Before one can talk about global arrays and multimodal detection, one must be clear about the concept of information: How is it different from energy and how is it detected? And can it come to specify a needed movement? We consider these issues in our commentary.

Psychophysical scaling models of the form R = f, with R the response and I some intensity of an attribute, all assume that people judge the amounts of an attribute. With simple biases excepted, most also assume that judgments are independent of space, time, and features of the situation other than the one being judged. Many data support these ideas: Magnitude estimations of brightness increase with luminance. Nevertheless, I argue that the general model is wrong. The stabilized retinal image literature (...) shows that nothing is seen if light does not change over time. The classification literature shows that dimensions often combine to produce emergent properties that cannot be described by the elements in the stimulus. These and other effects cannot be adjusted for by simply adding variables to the general model because some factors do not combine linearly. The proposed alternative is that people initially judge the entire stimulus – the object in terms of its environment. This agrees with the constancy literature that shows that objects and their attributes are identified through their relations to other aspects of the scene. That the environment determines judgments is masked in scaling studies where the standard procedure is to hold context constant. In a typical brightness study the essential stimulus might be the intensity of the light or a difference between the light and the background. The two are perfectly confounded. This issue is examined in the case of audition. Judgments of the loudness of a tone depend on how much that tone differs from the previous tone in both pitch and loudness. To judge loudness people first seem to process the stimulus object in terms of differences between it and other aspects in the situation; only then do they assess the feature of interest. Psychophysical judgments will therefore be better interpreted by theories of attention that are based in biology or psychology than those that are based in classical physics. (shrink)

The purpose of this study is to take preliminary steps to unify psychoacoustic techniques with reaction-time methodologies to address the perceptual mechanisms responsible for the detection of one vs. multiple sounds. We measured auditory redundancy gains for auditory detection of pure tones widely spaced in frequency using the tools of Systems Factorial Technology to evince the system architecture and workload capacity in two different scenarios. We adopted an experimental design in which the presence or absence of a target at each (...) of two frequencies was combined factorially with two stimulus levels. Replicating previous work, results did not allow an assessment of system architecture due to a failure to observe factor influence at the level of distribution ordering for dual-target stimuli for both SOFT and LOUD scenarios. All subjects demonstrated very modest redundancy gains for the dual-target compared to the single-target stimuli, and results were similar for both LOUD and SOFT. We propose that these results can be predicted by a mental architecture that falls into the class of integrated subadditive parallel systems, using a well-supported assumption that reaction time is driven by loudness. We demonstrate that modeled loudness of the experimental sounds is highly correlated with mean reaction time, and we provide a proof-of-concept model based on Steven’s Power law that predicts both a failure of distributional ordering for dual-target stimuli and very modest redundancy gains. (shrink)

An ongoing philosophical discussion concerns how various types of mental states fall within broad representational genera—for example, whether perceptual states are “iconic” or “sentential,” “analog” or “digital,” and so on. Here, I examine the grounds for making much more specific claims about how mental states are structured from constituent parts. For example, the state I am in when I perceive the shape of a mountain ridge may have as constituent parts my representations of the shapes of each peak and saddle (...) of the ridge. More specific structural claims of this sort are a guide to how mental states fall within broader representational kinds. Moreover, these claims have significant implications of their own about semantic, functional, and epistemic features of our mental lives. But what are the conditions on a mental state's having one type of constituent structure rather than another? Drawing on explanatory strategies in vision science, I argue that, other things being equal, the constituent structure of a mental state determines what I call its distributional properties—namely, how mental states of that type can, cannot, or must co‐occur with other mental states in a given system. Distributional properties depend critically on and are informative about the underlying structures of mental states, they abstract in important ways from aspects of how mental states are processed, and they can yield significant insights into the variegation of psychological capacities. (shrink)

PROBEX (PROBabilities from EXemplars), a model of probabilistic inference and probability judgment based on generic knowledge is presented. Its properties are that: (a) it provides an exemplar model satisfying bounded rationality; (b) it is a “lazy” algorithm that presumes no pre‐computed abstractions; (c) it implements a hybrid‐representation, similarity‐graded probability. We investigate the ecological rationality of PROBEX and find that it compares favorably with Take‐The‐Best and multiple regression (Gigerenzer, Todd, & the ABC Research Group, 1999). PROBEX is fitted to the point (...) estimates, decisions, and probability assessments by human participants. The best fit is obtained for a version that weights frequency heavily and retrieves only two exemplars. It is proposed that PROBEX implements speed and frugality in a psychologically plausible way. (shrink)

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model that incorporates varying degrees of prior belief in class-conditional independence, learns whether or not independence holds, (...) and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments—designed using optimal experimental design principles—were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class-conditional independence, before adapting to the true environmental structure. Class-conditional independence may be a strong and useful default assumption in category learning tasks. (shrink)