Separating perceptual and decisional attention processes in the identification and categorization of integral-dimension stimuli

Four observers performed matching, identification, and categorization with stimuli that varied along the integral dimensions: brightness and saturation. General recognition theory (F. G. Ashby & J. T. Townsend, 1986) was applied to quantify the separate influences of perceptual and decisional processes within and across tasks, with a focus on separating perceptual from decisional attention processes. Good accounts of the identification data were obtained from perceptual matching representation. This perceptual representation provided a good account of the categorization data, except when decisional selective attention to 1 stimulus dimension was required. Decisional selective attention reduced the attended-dimension perceptual variance relative to the unattended-dimension perceptual variance, with a larger reduction resulting when brightness, as opposed to saturation was attended. Implications for color vision research are discussed.     

Separating perceptual processes from decisional processes in identification and categorization

Four observers completed perceptual matching, identification, and categorization tasks using separable-dimension stimuli. A unified quantitative approach relating perceptual matching, identification, and categorization was proposed and tested. The approach derives from general recognition theory (Ashby & Townsend, 1986) and provides a powerful method for quantifying the separate influences of perceptual processes and decisional processes within and across tasks. Good accounts of the identification data were obtained from an initial perceptual representation derived from perceptual matching. The same perceptual representation provided a good account of the categorization data, except when selective attention to one stimulus dimension was required. Selective attention altered the perceptual representation by decreasing the perceptual variance along the attended dimension. These findings suggest that a complete understanding of identification and categorization performance requires an understanding of perceptual and decisional processes. Implications for other psychological tasks are discussed.     

Separating perceptual processes from decisional processes in identification and categorization.

Four observers completed perceptual matching, identification, and categorization tasks using separable-dimension stimuli. A unified quantitative approach relating perceptual matching, identification, and categorization was proposed and tested. The approach derives from general recognition theory (Ashby & Townsend, 1986) and provides a powerful method for quantifying the separate influences of perceptual processes and decisional processes within and across tasks. Good accounts of the identification data were obtained from an initial perceptual representation derived from perceptual matching. The same perceptual representation provided a good account of the categorization data, except when selective attention to one stimulus dimension was required. Selective attention altered the perceptual representation by decreasing the perceptual variance along the attended dimension. These findings suggest that a complete understanding of identification and categorization performance requires an understanding of perceptual and decisional processes. Implications for other psychological tasks are discussed.     

On the relation between decision rules and perceptual representation in multidimensional perceptual categorization

This article examines the relation between changing categorization decision rules and the nature of the underlying perceptual representation. Observers completed a matching task that required them to adjust the length and orientation of a single line stimulus until they perceived it to "match" a second line stimulus (Alfonso-Reese, 1996, 1997). The same observers then completed four categorization tasks with the same stimuli. Data from the matching task were used to estimate a perceptual representation for each stimulus and observer. Three hypotheses regarding potential interactions between categorization decision rules and perceptual representation were examined. One assumed that there was no interaction between decision rules and perceptual representation. The second assumed that linear categorization rules affect the perceptual representation differently from nonlinear categorization rules. The third assumed that dimensional integration rules affected the perceptual representation differently from decision rules that require the observer to set a criterion along one stimulus dimension while ignoring the other; this is referred to as decisional selective attention. The results suggested that (1) the matching task perceptual representation provided a good account of the categorization data, (2) decisional selective attention affected the perceptual representation differently from decisional integration, and (3) decisional selective attention generally decreased the perceptual variability along the attended dimension.     

Holistic processing of faces: perceptual and decisional components

Researchers have used several composite face paradigms to assess holistic processing of faces. In the selective attention paradigm, participants decide whether one face part (e.g., top) is the same as a previously seen face part. Their judgment is affected by whether the irrelevant part of the test face is the same as or different than the relevant part of the study face. This failure of selective attention implies holistic processing. However, the authors show that this task alone cannot distinguish between perceptual and decisional sources of holism. The distinction can be addressed by the complete identification paradigm, in which both face parts are judged to be same or different, combined with analyses based on general recognition theory (F. G. Ashby & J. T. Townsend, 1986). The authors used a different paradigm, sequential responses, to relate these 2 paradigms empirically and theoretically. Sequential responses produced the same results as did selective attention and complete identification. Moreover, disruptions of holistic processing by systematic misalignment of the faces corresponded with systematic and significant changes in the decisional components, but not in the perceptual components, that were extracted using general recognition theory measures. This finding suggests a significant decisional component of holistic face processing in the composite face task.     

