Inhibition of the LH Surge by Restraint Stress in Cyclic Rats: Studies on the Role of GABAA and GABAB Receptors

There is evidence that stress can alter the activity in the brain of gamma-aminobutyricacid (GABA), a neurotransmitter that has been implicated in the regulation of LH secretion. In the present study the role of GABA in the restraint stress-induced inhibition of the LH surge was investigated in the intact cyclic rat. Intracerebroventricular (icv) administration of the GABAA receptor agonist muscimol (0.1, 0.5 or 1 μg) 5 min before the presumed onset of the pro-oestrous LH surge (at 0900 h) caused a dose dependent suppression of the surge. A single dose of the GABAB receptor agonist baclofen (1 μg; icv) injected at 0855 h postponed the onset of the LH surge, and repeated injections at 0855 and 1130 h suppressed the surge. These data indicate that GABA-ergic activity in the brain can inhibit the LH surge in the cyclic rat via GABAA and GABAB receptors. Pro-oestrous rats were subjected to 5 hrs of restraint starting at 0855 h. Pretreatment with the GABAA receptor antagonist bicuculine (1 μg; icv) at 0840, 0940 and 1040 h or pretreatment with the GABAB receptor antagonist phaclofen (10 μg; icv) at 0840 h were ineffective in preventing the restraint-induced inhibition of the LH surge. The results suggest that GABAA and GABAB receptors are not involved in the inhibitory effect of restraint stress on the LH surge.     

Effects of domain-specific knowledge on memory for serial order

Knowledge concerning domain-specific regularities in sequential structure has long been known to affect recall for serial order. However, very little work has been done toward specifying the exact role such knowledge plays. The present article proposes a theory of serial recall in structured domains, based on Bayesian decision theory and a set of representational assumptions proceeding from recent computational and neurophysiologic research. The theory suggests that the accuracy with which a target sequence will be recalled is influenced by two interacting factors: (1) the 'goodness' of the sequence, i.e. its fit with the sequencing constraints that characterize its source domain, and (2) the sequence's neighborhood relations, i.e. the degree to which it resembles other sequences in the source domain. A specific prediction of the theory is that recall will be relatively poor for target lists with high-goodness near neighbors (the good neighbor effect). This prediction was tested and confirmed in an experiment evaluating recall for sequences based on an artificial grammar.     

Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates

Recent work has given rise to the view that reward-based decision making is governed by two key controllers: a habit system, which stores stimulus-response associations shaped by past reward, and a goal-oriented system that selects actions based on their anticipated outcomes. The current literature provides a rich body of computational theory addressing habit formation, centering on temporal-difference learning mechanisms. Less progress has been made toward formalizing the processes involved in goal-directed decision making. We draw on recent work in cognitive neuroscience, animal conditioning, cognitive and developmental psychology, and machine learning to outline a new theory of goal-directed decision making. Our basic proposal is that the brain, within an identifiable network of cortical and subcortical structures, implements a probabilistic generative model of reward, and that goal-directed decision making is effected through Bayesian inversion of this model. We present a set of simulations implementing the account, which address benchmark behavioral and neuroscientific findings, and give rise to a set of testable predictions. We also discuss the relationship between the proposed framework and other models of decision making, including recent models of perceptual choice, to which our theory bears a direct connection.     

Mechanisms of motivation–cognition interaction: challenges and opportunities

Recent years have seen a rejuvenation of interest in studies of motivation–cognition interactions arising from many different areas of psychology and neuroscience. The present issue of Cognitive, Affective, & Behavioral Neuroscience provides a sampling of some of the latest research from a number of these different areas. In this introductory article, we provide an overview of the current state of the field, in terms of key research developments and candidate neural mechanisms receiving focused investigation as potential sources of motivation–cognition interaction. However, our primary goal is conceptual: to highlight the distinct perspectives taken by different research areas, in terms of how motivation is defined, the relevant dimensions and dissociations that are emphasized, and the theoretical questions being targeted. Together, these distinctions present both challenges and opportunities for efforts aiming toward a more unified and cross-disciplinary approach. We identify a set of pressing research questions calling for this sort of cross-disciplinary approach, with the explicit goal of encouraging integrative and collaborative investigations directed toward them.     

