[Show abstract][Hide abstract] ABSTRACT: Women with a history of childhood sexual abuse (CSA) exhibit higher rates of sexual dysfunction than nonabused women.
Because sexual responses are affected by cognitive processes, this study investigated the implicit and explicit cognitive processing of sexual stimuli in women with a history of CSA.
Women with (CSA; N = 34) and without (no history of abuse [NSA]; N = 22) a history of CSA participated in a quasi-experimental study.
Implicit processes, described as involuntary or unintentional responses to stimuli, were assessed using the Implicit Association Test (IAT). Explicit processes, described as intentional, voluntary, or effortful processing of sexual stimuli, were assessed through a self-reported questionnaire (Sexual Self-Schema Scale).
Results showed initial evidence of between group differences in the cognitive processing of sexual stimuli. Regarding the implicit processes, women in the NSA group showed that sexual pictures were more strongly associated with positive valence (pleasure) than nonsexual pictures. For the CSA group, neutral and sexual pictures were similarly associated with pleasure. Conversely, for both groups, romantic pictures were more strongly associated with positive valence than sexual pictures. Moreover, sexual satisfaction was predicted by both implicit and explicit processes, suggesting that the sexuality of women is affected by both processes independently.
These results fit within the larger body of literature suggesting that implicit and explicit processes independently predict different aspects of behavior.
Journal of Sexual Medicine 06/2011; 8(11):3098-107. DOI:10.1111/j.1743-6109.2011.02356.x · 3.15 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This study investigated the mechanisms of grouping and segregation in natural scenes of close-up foliage, an important class of scenes for human and non-human primates. Close-up foliage images were collected with a digital camera calibrated to match the responses of human L, M, and S cones at each pixel. The images were used to construct a database of hand-segmented leaves and branches that correctly localizes the image region subtended by each object. We considered a task where a visual system is presented with two image patches and is asked to assign a category label (either same or different) depending on whether the patches appear to lie on the same surface or different surfaces. We estimated several approximately ideal classifiers for the task, each of which used a unique set of image properties. Of the image properties considered, we found that ideal classifiers rely primarily on the difference in average intensity and color between patches, and secondarily on the differences in the contrasts between patches. In psychophysical experiments, human performance mirrored the trends predicted by the ideal classifiers. In an initial phase without corrective feedback, human accuracy was slightly below ideal. After practice with feedback, human accuracy was approximately ideal.
Journal of Vision 04/2010; 10(4):10.1-19. DOI:10.1167/10.4.10 · 2.73 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Determining the features of natural stimuli that are most useful for specific natural tasks is critical for understanding perceptual systems. A new approach is described that involves finding the optimal encoder for the natural task of interest, given a relatively small population of noisy "neurons" between the encoder and decoder. The optimal encoder, which necessarily specifies the most useful features, is found by maximizing accuracy in the natural task, where the decoder is the Bayesian ideal observer operating on the population responses. The approach is illustrated for a patch identification task, where the goal is to identify patches of natural image, and for a foreground identification task, where the goal is to identify which side of a natural surface boundary belongs to the foreground object. The optimal features (receptive fields) are intuitive and perform well in the two tasks. The approach also provides insight into general principles of neural encoding and decoding.
Journal of Vision 12/2009; 9(13):17.1-16. DOI:10.1167/9.13.17 · 2.73 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Variability in the representation of the decision criterion is assumed in many category-learning models, yet few studies have directly examined its impact. On each trial, criterial noise should result in drift in the criterion and will negatively impact categorization accuracy, particularly in rule-based categorization tasks, where learning depends on the maintenance and manipulation of decision criteria. In three experiments, we tested this hypothesis and examined the impact of working memory on slowing the drift rate. In Experiment 1, we examined the effect of drift by inserting a 5-sec delay between the categorization response and the delivery of corrective feedback, and working memory demand was manipulated by varying the number of decision criteria to be learned. Delayed feedback adversely affected performance, but only when working memory demand was high. In Experiment 2, we built on a classic finding in the absolute identification literature and demonstrated that distributing the criteria across multiple dimensions decreases the impact of drift during the delay. In Experiment 3, we confirmed that the effect of drift during the delay is moderated by working memory. These results provide important insights into the interplay between criterial noise and working memory, as well as providing important constraints for models of rule-based category learning.
