Humans have an extremely flexible ability to categorize regularities in their environment, in part because of attentional systems that allow them to focus on important perceptual information. In formal theories of categorization, attention is typically modeled with weights that selectively bias the processing of stimulus features. These theories make differing predictions about the degree of flexibility with which attention can be deployed in response to stimulus properties. Results from 2 eye-tracking studies show that humans can rapidly learn to differently allocate attention to members of different categories. These results provide the first unequivocal demonstration of stimulus-responsive attention in a categorization task. Furthermore, the authors found clear temporal patterns in the shifting of attention within trials that follow from the informativeness of particular stimulus features. These data provide new insights into the attention processes involved in categorization.

Typically, the search for order in grapheme-color synesthesia has been conducted by looking at the frequency of certain letter-color associations. Here, we report stronger associations when second-order similarity mappings are examined-specifically, mappings between the synesthetic colors of letters and letter shape, frequency, and position in the alphabet. The analyses demonstrate that these relations are independent of one other. More strikingly, our analyses show that each of the letter-color mappings is restricted to one dimension of color, with letter shape and ordinality linked to hue, and letter frequency linked to luminance. These results imply that synesthetic associations are acquired as the alphabet is learned, with associations involving letter shape, ordinality, and frequency being made independently and idiosyncratically. Because these mappings of similarity structure between domains (letters and colors) are similar to those found in numerous other cognitive and perceptual domains, they imply that synesthetic associations operate on principles common to many aspects of human cognition.Keywords Human associative learning . Perceptual categorization and identification . Synaesthesia . SynesthesiaAt least 1% of the population reliably associates particular colors with letters and numerals (Simner et al., 2006). Despite an explosion of research on grapheme-color synesthesia over the past two decades, little is known about how these associations are made. Why does Jane see the letter M as a deep purple, while John associates the same letter with forest green? Here we verify that there are several different sources of synesthetic associations, and we investigate both how they interact with each other and what aspects of synesthetic color they influence.To date, synesthesia research has documented a number of regularities in the grapheme-color pairs of individuals. For example, English speakers often associate the letter B with blue or brown, G with green, and so on for the first letters of other common color names (Barnett et al., 2008;Rich, Bradshaw, & Mattingley, 2005;Simner et al., 2005). Similarly, some synesthetes have adopted the colors of letter-shaped fridge magnets used in their childhoods (Witthoft & Winawer, 2006). These are regularities in first-order relations-that is, between nonrelational properties of a letter (such as its shape or name) and dimensions of synesthetic color such as hue and lightness (see also Day, 2005).A parallel line of research has begun to investigate grapheme-color pairings by looking for second-order relations, or "relations between relations." For example, letters with similar shapes, such as E and F, tend to be associated with synesthetic colors that are similar in hue (Brang, Rouw,

Visual search involves the coordination of looking (moving one's gaze to new locations) and seeing (distinguishing targets and nontargets). These two aspects of visual search are distinct from one another because high-acuity vision is possible only in a small region at the center of gaze (the fovea), and only when the eyes are stationary (a fixation). To sample detailed information from an extended scene, the eyes must move abruptly (saccade) from one location to another. In the typical inspection of a scene, this fixation-saccade cycle is repeated 3-4 times/sec.The efficiency of visual search-how rapidly and accurately the target is found-is typically measured by the time that elapses between the first glimpse of a scene and a response indicating target detection. This entails a direct trading relation between seeing and looking: Longer fixations increase information fidelity from each location at the cost of exploring fewer locations, whereas quickly exploring many locations results in reduced fidelity at each one. Studies comparing human oculomotor behavior with an ideal psychophysical observer have indicated that many participants come close to optimizing this trade-off in search (Najemnik & Geisler, 2005.In the present study, we explored the consequences of adopting particular cognitive strategies on this trading relationship. Several studies have shown that participants who are instructed to search passively search more efficiently than those who are instructed to search actively (complete instructions are in the Method section) (Smilek, Dixon, & Merikle, 2006;Smilek, Enns, Eastwood, & Merikle, 2006). Smilek, Enns, et al. (2006) hypothesized that the passive strategy gives automatic processes more influence over spatial attention, whereas the active strategy encourages greater reliance on unnecessary executive processes (cf. Wolfe, Alvarez, & Horowitz, 2000). This interpretation was bolstered by a second experiment in Smilek, Enns, et al. (2006) showing that search was improved when participants performed a simultaneous task that occupied executive processes.In the present study, we asked three broad questions concerning cognitive strategies and eye movements. First, is there any relationship between the two at all? It may be that strategy has no effect on eye movements, in that all of the participants use their eyes to sample information in essentially the same way. If so, the passive advantage found by Smilek, Dixon, and Merikle (2006) and Smilek, Enns, et al. (2006) may be purely cognitive, reflecting differences in the way scene information is processed after the eyes have sampled it.Second, if strategies alter eye movements, which oculomotor measures are affected? We hypothesized that passively instructed searchers will shift their emphasis to looking less and seeing more, spending more time on individual fixations than do active searchers. We also expected differences in other oculomotor behaviors. There are at least two ways one could see more: by expanding the attentional window of each fixation (i...

