Cognitive Affective & Behavioral Neuroscience

Published by Springer Nature

Online ISSN: 1531-135X

·

Print ISSN: 1530-7026

Articles


Individual differences
  • Article

July 2005

·

18 Reads

·

Todd S Braver

·

John Jonides
Share

Fig. 1 Number forms from Experiment 2, shown schematically in the figure. The number forms in Experiment 1 were similar, but omitted the points labeled 0 and 0′. Forms differ along the rows in terms of which points can be compared (i.e., are >, <, or = ). Columns differ in how the numbers are bounded 
Fig. 2 Mean ratings of number forms as possible number systems in Experiment 1. Circles represent linear forms; squares, bilinear forms; diamonds, step forms; upward triangles, forward-branching forms; and 
Fig. 4 Number forms from Experiment 3, shown schematically in the figure. Forms differ along the rows in terms of which points can be compared (i.e., are >, <, or = ). Columns differ in how the numbers are bounded 
Figure 4 of 5
Figure 5 of 5
Possible number systems
  • Article
  • Full-text available

September 2013

·

220 Reads

Number systems-such as the natural numbers, integers, rationals, reals, or complex numbers-play a foundational role in mathematics, but these systems can present difficulties for students. In the studies reported here, we probed the boundaries of people's concept of a number system by asking them whether "number lines" of varying shapes qualify as possible number systems. In Experiment 1, participants rated each of a set of number lines as a possible number system, where the number lines differed in their structures (a single straight line, a step-shaped line, a double line, or two branching structures) and in their boundedness (unbounded, bounded below, bounded above, bounded above and below, or circular). Participants also rated each of a group of mathematical properties (e.g., associativity) for its importance to number systems. Relational properties, such as associativity, predicted whether participants believed that particular forms were number systems, as did the forms' ability to support arithmetic operations, such as addition. In Experiment 2, we asked participants to produce properties that were important for number systems. Relational, operation, and use-based properties from this set again predicted ratings of whether the number lines were possible number systems. In Experiment 3, we found similar results when the number lines indicated the positions of the individual numbers. The results suggest that people believe that number systems should be well-behaved with respect to basic arithmetic operations, and that they reject systems for which these operations produce ambiguous answers. People care much less about whether the systems have particular numbers (e.g., 0) or sets of numbers (e.g., the positives).
Download

DOI 10.3758/s13415-011-0027-0 Learning from delayed feedback: neural responses in temporal credit assignment

March 2011

·

100 Reads

When feedback follows a sequence of decisions, relationships between actions and outcomes can be difficult to learn. We used event-related potentials (ERPs) to understand how people overcome this temporal credit assignment problem. Participants performed a sequential decision task that required two decisions on each trial. The first decision led to an intermediate state that was predictive of the trial outcome, and the second decision was followed by positive or negative trial feedback. The feedback-related negativity (fERN), a component thought to reflect reward prediction error, followed negative feedback and negative intermediate states. This suggests that participants evaluated intermediate states in terms of expected future reward, and that these evaluations supported learning of earlier actions within sequences. We examine the predictions of several temporal-difference models to determine whether the behavioral and ERP results reflected a reinforcement-learning process.

Affective processing within 1/10th of a second: High arousal is necessary for early facilitative processing of negative but not positive words

December 2009

·

342 Reads

·

·

·

[...]

·

Lexical decisions to high- and low-arousal negative words and to low-arousal neutral and positive words were examined in an event-related potentials (ERP) study. Reaction times to positive and high-arousal negative words were shorter than those to neutral (low-arousal) words, whereas those to low-arousal negative words were longer. A similar pattern was observed in an early time window of the ERP response: Both positive and high-arousal negative words elicited greater negative potentials in a time frame of 80 to 120 msec after stimulus onset. This result suggests that arousal has a differential impact on early lexical processing of positive and negative words. Source localization in the relevant time frame revealed that the arousal effect in negative words is likely to be localized in a left occipito-temporal region including the middle temporal and fusiform gyri. The ERP arousal effect appears to result from early lexico-semantic processing in high-arousal negative words.

Impact of anxiety profiles on cognitive performance in BALB/c and 129P2 mice

July 2012

·

108 Reads

It has been suggested over the decades that dysfunctional anxiety may be caused by distinct alterations in cognitive processing. To learn more about the relation between anxiety and cognitive functioning, two mouse strains that display either adaptive (BALB/c) or nonadaptive (129P2) anxiety, as reflected by their ability to habituate when repeatedly exposed to a novel environment, were tested for their cognitive performance in the modified hole board (mHB) task. In general, both strains showed successful acquisition of the task. The initially more anxious BALB/c mice revealed rapid habituation to the test setup, followed by decreased long-term and short-term memory errors across the experimental period and fast relearning after reversal of the task. By contrast, the nonadaptive 129P2 mice made more short-term memory errors and performed worse than the BALB/c animals after reversal of the test. The results confirm the proposed interaction of anxiety and cognition: In BALB/c mice, adaptive characteristics of anxiety were paralleled by more successful cognitive performance, while in 129P2 mice nonadaptive anxiety-related behaviour was accompanied by a higher level of short-term memory errors and less cognitive flexibility. Moreover, these results support our hypothesis that the nonadaptive anxiety phenotype in 129P2 mice may be the result of impaired cognitive control of emotional processes, resulting in impaired behavioural flexibility, for example in response to novelty.