[Show abstract][Hide abstract] ABSTRACT: The environments we live in and the tasks we perform in those environments have shaped the design of our visual systems through evolution and experience. This is an obvious statement, but it implies three fundamental components of research we must have if we are going to gain a deep understanding of biological vision systems: (a) a rigorous science devoted to understanding natural environments and tasks, (b) mathematical and computational analysis of how to use such knowledge of the environment to perform natural tasks, and (c) experiments that allow rigorous measurement of behavioral and neural responses, either in natural tasks or in artificial tasks that capture the essence of natural tasks. This approach is illustrated with two example studies that combine measurements of natural scene statistics, derivation of Bayesian ideal observers that exploit those statistics, and psychophysical experiments that compare human and ideal performance in naturalistic tasks.
Proceedings of SPIE - The International Society for Optical Engineering 02/2008; 8(17). DOI:10.1167/8.17.30 · 0.20 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: It has been proposed that a procedural-based classification system mediates the learning of information-integration categories, whereas a hypothesis-testing system mediates the learning of rule-based categories. Ashby, Ell and Waldron (2003) provided support for this claim by showing that a button switch introduced during classification transfer adversely affected information-integration but not rule-based performance. Nosofsky, Stanton and Zaki (2005) showed that increasing "cognitive complexity" can lead to button switch costs on rule-based performance. They argue that "cognitive complexity," and not the existence of separable classification systems, accounts for Ashby et al.'s empirical dissociation. The present study shows that experimental manipulations that increase "cognitive complexity" often have dissociable effects on information-integration and rule-based classification that are predicted a priori from the processing characteristics associated with the procedural-based and hypothesis-testing systems. These results suggest that manipulations of "cognitive complexity" can be dissociated, suggesting that "cognitive complexity" in not a unitary construct that affects a single psychological process.
[Show abstract][Hide abstract] ABSTRACT: An emerging theory of the neurobiology of category learning postulates that there are separate neural systems supporting the learning of categories based on verbalizeable rules (RB) or through implicit information integration (II). The medial temporal lobe (MTL) is thought to play a crucial role in successful RB categorization, whereas the posterior regions of the caudate are hypothesized to support II categorization. Functional neuroimaging was used to assess activity in these systems during category-learning tasks with category structures designed to afford either RB or II learning. Successful RB categorization was associated with relatively increased activity in the anterior MTL. Successful II categorization was associated with increased activity in the caudate body. The dissociation observed with neuroimaging is consistent with the roles of these systems in memory and dissociations reported in patient populations. Convergent evidence from these approaches consistently reinforces the idea of multiple neural systems supporting category learning.
[Show abstract][Hide abstract] ABSTRACT: Parkinson's disease (PD) patients and normal controls were tested in three category learning experiments to determine if previously observed rule-based category learning impairments in PD patients were due to deficits in selective attention or working memory. In Experiment 1, optimal categorization required participants to base their decision on a single stimulus dimension and ignore irrelevant variation on another dimension, thus emphasizing selective attention processes. In Experiment 2, optimal categorization required participants to base their decision on both stimulus dimensions using a conjunction of unidimensional decisions. Thus, this task placed less emphasis on selective attention and more on working memory. In Experiment 3, optimal categorization again required participants to base their decision on both stimulus dimensions using a disjunction of two unidimensional decisions in which an additional verbal operation was needed, thereby placing even greater emphasis on working memory. Results indicated that PD patients were impaired in the unidimensional rule-based condition, but not the other two rule-based conditions. These results are consistent with previous studies that demonstrate that PD patients are impaired in learning rule-based categories when selective attention demands are greatest, whereas these patients are normal in learning rule-based tasks when working memory demands are emphasized. Overall, these findings help to delineate the conditions under which PD patients display rule-based category learning deficits.
[Show abstract][Hide abstract] ABSTRACT: Two experiments were conducted that examined information integration and rule-based category learning, using stimuli that contained auditory and visual information. The results suggest that it is easier to perceptually integrate information within these sensory modalities than across modalities. Conversely, it is easier to perform a disjunctive rule-based task when information comes from different sensory modalities, rather than from the same modality. Quantitative model-based analyses suggested that the information integration deficit for across-modality stimulus dimensions was due to an increase in the use of hypothesis-testing strategies to solve the task and to an increase in random responding. The modeling also suggested that the across-modality advantage for disjunctive, rule-based category learning was due to a greater reliance on disjunctive hypothesis-testing strategies, as opposed to unidimensional hypothesis-testing strategies and random responding.
