Higher-order associative processing in Hermissenda suggests multiple sites of neuronal modulation.

Two important features of modern accounts of associative learning are (1) the capacity for contextual stimuli to serve as a signal for an unconditioned stimulus (US) and (2) the capacity for a previously conditioned (excitatory) stimulus to block learning about a redundant stimulus when both stimuli serve as a signal for the same US. Here, we examined the process of blocking, thought by some to reflect a cognitive aspect of classical conditioning, and its underlying mechanisms in the marine mollusc Hermissenda. In two behavioral experiments, a context defined by chemosensory stimuli was made excitatory by presenting unsignalled USs (rotation) in that context. The excitatory context subsequently blocked overt learning about a discrete conditioned stimulus (CS; light) paired with the US in that context. In a third experiment, the excitability of the B photoreceptors in the Hermissenda eye, which typically increases following light-rotation pairings, was examined in behaviorally blocked animals, as well as in animals that had acquired a normal CS-US association or animals that had been exposed to the CS and US unpaired. Both the behaviorally blocked and the normal learning groups exhibited increases in neuronal excitability relative to unpaired animals. However, light-induced multiunit activity in pedal nerves was suppressed following normal conditioning but not in blocked or unpaired control animals, suggesting that the expression of blocking is mediated by neuronal modifications not directly reflected in B-cell excitability, possibly within an extensive network of central light-responsive interneurons

Deletion of PEA-15 in mice is associated with specific impairments of spatial learning abilities

<p>Abstract</p> <p>Background</p> <p>PEA-15 is a phosphoprotein that binds and regulates ERK MAP kinase and RSK2 and is highly expressed throughout the brain. PEA-15 alters c-Fos and CREB-mediated transcription as a result of these interactions. To determine if PEA-15 contributes to the function of the nervous system we tested mice lacking PEA-15 in a series of experiments designed to measure learning, sensory/motor function, and stress reactivity.</p> <p>Results</p> <p>We report that PEA-15 null mice exhibited impaired learning in three distinct spatial tasks, while they exhibited normal fear conditioning, passive avoidance, egocentric navigation, and odor discrimination. PEA-15 null mice also had deficient forepaw strength and in limited instances, heightened stress reactivity and/or anxiety. However, these non-cognitive variables did not appear to account for the observed spatial learning impairments. The null mice maintained normal weight, pain sensitivity, and coordination when compared to wild type controls.</p> <p>Conclusion</p> <p>We found that PEA-15 null mice have spatial learning disabilities that are similar to those of mice where ERK or RSK2 function is impaired. We suggest PEA-15 may be an essential regulator of ERK-dependent spatial learning.</p

A Dopaminergic Gene Cluster in the Prefrontal Cortex Predicts Performance Indicative of General Intelligence in Genetically Heterogeneous Mice

Background: Genetically heterogeneous mice express a trait that is qualitatively and psychometrically analogous to general intelligence in humans, and as in humans, this trait co-varies with the processing efficacy of working memory (including its dependence on selective attention). Dopamine signaling in the prefrontal cortex (PFC) has been established to play a critical role in animals ’ performance in both working memory and selective attention tasks. Owing to this role of the PFC in the regulation of working memory, here we compared PFC gene expression profiles of 60 genetically diverse CD-1 mice that exhibited a wide range of general learning abilities (i.e., aggregate performance across five diverse learning tasks). Methodology/Principal Findings: Animals ’ general cognitive abilities were first determined based on their aggregate performance across a battery of five diverse learning tasks. With a procedure designed to minimize false positive identifications, analysis of gene expression microarrays (comprised of &lt;25,000 genes) identified a small number (,20) of genes that were differentially expressed across animals that exhibited fast and slow aggregate learning abilities. Of these genes, one functional cluster was identified, and this cluster (Darpp-32, Drd1a, and Rgs9) is an established modulator of dopamine signaling. Subsequent quantitative PCR found that expression of these dopaminegic genes plus one vascular gene (Nudt6) were significantly correlated with individual animal’s general cognitive performance. Conclusions/Significance: These results indicate that D1-mediated dopamine signaling in the PFC, possibly through it

