Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Statistical modeling is generally meant to describe patterns in data in service of the broader scientific goal of developing theories to explain those patterns. Statistical models support meaningful inferences when models are built so as to align parameters of the model with potential causal mechanisms and how they manifest in data. When statistical models are instead based on assumptions chosen by default, attempts to draw inferences can be uninformative or even paradoxical—in essence, the tail is trying to wag the dog. These issues are illustrated by van Doorn et al. (this issue) in the context of using Bayes Factors to identify effects and interactions in linear mixed models. We show that the problems identified in their applications (along with other problems identified here) can be circumvented by using priors over inherently meaningful units instead of default priors on standardized scales. This case study illustrates how researchers must directly engage with a number of substantive issues in order to support meaningful inferences, of which we highlight two: The first is the problem of coordination, which requires a researcher to specify how the theoretical constructs postulated by a model are functionally related to observable variables. The second is the problem of generalization, which requires a researcher to consider how a model may represent theoretical constructs shared across similar but non-identical situations, along with the fact that model comparison metrics like Bayes Factors do not directly address this form of generalization. For statistical modeling to serve the goals of science, models cannot be based on default assumptions, but should instead be based on an understanding of their coordination function and on how they represent causal mechanisms that may be expected to generalize to other related scenarios. PubDate: 2022-05-16
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract A new phenomenon is the spread and acceptance of misinformation and disinformation on an individual user level, facilitated by social media such as Twitter. So far, state-of-the-art socio-psychological theories and cognitive models focus on explaining how the accuracy of fake news is judged on average, with little consideration of the individual. In this paper, a breadth of core models are comparatively assessed on their predictive accuracy for the individual decision maker, i.e., how well can models predict an individual’s decision before the decision is made. To conduct this analysis, it requires the raw responses of each individual and the implementation and adaption of theories to predict the individual’s response. Building on methods formerly applied on smaller and more limited datasets, we used three previously collected large datasets with a total of 3794 participants and searched for, analyzed and refined existing classical and heuristic modeling approaches. The results suggest that classical reasoning, sentiment analysis models and heuristic approaches can best predict the “Accept” or “Reject” response of a person, headed by a model put together from research by Jay Van Bavel, while other models such as an implementation of “motivated reasoning” performed worse. Further, hybrid models that combine pairs of individual models achieve a significant increase in performance, pointing to an adaptive toolbox. PubDate: 2022-05-11
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract ANOVA—the workhorse of experimental psychology—seems well understood in that behavioral sciences have agreed-upon contrasts and reporting conventions. Yet, we argue this consensus hides considerable flaws in common ANOVA procedures, and these flaws become especially salient in the within-subject and mixed-model cases. The main thesis is that these flaws are in model specification. The specifications underlying common use are deficient from a substantive perspective, that is, they do not match reality in behavioral experiments. The problem, in particular, is that specifications rely on coincidental rather than robust statements about reality. We provide specifications that avoid making arguments based on coincidences, and note these Bayes factor model comparisons among these specifications are already convenient in the BayesFactor package. Finally, we argue that model specification necessarily and critically reflects substantive concerns, and, consequently, is ultimately the responsibility of substantive researchers. Source code for this project is at github/PerceptionAndCognitionLab/stat_aov2. PubDate: 2022-05-09
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The predictive processing account aspires to explain all of cognition using a single, unifying principle. Among the major challenges is to explain how brains are able to infer the structure of their generative models. Recent attempts to further this goal build on existing ideas and techniques from engineering fields, like Bayesian statistics and machine learning. While apparently promising, these approaches make specious assumptions that effectively confuse structure learning with Bayesian parameter estimation in a fixed state space. We illustrate how this leads to a set of theoretical problems for the predictive processing account. These problems highlight a need for developing new formalisms specifically tailored to the theoretical aims of scientific explanation. We lay the groundwork for a possible way forward. PubDate: 2022-04-28