Resolving conflict: A response to Martin and Cheng (2006)

Martin and Cheng (2006) report the results of an experiment aimed at disentangling the effects of association strength from those of competition on performance on a verb generation task. Their experiment is situated at the center of a putative debate regarding the function of the left inferior frontal gyrus in language processing (see, e.g., Wagner, Paré-Blagoev, Clark, & Poldrack, 2001). Following in this tradition, Martin and Cheng purport to contrast two processes—selection between competing representations and controlled retrieval of weak associates—that we argue can be reduced to the same mechanism. We contend that the distinction between competition and association strength is a false dichotomy, and we attempt to recast this discussion within a Bayesian framework in an attempt to guide research in this area in a more fruitful direction.     

Regularization in short-term memory for serial order

Previous research has shown that short-term memory for serial order can be influenced by background knowledge concerning regularities of sequential structure. Specifically, it has been shown that recall is superior for sequences that fit well with familiar sequencing constraints. The authors report a corresponding effect pertaining to serial recall errors. Undergraduate participants performed immediate serial recall on sequences of pseudowords generated on the basis of an artificial grammar. After extensive experience with this material, recall errors displayed a bias toward regularizing responses, response sequences more probable, with respect to the artificial grammar, than the originally presented stimulus sequence. This regularization effect squares well with recent trace redintegration and Bayesian models of serial recall, and appears to represent an analog of the schema-based error patterns observed in other domains of memory.     

Short-term memory for serial order: a recurrent neural network model

Despite a century of research, the mechanisms underlying short-term or working memory for serial order remain uncertain. Recent theoretical models have converged on a particular account, based on transient associations between independent item and context representations. In the present article, the authors present an alternative model, according to which sequence information is encoded through sustained patterns of activation within a recurrent neural network architecture. As demonstrated through a series of computer simulations, the model provides a parsimonious account for numerous benchmark characteristics of immediate serial recall, including data that have been considered to preclude the application of recurrent neural networks in this domain. Unlike most competing accounts, the model deals naturally with findings concerning the role of background knowledge in serial recall and makes contact with relevant neuroscientific data. Furthermore, the model gives rise to numerous testable predictions that differentiate it from competing theories. Taken together, the results presented indicate that recurrent neural networks may offer a useful framework for understanding short-term memory for serial order.     

Representing task context: proposals based on a connectionist model of action

Representations of task context play a crucial role in shaping human behavior. While the nature of these representations remains poorly understood, existing theories share a number of basic assumptions. One of these is that task representations are discrete, independent, and non-overlapping. We present here an alternative view, according to which task representations are instead viewed as graded, distributed patterns occupying a shared, continuous representational space. In recent work, we have implemented this view in a computational model of routine sequential action. In the present article, we focus specifically on this model's implications for understanding task representation, considering the implications of the account for two influential concepts: (1) cognitive underspecification, the idea that task representations may be imprecise or vague, especially in contexts where errors occur, and (2) information-sharing, the idea that closely related operations rely on common sets of internal representations.     

The conflict adaptation effect: It’s not just priming

Analyses of trial sequences in flanker tasks have revealed cognitive adaptation, reflected in a reduced interference effect following incompatible trials (Gratton, Coles, & Donchin, 1992). These effects have been explained on the basis of the response conflict monitoring model of Botvinick, Braver, Barch, Carter, and Cohen (2001), who proposed that preceding response conflict triggers stronger topdown control, leading to performance improvements on subsequent trials of similar context. A recent study (Mayr, Awh, & Laurey, 2003) has challenged this account, suggesting that the behavioral adaptations are confined to trial sequences of exact trial repetitions and can therefore be explained by repetition priming. Here, we present two experiments in which the sequential dependency effect was present even on trial sequences that did not involve stimulus repeats. We discuss the data with respect to the conflict-monitoring and repetition-priming accounts.     

Us versus them: social identity shapes neural responses to intergroup competition and harm

Intergroup competition makes social identity salient, which in turn affects how people respond to competitors' hardships. The failures of an in-group member are painful, whereas those of a rival out-group member may give pleasure-a feeling that may motivate harming rivals. The present study examined whether valuation-related neural responses to rival groups' failures correlate with likelihood of harming individuals associated with those rivals. Avid fans of the Red Sox and Yankees teams viewed baseball plays while undergoing functional magnetic resonance imaging. Subjectively negative outcomes (failure of the favored team or success of the rival team) activated anterior cingulate cortex and insula, whereas positive outcomes (success of the favored team or failure of the rival team, even against a third team) activated ventral striatum. The ventral striatum effect, associated with subjective pleasure, also correlated with self-reported likelihood of aggressing against a fan of the rival team (controlling for general aggression). Outcomes of social group competition can directly affect primary reward-processing neural systems, which has implications for intergroup harm.