Stress-induced Fos-like Immunoreactivity in the Pons and the Medulla Oblongata of Rats

Immunoreactivity of the immediate early gene c-fos was used to investigate changes in the activity of brainstem neurons in response to acute stressors like immobilization, formalin-induced pain, cold exposure, hemorrhage and insulin-induced hypoglycemia. Different stressors induced Fos-like immunoreactivity in different pontine and medullary neurons. A single, 3 hour immobilization was found to be a very strong stimulus that activated brainstem catecholaminergic (tyrosine hydroxylase-immunopositive) neurons and cells in the raphe and certain pontine tegmental nuclei, as well as in the reticular formation. Pain, induced by a subcutaneous injection of formalin was also effective on catecholamine-synthesizing neurons and on others cells in the nucleus of the solitary tract. Cold exposure activated cells mainly in the sensory spinal trigeminal and parabrachial nuclei and in the so-called "pontine thermoregulatory area". Moderate Fos-like immunoreactivity was induced by a hypotonic (25%) hemorrhage in medullary catecholaminergic neurons, the nucleus of the solitary tract and the Barrington nucleus. Among stressful stimuli used, insulin-induced hypoglycemia elicited the smallest Fos activation in the lower brainstem. The present observations indicate that different stressors may use different neuronal pathways in the central organization of the stress response.     

Multialternative context effects obtained using an inference task

When decision-makers are faced with a choice among multiple options that have several attributes, preferences are often influenced by how the options are related to one another. For example, consumer preferences can be influenced, and even reversed, by the context defined by available products. This article discusses three standard context effects found in the preferential-choice literature: the attraction, similarity, and compromise effects. While decision theorists have attempted to explain these three effects under single modeling accounts, it has never before been demonstrated that these effects can be obtained within the same experimental paradigm. A set of experiments is described that demonstrate the three effects in an inference task. The paradigm is completely novel, as no previous experimental work has examined the standard context effects in inference. The experiments also add to evidence that the effects are not confined to choices among options with affective value, such as consumer products. The experimental results provide evidence that these effects might be a general property of human choice behavior and bring into question explanations of the effects that are based on the concept of loss-aversion asymmetry.     

A quantum theoretical explanation for probability judgment errors

A quantum probability model is introduced and used to explain human probability judgment errors including the conjunction and disjunction fallacies, averaging effects, unpacking effects, and order effects on inference. On the one hand, quantum theory is similar to other categorization and memory models of cognition in that it relies on vector spaces defined by features and similarities between vectors to determine probability judgments. On the other hand, quantum probability theory is a generalization of Bayesian probability theory because it is based on a set of (von Neumann) axioms that relax some of the classic (Kolmogorov) axioms. The quantum model is compared and contrasted with other competing explanations for these judgment errors, including the anchoring and adjustment model for probability judgments. In the quantum model, a new fundamental concept in cognition is advanced--the compatibility versus incompatibility of questions and the effect this can have on the sequential order of judgments. We conclude that quantum information-processing principles provide a viable and promising new way to understand human judgment and reasoning.     

The Generalized Quantum Episodic Memory Model

Recent evidence suggests that experienced events are often mapped to too many episodic states, including those that are logically or experimentally incompatible with one another. For example, episodic over‐distribution patterns show that the probability of accepting an item under different mutually exclusive conditions violates the disjunction rule. A related example, called subadditivity, occurs when the probability of accepting an item under mutually exclusive and exhaustive instruction conditions sums to a number >1. Both the over‐distribution effect and subadditivity have been widely observed in item and source‐memory paradigms. These phenomena are difficult to explain using standard memory frameworks, such as signal‐detection theory. A dual‐trace model called the over‐distribution (OD) model (Brainerd & Reyna, 2008) can explain the episodic over‐distribution effect, but not subadditivity. Our goal is to develop a model that can explain both effects. In this paper, we propose the Generalized Quantum Episodic Memory (GQEM) model, which extends the Quantum Episodic Memory (QEM) model developed by Brainerd, Wang, and Reyna (2013). We test GQEM by comparing it to the OD model using data from a novel item‐memory experiment and a previously published source‐memory experiment (Kellen, Singmann, & Klauer, 2014) examining the over‐distribution effect. Using the best‐fit parameters from the over‐distribution experiments, we conclude by showing that the GQEM model can also account for subadditivity. Overall these results add to a growing body of evidence suggesting that quantum probability theory is a valuable tool in modeling recognition memory.     