Figure 4: The basal ganglia (striatum, globus pallidus and thalamus) are interconnected with frontal cortex through a series of parallel loops. Excitatory connections are in solid lines, and inhibitory ones are dashed. Frontal cortex projects excitatory connections to striatum, which then projects inhibition to the globus pallidus internal segment (GPi) or the substantia nigra pars reticulata (SNr), which again project inhibition to nuclei in the thalamus, which are reciprocally interconnected with the frontal cortex. Because GPi/SNr neurons are tonically active, they are constantly inhibiting the thalamus, except when the striatum fires and disinhibits the thalamus. This disinhibition provides a modulatory or gating-like function.
Figure 8: Settling time in the sequential network for the 2nd  
Figure 6: Working memory model with basal ganglia mediated selective gating mechanism. The network structure is analogous to figure 4, where the PFC has been subdivided into maintenance (PFC Maint) and gating (PFC Gate) layers. Three hierarchically organized " stripes " of the PFC and basal ganglia are represented as the three columns of units within each layer — each stripe is capable of being independently updated. The right-most task stripe encodes task-level information (i.e., 1 or 2). The middle sequence (seq) encodes sequence-level information within a task (i.e., or  
Frank MJ, Loughry B, O’Reilly RC. Interactions between frontal cortex and basal ganglia in working memory: a computational model. Cogn Affect Behav Neurosci 1: 137-160

July 2001

·

374 Reads

The frontal cortex and the basal ganglia interact via a relatively well understood and elaborate system of interconnections. In the context of motor function, these interconnections can be understood as disinhibiting, or "releasing the brakes," on frontal motor action plans: The basal ganglia detect appropriate contexts for performing motor actions and enable the frontal cortex to execute such actions at the appropriate time. We build on this idea in the domain of working memory through the use of computational neural network models of this circuit. In our model, the frontal cortex exhibits robust active maintenance, whereas the basal ganglia contribute a selective, dynamic gating function that enables frontal memory representations to be rapidly updated in a task-relevant manner. We apply the model to a novel version of the continuous performance task that requires subroutine-like selective working memory updating and compare and contrast our model with other existing models and theories of frontal-cortex-basal-ganglia interactions.

Ray RD, Ochsner KN, Cooper JC, Robertson ER, Gabrieli JD, Gross JJ. Individual differences in trait rumination and the neural systems supporting cognitive reappraisal. Cogn Affect Behav Neurosci 5: 156-168

June 2005

·

126 Reads

Cognitive reappraisal can alter emotional responses by changing one's interpretation of a situation's meaning. Functional neuroimaging has revealed that using cognitive reappraisal to increase or decrease affective responses involves left prefrontal activation and goal-appropriate increases or decreases in amygdala activation (Ochsner, Bunge, Gross, & Gabrieli, 2002; Ochsner, Ray, et al., 2004). The present study was designed to examine whether patterns of brain activation during reappraisal vary in relation to individual differences in trait rumination, which is the tendency to focus on negative aspects of one's self or negative interpretations of one's life. Individual differences in rumination correlated with increases in amygdala response when participants were increasing negative affect and with greater decreases in prefrontal regions implicated in self-focused thought when participants were decreasing negative affect. Thus, the propensity to ruminate may reflect altered recruitment of mechanisms that potentiate negative affect. These findings clarify relations between rumination and emotion regulation processes and may have important implications for mood and anxiety disorders.

Figure 1. Grand mean event-related potentials (for all subjects) over electrode position Oz for the differences between pleasant minus neutral and unpleasant minus neutral stimuli. In addition, the distribution of the difference waves over the scalp is displayed in a back view for both negative components. 
Figure 2. Interaction of CoMt genotype (Met/Met, Val/Met, Val/Val) and category (pleasant and unpleasant stimuli) for the ePn (more negative, more activity), with mean values and standard errors of the means over the three electrode positions: o1, oz, and o2.
Catechol-O-methyltransferase Val(158)Met genotype affects neural correlates of aversive stimuli processing

July 2009

·

227 Reads

It was previously shown that variation of the catechol-O-methyltransferase (COMT) gene modulates brain activity during the processing of stimuli with negative valence, but not for pleasant stimuli. Here, we tested whether the COMT genotype also modulates the electrophysiological correlates of emotional processing and explored whether the environmental factor of life stress influences this effect. Using the early posterior negativity (EPN) paradigm, event-related brain potentials were measured in 81 healthy individuals during the processing of pictures that evoked emotions of positive and negative valence. As was hypothesized, the COMT genotype affected the EPN amplitudes for unpleasant stimuli, but not for pleasant ones. Specifically, Met/Met carriers respond more sensitively to unpleasant stimuli, as compared with Val/Val carriers. We did not find evidence that life stress moderates the effect of the COMT genotype on emotional stimuli processing.

