A common source of variance (i.e., “general intelligence”) underlies an individual's performance across diverse tests of cognitive ability, and evidence indicates that the processing efficacy of working memory may serve as one such source of common variance. One component of working memory, selective attention, has been reported to co-vary with general intelligence, and dopamine D1 signaling in prefrontal cortex can modulate attentional abilities. Based on their aggregate performance across five diverse tests of learning, here we characterized the general cognitive ability (GCA) of CD-1 outbred mice. In response to a D1 agonist (SKF82958, 1 mg/kg), we then assessed the relationship between GCA and activation of D1 receptor (D1R)-containing neurons in the prelimbic region of the medial prefrontal cortex, the agranular insular cortex, and the dorsomedial striatum. Increased activation of D1R-containing neurons in the prelimbic cortex (but not the agranular insular cortex or dorsomedial striatum) was observed in animals of high GCA relative to those of low GCA (quantified by c-Fos activation in response to the D1 agonist). However, a Western blot analysis revealed no differences in the density of D1Rs in the prelimbic cortex between animals of high and low GCA. Last, it was observed that working memory training promoted an increase in animals’ GCA and enhanced D1R-mediated neuronal activation in the prelimbic cortex. These results suggest that the sensitivity (but not density) of D1Rs in the prelimbic cortex may both regulate GCA and be a target for working memory training.

Working memory and general intelligence are highly co-regulated (Engle et al. 1999; Conway et al. 2003; Colom et al. 2004), but the mechanisms that underlie this co-regulation have been difficult to assess in humans (Jensen 1998; Deary et al. 2009, 2010). Much like humans, the efficacy of an animal's working memory is correlated with, and may be a causal determinant of, general cognitive abilities (GCAs) (Kolata et al. 2007; Light et al. 2010; Matzel et al. 2011). Here we assessed whether innate GCA (as determined by an animal's aggregate performance across a diverse set of five learning tasks) and the beneficial influence of working memory training (WMT) on GCA shared a common substrate and target.

Imaging studies of humans have determined that the dorsolateral prefrontal cortex (dlPFC), as well as the parietal cortex, are engaged during working memory based tasks with a high dependence on selective attention (Rowe et al. 2000; Gray et al. 2003; Jung and Haier 2007; Barbey et al. 2013a,b), and it has been asserted that D1 activity levels in the dlPFC and D2 activity levels in the parietal cortex may play a role in modulating the relationship between working memory performance and intelligence (Aalto et al. 2005; Colom et al. 2007; Jung and Haier 2007; McNab et al. 2009; Barbey et al. 2013a). Furthermore, it has been suggested that working memory training (with high attentional demands) can positively impact an individual's performance on tests of fluid intelligence (Jaeggi et al. 2008; Tang and Posner 2009; Buschkuehl and Jaeggi 2010), and can produce functional changes in D1 dopaminergic binding in the prefrontal cortex (Olesen et al. 2004; McNab et al. 2009; Fischer et al. 2010). Although the interpretation of “working memory training” studies is highly controversial (Chooi and Thompson 2012; Shipstead et al. 2012; Redick et al. 2013), we have repeatedly observed that training procedures that tax working memory capacity and selective attention reliably improve the attentional performance of mice (Light et al. 2010; Matzel et al. 2011), and this facilitation of attention can promote performance on at least some of the tests that comprise our learning battery (see below).