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Various works have suggested that the memorability of an image is consistent across people, and thus can be treated as an intrinsic property of an image. Using computer vision models, we can make specific predictions about what people will remember or forget. While older work has used now-outdated deep learning architectures rooted in shallow visual processing to predict image memorability, innovations in the field have given us new techniques to apply to this problem. Here, we propose and evaluate five alternative deep learning models which exploit developments in the field from the last 5 years, largely the introduction of residual neural networks, which are intended to allow the model to use semantic information in the memorability estimation process. These new models were tested against the prior state of the art with a combined dataset built to optimize both within-category and across-category predictions. Our findings suggest that the key prior memorability network had overstated its generalizability and was overfit on its training set. Our new models outperform this prior model, leading us to conclude that residual networks outperform simpler convolutional neural networks in memorability regression. We make our new state-of-the-art model readily available to the research community, allowing memory researchers to make predictions about memorability on a wider range of images. PubDate: 2022-04-11
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract To make good decisions in the real world, people need efficient planning strategies because their computational resources are limited. Knowing which planning strategies would work best for people in different situations would be very useful for understanding and improving human decision-making. Our ability to compute those strategies used to be limited to very small and very simple planning tasks. Here, we introduce a cognitively inspired reinforcement learning method that can overcome this limitation by exploiting the hierarchical structure of human behavior. We leverage it to understand and improve human planning in large and complex sequential decision problems. Our method decomposes sequential decision problems into two sub-problems: setting a goal and planning how to achieve it. Our method can discover optimal human planning strategies for larger and more complex tasks than was previously possible. The discovered strategies achieve a better tradeoff between decision quality and computational cost than both human planning and existing planning algorithms. We demonstrate that teaching people to use those strategies significantly increases their level of resource-rationality in tasks that require planning up to eight steps ahead. By contrast, none of the previous approaches was able to improve human performance on these problems. These findings suggest that our cognitively informed approach makes it possible to leverage reinforcement learning to improve human decision-making in complex sequential decision problems. Future work can leverage our method to develop decision support systems that improve human decision-making in the real world. PubDate: 2022-04-01
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The prediction of everyday human behavior is a central goal in the behavioral sciences. However, efforts in this direction have been limited, as (1) the behaviors studied in most surveys and experiments represent only a small fraction of all possible behaviors, and (2) it has been difficult to generalize data from existing studies to predict arbitrary behaviors, owing to the difficulty in adequately representing such behaviors. Our paper attempts to address each of these problems. First, by sampling frequent verb phrases in natural language and refining these through human coding, we compile a dataset of nearly 4000 common human behaviors. Second, we use distributed semantic models to obtain vector representations for our behaviors, and combine these with demographic and psychographic data, to build supervised, deep neural network models of behavioral propensities for a representative sample of the US population. Our best models achieve reasonable accuracy rates when predicting propensities for novel (out-of-sample) participants as well as novel behaviors, and offer new insights for modeling psychographic and demographic differences in behavior. This work is a first step towards building predictive theories of everyday behavior, and thus improving the generality and naturalism of research in the behavioral sciences. PubDate: 2022-03-25
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract In reinforcement-learning studies, the environment is typically object-based; that is, objects are predictive of a reward. Recently, studies also adopted rule-based environments in which stimulus dimensions are predictive of a reward. In the current study, we investigated how people learned (1) in an object-based environment, (2) following a switch to a rule-based environment, (3) following a switch to a different rule-based environment, and (4) following a switch back to an object-based environment. To do so, we administered a reinforcement-learning task comprising of four blocks with consecutively an object-based environment, a rule-based environment, another rule-based environment, and an object-based environment. Computational-modeling results suggest that people (1) initially adopt rule-based learning despite its suboptimal nature in an object-based environment, (2) learn rules after a switch to a rule-based environment, (3) experience interference from previously-learned rules following a switch to a different rule-based environment, and (4) learn objects after a final switch to an object-based environment. These results imply people have a hard time adjusting to switches between object-based and rule-based environments, although they do learn to do so. PubDate: 2022-03-24