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This document describes the tests performed to characterize USE system latencies relating to the USE I/O Box. Test methods and results are summarized.

Learning and synesthesia are profoundly interconnected. On the one hand, the development of synesthesia is clearly influenced by learning. Synesthetic inducers – the stimuli that evoke these unusual experiences – often involve the perception of complex properties learned in early childhood, e.g., letters, musical notes, numbers, months of the year, and even swimming strokes. Further, recent research has shown that the associations individual synesthetes make with these learned inducers are not arbitrary, but are strongly influenced by the structure of the learned domain. For instance, the synesthetic colors of letters are partially determined by letter frequency and the relative positions of letters in the alphabet. On the other hand, there is also a small, but growing, body of literature which shows that synesthesia can influence or be helpful in learning. For instance, synesthetes appear to be able to use their unusual experiences as mnemonic devices and can even exploit them while learning novel abstract categories. Here we review these two directions of influence and argue that they are interconnected. We propose that synesthesia arises, at least in part, because of the cognitive demands of learning in childhood, and that it is used to aid perception and understanding of a variety of learned categories. Our thesis is that the structural similarities between synesthetic triggering stimuli and synesthetic experiences are the remnants, the fossilized traces, of past learning challenges for which synsethesia was helpful.

Many theories of category learning incorporate mechanisms for selective attention, typically implemented as attention weights that change on a trial-by-trial basis. This is because there is relatively little data on within-trial changes in attention. We used eye tracking and mouse tracking as finegrained measures of attention in three complex visual categorization tasks to investigate temporal patterns in overt attentional behavior within individual categorization decisions. In Experiments 1 and 2, we recorded participants' eye movements while they performed three different categorization tasks. We extended previous research by demonstrating that not only are participants less likely to fixate irrelevant features, but also, when they do, these fixations are shorter than fixations to relevant features. We also found that participants' fixation patterns show increasingly consistent temporal patterns. Participants were faster, although no more accurate, when their fixation sequences followed a consistent temporal structure. In Experiment 3, we replicated these findings in a task where participants used mouse movements to uncover features. Overall, we showed that there are important temporal regularities in information sampling during category learning that cannot be accounted for by existing models. These can be used to supplement extant models for richer predictions of how information is attended to during the buildup to a categorization decision.Keywords Attention . Eye tracking . Categorization . Eye movements . Optimization . Learning . Error . Modeling . Temporal regularity . Visual cognition To interact with the world, people must learn how to sort through the vast amount of information it presents. One of the difficulties in doing so is that much of this information is irrelevant to the task at hand. Consequently, it is essential to allocate limited cognitive resources to important aspects of the environment through selective attention. The notion of selective attention is recognized within a broad range of research areas, from working memory (Awh, Anllo-Vento, & Hillyard, 2000) and priming (Tipper & Cranston, 1985) to cognitive development over the human life span (Plude, Enns, & Brodeur, 1994). Category learning, a paradigm that elicits voluntary control of attention, provides a unique opportunity for observing and formally analyzing how attention shifts over time in response to the relevance of available information. The present study uses eye tracking and mouse tracking to document temporal characteristics of overt top-down attentional behavior during categorization tasks and how this changes over the course of learning.One of the most influential models in the categorylearning literature, the generalized context model (GCM;Nosofsky, 1986), models the role of attention in learning as a weighting mechanism for incoming stimulus information. This type of model hypothesizes that relevant information gains more attention weight as a consequence of learning. For example, after learning the task of di...

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