Because learned associations are quickly renewed following extinction, the extinction process must include processes other than unlearning. However, reinforcement learning models, such as the temporal difference reinforcement learning (TDRL) model, treat extinction as an unlearning of associated value and are thus unable to capture renewal. TDRL models are based on the hypothesis that dopamine carries a reward prediction error signal; these models predict reward by driving that reward error to zero. The authors construct a TDRL model that can accommodate extinction and renewal through two simple processes: (a) a TDRL process that learns the value of situation-action pairs and (b) a situation recognition process that categorizes the observed cues into situations. This model has implications for dysfunctional states, including relapse after addiction and problem gambling.

The understanding of decision-making systems has come together in recent years to form a unified theory of decision-making in the mammalian brain as arising from multiple, interacting systems (a planning system, a habit system, and a situation-recognition system). This unified decision-making system has multiple potential access points through which it can be driven to make maladaptive choices, particularly choices that entail seeking of certain drugs or behaviors. We identify 10 key vulnerabilities in the system: (1) moving away from homeostasis, (2) changing allostatic set points, (3) euphorigenic “reward-like” signals, (4) overvaluation in the planning system, (5) incorrect search of situation-action-outcome relationships, (6) misclassification of situations, (7) overvaluation in the habit system, (8) a mismatch in the balance of the two decision systems, (9) over-fast discounting processes, and (10) changed learning rates. These vulnerabilities provide a taxonomy of potential problems with decision-making systems. Although each vulnerability can drive an agent to return to the addictive choice, each vulnerability also implies a characteristic symptomology. Different drugs, different behaviors, and different individuals are likely to access different vulnerabilities. This has implications for an individual’s susceptibility to addiction and the transition to addiction, for the potential for relapse, and for the potential for treatment.

We initiated a genome-wide search for genes predisposing to schizophrenia by ascertaining 9 families, each containing three to five cases of schizophrenia. The 9 pedigrees were initially genotyped with 329 polymorphic DNA loci distributed throughout the genome. Assuming either autosomal dominant or recessive inheritance, 254 DNA loci yielded lod scores less than -2.0 at theta = 0.0, 101 DNA markers gave lod scores less than -2.0 at theta = 0.05, while 5 DNA loci produced maximum lod scores greater than 1: D4S35, D14S17, D15S1, D22S84, and D22S55. Of the DNA markers yielding lod scores greater than 1, D4S35 and D22S55 also were suggestive of linkage when the Affected-Pedigree-Member method was used. The families were then genotyped with four highly polymorphic simple sequence repeat markers; possible linkage diminished with DNA markers mapping nearby D4S35, while suggestive evidence of linkage remained with loci in the region of D22S55. Although follow-up investigation of these chromosomal regions may be warranted, our linkage results should be viewed as preliminary observations, as 35 unaffected persons are not past the age of risk.

In our target article, we proposed that addiction could be envisioned as misperformance of a decision-making machinery described by two systems (deliberative and habit systems). Several commentators have argued that Pavlovian learning also produces actions. We agree and note that Pavlovian action-selection will provide several additional vulnerabilities. Several commentators have suggested that addiction arises from sociological parameters. We note in our response how sociological effects can change decision-making variables to provide additional vulnerabilities. Commentators generally have agreed that our theory provides a framework within which to site addiction and treatment, but additional work will be needed to determine whether our taxonomy will help identify and treat subpopulations within the addicted community.

As science, technology, engineering, and mathematics (STEM) classrooms in higher education transition from lecturing to active learning, the frequency of student interactions in class increases. Previous research documents a gender bias in participation, with women participating less than would be expected on the basis of their numeric proportions. In the present study, we asked which attributes of the learning environment contribute to decreased female participation: the abundance of in-class interactions, the diversity of interactions, the proportion of women in class, the instructor's gender, the class size, and whether the course targeted lower division (first and second year) or upper division (third or fourth year) students. We calculated likelihood ratios of female participation from over 5300 student–instructor interactions observed across multiple institutions. We falsified several alternative hypotheses and demonstrate that increasing class size has the largest negative effect. We also found that when the instructors used a diverse range of teaching strategies, the women were more likely to participate after small-group discussions.