Research using nonhuman animals has suggested that the medial prefrontal cortex (mPFC) in rodents may be homologous to the dorsolateral prefrontal cortex in humans (Lidow et al. 2003; Uylings et al. 2003; Kellendonk et al. 2006). The mPFC in rodents consists of three subregions, the anterior cingulate cortex, prelimbic cortex, and the infralimbic cortex (Kahn and Muly 2011). Specifically, lesions to the prelimbic cortex have been shown to broadly impair working memory performance (Cai and Arnsten 1997; Delatour and Gisquet-Verrier 2000; Ragozzino et al. 2002a; Heidbreder and Groenewegen 2003; Di Pietro et al. 2004; Gisquet-Verrier and Delatour 2006), as well as performance on tests of attention (Muir et al. 1996; Delatour and Gisquet-Verrier 2000; Chudasama et al. 2003; Gisquet-Verrier and Delatour 2006). Relatedly, direct injections of a D1 agonist into the prelimbic cortex have been shown to enhance the performance of animals on working memory tasks (Cai and Arnsten 1997; Mizoguchi et al. 2000; Chudasama and Robbins 2004), as well as attention tasks (Granon et al. 2000; Chudasama and Robbins 2004; Paine et al. 2007, 2009). Conversely, D1 antagonists have been shown to impair an animal's performance on working memory and attention-based tasks (Sawaguchi and Goldman-Rakic 1994; Granon et al. 2000). Similarly, the agranular insular cortex (an area of the lateral PFC) has also been implicated in the regulation of working memory (Puumala and Sirvio 1998; Ragozzino et al. 1998, 2002a; Chudasama et al. 2003; Chudasama and Robbins 2004). Lesion studies have shown that the agranular insular cortex is involved in modulating an animal's ability to perform olfactory working memory tasks (Schoenbaum et al. 2003; Di Pietro et al. 2004), possibly due to its role in processing the incentive value of rewards (Schoenbaum et al. 1998; Gallagher et al. 1999; Kesner and Gilbert 2007). Many would suggest that these impairments/improvements in working memory and attentional performance (functions that are typically referred to as executive functions in primates) would, indeed, show that rodents possess a prefrontal cortical area that is homologous to the dlPFC in humans (Lidow et al. 2003; Uylings et al. 2003; Kellendonk et al. 2006). However, it should be noted that some would argue that rodents do not possess a prefrontal cortical area that truly parallels the dlPFC of primates (Preuss 1995; Brown and Bowman 2002). It is, indeed, possible that rodents do not possess such an area that can fully encompass all functions regulated by the dlPFC of humans, but the evidence described above suggests that subregions of the rodent mPFC, the prelimbic cortex in particular, have the ability to modulate executive functions.

Previously, we have used RNA microarrays to examine 25,000 genes in the frontal cortex of genetically heterogeneous CD-1 mice that had been characterized for their GCA. Using conservative selection criterion, 10 genes were found to be up-regulated in animals with high GCA. Of those 10 genes, three (Darpp-32, Rgs9, and Drd1a) formed a functional dopaminergic cluster which has the potential to modulate the sensitivity of dopamine binding to the D1R (Kolata et al. 2010). In conjunction with the results described above, our microarray analysis suggests that the efficacy of D1 signaling in the prefrontal cortex may modulate an individual's working memory/attentional abilities, and differences in D1R-containing neurons activity levels may therefore serve as one (of potentially many) (Deary et al. 2009, 2010, 2012) determinants of general cognitive performance. To test this hypothesis, in Experiment 1 we examined the level of D1-mediated neuronal activation in animals that had been quantified for their GCA (i.e., aggregate performance across a diverse set of five learning tasks). Experiment 2 then assessed whether any differences in D1R-mediated neuronal activation were related to the density of D1Rs in the prelimbic cortex. Last, Experiment 3 assessed whether the implementation of working memory training (with a high selective attention load) affected the same dopaminergic signaling mechanisms that may innately regulate GCA.

Differences in c-Fos immunoreactive nuclei were observed across treatment groups. (A) Visualized c-Fos immunoreactivity in animals that had undergone working memory training (1), simple exposure to the apparatus (3), and those which remained in their home cages (5) 60 min after the administration of SKF82958. No differences were observed in either group of animals that received working memory training (2), exposure to the apparatus (4), or those which remained in the home cages (6) 60 min after the administration of saline. (B) Mean ± SEM number of c-Fos immunoreactive nuclei expressed in the prelimbic cortex of animals that have been segregated into groups which received working memory training (WMT), exposure to the apparatus (EXP), or remained in their home cages (HM). Groups labeled with “D” following their respective grouping received an administration of SKF82958 (1 mg/kg), whereas groups labeled with “S” received saline. Post hoc analysis revealed a significant difference in D1 agonist induced c-Fos immunoreactivity between animals that have undergone working memory training and animals that had been exposed to the apparatus for an equivalent amount of time (P < 0.05) as well as animals that remained in the home cages (P < 0.05). No significant differences in D1 agonist induced c-Fos immunoreactivity were observed between groups in the agranular insular cortex (C) or the dorsomedial striatum (D).