The causes of variation in learning and behavior: Why individual differences matter

In a seminal paper written five decades ago, Cronbach discussed the two highly distinct approaches to scientific psychology: experimental and correlational. Today, although these two approaches are fruitfully implemented and embraced across some fields of psychology, this synergy is largely absent from other areas, such as in the study of learning and behavior. Both Tolman and Hull, in a rare case of agreement, stated that the correlational approach held little promise for the understanding of behavior. Interestingly, this dismissal of the study of individual differences was absent in the biologically-oriented branches of behavior analysis, namely, behavioral genetics and ethology. Here we propose that the distinction between causation and causes of variation (with its origins in the field of genetics) reveal the potential value of the correlational approach in understanding the full complexity of learning and behavior. Although the experimental approach can illuminate the causal variables that modulate learning, the analysis of individual differences can elucidate how much and in which way variables interact to support variations in learning in complex natural environments. For example, understanding that a past experience with a stimulus influences its associability provides little insight into how individual predispositions interact to modulate this influence on associability. In this new light, we discuss examples from studies of individual differences in animals’ performance in the Morris Water Maze and from our own work on individual differences in general intelligence in mice. These studies illustrate that, opposed to what Underwood famously suggested, studies of individual differences can do much more to psychology than merely providing preliminary indications of cause-effect relationships

A multi-faceted role of dual-state dopamine signaling in working memory, attentional control, and intelligence

Genetic evidence strongly suggests that individual differences in intelligence will not be reducible to a single dominant cause. However, some of those variations/changes may be traced to tractable, cohesive mechanisms. One such mechanism may be the balance of dopamine D1 (D1R) and D2 (D2R) receptors, which regulate intrinsic currents and synaptic transmission in frontal cortical regions. Here, we review evidence from human, animal, and computational studies that suggest that this balance (in density, activity state, and/or availability) is critical to the implementation of executive functions such as attention and working memory, both of which are principal contributors to variations in intelligence. D1 receptors dominate neural responding during stable periods of short-term memory maintenance (requiring attentional focus), while D2 receptors play a more specific role during periods of instability such as changing environmental or memory states (requiring attentional disengagement). Here we bridge these observations with known properties of human intelligence. Starting from theories of intelligence that place executive functions (e.g., working memory and attentional control) at its center, we propose that dual-state dopamine signaling might be a causal contributor to at least some of the variation in intelligence across individuals and its change by experiences/training. Although it is unlikely that such a mechanism can account for more than a modest portion of the total variance in intelligence, our proposal is consistent with an array of available evidence and has a high degree of explanatory value. We suggest future directions and specific empirical tests that can further elucidate these relationships

Individual Differences in Animal Intelligence: Learning, Reasoning, Selective Attentionand Inter-Species Conservation of a Cognitive Trait

Humans’ performance on most cognitive tasks are commonly regulated by an underlying latent variable (i.e., “general” intelligence), and the expression of this latent modulator of cognitive performance varies across individuals. While “intelligence” in humans is easily recognized, a precise definition of this trait has proven elusive, and has impeded efforts to compare the emergence of thi strait across species. Here we describe our efforts to characterize this cognitive trait in genetically heterogeneous laboratory mice. Using batteries of as many as eight learning tasks and various principal component analysis regimens, we have found a robust general factor that accounts fornearly 40% of the variance of individual animals across all tasks. This “” is not attributable to variations in stress reactivity or exploratory tendencies. However, like human intelligence, this general factor covaries with the efficacy of selective attention and working memory capacity. Importantly, we also find that general learning abilities covary with animals’ performance on novel tests of reasoning. In total, this work indicates that learning abilities, attentional control, andthe capacity for reasoning, features that constitute both colloquial and formal definitions of human intelligence, are commonly regulated in individual genetically heterogeneous mice. These results suggest an evolutionary conservation of the qualitative and quantitative properties of intelligence, and indicate that like humans, sub-human animals express individual differences in this trait