Sex differences in cognitive performance and alcohol consumption in High Alcohol-Drinking (HAD-1) rats.

Excessive alcohol (ethanol) consumption negatively impacts social, emotional, as well as cognitive function and well-being. Thus, identifying behavioral and/or biological predictors of excessive ethanol consumption is important for developing prevention and treatment strategies against alcohol use disorders (AUDs). Sex differences in alcohol consumption patterns are observed in humans, primates, and rodents. Selectively bred high alcohol-drinking rat lines, such as the "HAD-1" lines are recognized animal models of alcoholism. The present work examined sex differences in alcohol consumption, object recognition, and exploratory behavior in male and female HAD-1 rats. Naïve male and female HAD-1 rats were tested in an object recognition test (ORT) prior to a chronic 24 h intermittent ethanol access procedure for five weeks. Object recognition parameters measured included exploratory behavior, object investigation, and time spent near objects. During the initial training trial, rearing, active object investigation and amount of time spent in the object-containing section was significantly greater in female HAD-1 rats compared to their male counterparts. During the subsequent testing trial, time spent in the object-containing section was greater in female, compared to male, rats; but active object investigation and rearing did not statistically differ between females and males. In addition, female HAD-1 rats consumed significantly more ethanol than their male counterparts, replicating previous findings. Moreover, across all animals there was a significant positive correlation between exploratory behavior in ORT and ethanol consumption level. These results indicate there are significant sex differences in cognitive performance and alcohol consumption in HAD-1 rats, which suggests neurobiological differences as well.

Sex-specific ultrasonic vocalization patterns and alcohol consumption in high alcohol-drinking (HAD-1) rats.

Ultrasonic vocalizations (USVs) have been established as an animal model of emotional status and are often utilized in drug abuse studies as motivational and emotional indices. Further USV functionality has been demonstrated in our recent work showing accurate identification of selectively-bred high versus low alcohol-consuming male rats ascertained exclusively from 22 to 28kHz and 50-55kHz FM USV acoustic parameters. With the hypothesis that alcohol-sensitive sex differences could be revealed through USV acoustic parameters, the present study examined USVs and alcohol consumption in male and female selectively bred high-alcohol drinking (HAD-1) rats. For the current study, we examined USV data collected during a 12-week experiment in male and female HAD-1 rats. Experimental phases included Baseline (2weeks), 4-h EtOH Access (4weeks), 24-h EtOH Access (4weeks) and Abstinence (2weeks). Findings showed that both male and female HAD-1 rats spontaneously emitted a large number of 22-28kHz and 50-55kHz FM USVs and that females drank significantly more alcohol compared to males over the entire course of the experiment. Analyses of USV acoustic characteristics (i.e. mean frequency, duration, bandwidth and power) revealed distinct sex-specific phenotypes in both 50-55kHz FM and 22-28kHz USV transmission that were modulated by ethanol exposure. Moreover, by using a linear combination of these acoustic characteristics, we were able to develop binomial logistic regression models able to discriminate between male and female HAD-1 rats with high accuracy. Together these results highlight unique emotional phenotypes in male and female HAD-1 rats that are differentially modulated by alcohol experience.

Toward a dual-learning systems model of speech category learning.

More than two decades of work in vision posits the existence of dual-learning systems of category learning. The reflective system uses working memory to develop and test rules for classifying in an explicit fashion, while the reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-learning systems models hypothesize that in learning natural categories, learners initially use the reflective system and, with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in auditory category learning and more specifically in speech category learning has not been systematically examined. In this article, we describe a neurobiologically constrained dual-learning systems theoretical framework that is currently being developed in speech category learning and review recent applications of this framework. Using behavioral and computational modeling approaches, we provide evidence that speech category learning is predominantly mediated by the reflexive learning system. In one application, we explore the effects of normal aging on non-speech and speech category learning. Prominently, we find a large age-related deficit in speech learning. The computational modeling suggests that older adults are less likely to transition from simple, reflective, unidimensional rules to more complex, reflexive, multi-dimensional rules. In a second application, we summarize a recent study examining auditory category learning in individuals with elevated depressive symptoms. We find a deficit in reflective-optimal and an enhancement in reflexive-optimal auditory category learning. Interestingly, individuals with elevated depressive symptoms also show an advantage in learning speech categories. We end with a brief summary and description of a number of future directions.