Some of the animals received working memory training, simple exposure to the maze, or remained in their home cages, but underwent no behavioral testing in the learning battery (WMT, n = 16; EXP, n = 14; HOME, n = 14). Half of the animals in each of these groups received an intraperitoneal injection of a D1 agonist (SKF82958) and the remaining half received a saline injection (resulting in a total of six groups). One-hour post-injection, levels of c-Fos immunoreactive nuclei were assessed in the prelimbic cortex, agranular insular cortex, and the dorsomedial striatum. Marked differences in the average number of c-Fos immunoreactive nuclei (in response to the D1 agonist) were observed in the prelimbic cortex between animals that received working memory training compared to animals which were either exposed to the maze or remained in their home cages throughout the experiment ( Fig. 6 A). The average number of c-Fos immunoreactive nuclei (induced by the D1 agonist) in the prelimbic cortex was compared between groups using an ANOVA which revealed a main effect of group, F (2,19) = 5.11, P < 0.05. A Tukey HSD revealed a significant difference in D1 agonist-induced c-Fos immunoreactive nuclei between animals that had undergone working memory training and animals that had either simply been exposed to the apparatus (P < 0.05) or that remained in their home cages (P < 0.05), and these comparisons are illustrated in Figure 6 B. No significant differences were observed between any groups in the agranular insular cortex F (2,19) = .911, n.s. ( Fig. 6 C), but there was a trend toward significance for a main effect of group in c-Fos immunoreactive nuclei in the dorsomedial striatum, F (2,19) = 3.5, P = 0.051 ( Fig. 6 D). Further post hoc analysis revealed a trend toward a significant difference in the dorsomedial striatum between working memory trained animals and animals that were exposed to the apparatus for an equivalent amount of time (P = 0.075).

From principal components analysis of all learning tasks, GCAs (primary factor score) are plotted as a function of group wherein higher GCAs are indicated as higher factor score values. The imposition of a working memory training (group WMT) regimen promoted an enhancement of GCAs compared to animals that were exposed (EXP) to the training apparatus for an amount of time equivalent to the trained group, or remained in their home cages (HOME) throughout the experiment.

Here, animals received either working memory training (WMT, n = 30), simple exposure to the training apparatus (EXP, n = 28), or remained in their home cage (HOME, n = 26). In order to ensure that animals of high GCA were not spuriously assigned into a single experimental group, the groups were created so that each group had a sample of animals with similar innate exploratory tendencies assessed by levels of exploration in an open field. As innate exploratory tendencies have been shown to be co-regulated with an animal's GCA, this served as a viable way to ensure that animals of similar GCAs were equally represented in each of our treatment groups ( Matzel et al. 2006 ; Light et al. 2011 ). A subgroup of each of these groups (WMT, n = 14; EXP, n = 14; HOME, n = 12) was subsequently assessed for performance across the battery of learning tasks (described below). As in the prior experiment, the acquisition performance of these animals across all learning tasks was first analyzed with a principal component analysis. This analysis extracted a primary factor (GCA factor) with an eigenvalue of 1.79, which accounted for 36% of the variance in performance of individual animals across all tasks (see Table 3 ). A secondary factor was also extracted with an eigenvalue of 1.1 which accounted for 22% variance. However, performance variables did not load in a consistent direction on this factor (indicating no common source of underlying variance), and so this factor will not be further considered. Factor scores were derived from the primary factor that represented the GCAs of individual animals. Factor scores were then segregated according to the treatment that the animals had previously received (i.e., WMT, EXP, HOME). When factor scores from the three treatment conditions were compared (see Fig. 5 ) a main effect of treatment was observed, F (2,37) = 6.23, P < 0.01. Post hoc comparisons of factor scores revealed significant differences between the group that received working memory training (WMT) and the group that received simple exposure to the maze (EXP), P < 0.05, and between group WMT and the HOME cage control condition, P < 0.01. No significant difference was observed between groups EXP and HOME. These results indicate that 12 d of composite working memory training promoted an increase in the GCAs of treated animals.