Figure 1. Dorsal anterior cingulate cortex (dACC) activation associated with neuroticism and time courses for the activity in this cluster weighted by neuroticism, extraversion, and self-consciousness scores.  
Eisenberger NI, Lieberman MD, Satpute AB. Personality from a controlled processing perspective: an fMRI study of neuroticism, extraversion, and self-consciousness. Cogn Affect Behav Neurosci 5: 169-181

July 2005

·

532 Reads

Although neuroticism has been central to most theories of personality, self-reported neuroticism has had limited success in predicting expected behavioral outcomes. The reason for this may be due, in part, to the imprecision of self-reports. The purpose of this study was to examine the relationship between neural correlates of control systems and neuroticism, extraversion, and self-consciousness. In response to an oddball task, neuroticism was associated with increased dorsal anterior cingulate cortex (dACC) reactivity, typically associated with discrepancy detection, whereas extraversion and self-consciousness were associated with lateral and medial frontoparietal networks, respectively, typically associated with task-focused (lateral) or self-focused (medial) controlled processes. We also examined whether the neural measure of neuroticism would predict a relevant behavioral outcome better than self-reports would. Interoceptive accuracy, an outcome associated with neuroticism, was better accounted for by dACC reactivity (r 2=.74) than by self-reported neuroticism (r 2=.16), suggesting that neural reactivities may provide a more direct measure of personality than self-reports do.

Figure 2. Whole-brain direct contrasts, showing each contrast of interest for reference and comparison. The contrasts are presented at a threshold of p .001, uncorrected. The bottom right shows the color coding of the t(13) values. Images are shown in radiological conventions (right is left). Relevant Talairach coordinates are shown below the images. (A) Contrast of suppress neutral recall neutral. (B) Contrast of suppress negative recall negative. (C) Contrast of recall neutral recall negative. (D) Contrast of suppress negative suppress neutral (see the text for a further description). 
Figure 3. Anatomical region-of-interest results in both the whole and posterior bilateral hippocampus from all run repetitions combined. The whole and posterior bilateral hippocampus shows lowered activation for the suppression of neutral words, as compared with the recall of neutral words. However, no differences were found between the recall and suppression of negative words. Beta weights represent percent BOLD signal change. Bars represent mean peak BOLD response for each condition. Error bars represent 1 standard error of the mean. Significance bars represent * p .05, ** p .01, and *** p .001. 
Figure 4. Comparison of early (Runs/Repetitions 1, 2, and 3 combined) with late (Runs/Repetitions 4, 5, and 6 combined) suppression repetitions in the whole left hippocampus and left amygdala. The bars represent a difference score of the mean peak BOLD response for suppression subtracted from the mean response for recall within a given level of emotion. This indexes the amount of decrease or increase in the BOLD response for suppression, relative to recall. The and symbols represent a significant difference between suppression and recall in the negative and positive directions, respectively. The left hippocampus showed a significant decrease for suppression of neutral words during late, but not early, runs, with no difference for negative words. The amygdala showed a significant increase during the suppression of negative words, but not neutral words, in later repetitions. 
The neural correlates of attempting to suppress negative versus neutral memories. Cognitive, Affective, & Behavioral Neuroscience, 10(2), 182-194

May 2010

·

165 Reads

We performed an event-related fMRI study comparing attempts at suppressing recall of negative versus neutral memories. The hippocampus is crucial for successful explicit recall. Hippocampal activation has been shown to decrease during the suppression of previously learned neutral words. However, different effects may occur in the case of emotional memories. Participants first learned 40 word pairs consisting of a cue and either a neutral or a negative target. During fMRI scanning, the participants were shown the cues and were instructed to recall the targets or to suppress the targets, using attentional distraction. Similar right-lateralized frontoparietal regions were activated more during suppression than during recall, regardless of emotion. However, we show for the first time that lowered hippocampal activation occurs during the suppression of neutral, but not negative, words. Coinciding with this sustained hippocampal activation, the amygdala, insula, anterior cingulate, and fusiform gyrus showed greater activation during the suppression of negative memories than during suppression of neutral memories. Thus, during attempts to suppress negative memories, regions involved in the emotional and sensory aspects of memory reactivate, along with regions indexing conscious recall. Revealing the neural correlates and mechanisms of the suppression of negative memories has relevance for disorders such as posttraumatic stress disorder, in which traumatic memories often intrude and are associated with avoidance. Supplemental materials for this article may be downloaded from http://cabn.psychonomic-journals.org/content/supplemental.

Figure 1. Example of stimuli used in the present study. Participants were asked to indicate the direction of center arrows. Congruent and incongruent trials are shown.  
Figure 2. Mean response time by trial type. The x-axis shows how many times the current congruent or incongruent trial was preceded by the same flanker type. IC, congruent preceded by incongruent trial; CC, congruent preceded by single congruent trial; CCC, congruent preceded by two successive congruent trials; CI, incongruent preceded by congruent trial; II, incongruent preceded by single incongruent trial; III, incongruent preceded by two successive incongruent trials.  
Figure 3.  
Figure 4.  
Catechol-O-methyltransferase polymorphism modulates cognitive control in children with chromosome 22q11.2 deletion syndrome

April 2009

·

59 Reads

Dopamine plays a critical role in regulating neural activity in prefrontal cortex (PFC) and modulates cognition via a hypothesized inverse U function. We investigated PFC function in children with chromosome 22q11.2 deletion syndrome (22q11.2DS) in which one copy of catechol-O-methyltransferase (COMT) is deleted, thereby shifting them toward the lower end of dopamine turnover on the nonlinear function. A common polymorphism with valine to methionine substitution alters COMT activity that results in higher enzyme activity in the valine variant. Twenty-seven children with 22q11.2DS between 7 and 14 years old, and 21 age-matched typically developing children, performed a modified version of the Attention Network Test. Children with a single valine allele showed a reduction in response times when trials with incongruent flankers were repeated, whereas those who were hemizygous for the methionine allele did not show the same context-based response facilitation. Our results support that a single gene, COMT, could modulate PFC-dependent cognition.