Neural correlates of rule-based and information-integration visual category learning.

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.

On the relation between base-rate and cost-benefit learning in simulated medical diagnosis.

Observers completed a series of simulated medical diagnosis tasks that differed in category discriminability and base-rate/cost-benefit ratio. Point, accuracy, and decision criterion estimates were closer to optimal (a) for category d' = 2.2 than for category d' = 1.0 or 3.2, (b) when base-rates as opposed to cost-benefits were manipulated, and (c) when the cost of an incorrect response resulted in no point loss (nonnegative cost) as opposed to a point loss (negative cost). These results support the "flat-maxima" and competition between reward and accuracy (COBRA) hypotheses. A hybrid model that instantiated simultaneously both hypotheses was applied to the data. The model parameters indicated that (a) the reward-maximizing decision criterion quickly approached the optimal criterion, (b) the importance placed on accuracy maximization early in learning was larger when the cost of an incorrect response was negative as opposed to nonnegative, and (c) by the end of training the importance placed on accuracy was equal for negative and nonnegative costs.

Striatal contributions to category learning: quantitative modeling of simple linear and complex nonlinear rule learning in patients with Parkinson's disease.

The contribution of the striatum to category learning was examined by having patients with Parkinson's disease (PD) and matched controls solve categorization problems in which the optimal rule was linear or nonlinear using the perceptual categorization task. Traditional accuracy-based analyses, as well as quantitative model-based analyses were performed. Unlike accuracy-based analyses, the model-based analyses allow one to quantify and separate the effects of categorization rule learning from variability in the trial-by-trial application of the participant's rule. When the categorization rule was linear, PD patients showed no accuracy, categorization rule learning, or rule application variability deficits. Categorization accuracy for the PD patients was associated with their performance on a test believed to be sensitive to frontal lobe functioning. In contrast, when the categorization rule was nonlinear, the PD patients showed accuracy, categorization rule learning, and rule application variability deficits. Furthermore, categorization accuracy was not associated with performance on the test of frontal lobe functioning. Implications for neuropsychological theories of categorization learning are discussed.

Feedback effects on cost-benefit learning in perceptual categorization.

Two experiments were conducted in which the effects of different feedback displays on decision criterion learning were examined in a perceptual categorization task with unequal cost-benefits. In Experiment 1, immediate versus delayed feedback was combined factorially with objective versus optimal classifier feedback. Immediate versus delayed feedback had no effect. Performance improved significantly over blocks with optimal classifier feedback and remained relatively stable with objective feedback. Experiment 2 used a within-subjects design that allowed a test of model-based instantiations of the flat-maxima (von Winterfeldt & Edwards, 1982) and competition between reward and accuracy (Maddox & Bohil, 1998a) hypotheses in isolation and of a hybrid model that incorporated assumptions from both hypotheses. The model-based analyses indicated that the flat-maxima model provided a good description of early learning but that the assumptions of the hybrid model were necessary to account for later learning. An examination of the hybrid model parameters indicated that the emphasis placed on accuracy maximization generally declined with experience for optimal classifier feedback but remained high, and fairly constant for objective classifier feedback. Implications for cost-benefit training are discussed.

A possible role of the striatum in linear and nonlinear category learning: evidence from patients with Huntington's disease.

Linear and nonlinear categorization rule learning was examined in patients with Huntington's disease (HD) and a group of controls using the perceptual categorization task. Participants learned to categorize simple line stimuli into 1 of 2 categories over 600 trials. In addition to traditional measures of accuracy, quantitative model-based analyses were applied to each participant's data to characterize better the nature of any observed deficits. In the linear rule condition, HD patients displayed an early-training deficit relative to controls, whereas later in training the HD patients were not statistically different from controls. In the nonlinear rule condition, HD patients displayed both an early- and late-training deficit. The quantitative model-based analyses revealed that the HD patients' deficits in the linear condition were due to an impairment in learning the experimenter-defined rule and not in applying a learned rule inconsistently. In the nonlinear condition, in contrast, the HD patients' deficits were due to an impairment in learning the experimenter-defined rule and in applying a learned rule inconsistently. Overall, these results suggest that HD can result in a deficit in learning both linear and nonlinear categorization rules.

Category discriminability, base-rate, and payoff effects in perceptual categorization.

