The Evolution of Predator Resemblance in Avian Brood Parasites

Predators have profound effects on prey behavior and some adult brood parasites use predator resemblance to exploit the antipredator defenses of their hosts. Clarifying host perception of such stimuli is important for understanding the adaptive significance of adult brood parasite characteristics, and the mechanisms by which they misdirect hosts. Here I review the literature to explore the adaptive basis of predator resemblance in avian brood parasites, and natural variation in host responses to these stimuli. I also provide a framework for the information ecology of predator resemblance, which is based on the principles of signal detection theory and draws from empirical evidence from the common cuckoo, Cuculus canorus, as the most widely studied system. In this species, visual and acoustic hawk-like stimuli are effective in manipulating host defenses. Overall, contrasts across host responses suggest that different modalities of information can have independent effects on hosts, and that predator resemblance takes advantage of multiple sensory and cognitive processes. Host perception of these stimuli and the degree to which they are processed in an integrated manner, and the physiological processes underlying regulation of the responses, present new avenues for brood parasitism research.

Contested science: Individuals with higher metacognitive insight into interpretation of evidence are less likely to polarize

Societal polarization over contested science has increased in recent years. To explain this development, political, sociological, and psychological research has identified societal macro-phenomena as well as cognitive micro-level factors that explain how citizens reason about the science. Here we take a radically different perspective, and highlight the effects of metacognition: How citizens reason about their own reasoning. Leveraging methods from Signal Detection Theory, we investigated the importance of metacognitive insight for polarization for the heavily contested topic of climate change, and the less heavily contested topic of nanotechnology. We found that, for climate change (but not for nanotechnology), higher insight into the accuracy of own interpretations of the available scientific evidence related to a lower likelihood of polarization over the science. This finding held irrespective of the direction of the scientific evidence (endorsing or rejecting anthropogenicity of climate change). Furthermore, the polarizing effect of scientific evidence could be traced back to higher metacognitive insight fostering belief-updating in the direction of the evidence at the expense of own, prior beliefs. By demonstrating how metacognition links to polarization, the present research adds to our understanding of the drivers of societal polarization over science.

Stereotypical Processing of Emotional Faces: Perceptual and Decisional Components

People tend to associate anger with male faces and happiness or surprise with female faces. This angry-men-happy-women bias has been ascribed to either top-down (e.g., well-learned stereotypes) or bottom-up (e.g., shared morphological cues) processes. The dissociation between these two theoretical alternatives has proved challenging. The current effort addresses this challenge by harnessing two complementary metatheoretical approaches to dimensional interaction: Garner's logic of inferring informational structure and General Recognition Theory—a multidimensional extension of signal detection theory. Conjoint application of these two rigorous methodologies afforded us to: (a) uncover the internal representations that generate the angry-men-happy-women phenomenon, (b) disentangle varieties of perceptual (bottom-up) and decisional (top-down) sources of interaction, and (c) relate operational and theoretical meanings of dimensional independence. The results show that the dimensional interaction between emotion and gender is generated by varieties of perceptual and decisional biases. These outcomes document the involvement of both bottom-up (e.g., shared morphological structures) and top-down (stereotypes) factors in social perception.

The Pseudoscientific Structure of “Perceptual Decision-Making” and other “Signal Detection Theory"-based Research (or How Science Works When It Works)

The assumptions and formulas of “Signal Detection Theory” (SDT) dominate psychophysics and neuroscience, and serve as the basis of visual neuroscience under the rubric of “perceptual decision-making.” Here, I discuss how the overly simple, ad hoc assumptions of SDT served to rationalize the chronic failure of traditional psychophysics to achieve reliable results; how the constraints on outcomes imposed by the traditional methods combined with SDT to artificially immunize core assumptions from empirical challenge; and how consequently, research activity has been reduced to a seemingly uncomplicated - yet still non-replicable - matter of mere measurement and correlation. I contrast the structure of this ever-barren approach to the structure of research that respects reality and expands our knowledge of the natural world.

Performance-optimized neural networks as an explanatory framework for decision confidence

Previous work has sought to understand decision confidence as a prediction of the probability that a decision will be correct, leading to debate over whether these predictions are optimal, and whether they rely on the same decision variable as decisions themselves. This work has generally relied on idealized, low-dimensional modeling frameworks, such as signal detection theory or Bayesian inference, leaving open the question of how decision confidence operates in the domain of high-dimensional, naturalistic stimuli. To address this, we developed a deep neural network model optimized to assess decision confidence directly given high-dimensional inputs such as images. The model naturally accounts for a number of puzzling dissociations between decisions and confidence, suggests a principled explanation of these dissociations in terms of optimization for the statistics of sensory inputs, and makes the surprising prediction that, despite these dissociations, decisions and confidence depend on a common decision variable.

Predicting and measuring decision rules for social recognition in a Neotropical frog

Many animals use signals, such as vocalizations, to recognize familiar individuals. However, animals risk making recognition mistakes because the signal properties of different individuals often overlap due to within-individual variation in signal production. To understand the relationship between signal variation and decision rules for social recognition, we studied male golden rocket frogs, which recognize the calls of territory neighbors and respond less aggressively to a neighbor's calls than to the calls of strangers. We quantified patterns of individual variation in acoustic properties of calls and predicted optimal discrimination thresholds using a signal detection theory model of receiver utility that incorporated signal variation, the payoffs of correct and incorrect decisions, and the rates of encounters with neighbors and strangers. We then experimentally determined thresholds for discriminating between neighbors and strangers using a habituation-discrimination experiment with territorial males in the field. Males required a threshold difference between 9% and 12% to discriminate between calls differing in temporal properties; this threshold matched those predicted by a signal detection theory model under ecologically realistic assumptions of infrequent encounters with strangers and relatively costly missed detections of strangers. We demonstrate empirically that receivers group continuous variation in vocalizations into discrete social categories and show that signal detection theory can be applied to investigate evolved decision rules.