People’s beliefs about normality play an important role in many aspects of cognition and life (e.g., causal cognition, linguistic semantics, cooperative behavior). But how do people determine what sorts of things are normal in the first place? Past research has studied both people’s representations of statistical norms (e.g., the average) and their representations of prescriptive norms (e.g., the ideal). Four studies suggest that people’s notion of normality incorporates both of these types of norms. In particular, people’s representations of what is (...) normal were found to be influenced both by what they believed to be descriptively average and by what they believed to be prescriptively ideal. This is shown across three domains: people’s use of the word ‘‘normal” (Study 1), their use of gradable adjectives (Study 2), and their judgments of concept prototypicality (Study 3). A final study investigated the learning of normality for a novel category, showing that people actively combine statistical and prescriptive information they have learned into an undifferentiated notion of what is normal (Study 4). Taken together, these findings may help to explain how moral norms impact the acquisition of normality and, conversely, how normality impacts the acquisition of moral norms. (shrink)

Do we see more than we can report? Psychologists and philosophers have been hotly debating this question, in part because both possibilities are supported by suggestive evidence. On one hand, phenomena such as inattentional blindness and change blindness suggest that visual awareness is especially sparse. On the other hand, experiments relating to iconic memory suggest that our in-the-moment awareness of the world is much richer than can be reported. Recent research has attempted to resolve this debate by showing that observers (...) can accurately report the color diversity of a quickly flashed group of letters, even for letters that are unattended. If this ability requires awareness of the individual letters’ colors, then this may count as a clear case of conscious awareness overflowing cognitive access. Here we explored this requirement directly: can we perceive ensemble properties of scenes even without being aware of the relevant individual features? Across several experiments that combined aspects of iconic memory with measures of change blindness, we show that observers can accurately report the color diversity of unattended stimuli, even while their self-reported awareness of the individual elements is coarse or nonexistent — and even while they are completely blind to situations in which each individual element changes color mid-trial throughout the entire experiment. We conclude that awareness of statistical properties may occur in the absence of awareness of individual features, and that such results are fully consistent with sparse visual awareness. (shrink)

When solving problems, like making predictions or choices, people often “sample” possibilities into mind. Here, we consider whether there is structure to the kinds of thoughts people sample by default—that is, without an explicit goal. Across three experiments we found that what comes to mind by default are samples from a probability distribution that combines what people think is likely and what they think is good. Experiment 1 found that the first quantities that come to mind for everyday behaviors and (...) events are quantities that combine what is average and ideal. Experiment 2 found, in a manipulated context, that the distribution of numbers that come to mind resemble the mathematical product of the presented statistical distribution and a (softmax-transformed) prescriptive distribution. Experiment 3 replicated these findings in a visual domain. These results provide insight into the process generating people’s conscious thoughts and invite new questions about the value of thinking about things that are both likely and good. (shrink)

Four studies explored people's judgments about whether particular types of behavior are compatible with determinism. Participants read a passage describing a deterministic universe, in which everything that happens is fully caused by whatever happened before it. They then assessed the degree to which different behaviors were possible in such a universe. Other participants evaluated the extent to which each of these behaviors had various features. We assessed the extent to which these features predicted judgments about whether the behaviors were possible (...) in a deterministic universe. Experiments 1 and 2 found that people's judgments about whether a behavior was compatible with determinism were not predicted by their judgments about whether that behavior relies on physical processes in the brain and body, is uniquely human, is unpredictable, or involves reasoning. Experiment 3, however, found that a distinction between what we call “active” and “passive” behaviors can explain people's judgments. Experiment 4 extended these findings, showing that we can measure this distinction in several ways and that it is robustly predicted by two different cues. Taken together, these results suggest that people carve up mentally guided behavior into two distinct types—understanding one type to be compatible with determinism, but another type to be fundamentally incompatible with determinism. (shrink)

Why do people enter devoted relationships when they can continue looking for better partners? The “strategic ignorance” account holds that remaining ignorant about alternative partners is a signal that you are a high‐quality partner. Despite this intuition, the authors show that evolution favors a “look while allowing your partner to look” strategy, unless the costs of being rejected by a looking partner are extremely high. Thus, the origins of love must be found elsewhere.

Yarkoni argues that researchers making broad inferences often use impoverished statistical models that fail to include important sources of variation as random effects. We argue, however, that for many common study designs, random effects are inappropriate and insufficient to draw general inferences, as the source of variation is not random, but systematic.