It has been reported that the imposition of a working memory training regimen with a high demand on selective attention can promote an increase in the general cognitive performance of mice ( Light et al. 2010 ; Matzel et al. 2011 ). Here we ascertained if working memory training targeted the same dopaminergic signaling mechanisms (D1R- containing neurons) that underlie innate cognitive abilities. One might expect such a modulation since receptor use is a critical determinant of rate of turnover, and turnover regulates sensitivity ( Gygi et al. 1999 ; Ferguson 2001 ; Olesen et al. 2004 ). Since D1Rs in prefrontal networks are preferentially active during the execution of working memory tasks, intense working memory training might reasonably be expected to promote an increased rate of turnover and hence sensitivity.

Once the density of D1 protein in the prelimbic cortex was quantified for each subject, we then assessed whether there were differences in the density of D1Rs between high GCA animals and low GCA animals. In order to accomplish this we averaged the density of the eight animals with the highest GCA (13.3 ± 1.49 SEM ) and compared that to the average density of the eight animals with the lowest GCA (13.68 ± 2.09 SEM ) using an independent samples t-test. Results indicate that there were no significant differences in the average density of each group, t (14) = −0.15, n.s. ( Fig. 4 C). In addition to between-group comparisons (i.e., animals of high and low GCA), we also performed a simple correlation comparing D1R density level to factor scores, which again found no relationship between these two variables, r (14) = 0.004, n.s. These results indicate that the number of D1Rs does not differ between animals of high GCA and low GCA. Thus the increases in D1R-mediated neuronal activation observed in Experiment 1 were not likely the result of differential densities of D1 receptors.

Eight animals characterized as having high GCAs and eight animals characterized as having low GCAs (taken from a total sample of 32 animals) were assayed for their levels of D1 receptor protein. Electrophoresis was performed across two gels (A,B) in which animals of high GCA and low GCA alternated between gels as well as lanes within each gel. Twenty-five micrograms of protein from high GCA animals (S1, 3, 5, 7 in A; S9, 11, 13, 15 in B) and low GCA animals (S2, 4, 6, 8 in A; S10, 12, 14, 16 in B) were loaded into each lane. Anti-Drd1 antibodies were then blotted against PVDF membranes and a single protein band was visualized at ∼52 kDA. (C) The mean ± SEM density of D1 protein from eight animals with high GCAs was compared to the mean density of eight animals with low GCAs. An independent samples t-test revealed there was no significant difference in the mean density of D1 receptor protein levels between animals of high GCA compared to those of low GCA (P > 0.05).

A sample of 32 CD-1 mice were assessed for their learning performance in the five learning tasks, which were once again subjected to a principal components analysis in order to derive each individual animal's factor score (indicative of GCA). A primary factor was extracted with an eigenvalue of 1.94, which accounted for 32% of the variance in performance across the five tasks ( Table 2 ). From the primary factor, factor scores were extracted to represent animals’ GCAs. A secondary factor was also extracted with an eigenvalue of 1.39 which accounted for 21% of the variance. Performance measures did not load in a consistent direction on this factor, indicating that this factor did not capture a common source of variance across all tasks. This factor will not be further considered. Since this experiment was intended to determine whether there was a differential level in the density of D1Rs in the prelimbic cortex between animals of high GCA compared to those of low GCA, eight animals with the highest GCAs and eight animals with the lowest GCAs were assessed for their levels of D1R protein by Western blotting procedures ( Fig. 4 A,B).

Exogenous application of SKF82958-induced c-Fos immunoreactive nuclei. Three groups of animals were formed based on the top, middle, and bottom third of the distribution of factor scores (reflective of GCAs) obtained from the principal component analysis of learning test performance (high factor scores = better general cognitive performance). Values are expressed as the mean ± SEM. (A) Comparison of the mean number of c-Fos immunoreactive nuclei in the prelimbic cortex of animals that had been characterized as having high, intermediate, or low GCA revealed a significant difference between animals of high GCA and low GCA (P < 0.05). (B) Comparison of the mean number of c-Fos immunoreactive nuclei in the agranular insular cortex of animals characterized for their GCA; no significant differences were observed. (C) No significant difference between groups was observed when the mean number of Fos immunoreactive nuclei in the dorsomedial striatum was compared.