General recognition theory extended to include response times: Predictions for a class of parallel systems

General Recognition Theory (GRT; Ashby & Townsend, 1986) is a multidimensional theory of classification. Originally developed to study various types of perceptual independence, it has also been widely employed in diverse cognitive venues, such as categorization. The initial theory and applications have been static, that is, lacking a time variable and focusing on patterns of responses, such as confusion matrices. Ashby proposed a parallel, dynamic stochastic version of GRT with application to perceptual independence based on discrete linear systems theory with imposed noise (Ashby, 1989). The current study again focuses on cognitive/perceptual independence within an identification classification paradigm. We extend stochastic GRT and its implicated methodology for cognitive/perceptual independence, to an entire class of parallel systems. This goal is met in a distribution-free manner and includes all linear and non-linear systems satisfying very general conditions. A number of theorems are proven concerning stochastic forms of independence. However, the theorems all assume the stochastic version of decisional separability. A vital task remains to investigate the consequences of failures of stochastic decisional separability.     

The effects of concurrent verbal and visual tasks on category learning

Current theories of category learning posit separate verbal and nonverbal learning systems. Past research suggests that the verbal system relies on verbal working memory and executive functioning and learns rule-defined categories; the nonverbal system does not rely on verbal working memory and learns non-rule-defined categories (E. M. Waldron & F. G. Ashby, 2001; D. Zeithamova & W. T. Maddox, 2006). However, relatively little research has explored the importance of visual working memory or visual processing for either system. The authors investigated the role of working memory (Experiment 1a and 1b), visual processing (Experiment 2), and executive functioning for each system, using a concurrent task methodology. It was found that visual tasks with high executive functioning demands and verbal tasks with high or low executive demands disrupted rule-defined learning, whereas any visual task, regardless of executive functioning demand, disrupted non-rule-defined learning. Taken together, these results confirm the importance of verbal working memory and executive functioning for the verbal system and provide new evidence for the importance of visual processing for the nonverbal system. These results help to clarify understanding of the nonverbal system and have implications for multiple systems theories of category learning (F. G. Ashby, L. A. Alfonso-Reese, A. U. Turken, & E. M. Waldron, 1998).     

Mixture Models of Categorization

Many currently popular models of categorization are either strictly parametric (e.g., prototype models, decision bound models) or strictly nonparametric (e.g., exemplar models) (F. G. Ashby & L. A. Alfonso-Reese, 1995, Journal of Mathematical Psychology, 39, 216-233). In this article, a family of semiparametric classifiers is investigated where categories are represented by a finite mixture distribution. The advantage of these mixture models of categorization is that they contain several parametric models and nonparametric models as a special case. Specifically, it is shown that both decision bound models (F. G. Ashby & W. T. Maddox, 1992, Journal of Experimental Psychology: Human Perception and Performance, 16, 598-612; 1993, Journal of Mathematical Psychology, 37, 372-400) and the generalized context model (R. M. Nosofsky, 1986, Journal of Experimental Psychology: General, 115, 39-57) can be interpreted as two extreme cases of a common mixture model. Furthermore, many other (semiparametric) models of categorization can be derived from the same generic mixture framework. In this article, several examples are discussed and a parameter estimation procedure for fitting these models is outlined. To illustrate the approach, several specific models are fitted to a data set collected by S. C. McKinley and R. M. Nosofsky (1995, Journal of Experimental Psychology: Human Perception and Performance, 21, 128-148). The results suggest that semi-parametric models are a promising alternative for future model development.     

Multiple attention systems in perceptual categorization

Five observers categorized inverted L-shaped stimuli according to the length of the horizontal line segment. A centrally located spatial cue preceded the stimulus on each trial. On 80% of the trials, the (relevant) horizontal line segment fell within the cued location, and on 20% of the trials the (irrelevant) vertical line segment fell within the cued location. The empirical results provide support for the hypothesis that perceptual attention can focus on the stimulus attribute inside the spatially cued location at the same time that decisional attention is focused on the (relevant) horizontal attribute--that is, the results suggest that perceptual and decisional attention can function independently during categorization. Decision bound models and extended generalized context models that assume separate perceptual and decisional attention systems were fitted to the data. Versions of the models that assume that the spatial cue affected perceptual attention were superior to versions that assume no effect on perceptual attention. These theoretical analyses support the functional independence hypothesis and suggest that formal theories of categorization should model the effects of perceptual and decisional attention separately.     

