Anhedonia (hyposensitivity to rewards) and negative bias (hypersensitivity to punishments) are core features of major depressive disorder (MDD), which could stem from abnormal reinforcement learning. Emerging evidence highlights blunted reward learning and reward prediction error (RPE) signaling in the striatum in MDD, although inconsistencies exist. Preclinical studies have clarified that ventral tegmental area (VTA) neurons encode RPE and habenular neurons encode punishment prediction error (PPE), which are then transmitted to the striatum and cortex to guide goal-directed behavior. However, few studies have probed striatal activation, and functional connectivity between VTA-striatum and VTA-habenula during reward and punishment learning respectively, in unmedicated MDD. To fill this gap, we acquired fMRI data from 25 unmedicated MDD and 26 healthy individuals during a monetary instrumental learning task and utilized a computational modeling approach to characterize underlying neural correlates of RPE and PPE. Relative to controls, MDD individuals showed impaired reward learning, blunted RPE signal in the striatum and overall reduced VTA-striatal connectivity to feedback. Critically, striatal RPE signal was increasingly blunted with more major depressive episodes (MDEs). No group differences emerged in PPE signals in the habenula and VTA or in connectivity between these regions. However, PPE signals in the habenula correlated positively with number of MDEs. These results highlight impaired reward learning, disrupted RPE signaling in the striatum (particularly among individuals with more lifetime MDEs) as well as reduced VTA-striatal connectivity in MDD. Collectively, these findings highlight reward-related learning deficits in MDD and their underlying pathophysiology.

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Analyzing the data of individuals has several advantages over analyzing the data combined across the individuals (the latter we term group analysis): Grouping can distort the form of data, and different individuals might perform the task using different processes and parameters. These factors notwithstanding, we demonstrate conditions in which group analysis outperforms individual analysis. Such conditions include those in which there are relatively few trials per subject per condition, a situation that sometimes introduces distortions and biases when models are fit and parameters are estimated. We employed a simulation technique in which data were generated from each of two known models, each with parameter variation across simulated individuals. We examined how well the generating model and its competitor each fared in fitting (both sets of) the data, using both individual and group analysis. We examined the accuracy of model selection (the probability that the correct model would be selected by the analysis method). Trials per condition and individuals per experiment were varied systematically. Three pairs of cognitive models were compared: exponential versus power models of forgetting, generalized context versus prototype models of categorization, and the fuzzy logical model of perception versus the linear integration model of information integration. We show that there are situations in which small numbers of trials per condition cause group analysis to outperform individual analysis. Additional tables and figures may be downloaded from the Psychonomic Society Archive of Norms, Stimuli, and Data, www.psychonomic.org/archive.

In a recent article, J. P. Minda and J. D. Smith (2002) argued that an exemplar model provided worse quantitative fits than an alternative prototype model to individual subject data from the classic D. L. Medin and M. M. Schaffer (1978) 5/4 categorization paradigm. In addition, they argued that the exemplar model achieved its fits by making untenable assumptions regarding how observers distribute their attention. In this article, we demonstrate that when the models are equated in terms of their response-rule flexibility, the exemplar model provides a substantially better account of the categorization data than does a prototype or mixed model. In addition, we point to shortcomings in the attention-allocation analyses conducted by J. P. Minda and J. D. Smith (2002). When these shortcomings are corrected, we find no evidence that challenges the attention-allocation assumptions of the exemplar model.A classic issue in the categorization literature has been whether people represent categories in terms of abstracted prototypes or in terms of specific exemplars. According to prototype models, people represent categories in terms of some central tendency computed over the category training instances and classify objects on the basis of how similar they are to the prototypes of the alternative categories (Homa & Vosburgh, 1976;Posner & Keele, 1968;Reed, 1972). By contrast, according to exemplar models, people represent categories by storing the individual training instances themselves (Hintzman, 1986;Medin & Schaffer, 1978;Nosofsky, 1986).A well-known experimental paradigm that has been used for contrasting the predictions of exemplar and prototype models is the Medin and Schaffer (1978) 5/4 category structure, which is listed in Table 1. 1 In this paradigm, the stimuli are simple perceptual forms that vary along four salient binary-valued dimensions. The stimuli are divided into two categories. The logical values of the prototype of Category A are assumed to be 0 0 0 0, and the logical values of the prototype of Category B are assumed to be 1 1 1 1. Subjects are trained on the first nine items and are then given a transfer test that includes all the items in the list. This category structure is diagnostic because prototype and exemplar models tend to make opposite predictions for specific items. Most critically, prototype models predict that people will perform better on Stimulus A1 than on Stimulus A2 because A1 shares more features with its category prototype. In contrast, exemplar models generally predict an A2 advantage because A2 is highly similar to (i.e., shares three features with) two Category A exemplars and no Category B exemplars. In fact, the A2 advantage has been observed in numerous studies. Furthermore, when exemplar and prototype models are fitted to the classification data in this design, the results generally favor the predictions from the exemplar model (for reviews, see Nosofsky, 1992Nosofsky, , 2000 but see Smith & Minda, 2000, for an opposing viewpoint).However, whereas previous research concentr...

Throughout tropical moist climates, Dicranopteris linearis fernlands can develop as a result of rain forest clearance followed by frequent burning. In Sri Lanka, D. linearis fern‐lands are capable of suppressing the regeneration of rain forest. Field experiments were conducted at Sinharaja Man and Biosphere Reserve, a rain forest where fernlands occupy substantial areas of the reserve boundary. The experiment's objective was to identify methods for initiating forest regeneration in fernlands dominated by D. linearis Three disturbance treatments were used to initiate seedling regeneration: clean weed, root removal, and till. We hypothesized that increasing the severity of the soil disturbance would establish vegetation with higher species richness and diversity, greater above‐ground dry biomass, and higher percentage cover and seedling density. Results indicate only partial support for this hypothesis. Dry biomass was greatest in till treatments, the most severe soil disturbance. By comparison, species richness and diversity, seedling density, and percentage cover were greatest in root‐removal treatments, though in many instances the differences were not significant. The study clearly demonstrated that any kind of soil disturbance can facilitate the establishment of herbs, shrubs, and trees in a fernland dominated by D. linearis. Results showed that herbs, sedges, grasses, and pioneer shrubs represented greater proportions of seedling recruits than did pioneer trees. Seedlings of primary‐forest tree species were nearly nonexistent. In general, results showed that soil disturbance can play an important role in site preparation for the purpose of initiating non‐fern vegetation in fernlands dominated by D. linearis.