Color realism and color science

The target article is an attempt to make some progress on the problem of color realism. Are objects colored? And what is the nature of the color properties? We defend the view that physical objects (for instance, tomatoes, radishes, and rubies) are colored, and that colors are physical properties, specifically, types of reflectance. This is probably a minority opinion, at least among color scientists. Textbooks frequently claim that physical objects are not colored, and that the colors are "subjective" or "in the mind." The article has two other purposes: First, to introduce an interdisciplinary audience to some distinctively philosophical tools that are useful in tackling the problem of color realism and, second, to clarify the various positions and central arguments in the debate. The first part explains the problem of color realism and makes some useful distinctions. These distinctions are then used to expose various confusions that often prevent people from seeing that the issues are genuine and difficult, and that the problem of color realism ought to be of interest to anyone working in the field of color science. The second part explains the various leading answers to the problem of color realism, and (briefly) argues that all views other than our own have serious difficulties or are unmotivated. The third part explains and motivates our own view, that colors are types of reflectances and defends it against objections made in the recent literature that are often taken as fatal.     

Reinforcement learning, conditioning, and the brain: Successes and challenges

The field of reinforcement learning has greatly influenced the neuroscientific study of conditioning. This article provides an introduction to reinforcement learning followed by an examination of the successes and challenges using reinforcement learning to understand the neural bases of conditioning. Successes reviewed include (1) the mapping of positive and negative prediction errors to the firing of dopamine neurons and neurons in the lateral habenula, respectively; (2) the mapping of model-based and model-free reinforcement learning to associative and sensorimotor cortico-basal ganglia-thalamo-cortical circuits, respectively; and (3) the mapping of actor and critic to the dorsal and ventral striatum, respectively. Challenges reviewed consist of several behavioral and neural findings that are at odds with standard reinforcement-learning models, including, among others, evidence for hyperbolic discounting and adaptive coding. The article suggests ways of reconciling reinforcement-learning models with many of the challenging findings, and highlights the need for further theoretical developments where necessary. Additional information related to this study may be downloaded from http://cabn.psychonomic-journals.org/content/supplemental.     

Gestural communication of the gorilla (Gorilla gorilla): repertoire, intentionality and possible origins

Social groups of gorillas were observed in three captive facilities and one African field site. Cases of potential gesture use, totalling 9,540, were filtered by strict criteria for intentionality, giving a corpus of 5,250 instances of intentional gesture use. This indicated a repertoire of 102 gesture types. Most repertoire differences between individuals and sites were explicable as a consequence of environmental affordances and sampling effects: overall gesture frequency was a good predictor of universality of occurrence. Only one gesture was idiosyncratic to a single individual, and was given only to humans. Indications of cultural learning were few, though not absent. Six gestures appeared to be traditions within single social groups, but overall concordance in repertoires was almost as high between as within social groups. No support was found for the ontogenetic ritualization hypothesis as the chief means of acquisition of gestures. Many gestures whose form ruled out such an origin, i.e. gestures derived from species-typical displays, were used as intentionally and almost as flexibly as gestures whose form was consistent with learning by ritualization. When using both classes of gesture, gorillas paid specific attention to the attentional state of their audience. Thus, it would be unwarranted to divide ape gestural repertoires into ‘innate, species-typical, inflexible reactions’ and ‘individually learned, intentional, flexible communication’. We conclude that gorilla gestural communication is based on a species-typical repertoire, like those of most other mammalian species but very much larger. Gorilla gestures are not, however, inflexible signals but are employed for intentional communication to specific individuals.     

SOME LIKE IT HOT: CONSCIOUSNESS AND HIGHER-ORDER THOUGHTS

Philosophical Studies -     

Risk for Injury in Preschoolers: Relationship to Attention Deficit Hyperactivity Disorder

Parental ratings of preschoolers’ risk for injury, direct assessment of preschoolers’ behavior thought related to risk for injury (e.g., Inattention, impulsivity) and number of documented injuries were examined in preschoolers with Attention Deficit Hyperactivity Disorder (ADHD) and their non-ADHD peers (Control). Of preschoolers with ADHD, 58.3% exhibited behavior which placed them at-risk for physical injury (0% Control), and their performance was significantly poorer on clinic-based tests. Nonetheless, preschoolers with ADHD did not actually sustain significantly more injuries which warranted medical treatment in an emergency department. Although preschoolers with ADHD may be at increased risk for minor injuries, further research is needed to determine whether they more frequently sustain more serious injuries.     

Counterfactual and prefactual conditionals.

We consider reasoning about prefactual possibilities in the future, for example, "if I were to win the lottery next year I would buy a yacht" and counterfactual possibilities, for example, "if I had won the lottery last year, I would have bought a yacht." People may reason about indicative conditionals, for example, "if I won the lottery I bought a yacht" by keeping in mind a few true possibilities, for example, "I won the lottery and I bought a yacht." They understand counterfactuals by keeping in mind two possibilities, the conjecture, "I won the lottery and I bought a yacht" and the presupposed facts, "I did not win the lottery and I did not buy a yacht." We report the results of three experiments on prefactuals that examine what people judge them to imply, the possibilities they judge to be consistent with them, and the inferences they judge to follow from them. The results show that reasoners keep a single possibility in mind to understand a prefactual.     

Emotion Dysregulation and Eating Disorder Symptoms: Examining Distinct Associations and Interactions in Adolescents

Research on Child and Adolescent Psychopathology - Emotion dysregulation has been posited as a key transdiagnostic factor of mental health difficulties, including eating disorders. However, how...     

State-based versus reward-based motivation in younger and older adults

Recent decision-making work has focused on a distinction between a habitual, model-free neural system that is motivated toward actions that lead directly to reward and a more computationally demanding goal-directed, model-based system that is motivated toward actions that improve one’s future state. In this article, we examine how aging affects motivation toward reward-based versus state-based decision making. Participants performed tasks in which one type of option provided larger immediate rewards but the alternative type of option led to larger rewards on future trials, or improvements in state. We predicted that older adults would show a reduced preference for choices that led to improvements in state and a greater preference for choices that maximized immediate reward. We also predicted that fits from a hybrid reinforcement-learning model would indicate greater model-based strategy use in younger than in older adults. In line with these predictions, older adults selected the options that maximized reward more often than did younger adults in three of the four tasks, and modeling results suggested reduced model-based strategy use. In the task where older adults showed similar behavior to younger adults, our model-fitting results suggested that this was due to the utilization of a win-stay–lose-shift heuristic rather than a more complex model-based strategy. Additionally, within older adults, we found that model-based strategy use was positively correlated with memory measures from our neuropsychological test battery. We suggest that this shift from state-based to reward-based motivation may be due to age related declines in the neural structures needed for more computationally demanding model-based decision making.