A decisional component of holistic encoding

It has been proposed (see, specifically, M. J. Farah, K. D. Wilson, M. Drain, & J. N. Tanaka, 1998) that human faces are used in cognition as undifferentiated wholes. General recognition theory (GRT; F. G. Ashby & J. T. Townsend, 1986) is used to represent hypotheses regarding the possible sources for the behavioral evidence supporting holistic representation. Specifically, it is suggested that holism can be understood in terms of violations of informational independence, informational separability, or decisional separability, as these constructs are defined in GRT. Stimuli were presented upright, inverted, and in an encoding task that emphasized the meaningful nature of the stimuli. Patterns of performance (recognition hit rates) were consistent with prior studies. However, there were only a handful of violations of informational separability. Instead, consistent violations of decisional separability suggested a decisional basis for holistic effects.     

Perceptual fluency can be used as a cue for categorization decisions

Learning in the prototype distortion task is thought to involve perceptual learning in which category members experience an enhanced visual response (Ashby & Maddox. Annual Review of Psychology, 56, 149-178, 2005). This response likely leads to more-efficient processing, which in turn may result in a feeling of perceptual fluency for category members. We examined the perceptual-fluency hypothesis by manipulating fluency independently from category membership. We predicted that when perceptual fluency was induced using subliminal priming, this fluency would be misattributed to category membership and would affect categorization decisions. In a prototype distortion task, the participants were more likely to judge stimuli that were not members of the category as category members when the nonmembers were made perceptually fluent with a matching subliminal prime. This result suggests that perceptual fluency can be used as a cue during some categorization decisions. In addition, the results provided converging evidence that some types of categorization are based on perceptual learning.     

Predicting similarity and categorization from identification.

In this article, the relation between the identification, similarity judgment, and categorization of multidimensional perceptual stimuli is studied. The theoretical analysis focused on general recognition theory (GRT), which is a multidimensional generalization of signal detection theory. In one application, 2 Ss first identified a set of confusable stimuli and then made judgments of their pairwise similarity. The second application was to Nosofsky's (1985b, 1986) identification-categorization experiment. In both applications, a GRT model accounted for the identification data better than Luce's (1963) biased-choice model. The identification results were then used to predict performance in the similarity judgment and categorization conditions. The GRT identification model accurately predicted the similarity judgments under the assumption that Ss allocated attention to the 2 stimulus dimensions differently in the 2 tasks. The categorization data were predicted successfully without appealing to the notion of selective attention. Instead, a simpler GRT model that emphasized the different decision rules used in identification and categorization was adequate.     

Indecision on decisional separability

The theoretical framework of General Recognition Theory (GRT; Ashby & Townsend, Psychological Review, 93, 154-179, 1986) coupled with the empirical analysis tools of Multidimensional Signal Detection Analysis (MSDA; Kadlec & Townsend, Multidimensional models of perception and recognition, pp. 181-228, 1992) have become one important method for assessing dimensional interactions in perceptual decision-making. In this article, we critically examine MSDA and characterize cases where it is unable to discriminate two kinds of dimensional interactions: perceptual separability and decisional separability. We performed simulations with known instances of violations of perceptual or decisional separability, applied MSDA to the data generated by these simulations, and evaluated MSDA on its ability to accurately characterize the perceptual versus decisional source of these simulated dimensional interactions. Critical cases of violations of perceptual separability are often mischaracterized by MSDA as violations of decisional separability.     

Attention and implicit memory in the category-verification and lexical decision tasks

Prior research on implicit memory appeared to support 3 generalizations: Conceptual tests are affected by divided attention, perceptual tasks are affected by certain divided-attention manipulations, and all types of priming are affected by selective attention. These generalizations are challenged in experiments using the implicit tests of category verification and lexical decision. First, both tasks were unaffected by divided-attention tasks known to impact other priming tasks. Second, both tasks were unaffected by a manipulation of selective attention in which colored words were either named or their colors identified. Thus, category verification, unlike other conceptual tasks, appears unaffected by divided attention, and some selective-attention tasks, and lexical decision, unlike other perceptual tasks, appears unaffected by a difficult divided-attention task and some selective-attention tasks. Finally, both tasks were affected by a selective-attention task in which attention was manipulated across objects (rather than within objects), indicating some susceptibility to selective attention. The results contradict an analysis on the basis of the conceptual-perceptual distinction and other more specific hypotheses but are consistent with the distinction between production and identification priming.     

Independence and separability of volume and mass in the size-weight illusion

Numerous size-weight illusion models were classified in the present article according to general recognition theory (Ashby & Townsend, 1986), wherein the illusion results from a lack of perceptual separability, perceptual independence, decisional separability, or a combination of the three. These options were tested in two experiments in which a feature-complete factorial design and multidimensional signal detection analysis were used (Kadlec & Townsend, 1992a, 1992b). With haptic touch alone, the illusion was associated with a lack of perceptual and decisional separability. When the participant viewed the stimulus in his or her hand, the illusion was associated only with a lack of decisional separability. Visual input appeared to improve the discrimination of mass, leaving only the response bias due to expectation.     