SKF82958-induced expression of c-Fos immunoreactivity in the prelimbic cortex of animals that have been characterized for their GCAs. (Left) Schematic illustration of the regions of interest (marked by an arrow) for the prelimbic cortex (center arrow), agranular insular cortex (in a cross-section taken 5.9 mm rostral to the interaural line, left arrow), and the dorsomedial striatum (located 4.48 mm rostral to the interaural line, right arrow). Coordinates conform to Franklin and Paxinos (1997) . (Right) Marked differences in the expression of c-Fos immunoreactivity were detected in the prelimbic cortex 60 min after SKF82958 administration between animals of high GCAs (A) when compared to animals of low GCAs (C). No measurable difference was observed between animals of high GCAs (B) and low GCAs (D) when administered saline. No c-Fos immunoreactivity was observed in a positive control (E).

Groups of animals representing different GCAs were then compared based on their aggregate performance across all learning tasks. That is, the factor scores (of each individual) were ranked, and the top, middle, and bottom thirds of these ranked scores were used to construct groups of animals representing high, intermediate, and low GCAs. Marked differences in the average number of D1R-mediated c-Fos immunoreactive nuclei in the prelimbic cortex were observed in animals of high, intermediate, and low GCAs (representative slices provided in Fig. 2 ). The average number of c-Fos immunoreactive nuclei in the prelimbic cortex (see Fig. 3 A) was then compared between groups using an ANOVA which revealed a main effect of group, F (2,21) = 3.59, P < 0.05. An LSD post hoc analysis revealed a significant difference between animals of high GCA and low GCA in the prelimbic cortex, P < 0.05. No significant differences were observed between animals of different cognitive abilities in either the agranular insular cortex (F (2,21) = 2.25, n.s.) or the dorsomedial striatum (F (2,21) = 1.12, n.s.) ( Fig. 3 B,C).

Number of Fos immunoreactive nuclei from individual animals following an administration of SKF82958. Factor scores for each animal were derived from a principal component analysis of all animals’ performance on five learning tasks. These scores reflect each animal's aggregate performance across all five tasks (higher scores reflect higher GCAs). (A) A significant correlation (P < 0.05) was observed between animals’ factor scores and the average number of Fos immunoreactive nuclei in the prelimbic cortex, indicating that animals with higher GCAs exhibit an increased expression of c-Fos after the administration of SKF82958 relative to animals of lower GCAs. No other comparisons were found to be significant when the number of c-Fos immunoreactive nuclei in the agranular insular cortex (B) or the dorsomedial striatum (C) were compared to the animals’ factor scores.

Once each animal's factor score was obtained (where higher factor scores = higher GCAs), we then compared the factor scores of the animals that received a D1 agonist to their c-Fos immunoreactive nuclei in the prelimbic cortex (r (22) = 0.48, P < 0.02) ( Fig. 1 A), agranular insular cortex (r (22) = 0.29, n.s.) ( Fig. 1 B), and the dorsomedial striatum (r (22) = 0.06, n.s.) ( Fig. 1 C). These results indicate that animals which have higher GCAs expressed an increased level of D1R-mediated neuronal activation in the prelimbic cortex. There was no significant correlation between factor scores and c-Fos immunoreactive nuclei for any of the above regions in the animals that were treated with saline: prelimbic cortex (r (22) = 0.08, n.s.), agranular insular cortex (r (22) = 0.26, n.s.), and the dorsomedial striatum (r (22) = 0.1, n.s.).