Frank MJ, D’Lauro C, Curran T. Cross-task individual differences in error processing: neural, electrophysiological, and genetic components. Cognitive Affect Behav Neurosci 7: 297-308

January 2008

·

131 Reads

The error-related negativity (ERN) and error positivity (Pe) are electrophysiological markers of error processing thought to originate in the medial frontal cortex. Previous studies using probabilistic reinforcement showed that individuals who learn more from negative than from positive feedback (negative learners) had larger ERNs than did positive learners. These findings support the dopamine (DA) reinforcement-learning hypothesis of the ERN and associated computational models. However, it remains unclear (1) to what extent these effects generalize to tasks outside the restricted probabilistic reinforcement-learning domain and (2) whether there is a dopaminergic source of these effects. To address these issues, we tested subjects' reinforcement-learning biases behaviorally and recorded EEG during an unrelated recognition memory experiment. Initial recognition responses were speeded, but the subjects were subsequently allowed to self-correct their responses. We found that negative learners, as assessed via probabilistic learning, had larger ERNs in the recognition memory task, suggestive of a common underlying enhanced error-processing mechanism. Negative learners also had enhanced Pes when self-correcting errors than did positive learners. Moreover, the ERN and Pe components contributed independently to negative learning. We also tested for a dopaminergic genetic basis of these ERP components. We analyzed the COMT val/met polymorphism, which has been linked to frontal DA levels. The COMT genotype affected Pe (but not ERN) magnitude; met/met homozygotes showed enhanced Pes to self-corrected errors, as compared with val carriers. These results are consistent with a role for the Pe and frontal monoamines in error awareness.

Knight DC, Smith CN, Cheng DT, Stein EA, Helmstetter FJ. Amygdala and hippocampal activity during acquisition and extinction of human fear conditioning. Cogn Affect Behav Neurosci 4: 317-325

October 2004

·

166 Reads

Previous functional magnetic resonance imaging (fMRI) studies have characterized brain systems involved in conditional response acquisition during Pavlovian fear conditioning. However, the functional neuroanatomy underlying the extinction of human conditional fear remains largely undetermined. The present study used fMRI to examine brain activity during acquisition and extinction of fear conditioning. During the acquisition phase, participants were either exposed to light (CS) presentations that signaled a brief electrical stimulation (paired group) or received light presentations that did not serve as a warning signal (control group). During the extinction phase, half of the paired group subjects continued to receive the same treatment, whereas the remainder received light alone. Control subjects also received light alone during the extinction phase. Changes in metabolic activity within the amygdala and hippocampus support the involvement of these regions in each of the procedural phases of fear conditioning. Hippocampal activity developed during acquisition of the fear response. Amygdala activity increased whenever experimental contingencies were altered, suggesting that this region is involved in processing changes in environmental relationships. The present data show learning-related amygdala and hippocampal activity during human Pavlovian fear conditioning and suggest that the amygdala is particularly important for forming new associations as relationships between stimuli change.

Figure 1. The top panel represents the mean number of beam breaks (6 SEM ) in the horizontal crossing task of intact (black bar), GDX (white bar), and GDX 1 T rats (gray bar). Bars with different letters are significantly different from each other ( p , .05). The bottom left panel represents the mean number of total squares entered (6 SEM ) in the open field of intact (black bar), GDX (white bar), and GDX 1 T rats (gray bar). Bars with different letters represent a tendency to be different from each other ( p 5 .06). The bottom right panel represents the mean number of peripheral squares entered (6 SEM ) in the open field of intact (black bar), GDX (white bar), and GDX 1 T rats (gray bar). Bars with different letters are significantly different from each other ( p , .05).
Figure 3. Mean duration of time spent interacting with conspecific in seconds (6 SEM ) of intact (black bar), GDX (white bar), and GDX 1 T rats (gray bar). Bars with different letters are significantly different from each other ( p , .05).
Figure 4. Mean duration of postshock burial (6 ) of intact (black bar), GDX (white bar), and GDX 1 T rats (gray bar). Bars with different letters are significantly different from each other ( p , .05).
Frye CA, Seliga AM. Testosterone increases analgesia, anxiolysis, and cognitive performance of male rats. Cogn Affect Behav Neurosci 1: 371-381