Comparing decision bound and exemplar models of categorization

The performance of a decision bound model of categorization (Ashby, J992a; Ashby & Maddox, in press) is compared with the performance of two exemplar models. The first is the generalized context model (e.g., Nosofsky, 1986, 1992) and the second is a recently proposed deterministic exemplar model (Ashby & Maddox, in press), which contains the generalized context model as a special case. When the exemplars from each category were normally distributed and the optimal decision bound was linear, the deterministic exemplar model and the decision bound model provided roughly equivalent accounts of the data. When the optimal decision bound was nonlinear, the decision bound model provided a more accurate account of the data than did either exemplar model. When applied to categorization data collected by Nosofsky (1986, 1989), in which the category exemplars are not normally distributed, the decision bound model provided excellent accounts of the data, in many cases significantly outperforming the exemplar models. The decision bound model was found to be especially successful when(1) single subject analyses were performed, (2) each subject was given relatively extensive training, and (3) the subject's performance was characterized by complex suboptimalities. These results support the hypothesis that the decision bound is of fundamental importance in predicting asymptotic categorization performance and that the decision bound models provide a viable alternative to the currently popular exemplar models of categorization.     

Decisional separability, model identification, and statistical inference in the general recognition theory framework

Recent work in the general recognition theory (GRT) framework indicates that there are serious problems with some of the inferential machinery designed to detect perceptual and decisional interactions in multidimensional identification and categorization (Mack, Richler, Gauthier, & Palmeri, 2011). These problems are more extensive than previously recognized, as we show through new analytic and simulation-based results indicating that failure of decisional separability is not identifiable in the Gaussian GRT model with either of two common response selection models. We also describe previously unnoticed formal implicational relationships between seemingly distinct tests of perceptual and decisional interactions. Augmenting these formal results with further simulations, we show that tests based on marginal signal detection parameters produce unacceptably high rates of incorrect statistical significance. We conclude by discussing the scope of the implications of these results, and we offer a brief sketch of a new set of recommendations for testing relationships between dimensions in perception and response selection in the full-factorial identification paradigm.     

Choice and response time processes in the identification and categorization of unidimensional stimuli

Lacouture and Marley (1991, 1995, 2001) have successfully modeled the probabilities of correct responses and the mean correct response times (RTs) in unidimensional absolute identification tasks for various stimulus ranges and stimulus/response set sizes, for individual and group data. These fits include those to a set of phenomena often referred to as end-anchor effects. A revised model, with the independent accumulator decision process replaced by a leaky competing accumulator decision process, fits the probabilities of correct responses and the full distributions of RTs in unidimensional absolute identification. The revised model is also applied successfully to a particular class of unidimensional categorization tasks. We discuss possible extensions for handling sequential effects in unidimensional absolute identification, and other extensions of the given class of categorization tasks that are of potential empirical and theoretical importance as a supplement to the study of multidimensional absolute identification tasks.     

Attention, similarity, and the identification-categorization relationship

A unified quantitative approach to modeling subjects' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification data were modeled using Shepard's (1957) multidimensional scaling-choice framework. This framework was then extended to model the subjects' categorization performance. The categorization model, which generalizes the context theory of classification developed by Medin and Schaffer (1978), assumes that subjects store category exemplars in memory. Classification decisions are based on the similarity of stimuli to the stored exemplars. It is assumed that the same multidimensional perceptual representation underlies performance in both the identification and categorization paradigms. However, because of the influence of selective attention, similarity relationships change systematically across the two paradigms. Some support was gained for the hypothesis that subjects distribute attention among component dimensions so as to optimize categorization performance. Evidence was also obtained that subjects may have augmented their category representations with inferred exemplars. Implications of the results for theories of multidimensional scaling and categorization are discussed.     

The prioritization of perceptual processing in categorization

In three experiments, the effects of selective attention on perceptual processes in a complex multidimensional object categorization task were investigated. In each experiment, participants completed a perceptual-matching task to gain estimates of the perceptual salience of each stimulus dimension, then a categorization task using the same stimuli. In Experiments 1 and 2, the perceptual processing of stimulus dimensions was faster when dimensions were more diagnostic of category membership, regardless of their perceptual salience. Experiment 3 demonstrated that this prioritization of perceptual processing was evident even when stimuli were presented in unpredictable locations during categorization, indicating that the physical characteristics of the stimulus guide selective attention to diagnostic stimulus dimensions.