The optimality of perceptual categorization performance under manipulations of category discriminability (i.e., d' level), base rates, and payoffs was examined. Base-rate and payoff manipulations across two category discriminabilities allowed a test of the hypothesis that the steepness of the objective reward function affects performance (i.e., the flat-maxima hypothesis), as well as the hypothesis that observers combine base-rate and payoff information independently. Performance was (1) closer to optimal for the steeper objective reward function, in line with the flat-maxima hypothesis, (2) closer to optimal in base-rate conditions than in payoff conditions, and (3) in partial support of the hypothesis that base-rate and payoff knowledge is combined independently. Implications for current theories of base-rate and payoff learning are discussed.

Quantitative modeling of category learning in amnesic patients.

Category rule learning was examined in two amnesic patients using the perceptual categorization task (e.g., Ashby & Gott, 1988; Filoteo & Maddox, 1999). Traditional accuracy-based analyses as well as quantitative model-based analyses were performed. Unlike accuracy-based analyses, the model-based approach allowed us to examine both categorization rule learning and variability in the trial-by-trial application of the participant's categorization rule. The results indicated that the amnesic patients were as accurate as the controls in learning a complex, nonlinear rule over a large number of trials. The model-based analysis indicated that, in general, the amnesic patients learned the categorization rule as well as controls and applied their rule as consistently as controls. Categorization performance on a second day of testing revealed that amnesic patients can retain the categorization rule over a 24-h period. These results suggest that the brain regions damaged in amnesia are not involved in category learning or memory for the category structures.

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.

Costs and benefits in perceptual categorization.

Observers categorized perceptual stimuli when the category costs and benefits were manipulated across conditions, and costs were either zero or nonzero. The cost-benefit structures were selected so that performance across conditions was equivalent with respect to the optimal classifier. Each observer completed several blocks of trials in each of the experimental conditions, and a series of nested models was applied to the individual observer data from all conditions. In general, performance became more nearly optimal as observers gained experience with the cost-benefit structures, but performance reached asymptote at a suboptimal level. Observers behaved differently in the zero- and nonzero-cost conditions, performing consistently worse when costs were nonzero. A test of the hypothesis that observers weight costs more heavily than benefits was inconclusive. Some aspects of the data supported this differential weighting hypothesis, but others did not. Implications for current theories of cost-benefit learning are discussed.

Quantitative modeling of visual attention processes in patients with Parkinson's disease: effects of stimulus integrality on selective attention and dimensional integration.

Parkinson's disease (PD) patients and normal controls (NCs) were administered a series of visual attention tasks. The dimensional integration task required integration of information from 2 stimulus dimensions. The selective attention task required selective attention to 1 stimulus dimension while ignoring the other stimulus dimension. Both integral- and separable-dimension stimuli were examined. A series of quantitative models of attentional processing was applied to each participant's data. The results suggest that (a) PD patients were not impaired in integrating information from 2 stimulus dimensions, (b) PD patients were impaired in selective attention, (c) selective attention deficits in PD patients were not due to perceptual interference, and (d) PD patients were affected by manipulations of stimulus integrality and separability in much the same way as were NCs.

On the dangers of averaging across observers when comparing decision bound models and generalized context models of categorization.

Averaging across observers is common in psychological research. Often, averaging reduces the measurement error and, thus, does not affect the inference drawn about the behavior of individuals. However, in other situations, averaging alters the structure of the data qualitatively, leading to an incorrect inference about the behavior of individuals. In this research, the influence of averaging across observers on the fits of decision bound models (Ashby, 1992a) and generalized context models (GCM; Nosofsky, 1986) was investigated through Monte Carlo simulation of a variety of categorization conditions, perceptual representations, and individual difference assumptions and in an experiment. The results suggest that (1) averaging has little effect when the GCM is the correct model, (2) averaging often improves the fit of the GCM and worsens the fit of the decision bound model when the decision bound model is the correct model, (3) the GCM is quite flexible and, under many conditions, can mimic the predictions of the decision bound model, whereas the decision bound model is generally unable to mimic the predictions of the GCM, (4) the validity of the decision bound model's perceptual representation assumption can have a large effect on the inference drawn about the form of the decision bound, and (5) the experiment supported the claim that averaging improves the fit of the GCM. These results underscore the importance of performing single-observer analysis if one is interested in understanding the categorization performance of individuals.

Base-rate and payoff effects in multidimensional perceptual categorization.