[Show abstract][Hide abstract] ABSTRACT: This study examined the impact of irrelevant dimensional variation on rule-based category learning in patients with Parkinson's disease (PD), older controls (OC), and younger controls (YC). Participants were presented with 4-dimensional, binary-valued stimuli and were asked to categorize each into 1 of 2 categories. Category membership was based on the value of a single dimension. Four experimental conditions were administered in which there were zero, 1, 2, or 3 randomly varying irrelevant dimensions. Results indicated that patients with PD were impacted to a greater extent than both the OC and YC participants when the number of randomly varying irrelevant dimensions increased. These results suggest that the degree of working memory and selective attention requirements of a categorization task will impact whether PD patients are impaired in rule-based category learning, and help to clarify recent discrepancies in the literature.
Journal of the International Neuropsychological Society 10/2005; 11(5):503-13. DOI:10.1017/S1355617705050617 · 3.01 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The brain regions contributing to rule-based category learning were examined using fMRI. Participants categorized single lines that varied in length and orientation into one of two categories. Category membership was based on the length of the line. Results indicated that left frontal and parietal regions were differentially activated in those participants who learned the task as compared to those who did not. Further, the head of the caudate displayed relative decreases in activation on incorrect trials relative to correct trials. The involvement of this latter structure is likely related to (1) processing an error signal, or (2) volitional switching between potential category rules. Results are consistent with theories suggesting that a frontal-striatal circuit is involved in rule-based category learning.
[Show abstract][Hide abstract] ABSTRACT: W. T. Maddox, F. G. Ashby, and C. J. Bohil (2003) found that delayed feedback adversely affects information-integration but not rule-based category learning in support of a multiple-systems approach to category learning. However, differences in the number of stimulus dimensions relevant to solving the task and perceptual similarity failed to rule out 2 single-system interpretations. The authors conducted an experiment that remedied these problems and replicated W. T. Maddox et al.'s findings. The experiment revealed a strong performance decrement for information-integration but not rule-based category learning under delayed feedback that was due to an increase in the number of observers using hypothesis-testing strategies to solve the information-integration task, and lower accuracy rates for the few observers using information-integration strategies.
Journal of Experimental Psychology Learning Memory and Cognition 02/2005; 31(1):100-7. DOI:10.1037/0278-73126.96.36.199 · 3.10 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The consistency of the mapping from category to response location was investigated to test the hypothesis that abstract category labels are learned by the hypothesis testing system to solve rule-based tasks, whereas response position is learned by the procedural-learning system to solve information-integration tasks. Accuracy rates were examined to isolate global performance deficits, and model-based analyses were performed to identify the types of response strategies used by observers. A-B training (consistent mapping) led to more accurate responding relative to yes-no training (variable mapping) in the information-integration category learning task. Model-based analyses indicated that the yes-no accuracy decline was due to an increase in the use of rule-based strategies to solve the information-integration task. Yes-no training had no effect on the accuracy of responding or distribution of best-fitting models relative to A-B training in the rule-based category learning tasks. These results both provide support for a multiple-systems approach to category learning in which one system is procedural-learning-based and argue against the validity of single-system approaches.
[Show abstract][Hide abstract] ABSTRACT: The effect of a sequentially presented memory scanning task on rule-based and information-integration category learning was investigated. On each trial in the short feedback-processing time condition, memory scanning immediately followed categorization. On each trial in the long feedback-processing time condition, categorization was followed by a 2.5-sec delay and then memory scanning. In the control condition, no memory scanning was required. Rule-based category learning was significantly worse in the short feedback-processing time condition than in the long feedback-processing time condition or control condition, whereas information-integration category learning was equivalent across conditions. In the rule-based condition, a smaller proportion of observers learned the task in the short feedback-processing time condition, and those who learned took longer to reach the performance criterion than did those in the long feedback-processing time or control condition. No differences were observed in the information integration task. These results provide support for a multiple-systems approach to category learning and argue against the validity of single-system approaches.
[Show abstract][Hide abstract] ABSTRACT: Category number effects on rule-based and information-integration category learning were investigated. Category number affected accuracy and the distribution of best-fitting models in the rule-based task but had no effect on accuracy and little effect on the distribution of best-fining models in the information-integration task. In the 2 category conditions, rule-based learning was better than information-integration learning, whereas in the 4 category conditions, unidimensional and conjunctive rule-based learning was worse than information-integration learning. Rule-based strategies were used in the 2-category/rule-based condition, but about half of the observers used rule-based strategies in the 4-category unidimensional and conjunctive rule-based conditions. Information-integration strategies were used in the 4-category/ information-integration condition and by the end of training were used in the 2-category/information-integration condition.
Journal of Experimental Psychology Learning Memory and Cognition 02/2004; 30(1):227-45. DOI:10.1037/0278-73188.8.131.52 · 3.10 Impact Factor