Subscribe: Psychological Review - Vol 117, Iss 1
Preview: Psychological Review - Vol 117, Iss 1

Psychological Review - Vol 124, Iss 2

Psychological Review publishes articles that make important theoretical contributions to any area of scientific psychology.

Last Build Date: Tue, 28 Mar 2017 05:00:30 EST

Copyright: Copyright 2017 American Psychological Association

Robust social categorization emerges from learning the identities of very few faces.


Viewers are highly accurate at recognizing sex and race from faces—though it remains unclear how this is achieved. Recognition of familiar faces is also highly accurate across a very large range of viewing conditions, despite the difficulty of the problem. Here we show that computation of sex and race can emerge incidentally from a system designed to compute identity. We emphasize the role of multiple encounters with a small number of people, which we take to underlie human face learning. We use highly variable everyday ‘ambient’ images of a few people to train a Linear Discriminant Analysis (LDA) model on identity. The resulting model has human-like properties, including a facility to cohere previously unseen ambient images of familiar (trained) people—an ability which breaks down for the faces of unknown (untrained) people. The first dimension created by the identity-trained LDA classifies both familiar and unfamiliar faces by sex, and the second dimension classifies faces by race—even though neither of these categories was explicitly coded at learning. By varying the numbers and types of face identities on which a further series of LDA models were trained, we show that this incidental learning of sex and race reflects covariation between these social categories and face identity, and that a remarkably small number of identities need be learnt before such incidental dimensions emerge. The task of learning to recognize familiar faces is sufficient to create certain salient social categories. (PsycINFO Database Record (c) 2017 APA, all rights reserved)(image)

Cocaine addiction as a homeostatic reinforcement learning disorder.


Drug addiction implicates both reward learning and homeostatic regulation mechanisms of the brain. This has stimulated 2 partially successful theoretical perspectives on addiction. Many important aspects of addiction, however, remain to be explained within a single, unified framework that integrates the 2 mechanisms. Building upon a recently developed homeostatic reinforcement learning theory, the authors focus on a key transition stage of addiction that is well modeled in animals, escalation of drug use, and propose a computational theory of cocaine addiction where cocaine reinforces behavior due to its rapid homeostatic corrective effect, whereas its chronic use induces slow and long-lasting changes in homeostatic setpoint. Simulations show that our new theory accounts for key behavioral and neurobiological features of addiction, most notably, escalation of cocaine use, drug-primed craving and relapse, individual differences underlying dose-response curves, and dopamine D2-receptor downregulation in addicts. The theory also generates unique predictions about cocaine self-administration behavior in rats that are confirmed by new experimental results. Viewing addiction as a homeostatic reinforcement learning disorder coherently explains many behavioral and neurobiological aspects of the transition to cocaine addiction, and suggests a new perspective toward understanding addiction. (PsycINFO Database Record (c) 2017 APA, all rights reserved)(image)

The neural representation of the gender of faces in the primate visual system: A computer modeling study.


We use an established neural network model of the primate visual system to show how neurons might learn to encode the gender of faces. The model consists of a hierarchy of 4 competitive neuronal layers with associatively modifiable feedforward synaptic connections between successive layers. During training, the network was presented with many realistic images of male and female faces, during which the synaptic connections are modified using biologically plausible local associative learning rules. After training, we found that different subsets of output neurons have learned to respond exclusively to either male or female faces. With the inclusion of short range excitation within each neuronal layer to implement a self-organizing map architecture, neurons representing either male or female faces were clustered together in the output layer. This learning process is entirely unsupervised, as the gender of the face images is not explicitly labeled and provided to the network as a supervisory training signal. These simulations are extended to training the network on rotating faces. It is found that by using a trace learning rule incorporating a temporal memory trace of recent neuronal activity, neurons responding selectively to either male or female faces were also able to learn to respond invariantly over different views of the faces. This kind of trace learning has been previously shown to operate within the primate visual system by neurophysiological and psychophysical studies. The computer simulations described here predict that similar neurons encoding the gender of faces will be present within the primate visual system. (PsycINFO Database Record (c) 2017 APA, all rights reserved)(image)

Goal relevance as a quantitative model of human task relevance.