The optimality of multidimensional perceptual categorization performance with unequal base rates and payoffs was examined. In Experiment 1, observers learned simultaneously the category structures and base rates or payoffs. Observers showed conservative cutoff placement when payoffs were unequal and extreme cutoff placement when base rates were unequal. In Experiment 2, observers were trained on the category structures before the base-rate or payoff manipulation. Simultaneous base-rate and payoff manipulations tested the hypothesis that base-rate information and payoff information are combined independently. Observers showed (a) small suboptimalities in base-rate and payoff estimation, (b) no qualitative differences across base-rate and payoff conditions, and (c) support for the hypothesis that base-rate and payoff information is combined independently. Implications for current theories of base-rate and payoff learning are discussed.

Effects of stimulus integrality on visual attention in older and younger adults: a quantitative model-based analysis.

Twenty-one older and 21 younger adults were administered a series of visual attention tasks. A series of quantitative models was applied to each observer's data to determine whether he or she performed optimally or suboptimally or showed a deficit-in-attentional processing. The results suggested that (a) older and younger observers were affected equally by the integrality-separability manipulation, (b) there are no age-related differences in selective attention performance for either integral or separable-dimension stimuli, (c) there are no age-related differences in dimensional integration performance with separable-dimension stimuli, and (d) older observers were more likely to be suboptimal when asked to integrate information from integral-dimension stimuli. Implications for current theories of attentional processing in normal aging are discussed.

Overestimation of base-rate differences in complex perceptual categories.

The optimality of multidimensional perceptual categorization performance was examined for several base-rate ratios, for both integral and separable dimension stimuli, and for complex category structures. In all cases, the optimal decision bound was highly nonlinear. Observers completed several experimental sessions, and all analyses were performed at the single-observer level using a series of nested models derived from decision-bound theory (Maddox, 1995; Maddox & Ashby, 1993). In every condition, all observers were found to be sensitive to the base-rate manipulations, but the majority of observers appeared to overestimate the base-rate difference. These findings converge with those for cases in which the optimal decision bound was linear (Maddox, 1995) and suggest that base-rates are learned in a similar fashion regardless of the complexity of the optimal decision bound. Possible explanations for the consistent overestimate of the base-rate difference are discussed. Several continuous-valued analogues of Kruschke's (1996) theory of base-rate learning with discrete-valued stimuli were tested. These models found some support, but in all cases were outperformed by a version of decision-bound theory that assumed accurate knowledge of the category structure and an overestimate of the base-rate difference.

Response time distributions in multidimensional perceptual categorization.

Three speeded categorization experiments were conducted using separable dimension stimuli. The form of the category boundary was manipulated across experiments, and the distance from category exemplars to the category boundary was manipulated within each experiment. Observers completed several sessions in each experiment, yielding 300-400 repetitions of each stimulus. The large sample sizes permitted accurate estimates of the response time (RT) distributions and RT hazard functions. Analyses of these data indicated: (1) RT was faster for stimuli farther from the category boundary, and this stochastic dominance held at the level of the RT distributions; (2) RT was invariant for all stimuli the same distance from the category boundary; (3) when task difficulty was high, errors were slower than correct responses, whereas this difference disappeared when difficulty was low; (4) small, consistent response biases appeared to have a large effect on the relation between correct and error RT; (5) the shape of the RT hazard function was qualitatively affected by distance to the category boundary. These data establish a rich set of empirical constraints for testing developing models of categorization RT.

Selective attention and the formation of linear decision boundaries: comment on McKinley and Nosofsky (1996).

S. C. McKinley and R. M. Nosofsky (1996) compared a linear decision-bound model with the generalized context model (GCM) in their ability to account for categorization data from experiments that used integral- or separable-dimension stimuli and required selective attention or attention to both dimensions. McKinley and Nosofsky (1996) found support for the GCM and concluded that decision-bound theory needs to incorporate assumptions about selective attention. In this commentary it is argued that (a) unlike the GCM, decision-bound theory provides a framework for independently investigating perceptual and decisional forms of selective attention; (b) the effect of stimulus integrality on the form of the optimal decision bound is misinterpreted; (c) averaged data is biased against decision-bound theory and toward the GCM; (d) many a priori predictions of the GCM are violated empirically; and (e) exemplar theory has lost much of its initial theoretical structure.