January 2002

·

61 Reads

Preliminary evidence suggests that testosterone (T) may have anxiety-reducing and cognitive-enhancing properties in animals and people. Performance in a number of affective and cognitive behavioral tasks was examined in intact, T-depleted, and T-depleted and T-replaced male rats. Rats that were gonadally intact (n = 33), gonadectomized (GDX; n = 30), or GDX with silastic capsules of T implanted (n = 28) were tested through a battery of affective tasks (horizontal crossing, open field, elevated plus-maze, emergence, holeboard, social interaction, tailflick, pawlick, and defensive burying) and in the inhibitory avoidance task for cognitive performance. An additional 6 rats per group had plasma androgen concentrations measured and were determined to be physiological for intact rats, supraphysiological for T-implanted rats, and near the nadir for GDX rats. Testosterone implants produced analgesia as shown by the increased tailflick latencies of the GDX rats with silastic capsules of T implanted, relative to intact or GDX rats. Testosterone also produced anxiolysis. Intact rats spent more time interacting with a conspecific and less time burying an electrified prod than did the GDX or T-implanted rats. Intact rats or GDX rats with T implants also spent more time on the open arms of the elevated plus-maze than did GDX rats. Testosterone also enhanced cognitive performance in the inhibitory avoidance task. Intact rats had longer crossover latencies in the inhibitory avoidance task relative to GDX rats; GDX rats with T implants had longer crossover latencies relative to GDX or intact rats. Together, these data demonstrate that endogenous T or administration of T produced analgesia and enhanced affect and cognitive performance of adult male rats.

Dayan P, Daw ND. Decision theory, reinforcement learning, and the brain. Cogn Affect Behav Neurosci 8: 429-453

January 2009

·

150 Reads

Decision making is a core competence for animals and humans acting and surviving in environments they only partially comprehend, gaining rewards and punishments for their troubles. Decision-theoretic concepts permeate experiments and computational models in ethology, psychology, and neuroscience. Here, we review a well-known, coherent Bayesian approach to decision making, showing how it unifies issues in Markovian decision problems, signal detection psychophysics, sequential sampling, and optimal exploration and discuss paradigmatic psychological and neural examples of each problem. We discuss computational issues concerning what subjects know about their task and how ambitious they are in seeking optimal solutions; we address algorithmic topics concerning model-based and model-free methods for making choices; and we highlight key aspects of the neural implementation of decision making.

Figure 1. (A) The trial structure for the abstract rule study. On each trial, subjects saw an instructional cue-either a nonsense shape or a nonword-that they had previously learned to associate with a specific rule. Presentation of this cue was followed by a long and variable delay period, over which the subjects were expected to actively keep the relevant rule in mind. Then, a sample stimulus appeared on the screen, followed by a probe stimulus. On presentation of the probe stimulus, the subjects were to press either a left or a right button depending on the rule being followed and on whether the sample and probe stimuli matched. From "Neural Circuits Subserving the Retrieval and Maintenance of Abstract Rules," by S. A. Bunge, I. Kahn, J. D. Wallis, E. K. Miller, and A. D. Wagner, 2003, Journal of Neurophysiology, 90, p. 3420. Copyright 2003 by the Americal Physiological Society. Reprinted with permission. (B) The regions in left PFC (anterior and posterior VLPFC and FPC) and postMTG modulated by rule complexity (compound simple rules) during presentation of the instructional cue (Bunge et al., 2003). Group-averaged data for 14 adults are rendered on a canonical brain. ( p .005 uncorrected, masked at p .005 to include only regions that were active relative to fixation during cue presentation). (C) Delay period activity associated with maintenance of a specific response plan (simple rules). (D) a set of response contingencies (compound rules). See Bunge et al. (2003) for a direct comparison between regions engaged more strongly by maintenance of compound versus maintenance of simple rules. 
Figure 1. (A) The trial structure for the abstract rule study. On each trial, subjects saw an instructional cue-either a nonsense shape or a nonword-that they had previously learned to associate with a specific rule. Presentation of this cue was followed by a long and variable delay period, over which the subjects were expected to actively keep the relevant rule in mind. Then, a sample stimulus appeared on the screen, followed by a probe stimulus. On presentation of the probe stimulus, the subjects were to press either a left or a right button depending on the rule being followed and on whether the sample and probe stimuli matched. From "Neural Circuits Subserving the Retrieval and Maintenance of Abstract Rules," by S. A. Bunge, I. Kahn, J. D. Wallis, E. K. Miller, and A. D. Wagner, 2003, Journal of Neurophysiology, 90, p. 3420. Copyright 2003 by the Americal Physiological Society. Reprinted with permission. (B) The regions in left PFC (anterior and posterior VLPFC and FPC) and postMTG modulated by rule complexity (compound simple rules) during presentation of the instructional cue (Bunge et al., 2003). Group-averaged data for 14 adults are rendered on a canonical brain. ( p .005 uncorrected, masked at p .005 to include only regions that were active relative to fixation during cue presentation). (C) Delay period activity associated with maintenance of a specific response plan (simple rules). (D) a set of response contingencies (compound rules). See Bunge et al. (2003) for a direct comparison between regions engaged more strongly by maintenance of compound versus maintenance of simple rules. 
Figure 2. (A) Brain regions that were modulated by rule knowledge during passive viewing of road signs (14 adults; p .001 uncorrected). PostMTG was more active for signs of which subjects knew the meanings than for signs whose meanings they did not know, on the basis of a postscan test. (B) Foci of activation from a meta-analysis of MTG activations are plotted on a canonical brain. Activations in postMTG from our rule studies are near those from studies of action knowledge. Orange foci are from Bunge, Kahn, Wallis, Miller, and Wagner, 2003; red foci are from Donohue, Wendelken, Crone, and Bunge, 2004; and yellow foci are from studies on action knowledge (Beauchamp, Lee, Haxby, & Martin, 2003; Chao, Haxby, & Martin, 1999; Choi et al., 2001; Damasio et al., 2001; Damasio, Tranel, Grabowski, Adolphs, & Damasio, 2004; Devlin et al., 2002; Emmorey et al., 2004; Grossman et al., 2002; Kellenbach, Brett, & Patterson, 2003; Kounios et al., 2003; Noppeney & Price, 2003; Perani et al., 1995; Tyler et al., 2003). 
Bunge, S. A. How we use rules to select actions: a review of evidence from cognitive neuroscience. Cogn. Affect. Behav. Neurosci. 4, 564-579
Much of our behavior is guided by rules, or prescribed guides for action. In this review, I consider the current state of knowledge of how rules are learned, stored in the brain, and retrieved and used as the need arises. The focus is primarily on studies in humans, but the review is informed by relevant studies in nonhuman primates. Ventrolateral prefrontal cortex (VLPFC) has been implicated in rule learning, retrieval from long-term memory, and on-line maintenance during task preparation. Interactions between VLPFC and temporal cortex are required for rule retrieval in nonhuman primates, and brain imaging findings in humans suggest that rule knowledge is stored in the posterior middle temporal gyrus. Dorsolateral PFC appears to be more closely related to rule-based response selection than to rule retrieval. An important task for the future is to explain how PFC, basal ganglia, and temporal, parietal, and motor cortices interact to produce rule-guided behavior.

