A hierarchical bayesian model of human decision-making on an optimal stopping problem

We consider human performance on an optimal stopping problem where people are presented with a list of numbers independently chosen from a uniform distribution. People are told how many numbers are in the list, and how they were chosen. People are then shown the numbers one at a time, and are instructed to choose the maximum, subject to the constraint that they must choose a number at the time it is presented, and any choice below the maximum is incorrect. We present empirical evidence that suggests people use threshold-based models to make decisions, choosing the first currently maximal number that exceeds a fixed threshold for that position in the list. We then develop a hierarchical generative account of this model family, and use Bayesian methods to learn about the parameters of the generative process, making inferences about the threshold decision models people use. We discuss the interesting aspects of human performance on the task, including the lack of learning, and the presence of large individual differences, and consider the possibility of extending the modeling framework to account for individual differences. We also use the modeling results to discuss the merits of hierarchical, generative and Bayesian models of cognitive processes more generally.     

The Enhanced Agentic Diversity Perspective (EADP): An appeal for Co-creating an accommodative social space so that diversity unlocks innovativeness rather than problems

We propose in this article an Enhanced Agentic Diversity Perspective (EADP), which is derived from the concept of agentic resources in social relational theory. EADP recognizes that all human beings are equally agents, that agentic expressions differ due to variation of individual access to power resources (individual, relational and cultural) and that the current social conditions serve to promote agentic diversity by enhancing all the three domains of power resources. In addition, EADP comprises a proposal for the development of accommodative social spaces. We argue that only in an accommodative social space can diversity unlock innovation; otherwise, it serves as a source for conflict. A constructive, nonhierarchical account of diversity, such as EADP is needed under current social conditions for the optimal becoming of idiosyncratic human agents as well as human society as a whole.     

Context effects in multi-alternative decision making: empirical data and a Bayesian model

For decisions between many alternatives, the benchmark result is Hick's Law: that response time increases log-linearly with the number of choice alternatives. Even when Hick's Law is observed for response times, divergent results have been observed for error rates-sometimes error rates increase with the number of choice alternatives, and sometimes they are constant. We provide evidence from two experiments that error rates are mostly independent of the number of choice alternatives, unless context effects induce participants to trade speed for accuracy across conditions. Error rate data have previously been used to discriminate between competing theoretical accounts of Hick's Law, and our results question the validity of those conclusions. We show that a previously dismissed optimal observer model might provide a parsimonious account of both response time and error rate data. The model suggests that people approximate Bayesian inference in multi-alternative choice, except for some perceptual limitations.     

Obtaining the best value for money in adaptive sequential estimation

In [Kujala, J. V., Richardson, U., &; Lyytinen, H. (2010). A Bayesian-optimal principle for learner-friendly adaptation in learning games. Journal of Mathematical Psychology, 54(2), 247–255], we considered an extension of the conventional Bayesian adaptive estimation framework to situations where each observable variable is associated with a certain random cost of observation. We proposed an algorithm that chooses each placement by maximizing the expected gain in utility divided by the expected cost. In this paper, we formally justify this placement rule as an asymptotically optimal solution to the problem of maximizing the expected utility of an experiment that terminates when the total cost overruns a given budget. For example, the cost could be defined as the random time taken by each trial in an experiment, and one might wish to maximize the expected total information gain over as many trials as can be completed in 15 min. A simple, analytically tractable example is considered.     

