We study whether experts and novices differ in the way they make predictionsabout National Football League games. In particular, we measure to what extenttheir predictions are consistent with five environmental regularities that couldsupport decision making based on heuristics. These regularities involve the hometeam winning more often, the team with the better win-loss record winning moreoften, the team favored by the majority of media experts winning more often, andtwo others related to surprise wins and losses in the teams’ previousgame. Using signal detection theory and hierarchical Bayesian analysis, we showthat expert predictions for the 2017 National Football League (NFL) seasongenerally follow these regularities in a near optimal way, but novicepredictions do not. These results support the idea that using heuristics adaptedto the decision environment can support accurate predictions and be an indicatorof expertise.

Signal detection theory (SDT) was developed to analyze the behavior of a single judge but also can be used to analyze decisions made by organizations or other social systems. SDT quantifies the ability to distinguish between signal and noise by separating accuracy of the detection system from response bias—the propensity to over-warn (too many false positives) or under-warn (too many misses). We apply SDT techniques to national and state-level data sets to analyze the ability of the child welfare services systems to detect instances of child maltreatment. Blacks have higher rates of referral and the system is less accurate for them than for Whites or Hispanics. The incidence of false positives—referrals leading to unsubstantiated findings—is higher for Blacks than for other groups, as is the incidence of false negatives—children for whom no referral was made but who are in fact neglected or abused. The rate of true positives–children for whom a referral was made and for whom the allegation was substantiated–is higher for Blacks. Values of d′ (signal strength) are roughly the same for Whites, Blacks, and Hispanics but there are pronounced group differences in C (a measure of the location of the decision threshold). Analyses show that the child welfare services system treats Blacks differently from Hispanics and Whites in ways that cannot be justified readily in terms of objective measures of group differences. This study illustrates the potential for JDM techniques such as SDT to contribute to understanding of system-level decision making processes.

The recognition heuristic (RH) — which predicts non-compensatory reliance on recognition in comparative judgments — has attracted much research and some disagreement, at times. Most studies have dealt with whether or under which conditions the RH is truly used in paired-comparisons. However, even though the RH is a precise descriptive model, there has been less attention concerning the precision of the methods applied to measure RH-use. In the current work, I provide an overview of different measures of RH-use tailored to the paradigm of natural recognition which has emerged as a preferred way of studying the RH. The measures are compared with respect to different criteria — with particular emphasis on how well they uncover true use of the RH. To this end, both simulations and a re-analysis of empirical data are presented. The results indicate that the adherence rate — which has been pervasively applied to measure RH-use — is a severely biased measure. As an alternative, a recently developed formal measurement model emerges as the recommended candidate for assessment of RH-use.

Focusing on the Sonority Sequencing Principle (SSP), we investigated the extent to which adult native speakers of French are sensitive to sonority-related constraints compared to lexical attestedness. In a non-word acceptability task, participants were asked to rate the acceptability of three types of non-words using a 6-point scale: non-words with attested sonority rising onset, non-words with unattested sonority rising onset, and non-words with unattested sonority falling onset. Data analysis was done using the signal detection theory approach to measure sensitivity of participants to lexical attestedness and to phonological well-formedness (i.e., respecting or violating the SSP). The results showed that speakers distinguished well-formed and ill-formed forms even when lexical attestedness was controlled for. This is consistent with previous findings on sonority projection effects. Participants were more sensitive to lexical attestedness than phonological well-formedness. Future research using computational models should investigate mechanisms that could account for these findings, namely whether a similar result would be obtained without including any assumption about the SSP in these models.

People tend to see faces from non-face objects or meaningless patterns. Such illusory face perception is called face pareidolia. Previous studies have revealed an interesting fact that there are huge individual differences in face pareidolia experience among the population. Here, we review previous findings on individual differences in face pareidolia experience from four categories: sex differences, developmental factors, personality traits and neurodevelopmental factors. We further discuss underlying cognitive or neural mechanisms to explain why some perceive the objects as faces while others do not. The individual differences in face pareidolia could not only offer scientific insights on how the brain works to process face information, but also suggest potential clinical applications.