Figure 3. Progressive ratio responding for sucrose reinforcement. The D 2 R / mice (n 10) responded for significantly more reinforcers than did the D 2 R / mice (n 10) ( p < .05) and showed a trend toward increased responding in comparison with D 2 R / mice (n 8) ( p .10) during the food-deprived phase. Each genotype earned significantly fewer reinforcers during the free-feeding manipulation than during the food-deprived phase of the experiment ( p < .05). 
Dopamine D2 receptors mediate reversal learning in male C57BL/6J mice

April 2006

·

98 Reads

Dopamine is critical for directing goal-oriented behavior. We investigated dopamine D2 receptor involvement in reversal learning and reinforcement efficacy in mice lacking functional dopamine D2 receptors and their heterozygous and wild-type littermates. Mice discriminated between two odors to receive a food reinforcer. One odor signaled a reinforcer (S+); the other odor signaled no reinforcer (S-). After mice learned the S+/S- relationship, we inverted the reinforcement contingencies. The necessary number of trials to relearn the new reinforcement contingencies served as our index of reversal learning. Mice lacking functional dopamine D2 receptors repeatedly failed to inhibit previously reinforced responses during reversal trials. In a separate experiment, mice responded for reinforcers on a progressive ratio schedule of reinforcement. Mice lacking functional dopamine D2 receptors earned significantly fewer reinforcers than did heterozygous mice. Our results suggest that dopamine D2 receptors regulate reversal learning and influence the reinforcing efficacy of natural rewards.

Low alpha power (7.5-9.5 Hz) changes during positive and negative affective learning

April 2003

·

97 Reads

There is evidence that the positive and the negative word lists of the Affective Auditory Verbal Learning Test (AAVL) are useful with regard to mood induction. To date, however, changes in brain activation, as indicated by quantitative electroencephalographic recording, have not been examined. Thus, changes in low alpha power (7.5-9.5 Hz) were examined during and after completion of the positive or the negative learning list of the AAVL among 37 undergraduate men and women. Three primary findings from the study include the following: (1) Previously reported recall patterns were replicated; (2) participants who completed the negative list reported a significant decline in mood state at the end of the session; and (3) participants who completed the negative word list evidenced a significant reduction in low alpha power (in comparison with baseline) within the parietal regions. The findings noted above are seemingly counter to contemporary theories of mood regulation (i.e., asymmetrical changes in anterior activity, rather than changes in parietal regions). Although the AAVL may have limited utility as a tool for mood induction, it may serve as a functional tool for examination of the cerebral processes associated with affective verbal memory.

Figure 1. Functions of neurotrophins and Trk receptors. The neurotrophins nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), neurotrophin-3 (NT-3), and neurotrophin-4 (NT-4) bind selectively to specific Trk receptors. Neurotrophin-mediated activation of Trk receptors leads to a variety of biological responses, which include cell survival, axonal and dendritic growth, and synaptic plasticity.  
Bath KG, Lee FS. Variant BDNF (Val66Met) impact on brain structure and function. Cogn Affect Behav Neurosci 6: 79-85
Neurotrophins, such as brain-derived neurotrophic factor (BDNF), are a unique family of polypeptide growth factors that influence differentiation and survival of neurons in the developing nervous system. In adults, BDNF is important in regulating synaptic plasticity and connectivity in the brain. Recently, a common single-nucleotide polymorphism in the human BDNF gene, resulting in avaline to methionine substitution in the prodomain (Val66Met), has been shown to lead to memory impairment and susceptibility to neuropsychiatric disorders. An understanding of how this naturally occurring polymorphism affects behavior, anatomy, and cognition in adults is an important first step in linking genetic alterations in the neurotrophin system to definable biological outcomes in humans. We review the recent literature linking this BDNF polymorphism to cognitive impairment in the context of in vitro and transgenic animal studies that have established BDNF's central role in neuronal functioning in the adult brain.