Here we assessed 48 genetically heterogeneous CD-1 mice on a battery of five learning tasks designed to tax different sensory/motor and information-processing systems. Each animal's aggregate performance (i.e., rate of acquisition) across these five tests of learning served as an indicator of that animal's general cognitive ability (GCA). The performance of individual animals across all learning tasks was first analyzed with a principal component analysis. This is a variable reduction procedure that uses an orthogonal transformation to convert a set of independent observations (potentially correlated variables) into a set of uncorrelated variables (i.e., principal components). The principal component factor analysis of animals’ performance on the five learning tasks (see Table 1 ) indicated that performance on all tasks loaded in a consistent direction under a primary factor (GCA factor). That factor (eigenvalue 1.79) accounted for ∼30% of the variance in performance of individual animals across all tasks. A secondary factor, with an eigenvalue of 1.34, was also extracted and accounted for ∼22% of the variance. However, performance variables did not load in a consistent direction on this factor, meaning that there was not a common source of variance influencing the animals’ performance across all tasks. Since the focus of our study is understanding the mechanisms modulating GCAs, the secondary factory was not further considered. From the primary factor, GCA factor scores were calculated for each of the animals. A factor score is essentially an average z-score of an animal's performance on the five learning tasks, with each score weighted according to the individual task's loading on the primary (GCA) factor. Thus an animal's factor score is a quantification of that animal's relative position in the distribution of GCAs represented by this sample.

Discussion

The present experiments provide evidence that there is a differential level of D1R-mediated neuronal activation in the prelimbic cortex (the area in rodents that is thought to be homologous to the dlPFC in humans; Uylings et al. 2003) of animals that express high GCAs relative to animals of low GCAs. We had speculated that this difference in activation would arise as a consequence of a higher density of D1Rs, a result that would be consistent with our observation of elevated DRD1 mRNA levels in animals characterized as having high GCA (Kolata et al. 2010). However, no evidence for an increased density of D1Rs associated with higher cognitive abilities was observed here (Experiment 2). Nevertheless, it was observed (Experiment 3) that the imposition of a working memory training regimen promoted an increase in the neuronal activation of D1R-containing neurons in the prelimbic cortex, suggesting that training-induced differences in GCA may reflect a mechanism analogous to that which is associated with innate differences in GCA. It should be noted that we did not observe any differences in the number of Fos immunoreactive nuclei between the groups that received only a saline injection. That is, all saline-treated animals, regardless of whether they underwent working memory training or remained in their home cages, had similar basal levels of Fos immunoreactivity. One possible explanation for these results could be that since no cognitive demand was placed on the animals there was not a need for an increase in neuronal activation, an effect that is consistent with that reported in the human literature (Cohen et al. 1997). An increase in basal activity (as would be reflected in elevated Fos immunoreactivity) may be disadvantageous owing to the necessary energy consumption relative to reserving that activity for times of use (e.g., a cognitive demand upon stimulation of the D1 receptor). Thus, the administration of the D1 agonist (which simulates a cognitive demand) might result in an increase in neuronal excitability in the group that underwent working memory training compared to the groups that did not receive that training. These results extend the results obtained with humans showing that working memory training designed to heavily tax selective attention produces a functional change in dopaminergic binding in the dorsolateral prefrontal cortex (McNab et al. 2009), and facilitates the execution of behaviors that in aggregate are indicative of fluid intelligence (for review, see Buschkuehl and Jaeggi 2010).

The question of whether or not increases in mRNA levels should correlate with protein expression has been a central concern in the field of biology. In some instances, mRNA levels correlate highly with levels of protein expression (Futcher 1999), whereas in other cases, no correlation exists between the two (Gygi et al. 1999). The central supposition that DNA is transcribed into RNA and directly determines protein levels presumes that this transcription occurs independently of other rate-limiting factors. Although the results of mRNA analyses aid in the elucidation of how specific phenotypes may manifest, a myriad of factors mediating these processes need to be taken into account since an increase in mRNA simply increases the likelihood that the protein target will be differentially expressed. Factors such as protein half-life (rate of protein turnover), mutations in the mRNA causing them to be silenced (possibly through RNA interference or DNA methylation), or transporter variations can all determine protein levels (Greenbaum et al. 2003).