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Assessment of mind wandering commonly entails the use of experience sampling to capture episodes in the midst of their occurrence. Time series sequences of responses to experience sampling probes have been shown to exhibit consequential temporal dynamics, including power-law scaling behavior in their fluctuations across increasing time scales. This scaling behavior implies long-range temporal correlations in the time series, in which mind wandering occurring far in the past can exert some influence on future episodes over increasingly large temporal intervals. Yet, it is known that short-range correlated Markov processes can also exhibit similar scaling behavior, making it critical that apparent long-range temporal correlations in time series measurements of mind wandering are evaluated with respect to short-range correlated null hypotheses. In the present study, we examine long-range temporal correlations in sequences of mind wandering probe ratings as well as in simulated sequences generated by short-range correlated Markov processes. To evaluate whether mind wandering probe ratings display genuine long-range temporal dependence, we contrast patterns of autodependence in mind wandering probe rating data with confidence intervals derived from short-range correlated Markov models and sequences temporally permuted to remove any temporal structure. We find that autodependence generally extended over short and intermediate ranges but was undifferentiated from short-range correlated processes later in time. This suggests that sequences of mind wandering probe ratings on average have finite memory with dynamics operating at shorter-range characteristic scales beyond which autodependence decays to that of a short-range correlated process. PubDate: 2022-03-08 DOI: 10.1007/s42113-022-00130-9
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Without having seen a bigram like “her buffalo”, you can easily tell that it is congruent because “buffalo” can be aligned with more common nouns like “cat” or “dog” that have been seen in contexts like “her cat” or “her dog”—the novel bigram structurally aligns with representations in memory. We present a new class of associative nets we call Dynamic-Eigen-Nets, and provide simulations that show how they generalize to patterns that are structurally aligned with the training domain. Linear-Associative-Nets respond with the same pattern regardless of input, motivating the introduction of saturation to facilitate other response states. However, models using saturation cannot readily generalize to novel, but structurally aligned patterns. Dynamic-Eigen-Nets address this problem by dynamically biasing the eigenspectrum towards external input using temporary weight changes. We demonstrate how a two-slot Dynamic-Eigen-Net trained on a text corpus provides an account of bigram judgment-of-grammaticality and lexical decision tasks, showing it can better capture syntactic regularities from the corpus compared to the Brain-State-in-a-Box and the Linear-Associative-Net. We end with a simulation showing how a Dynamic-Eigen-Net is sensitive to syntactic violations introduced in bigrams, even after the associations that encode those bigrams are deleted from memory. Over all simulations, the Dynamic-Eigen-Net reliably outperforms the Brain-State-in-a-Box and the Linear-Associative-Net. We propose Dynamic-Eigen-Nets as associative nets that generalize at retrieval, instead of encoding, through recurrent feedback. PubDate: 2022-03-04 DOI: 10.1007/s42113-022-00127-4
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract We discuss an important issue that is not directly related to the main theses of the van Doorn et al. (Computational Brain and Behavior, 2021) paper, but which frequently comes up when using Bayesian linear mixed models: how to determine sample size in advance of running a study when planning a Bayes factor analysis. We adapt a simulation-based method proposed by Wang and Gelfand (Statistical Science 193–208, 2002) for a Bayes factor-based design analysis, and demonstrate how relatively complex hierarchical models can be used to determine approximate sample sizes for planning experiments. PubDate: 2022-03-04 DOI: 10.1007/s42113-021-00125-y
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Numerals are part of our everyday lives and are regularly viewed in less-than ideal conditions. Mistaking one numeral for another is almost an inevitability, and the cost of these confusions could be insignificant or hugely expensive! Numeral confusions can be explained by distances between our mental representations — how we internally represent the external world — resulting from their perceived similarities; yet, how expertise interacts with the mental space of numerals is largely unexplored. We used an identification paradigm to investigate the mental representations of familiar and unfamiliar numerals (4 sets: Arabic, Chinese, Thai, and non-symbolic dots) in a first-language English and a first-language Chinese speaking cohort. Using Luce’s choice model, we removed the undesired effect of response bias and conducted multidimensional scaling analyses. Results showed that expertise with numerals alters distances in the mental space, that unfamiliar numerals are represented identically across cultures, that non-symbolic numerals (dots) may be represented both perceptually and numerically in the mental space, and that Arabic, Thai and Chinese numerals are represented by their perceptual similarities. The findings and methods of this study provide a principled foundation for future investigations into how expertise shapes people’s mental representations. PubDate: 2022-01-14 DOI: 10.1007/s42113-021-00122-1