Individual differences in electrophysiological responses to performance feedback predict AB magnitude

December 2012

·

14 Reads

The attentional blink (AB) is observed when report accuracy for a second target (T2) is reduced if T2 is presented within approximately 500 ms of a first target (T1), but accuracy is relatively unimpaired at longer T1-T2 separations. The AB is thought to represent a transient cost of attending to a target, and reliable individual differences have been observed in its magnitude. Some models of the AB have suggested that cognitive control contributes to production of the AB, such that greater cognitive control is associated with larger AB magnitudes. Performance-monitoring functions are thought to modulate the strength of cognitive control, and those functions are indexed by event-related potentials in response to both endogenous and exogenous performance evaluation. Here we examined whether individual differences in the amplitudes to internal and external response feedback predict individual AB magnitudes. We found that electrophysiological responses to externally provided performance feedback, measured in two different tasks, did predict individual differences in AB magnitude, such that greater feedback-related N2 amplitudes were associated with larger AB magnitudes, regardless of the valence of the feedback.

Face-sex categorisation is better above-fixation than below: Evidence from the Reach-to-Touch paradigm

April 2014

·

586 Reads

The masked congruence effect (MCE) elicited by nonconsciously presented faces in a sex-categorization task has recently been shown to be sensitive to the effects of attention. Here we investigated how spatial location along the vertical meridian modulates the MCE for face-sex categorization. Participants made left and right reaching movements to classify the sex of a target face that appeared either immediately above or below central fixation. The target was preceded by a masked prime face that was either congruent (i.e., same sex) or incongruent (i.e., opposite sex) with the target. In the reach-to-touch paradigm, participants typically classify targets more efficiently (i.e., their finger heads in the correct direction earlier and faster) on congruent than on incongruent trials. We observed an upper-hemifield advantage in the time course of this MCE, such that primes affected target classification sooner when they were presented in the upper visual field (UVF) rather than the lower visual field (LVF). Moreover, we observed a differential benefit of attention between the vertical hemifields, in that the MCE was dependent on the appropriate allocation of spatial attention in the LVF, but not the UVF. Taken together, these behavioral findings suggest that the processing of faces qua faces (e.g., sex-categorization) is more robust in upper-hemifield locations.

Figure 1. Scatterplots of (A) error-related negativity (ERN) amplitude in the first interval as a function of absorption. (B) Frontal asymmetry in the first interval as a function of absorption. (C) Frontal asymmetry in the first interval as a function of constraint. (d) Change in frontal asymmetry after the reward manipulation as a function of drive for reward.  
Table 1 Correlations From Studies 1 and 2 Between Persistence-Related Traits 
Table 2 Varimax Rotated Component Matrix of Traits 
Figure 2. Response-locked event-related potentials from the first interval of 20 min from electrodes Fz, FCz, Cz, and Pz as a function of high (black line) versus low (gray line) absorption scores. Only for graphical purposes, high-and low-scoring groups were created on the basis of median split.  
Table 3 Regression Analysis Predicting Persistence Scores 
Absorbed in the task: Personality measures predict engagement during task performance as tracked by error negativity and asymmetrical frontal activity

December 2010

·

272 Reads

We hypothesized that interactions between traits and context predict task engagement, as measured by the amplitude of the error-related negativity (ERN), performance, and relative frontal activity asymmetry (RFA). In Study 1, we found that drive for reward, absorption, and constraint independently predicted self-reported persistence. We hypothesized that, during a prolonged monotonous task, absorption would predict initial ERN amplitudes, constraint would delay declines in ERN amplitudes and deterioration of performance, and drive for reward would predict left RFA when a reward could be obtained. Study 2, employing EEG recordings, confirmed our predictions. The results showed that most traits that have in previous research been related to ERN amplitudes have a relationship with the motivational trait persistence in common. In addition, trait-context combinations that are likely associated with increased engagement predict larger ERN amplitudes and RFA. Together, these results support the hypothesis that engagement may be a common underlying factor predicting ERN amplitude.

Dissociable neural subsystems underlie visual working memory for abstract categories and specific exemplars

April 2008

·

132 Reads

An ongoing debate concerns whether visual object representations are relatively abstract, relatively specific, both abstract and specific within a unified system, or abstract and specific in separate and dissociable neural subsystems. Most of the evidence for the dissociable subsystems theory has come from experiments that used familiar shapes, and the usage of familiar shapes has allowed for alternative explanations for the results. Thus, we examined abstract and specific visual working memory when the stimuli were novel objects viewed for the first and only time. When participants judged whether cues and probes belonged to the same abstract visual category, they performed more accurately when the probes were presented directly to the left hemisphere than when they were presented directly to the right hemisphere. In contrast, when participants judged whether or not cues and probes were the same specific visual exemplar, they performed more accurately when the probes were presented directly to the right hemisphere than when they were presented directly to the left hemisphere. For the first time, results from experiments using visual working memory tasks support the dissociable subsystems theory.