There is a widespread attitude in epistemology that, if you know on the basis of perception, then you couldn't have been wrong as a matter of chance. Despite the apparent intuitive plausibility of this attitude, which I'll refer to here as “stochastic infallibilism”, it fundamentally misunderstands the way that human perceptual systems actually work. Perhaps the most important lesson of signal detection theory (SDT) is that our percepts are inherently subject to random error, and here I'll highlight some key empirical research that underscores this point. In doing so, it becomes clear that we are in fact quite willing to attribute knowledge to S that p even when S's perceptual belief that p could have been randomly false. In short, perceptual processes can randomly fail, and perceptual knowledge is stochastically fallible. The narrow implication here is that any epistemological account that entails stochastic infallibilism, like safety, is simply untenable. More broadly, this myth of stochastic infallibilism provides a valuable illustration of the importance of integrating empirical findings into epistemological thinking.

We examined the influence of the type of radio program on the memory for radio advertisements. We also investigated the role in memory of the typicality (high or low) of the elements of the products advertised. Participants listened to three types of programs (interesting, boring, enjoyable) with two advertisements embedded in each. After completing a filler task, the participants performed a true/false recognition test. Hits and false alarm rates were higher for the interesting and enjoyable programs than for the boring one. There were also more hits and false alarms for the high-typicality elements. The response criterion for the advertisements embedded in the boring program was stricter than for the advertisements in other types of programs. We conclude that the type of program in which an advertisement is inserted and the nature of the elements of the advertisement affect both the number of hits and false alarms and the response criterion, but not the accuracy of the memory.

Solving theoretical or empirical issues sometimes involves establishing the equality of two variables with repeated measures. This defies the logic of null hypothesis significance testing, which aims at assessing evidence against the null hypothesis of equality, not for it. In some contexts, equivalence is assessed through regression analysis by testing for zero intercept and unit slope (or simply for unit slope in case that regression is forced through the origin). This paper shows that this approach renders highly inflated Type I error rates under the most common sampling models implied in studies of equivalence. We propose an alternative approach based on omnibus tests of equality of means and variances and in subject-by-subject analyses (where applicable), and we show that these tests have adequate Type I error rates and power. The approach is illustrated with a re-analysis of published data from a signal detection theory experiment with which several hypotheses of equivalence had been tested using only regression analysis. Some further errors and inadequacies of the original analyses are described, and further scrutiny of the data contradict the conclusions raised through inadequate application of regression analyses.

Contrast sensitivity is a measure of the ability of an observer to detect contrast signals of particular spatial and temporal frequencies. A formal definition of contrast sensitivity that can be applied to individual linear visual neurons is derived. A neuron is modeled by a contrast transfer function and its modulus, contrast gain, and by a noise power spectrum. The distributions of neural responses to signal and blank presentations are derived, and from these, a definition of contrast sensitivity is obtained. This formal definition may be used to relate the sensitivities of various populations of neurons, and to relate the sensitivities of neurons to that of the behaving animal.

Although the study of visual perception has made more progress in the past 40 years than any other area of cognitive science, there remain major disagreements as to how closely vision is tied to cognition. This target article sets out some of the arguments for both sides (arguments from computer vision, neuroscience, psychophysics, perceptual learning, and other areas of vision science) and defends the position that an important part of visual perception, corresponding to what some people have called early vision, is prohibited from accessing relevant expectations, knowledge, and utilities in determining the function it computes – in other words, it is cognitively impenetrable. That part of vision is complex and involves top-down interactions that are internal to the early vision system. Its function is to provide a structured representation of the 3-D surfaces of objects sufficient to serve as an index into memory, with somewhat different outputs being made available to other systems such as those dealing with motor control. The paper also addresses certain conceptual and methodological issues raised by this claim, such as whether signal detection theory and event-related potentials can be used to assess cognitive penetration of vision.

A distinction is made among several stages in visual processing, including, in addition to the inflexible early-vision stage, a pre-perceptual attention-allocation stage and a post-perceptual evaluation, selection, and inference stage, which accesses long-term memory. These two stages provide the primary ways in which cognition can affect the outcome of visual perception. The paper discusses arguments from computer vision and psychology showing that vision is “intelligent” and involves elements of “problem solving.” The cases of apparently intelligent interpretation sometimes cited in support of this claim do not show cognitive penetration; rather, they show that certain natural constraints on interpretation, concerned primarily with optical and geometrical properties of the world, have been compiled into the visual system. The paper also examines a number of examples where instructions and “hints” are alleged to affect what is seen. In each case it is concluded that the evidence is more readily assimilated to the view that when cognitive effects are found, they have a locus outside early vision, in such processes as the allocation of focal attention and the identification of the stimulus.