A quantum probability account of order effects in inference

Order of information plays a crucial role in the process of updating beliefs across time. In fact, the presence of order effects makes a classical or Bayesian approach to inference difficult. As a result, the existing models of inference, such as the belief-adjustment model, merely provide an ad hoc explanation for these effects. We postulate a quantum inference model for order effects based on the axiomatic principles of quantum probability theory. The quantum inference model explains order effects by transforming a state vector with different sequences of operators for different orderings of information. We demonstrate this process by fitting the quantum model to data collected in a medical diagnostic task and a jury decision-making task. To further test the quantum inference model, a new jury decision-making experiment is developed. Using the results of this experiment, we compare the quantum inference model with two versions of the belief-adjustment model, the adding model and the averaging model. We show that both the quantum model and the adding model provide good fits to the data. To distinguish the quantum model from the adding model, we develop a new experiment involving extreme evidence. The results from this new experiment suggest that the adding model faces limitations when accounting for tasks involving extreme evidence, whereas the quantum inference model does not. Ultimately, we argue that the quantum model provides a more coherent account for order effects that was not possible before.     

Bayesian analysis of the piecewise diffusion decision model

Most past research on sequential sampling models of decision-making have assumed a time homogeneous process (i.e., parameters such as drift rates and boundaries are constant and do not change during the deliberation process). This has largely been due to the theoretical difficulty in testing and fitting more complex models. In recent years, the development of simulation-based modeling approaches matched with Bayesian fitting methodologies has opened the possibility of developing more complex models such as those with time-varying properties. In the present work, we discuss a piecewise variant of the well-studied diffusion decision model (termed pDDM) that allows evidence accumulation rates to change during the deliberation process. Given the complex, time-varying nature of this model, standard Bayesian parameter estimation methodologies cannot be used to fit the model. To overcome this, we apply a recently developed simulation-based, hierarchal Bayesian methodology called the probability density approximation (PDA) method. We provide an analysis of this methodology and present results of parameter recovery experiments to demonstrate the strengths and limitations of this approach. With those established, we fit pDDM to data from a perceptual experiment where information changes during the course of trials. This extensible modeling platform opens the possibility of applying sequential sampling models to a range of complex non-stationary decision tasks.     

The Phantom Decoy Effect in Perceptual Decision Making

A phantom decoy is an alternative that is superior to another “target” option but is unavailable at the time of choice. In value‐based decisions involving phantom decoys (e.g., consumer choices), individuals often show increased preference for the similar, inferior target option over a non‐dominated competitor alternative. Unlike value‐based decisions that are driven by subjective goals, perceptual decisions typically have an outside criterion that defines the goal of the task (e.g., target is present or absent). Despite their obvious differences, past research has documented a number of commonalities between both types of decisions. In a set of three experiments, we examine the influence of phantom options on simple perceptual decisions and point out a critical difference between perceptual and value‐based decisions. Our results show that in perceptual choice, participants prefer competitor options to target options, the opposite of the pattern typically found in consumer choice. We use the results of the experiments to examine the predictions of four different models of context effects including loss aversion and dynamic, preference accumulation models. We find that accumulation models provide the best explanation for our results as well as being able to generalize to other context effects. Copyright © 2015 John Wiley & Sons, Ltd.     

The Quality of Response Time Data Inference: A Blinded,Collaborative Assessment of the Validity of Cognitive Models

Psychonomic Bulletin & Review - Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into...