Lower dorsal striatum activation in association with neuroticism during the acceptance of unfair offers

February 2015

·

144 Reads

Unfair treatment may evoke more negative emotions in individuals scoring higher on neuroticism, thereby possibly impacting their decision-making in these situations. To investigate the neural basis of social decision-making in these individuals, we examined interpersonal reactions to unfairness in the Ultimatum Game (UG). We measured brain activation with fMRI in 120 participants selected based on their neuroticism score, while they made decisions to accept or reject proposals that were either fair or unfair. The anterior insula and anterior cingulate cortex were more activated during the processing of unfair offers, consistent with prior UG studies. Furthermore, we found more activation in parietal and temporal regions for the two most common decisions (fair accept and unfair reject), involving areas related to perceptual decision-making. Conversely, during the decision to accept unfair offers, individuals recruited more frontal regions previously associated with decision-making and the implementation of reappraisal in the UG. High compared to low neurotic individuals did not show differential activation patterns during the proposal of unfair offers; however, they did show lower activation in the right dorsal striatum (putamen) during the acceptance of unfair offers. This brain region has been involved in the formation of stimulus-action-reward associations and motivation/arousal. In conclusion, the findings suggest that both high and low neurotic individuals recruit brain regions signaling social norm violations in response to unfair offers. However, when it comes to decision-making, it seems that neural circuitry related to reward and motivation is altered in individuals scoring higher on neuroticism, when accepting an unfair offer.

Table 2 Results of the full ANOVAs, including detection and congruency for all of the extracted parameters
Example of a partial error, along with the extracted indices. a Typical electromyographic (EMG) recording showing a partial error. Time 0 is stimulus onset, and the long vertical dashed line indicates the mechanical response. The bottom trace presents the rectified EMG activity of the muscle involved in the correct response. A large EMG burst starts slightly before the mechanical response. This correct EMG burst is preceded by a small burst on the incorrect muscle (top trace), which is far too small to produce an overt response. The extracted indices are the latency of the partial error (IncLat), the correction time (CT, between the incorrect and the correct EMG burst onsets), and the motor time between the correct EMG burst onset and the mechanical response. b Zoom depiction of the partial error, depicting the extracted EMG burst parameters. First, we computed the maximum of the rectified trace. Then we extracted the earliest point preceding, and the latest point following, the peak whose amplitudes were equal to or larger than half of the max amplitude. The time separating the two values was taken as the measure of EMG burst duration (IncDur and CorDur, for incorrect and correct EMG bursts, respectively). The surface under the curve between these two points (shaded area in panel b) was taken as a measure of the EMG burst amplitude (IncSurf and CorSurf, for incorrect and correct bursts, respectively). c Slope extraction: The cumulative sum of the rectified EMG trace was computed, becoming monotonically increasing. The linear trend was then removed to get a “flat” signal. A linear regression was computed on the first 30 points of the cumulative signal following the burst onset (i.e., on about the first 15 ms), and the slope of the regression (dashed line in panel c) is taken as a measure of the steepness of the EMG burst (IncSlope and CorSlope, for incorrect and correct EMG bursts, respectively)
a Grand average of the incorrect EMG bursts: The EMG bursts corresponding to partial errors or overt errors were averaged, time-locked to their onsets, for the three detection categories. b Grand average of the correct EMG bursts observed on partial-error trials for the three detection categories, and for pure-correct trials. For the sake of visibility, the averaged EMG bursts have been smoothed, but all analyses were performed on the raw, unfiltered signals. (Inset: Grand average of pure-correct and error trials.) c Mean cumulative density functions of partial-error surfaces (IncSurf ) for undetected (gray diamonds) and detected (black diamonds) partial errors. Although the lowest values of the two distributions are pretty similar, they quickly diverge. (Inset: For the sake of comparison, this graph also shows the cumulative density function of surfaces for overt errors [black crosses].) d Mean cumulative density functions of CTs for undetected (gray diamonds) and detected (black diamonds) partial errors. The two distribution shapes are more similar than for those for surfaces, showing a more constant shift.
a Scatterplot of IncSurf as a function of CT for all participants (after z-score computation). The overlap between undetected and detected partial errors is large, and it is clear that neither of the two parameters in itself allows for a clear prediction of partial-error detection. It can be noted, however, that above a virtual decreasing diagonal, most of the points belong to the detected class, confirming that the combination of the two parameters is necessary for classifying the trials. b Receiver operating characteristic curves for EMG surface (solid line) and CT (dashed line). The area under the curve (AUC) is larger for EMG surface than for CT
Detecting and correcting partial errors: Evidence for efficient control without conscious access

December 2013

·

138 Reads

Appropriate reactions to erroneous actions are essential to keeping behavior adaptive. Erring, however, is not an all-or-none process: electromyographic (EMG) recordings of the responding muscles have revealed that covert incorrect response activations (termed "partial errors") occur on a proportion of overtly correct trials. The occurrence of such "partial errors" shows that incorrect response activations could be corrected online, before turning into overt errors. In the present study, we showed that, unlike overt errors, such "partial errors" are poorly consciously detected by participants, who could report only one third of their partial errors. Two parameters of the partial errors were found to predict detection: the surface of the incorrect EMG burst (larger for detected) and the correction time (between the incorrect and correct EMG onsets; longer for detected). These two parameters provided independent information. The correct(ive) responses associated with detected partial errors were larger than the "pure-correct" ones, and this increase was likely a consequence, rather than a cause, of the detection. The respective impacts of the two parameters predicting detection (incorrect surface and correction time), along with the underlying physiological processes subtending partial-error detection, are discussed.

Top-cited authors