Constraining bridges between levels of analysis : a computational justification for locally Bayesian learning

Different levels of analysis provide different insights into behavior: computational-level analyses determine the problem an organism must solve and algorithmic-level analyses determine the mechanisms that drive behavior. However, many attempts to model behavior are pitched at a single level of analysis. Research into human and animal learning provides a prime example, with some researchers using computational-level models to understand the sensitivity organisms display to environmental statistics but other researchers using algorithmic-level models to understand organisms’ trial order effects, including effects of primacy and recency. Recently, attempts have been made to bridge these two levels of analysis. Locally Bayesian Learning (LBL) creates a bridge by taking a view inspired by evolutionary psychology: Our minds are composed of modules that are each individually Bayesian but communicate with restricted messages. A different inspiration comes from computer science and statistics: Our brains are implementing the algorithms developed for approximating complex probability distributions. We show that these different inspirations for how to bridge levels of analysis are not necessarily in conflict by developing a computational justification for LBL. We demonstrate that a scheme that maximizes computational fidelity while using a restricted factorized representation produces the trial order effects that motivated the development of LBL. This scheme uses the same modular motivation as LBL, passing messages about the attended cues between modules, but does not use the rapid shifts of attention considered key for the LBL approximation. This work illustrates a new way of tying together psychological and computational constraints

Physical Attractiveness and Altruism in Two Modified Dictator Games

Several studies find that male individuals are more altruistic toward attractive women, suggesting altruism may serve as a courtship display. Many studies exploring this phenomenon have used vignettes and facial images. We tested the sexual selection hypothesis as an explanation for altruistic behavior, where players played the dictator game with “live” participants. Two studies were conducted (Study 1, n = 212; Study 2, n = 188) where we manipulated stakes and anonymity between participants to explore the relationship between the dictator’s allocations and their perceived attractiveness of the recipient. We found no relationship between attractiveness and altruism. Dictators were consistently fair when allocating stakes, irrespective of the recipients’ attractiveness

Justice and feelings: Toward a new era in justice research

In this special issue, the relationship between feelings and justice and its consequences are highlighted. Five articles discuss the role that affect, feelings, and emotions play in justice processes across a variety of social settings. In the present introductory article, the position of past and present justice research in relationship to these topics is briefly reviewed. In addition, reasons are outlined to show why a focus on these issues may be pivotal for a better understanding of social justice and how this may pave the way for a new, more process-oriented era in social justice research, focusing more on “hot” cognitive aspects as they pertain to social justice concerns

Networks of Emotion Concepts

The aim of this work was to study the similarity network and hierarchical clustering of Finnish emotion concepts. Native speakers of Finnish evaluated similarity between the 50 most frequently used Finnish words describing emotional experiences. We hypothesized that methods developed within network theory, such as identifying clusters and specific local network structures, can reveal structures that would be difficult to discover using traditional methods such as multidimensional scaling (MDS) and ordinary cluster analysis. The concepts divided into three main clusters, which can be described as negative, positive, and surprise. Negative and positive clusters divided further into meaningful sub-clusters, corresponding to those found in previous studies. Importantly, this method allowed the same concept to be a member in more than one cluster. Our results suggest that studying particular network structures that do not fit into a low-dimensional description can shed additional light on why subjects evaluate certain concepts as similar. To encourage the use of network methods in analyzing similarity data, we provide the analysis software for free use (http://www.becs.tkk.fi/similaritynets/)

Conflicted Emotions Following Trust-based Interaction

We investigated whether 20 emotional states, reported by 170 participants after participating in a Trust game, were experienced in a patterned way predicted by the “Recalibrational Model” or Valence Models. According to the Recalibrational Model, new information about trust-based interaction outcomes triggers specific sets of emotions. Unlike Valence Models that predict reports of large sets of either positive or negative emotional states, the Recalibrational Model predicts the possibility of conflicted (concurrent positive and negative) emotional states. Consistent with the Recalibrational Model, we observed reports of conflicted emotional states activated after interactions where trust was demonstrated but trustworthiness was not. We discuss the implications of having conflicted goals and conflicted emotional states for both scientific and well-being pursuits

Understanding cumulative risk

This paper summarizes the theory of simple cumulative risks—for example, the risk of food poisoning from the consumption of a series of portions of tainted food. Problems concerning such risks are extraordinarily difficult for naı¨ve individuals, and the paper explains the reasons for this difficulty. It describes how naı¨ve individuals usually attempt to estimate cumulative risks, and it outlines a computer program that models these methods. This account predicts that estimates can be improved if problems of cumulative risk are framed so that individuals can focus on the appropriate subset of cases. The paper reports two experiments that corroborated this prediction. They also showed that whether problems are stated in terms of frequencies (80 out of 100 people got food poisoning) or in terms of percentages (80% of people got food poisoning) did not reliably affect accuracy

Peperiksaan Perkhidmatan Penolong Pegawai Sains C29 Kertas II (Hospital Universiti Sains Malaysia) 28 November 2018

Tarikh : 28 November 2018 (Rabu) Masa : 9.00 pagi – 11.30 pagi (2 ½ jam) Tempat : Kampus Kesihata