The concept of relevance is used ubiquitously in everyday life. However, a general quantitative definition of relevance has been lacking, especially as pertains to quantifying the relevance of sensory observations to one’s goals. We propose a theoretical definition for the information value of data observations with respect to a goal, which we call “goal relevance.” We consider the probability distribution of an agent’s subjective beliefs over how a goal can be achieved. When new data are observed, its goal relevance is measured as the Kullback-Leibler divergence between belief distributions before and after the observation. Theoretical predictions about the relevance of different obstacles in simulated environments agreed with the majority response of 38 human participants in 83.5% of trials, beating multiple machine-learning models. Our new definition of goal relevance is general, quantitative, explicit, and allows one to put a number onto the previously elusive notion of relevance of observations to a goal. (PsycINFO Database Record (c) 2017 APA, all rights reserved)(image)

Cassandra’s regret: The psychology of not wanting to know.


Ignorance is generally pictured as an unwanted state of mind, and the act of willful ignorance may raise eyebrows. Yet people do not always want to know, demonstrating a lack of curiosity at odds with theories postulating a general need for certainty, ambiguity aversion, or the Bayesian principle of total evidence. We propose a regret theory of deliberate ignorance that covers both negative feelings that may arise from foreknowledge of negative events, such as death and divorce, and positive feelings of surprise and suspense that may arise from foreknowledge of positive events, such as knowing the sex of an unborn child. We conduct the first representative nationwide studies to estimate the prevalence and predictability of deliberate ignorance for a sample of 10 events. Its prevalence is high: Between 85% and 90% of people would not want to know about upcoming negative events, and 40% to 70% prefer to remain ignorant of positive events. Only 1% of participants consistently wanted to know. We also deduce and test several predictions from the regret theory: Individuals who prefer to remain ignorant are more risk averse and more frequently buy life and legal insurance. The theory also implies the time-to-event hypothesis, which states that for the regret-prone, deliberate ignorance is more likely the nearer the event approaches. We cross-validate these findings using 2 representative national quota samples in 2 European countries. In sum, we show that deliberate ignorance exists, is related to risk aversion, and can be explained as avoiding anticipatory regret. (PsycINFO Database Record (c) 2017 APA, all rights reserved)(image)

Fechner’s law in metacognition: A quantitative model of visual working memory confidence.


Although visual working memory (VWM) has been studied extensively, it is unknown how people form confidence judgments about their memories. Peirce (1878) speculated that Fechner’s law—which states that sensation is proportional to the logarithm of stimulus intensity—might apply to confidence reports. Based on this idea, we hypothesize that humans map the precision of their VWM contents to a confidence rating through Fechner’s law. We incorporate this hypothesis into the best available model of VWM encoding and fit it to data from a delayed-estimation experiment. The model provides an excellent account of human confidence rating distributions as well as the relation between performance and confidence. Moreover, the best-fitting mapping in a model with a highly flexible mapping closely resembles the logarithmic mapping, suggesting that no alternative mapping exists that accounts better for the data than Fechner’s law. We propose a neural implementation of the model and find that this model also fits the behavioral data well. Furthermore, we find that jointly fitting memory errors and confidence ratings boosts the power to distinguish previously proposed VWM encoding models by a factor of 5.99 compared to fitting only memory errors. Finally, we show that Fechner’s law also accounts for metacognitive judgments in a word recognition memory task, which is a first indication that it may be a general law in metacognition. Our work presents the first model to jointly account for errors and confidence ratings in VWM and could lay the groundwork for understanding the computational mechanisms of metacognition. (PsycINFO Database Record (c) 2017 APA, all rights reserved)(image)

The Menstrual Cycle-Response and Developmental Affective-Risk Model: A multilevel and integrative model of influence.


An integrative developmental model is presented in which menstrual cycle-related symptoms are hypothesized to result in a cascade of developmental challenges that contribute to increased affective symptoms among adolescent girls, and to long-term developmental sequelae. To provide the basis for this model a broad foundation is developed considering (a) psychological symptoms and disorders associated with reproductive events across the life span, and (b) the many and complicated effects that female reproductive steroids (estrogen & progesterone) have which trigger a variety of physical and psychological changes that are commonly associated with the menstrual cycle. The Menstrual Cycle-Response and Developmental Affective-Risk Model is driven by 3 central concepts: (a) individual differences in response to steroids are very large and thus require analysis of individual response, rather than group-level tendencies; (b) the menstrual cycle itself represents an important and complex set of biological, physical, psychological, behavioral, and social changes, and should not be studied exclusively as changing steroid levels; and (c) the effects of the menstrual cycle during adolescence and early adulthood may have long-term developmental consequences. This model integrates specific effects of the menstrual cycle with contextual and social developmental variables, and with past theoretical models. (PsycINFO Database Record (c) 2017 APA, all rights reserved)(image)