Feeling Good,but Lacking Autonomy: Closed-Mindedness on Social and Moral Issues in New Religious Movements

According to previous research, New Religious Movements (NRMs) seem to have a positive effect on the mental health of members who join NRMs with some previous affective, cognitive or other vulnerabilities. The present study investigates the other, less positive, side of the psychology of NRMs, i.e. elements that may be an obstacle to optimal development, such as rigidity and low autonomy. In comparison to non-NRM members, members of various NRMs in Belgium (N = 120) were found to be low in quest religious orientation (Altemeyer and Hunsberger, Int J Psychol Religion 2:113–133, 1992), to privilege conservation values to the detriment of openness to change values (Schwartz, Advances in experimental social psychology (vol 25, pp. 1–65). Orlando, FL: Academic Press, 1992), to show submissiveness to authority in hypothetical situations (projective measure), and to highly moralize judgments of transgression relative to conventional domains (Turiel, The development of social knowledge: Morality and social convention. Cambridge: Cambridge University Press, 1983). Discussion points out the idea that rigidity and restriction of autonomy may be the price to be paid for the structuring role NRMs play with regard to previous vulnerabilities.

Does surrounding traffic benefit from an assisted driver with traffic light assistance system?

Traffic light assistance systems enable drivers more energy and time efficient driving behavior at signalized intersections. However, most vehicles will not be equipped with such systems in the next years. These unequipped vehicles’ drivers (UVDs) may benefit from assisted drivers, if they would adapt their behavior. This paper outlines how UVDs (N = 60) interpreted and reacted to a driver with traffic light assistance system. We used a multi-driver simulator with three drivers driving in a car-following scenario. The lead driver was not a participant, but a confederate who was followed by two UVDs. The confederate was apparently equipped either with or without a traffic light assistance system. The traffic light assistance system consisted of two functionalities: a Green Light Optimal Speed Advisory and a start-up assistance system with two different parametrizations. These functionalities aimed at preventing unnecessary changes in speed and reducing the start-up lost time after signal change. The results showed that UVDs benefited from the driving behavior of the confederate with traffic light assistance system. However, the assisted driving behavior was hardly understood and partly rated as aversive by the UVDs. We discuss how to enhance behavioral adaptation of UVDs. We also outline which negative consequences may result from encounters of driver with systems and UVDs. We assume that how UVDs react towards drivers with systems may be one factor contributing to a successful launch of such systems.     

Multi-attribute sequential search

This article describes empirical and theoretical results from two multi-attribute sequential search tasks. In both tasks, the DM sequentially encounters options described by two attributes and must pay to learn the values of the attributes. In the continuous version of the task the DM learns the precise numerical value of an attribute when she pays to view it. In the threshold version the DM learns only whether the value of an attribute is above or below a threshold that she sets herself. Results from the continuous condition reveal that DMs tended to terminate their searches too early relative to the optimal policy. The pattern reversed in the threshold condition: DMs searched for too long. Maximum likelihood comparisons of two different stochastic decision models showed that DMs under both information conditions performed in ways consistent with the optimal policies. Those offered continuous-valued attribute information did not, however, spontaneously degrade this information into binary (acceptable/unacceptable) form, despite the theoretical finding that satisficing can be a very effective and efficient search strategy.     

On Revolutionizing the Theory and Practice of Psychotherapy

The original concepts psychological hurt and therapeutic catharsis are proposed as the basis for a revolutionary change in the theory and practice of psychotherapy. Psychological hurt, understood as any residual interference with psychological functioning from an adverse event(s), is proposed as the irreducible cause of any such difficulty. Therapeutic catharsis is proposed as the optimal healing process for psychological hurt. Such a transformational view of the theory and practice of psychotherapy is deemed as necessary because of the lack of unequivocal answers to the following fundamental questions: What causes psychological problems? What resolves them in the most optimal way?     

Observing evidence accumulation during multi-alternative decisions

Most decision-making research has focused on choices between two alternatives. For choices between many alternatives, the primary result is Hick’s Law—that mean response time increases logarithmically with the number of alternatives. Various models for this result exist within specific paradigms, and there are some more general theoretical results, but none of those have been tested stringently against data. We present an experimental paradigm that supports detailed examination of multi-choice data, and analyze predictions from a Bayesian ideal observer model for this paradigm. Data from the experiment deviate from the predictions of the Bayesian model in interesting ways. A simple heuristic model based on evidence accumulation provides a good account for the data, and has attractive properties as a limit case of the Bayesian model.