categorization. We also show that measures based on a mechanism of spreading activation derived from this new resource are highly predictive of direct judgments of similarity. Finally, a comparison with existing English word association sets further highlights systematic improvements provided through these new norms.
It is commonplace, when discussing the subject of psychological theory, to write articles from the assumption that psychology differs from the physical sciences in that we have no theories that would ...support cumulative, incremental science. In this brief article I discuss one counterexample: Shepard’s law of generalization and the various Bayesian extensions that it inspired over the past 3 decades. Using Shepard’s law as a running example, I argue that psychological theory building is not a statistical problem, mathematical formalism is beneficial to theory, measurement and theory have a complex relationship, rewriting old theory can yield new insights, and theory growth can drive empirical work. Although I generally suggest that the tools of mathematical psychology are valuable to psychological theorists, I also comment on some limitations to this approach.
Current attempts at methodological reform in sciences come in response to an overall lack of rigor in methodological and scientific practices in experimental sciences. However, most methodological ...reform attempts suffer from similar mistakes and over-generalizations to the ones they aim to address. We argue that this can be attributed in part to lack of formalism and first principles. Considering the costs of allowing false claims to become canonized, we argue for formal statistical rigor and scientific nuance in methodological reform. To attain this rigor and nuance, we propose a five-step formal approach for solving methodological problems. To illustrate the use and benefits of such formalism, we present a formal statistical analysis of three popular claims in the metascientific literature: (i) that reproducibility is the cornerstone of science; (ii) that data must not be used twice in any analysis; and (iii) that exploratory projects imply poor statistical practice. We show how our formal approach can inform and shape debates about such methodological claims.
One of the main limitations of natural language‐based approaches to meaning is that they do not incorporate multimodal representations the way humans do. In this study, we evaluate how well different ...kinds of models account for people's representations of both concrete and concepts. The models we compare include unimodal distributional linguistic models as well as multimodal models which combine linguistic with perceptual or affective information. There are two types of linguistic models: those based on text corpora and those derived from word association data. We present two new studies and a reanalysis of a series of previous studies. The studies demonstrate that both visual and affective multimodal models better capture behavior that reflects human representations than unimodal linguistic models. The size of the multimodal advantage depends on the nature of semantic representations involved, and it is especially pronounced for basic‐level concepts that belong to the same superordinate category. Additional visual and affective features improve the accuracy of linguistic models based on text corpora more than those based on word associations; this suggests systematic qualitative differences between what information is encoded in natural language versus what information is reflected in word associations. Altogether, our work presents new evidence that multimodal information is important for capturing both and concrete words and that fully representing word meaning requires more than purely linguistic information. Implications for both embodied and distributional views of semantic representation are discussed.
The exploration/exploitation trade-off (EE trade-off) describes how, when faced with several competing alternatives, decision-makers must often choose between a known good alternative (exploitation) ...and one or more unknown but potentially more rewarding alternatives (exploration). Prevailing theory on how humans perform the EE trade-off states that uncertainty is a major motivator for exploration: the more uncertain the environment, the more exploration that will occur. The current article examines whether exploratory behavior in both choice and attention may be impacted differently depending on whether uncertainty is onset suddenly (unexpected uncertainty), or more slowly (expected uncertainty). It is shown that when uncertainty was expected, participants tended to explore less with their choices, but not their attention, than when it was unexpected. Crucially, the impact of this "protection from uncertainty" on exploration only occurred when participants had an opportunity to learn the structure of the task before experiencing uncertainty. This suggests that the interaction between uncertainty and exploration is more nuanced than simply more uncertainty leading to more exploration, and that attention and choice behavior may index separate aspects of the EE trade-off.
Forensic handwriting examiners currently testify to the origin of questioned handwriting for legal purposes. However, forensic scientists are increasingly being encouraged to assign probabilities to ...their observations in the form of a likelihood ratio. This study is the first to examine whether handwriting experts are able to estimate the frequency of US handwriting features more accurately than novices. The results indicate that the absolute error for experts was lower than novices, but the size of the effect is modest, and the overall error rate even for experts is large enough as to raise questions about whether their estimates can be sufficiently trustworthy for presentation in courts. When errors are separated into effects caused by miscalibration and those caused by imprecision, we find systematic differences between individuals. Finally, we consider several ways of aggregating predictions from multiple experts, suggesting that quite substantial improvements in expert predictions are possible when a suitable aggregation method is used.
Skilled Bandits: Learning to Choose in a Reactive World Hotaling, Jared M.; Navarro, Danielle J.; Newell, Ben R.
Journal of experimental psychology. Learning, memory, and cognition,
06/2021, Letnik:
47, Številka:
6
Journal Article
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In uncertain environments we must balance our need to gather information with our desire to reap rewards by exploiting current knowledge. Achieving this balance is further complicated in reactive ...environments where actions produce long-lasting change to the system. In four experiments, we investigate how people learn to make effective decisions from experience in a dynamic multiarmed bandit task. In contrast to the typical exploitation-dependent diminishing rewards found in previous studies, options were framed as skills that developed greater rewards the more they were chosen. In Experiment 1, we provide a proof of concept, and in Experiments 2-4 we explore the boundaries of participants' sensitivity to reactive dynamics. Our results suggest that most individuals can learn effective strategies for coping with these reactive environments. A two-part comparison of several competing psychological models supports several conclusions: (a) a sizable minority of individuals learned that their environment was reactive, (b) evidence suggests several distinct groups of individuals employed unique decision strategies, and (c) testing models with the simulation method reveals qualitative misfits that motivate future theory development.
The samples of evidence we use to make inferences in everyday and formal settings are often subject to selection biases. Two property induction experiments examined group and individual sensitivity ...to one type of selection bias: sampling frames - causal constraints that only allow certain types of instances to be sampled. Group data from both experiments indicated that people were sensitive to the effects of such frames, showing narrower generalization when sample instances were selected because they shared a target property (property sampling) than when instances were sampled because they belonged to a particular group (category sampling). Group generalization patterns conformed to the predictions of a Bayesian model of property induction that incorporates a selective sampling mechanism. In each experiment, however, there was considerable individual variation, with a nontrivial minority showing little sensitivity to sampling frames. Experiment 2 examined correlates of frames sensitivity. A composite measure of working memory capacity predicted individual sensitivity to sampling frames. These results have important implications for current debates about people's ability to factor sample selection mechanisms into their inferences and for the development of formal models of inductive inference.
A key phenomenon in inductive reasoning is the diversity effect, whereby a novel property is more likely to be generalized when it is shared by an evidence sample composed of diverse instances than a ...sample composed of similar instances. We outline a Bayesian model and an experimental study that show that the diversity effect depends on the assumption that samples of evidence were selected by a helpful agent (strong sampling). Inductive arguments with premises containing either diverse or nondiverse evidence samples were presented under different sampling conditions, where instructions and filler items indicated that the samples were selected intentionally (strong sampling) or randomly (weak sampling). A robust diversity effect was found under strong sampling, but was attenuated under weak sampling. As predicted by our Bayesian model, the largest effect of sampling was on arguments with nondiverse evidence, where strong sampling led to more restricted generalization than weak sampling. These results show that the characteristics of evidence that are deemed relevant to an inductive reasoning problem depend on beliefs about how the evidence was generated.
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