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The ability to accurately estimate and reproduce the magnitude of stimulus features is critical for many daily tasks. However, experimental psychology has repeatedly confronted researchers with unexplained estimation biases stemming from preceding stimulus features. Serial dependency and central tendency bias are two particularly representative and ubiquitous examples. The core commonalities across these two response patterns raise a question: Are these seemingly different constructs re-describing a single phenomenon' The current paper tests this possibility by proposing a fidelity-based integration model (FIM) and testing it with three single item estimation experiments focusing on the visual features of line length and spatial frequency. The critical assumption of FIM is a fidelity-based sampling process that integrates information from both the target and recent non-target items. Our results suggest that central tendency bias and serial dependency reflect a single underlying process and that the distinct response patterns constituting these two phenomena are a by-product of the analytical methods traditionally favored within each domain. FIM also suggests that this implicit ensemble bias might be the basis for observed accuracy differences across implicit and explicit ensemble coding tasks. PubDate: 2022-01-03 DOI: 10.1007/s42113-021-00123-0
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The progressive ratio task (e.g., Wolf et al., Schizophrenia Bulletin, 40(6):1328–1337, 2014) is often used to assess motivational deficits of individuals with mental health conditions, yet the number of studies investigating its underlying mechanisms is limited. In this paper, we present a hierarchical Bayesian model for the cognitive structure of the progressive ratio task. This model may be used to investigate the underlying mechanisms of human behavior in progressive ratio tasks, which can identify the factors contributing to participants’ performance. A simulation study shows satisfactory parameter recovery results for this model. We apply the model to a progressive ratio data set involving people with schizophrenia, first-degree relatives of people with schizophrenia, and people without schizophrenia. Our analysis reveals that people with schizophrenia are more affected by elapsed time than people without schizophrenia, tending to lose motivation to exert effort as they spend more time and effort in the task, regardless of the effort-reward ratio. The first-degree relatives show intermediate effects of time and effort-reward optimization between people with and without schizophrenia, which indicates that first-degree relatives might share some deficits with people with schizophrenia, only not as severe. PubDate: 2022-01-03 DOI: 10.1007/s42113-021-00114-1
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The purpose of the current experiment was to examine the effect of perceptual separability on human-automation team efficiency in a speeded judgment task. Human operators in applied environments interact with automated systems via a visual display which contain both complex raw data and automated support, requiring both sources of information to be mentally integrated by operators. Participants performed a speeded length-judgment task with or without decisional cues issued by a reliable automated aid. The cue was rendered in the format perceptually separable (color) or configural (area) to raw stimulus information (length). Workload capacity measures quantified human-automation team efficiency. Participants responded more slowly following the onset of the aid’s decisional cue in the area display format in the form of limited-capacity processing than the color display format, which led to unlimited-capacity processing. The color display format can support unlimited-capacity processing without moderating operators’ response speed while the area display format may produce limited-capacity processing, delaying their responses. Automation and display designers should consider utilizing separable perceptual characteristics of display elements in visual interfaces to improve human-automation team efficiency in a speeded perceptual-cognitive task. PubDate: 2021-12-01 DOI: 10.1007/s42113-021-00108-z