Our present experiments do not allow us to determine what factors impacted the level of D1R expression in high GCA animals, although an increase in the rate of receptor turnover may underlie the increase in DRD1 mRNA levels in high GCA animals that we have previously reported (Kolata et al. 2010). Importantly, an increase in receptor turnover rates would also correspond to an increase in the level of sensitivity of the D1R (Ferguson 2001). When a D1 agonist binds to the receptor it facilitates specific signaling cascades and, once that signaling cascade is initiated, the receptor is then removed from the membrane through sequestration. It has been long thought that sequestration's primary role was for receptor desensitization, but more recent evidence has shown that this process effectively promotes receptor re-sensitization which positively regulates receptor signaling (Ferguson 2001; Pierce et al. 2002). Therefore an enhanced rate of receptor turnover would enhance neuronal signaling. That increase in a neuron's signaling potential may underlie the increase in neuronal activity observed in animals of high GCA compared to those of low GCA.

Simulations of prefrontal cortex firing patterns have led to the hypothesis that D1 modulation of the prefrontal cortex implements a gating function that serves to regulate the maintenance of information in active memory in order to protect the memory from interference (i.e., focusing attention on task-relevant information). This attentional gating feature is thought to be regulated by top-down processing mechanisms. According to this model, D1Rs in the prefrontal cortex underlie the maintenance of relevant information by increasing the tonic activity (via increasing the gain) of dopaminergic neurons, thus protecting the memory from interference (Cohen et al. 2002; Costa 2007; Costa et al. 2007). This increase in gain promotes persistent neuronal firing in order to stabilize the actively stored memory. In order to then incorporate/update the contents of working memory to ensure that a behavior is guided toward a goal, dopamine D2 receptors are activated in the dorsomedial striatum which “opens the gates” by increasing the phasic activity of dopaminergic neurons and allowing the memory to be updated (Cohen et al. 2002; Wickens et al. 2007). This model has been supported by previous research showing that pharmacological blockade of D2 receptors or DA denervation in the dorsomedial striatum is crucial to the efficient shifting of behaviors (i.e., “cognitive flexibility”) and important in facilitating corticostriatal plasticity (Centonze et al. 2001; O'Neill and Brown 2007).

This model of attentional regulation of information fits well with the present results in that animals with a higher level of GCAs exhibited more robust D1R-mediated neuronal activation. Such a characteristic would not only improve performance on a working memory task, but owing to the role of working memory in the execution of more basic learning tasks, would promote improvements in more general cognitive abilities, as working memory training did here.

The above hypothesis is consistent with the observation in Experiment 3 that animals which underwent extensive working memory training exhibited a consequent increase in D1R-mediated neuronal activation. Since working memory training required animals to actively maintain a memory of locations in the face of interfering external stimuli, that taxation (and its use of the D1R) may have increased the sensitivity of D1R-containing neurons. It is notable in this regard that implementation of a similar working memory training regimen also resulted in improved performance on specific tests of selective attention (Light et al. 2010; Matzel and Kolata 2010; Matzel et al. 2011).

One pathway that could incorporate the current findings with the model presented above may arise from the D1R's ability to inhibit protein phosphatase 1's (PP1) negative regulation of downstream proteins and kinases. PP1's inactivation results from the stimulation of D1Rs, which activate adenylate cyclase. Adenylate cyclase then converts adenosine triphosphate (ATP) to cyclic adenosine monophosphate (cAMP), which then phosphorylates protein kinase A (PKA) which in turn phosphorylates Darpp-32. When Darpp-32 is phosphorylated by PKA, it becomes a potent inhibitor of PP1 (Neve et al. 2004; Williams and Castner 2006). The suppression of PP1 leads to an increase in neuronal excitability which results in an increase in downstream proteins and kinases important for synaptic plasticity and the facilitation of learning and memory (Calabresi et al. 2000; Centonze et al. 2001; Genoux et al. 2002; Neve et al. 2004; Allen et al. 2006).

The discussion above is congruent with the hypothesized role that D1Rs in the prefrontal cortex may play a role in the modulation of animals’ GCAs through their regulation of the efficacy of selective attention (a component of working memory). Taking the current results together with the model presented above, it seems likely that the increase in neuronal activation could be due to a differential sensitivity of the D1R-containing neurons in animals of high GCA compared to animals of low GCA. An increased level of sensitivity would allow for an actively stored memory to be less prone to interference through a process of gain modulation. Although the current experiment cannot ascertain what the direct cause is of the increase in receptor sensitivity, it does suggest that behavioral training regimens and/or pharmacological manipulations could potentially serve to increase an individual's general intelligence.