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract How do people decide how general a causal relationship is, in terms of the entities or situations it applies to' What features do people use to decide whether a new situation is governed by a new causal law or an old one' How can people make these difficult judgments in a fast, efficient way' We address these questions in two experiments that ask participants to generalize from one (Experiment 1) or several (Experiment 2) causal interactions between pairs of objects. In each case, participants see an agent object act on a recipient object, causing some changes to the recipient. In line with the human capacity for few-shot concept learning, we find systematic patterns of causal generalizations favoring simpler causal laws that extend over categories of similar objects. In Experiment 1, we find that participants’ inferences are shaped by the order of the generalization questions they are asked. In both experiments, we find an asymmetry in the formation of causal categories: participants preferentially identify causal laws with features of the agent objects rather than recipients. To explain this, we develop a computational model that combines program induction (about the hidden causal laws) with non-parametric category inference (about their domains of influence). We demonstrate that our modeling approach can both explain the order effect in Experiment 1 and the causal asymmetry, and outperforms a naïve Bayesian account while providing a computationally plausible mechanism for real-world causal generalization. PubDate: 2021-11-30 DOI: 10.1007/s42113-021-00124-z
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Human behavior and interaction in road traffic is highly complex, with many open scientific questions of high applied importance, not least in relation to recent development efforts toward automated vehicles. In parallel, recent decades have seen major advances in cognitive neuroscience models of human decision-making, but these models have mainly been applied to simplified laboratory tasks. Here, we demonstrate how variable-drift extensions of drift diffusion (or evidence accumulation) models of decision-making can be adapted to the mundane yet non-trivial scenario of a pedestrian deciding if and when to cross a road with oncoming vehicle traffic. Our variable-drift diffusion models provide a mechanistic account of pedestrian road-crossing decisions, and how these are impacted by a variety of sensory cues: time and distance gaps in oncoming vehicle traffic, vehicle deceleration implicitly signaling intent to yield, as well as explicit communication of such yielding intentions. We conclude that variable-drift diffusion models not only hold great promise as mechanistic models of complex real-world decisions, but that they can also serve as applied tools for improving road traffic safety and efficiency. PubDate: 2021-10-05 DOI: 10.1007/s42113-021-00116-z
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Speeded decision tasks are usually modeled within the evidence accumulation framework, enabling inferences on latent cognitive parameters, and capturing dependencies between the observed response times and accuracy. An example is the speed-accuracy trade-off, where people sacrifice speed for accuracy (or vice versa). Different views on this phenomenon lead to the idea that participants may not be able to control this trade-off on a continuum, but rather switch between distinct states (Dutilh et al., Cognitive Science 35(2):211–250, 2010). Hidden Markov models are used to account for switching between distinct states. However, combining evidence accumulation models with a hidden Markov structure is a challenging problem, as evidence accumulation models typically come with identification and computational issues that make them challenging on their own. Thus, an integration of hidden Markov models with evidence accumulation models has still remained elusive, even though such models would allow researchers to capture potential dependencies between response times and accuracy within the states, while concomitantly capturing different behavioral modes during cognitive processing. This article presents a model that uses an evidence accumulation model as part of a hidden Markov structure. This model is considered as a proof of principle that evidence accumulation models can be combined with Markov switching models. As such, the article considers a very simple case of a simplified Linear Ballistic Accumulation. An extensive simulation study was conducted to validate the model’s implementation according to principles of robust Bayesian workflow. Example reanalysis of data from Dutilh et al. (Cognitive Science 35(2):211–250, 2010) demonstrates the application of the new model. The article concludes with limitations and future extensions or alternatives to the model and its application. PubDate: 2021-09-14 DOI: 10.1007/s42113-021-00115-0
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Humans often face sequential decision-making problems, in which information about the environmental reward structure is detached from rewards for a subset of actions. In the current exploratory study, we introduce an information-selective symmetric reversal bandit task to model such situations and obtained choice data on this task from 24 participants. To arbitrate between different decision-making strategies that participants may use on this task, we developed a set of probabilistic agent-based behavioral models, including exploitative and explorative Bayesian agents, as well as heuristic control agents. Upon validating the model and parameter recovery properties of our model set and summarizing the participants’ choice data in a descriptive way, we used a maximum likelihood approach to evaluate the participants’ choice data from the perspective of our model set. In brief, we provide quantitative evidence that participants employ a belief state-based hybrid explorative-exploitative strategy on the information-selective symmetric reversal bandit task, lending further support to the finding that humans are guided by their subjective uncertainty when solving exploration-exploitation dilemmas. PubDate: 2021-08-02 DOI: 10.1007/s42113-021-00112-3
Hybrid journal (It can contain Open Access articles)
[2469 journals]
