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In tasks that measure serial-order memory, it is common to observe a “fill-in tendency”—when a person skips an item, the next item they report is more likely to be the skipped item (a fill-in response) than the next list item (an infill response). They tend to “fill in” the blank they skipped. The fill-in tendency has informed the modeling of serial-order memory—it presents strong evidence against associative chaining accounts because they predict more infill responses than fill-in responses. Despite the failures of associative chaining theories, evidence grows for the use of chaining-like item-dependent cues in serial-order memory. In this paper, we analyzed fill-in and infill responses from nine serial learning experiments (one new experiment and eight previously published experiments) that used variants of the spin list procedure and found strong evidence of item-dependent retrieval cues in serial-order memory. The current analyses revealed a fill-in tendency in all lists—even in those in which item-dependent cues were suspected to have been used. However, in those lists the likelihood of infill responses was higher, and consequently, the fill-in tendency was weaker. Our results expose a flaw in the conventional understanding of fill-in and infill responses. That is, the presence (or absence) of the fill-in tendency is not a strong test of item-dependent cues. Instead, changes in the magnitude of the fill-in tendency—more specifically, an increase in the likelihood of infill responses—across task conditions seem to better indicate the use of item-dependent cues.

Here the authors report that repeated presentations of a visual sequence over a course of days causes evoked response potentiation in mouse V1 that is highly specific for stimulus order and timing. After V1 is trained to recognize a sequence, cortical activity regenerates the full sequence even when individual stimulus elements are omitted. Learning to recognize and predict temporal sequences is fundamental to sensory perception and is impaired in several neuropsychiatric disorders, but little is known about where and how this occurs in the brain. We discovered that repeated presentations of a visual sequence over a course of days resulted in evoked response potentiation in mouse V1 that was highly specific for stimulus order and timing. Notably, after V1 was trained to recognize a sequence, cortical activity regenerated the full sequence even when individual stimulus elements were omitted. Our results advance the understanding of how the brain makes 'intelligent guesses' on the basis of limited information to form visual percepts and suggest that it is possible to study the mechanistic basis of this high-level cognitive ability by studying low-level sensory systems.

Recent research suggests that socio-ecological factors such as dietary specialization and social complexity may be drivers of advanced cognitive skills among primates. Therefore, we assessed the ability of 12 black-handed spider monkeys ( Ateles geoffroyi ), a highly frugivorous platyrrhine primate with strong fission-fusion dynamics, to succeed in a serial visual reversal learning task. Using a two-alternative choice paradigm we first trained the animals to reliably choose a rewarded visual stimulus over a non-rewarded one. Upon reaching a pre-set learning criterion we then switched the reward values of the two stimuli and assessed if and how quickly the animals learned to reverse their choices, again to a pre-set learning criterion. This stimulus reversal procedure was then continued for a total of 80 sessions of 10 trials each. We found that the spider monkeys quickly learned to reliably discriminate between two simultaneously presented visual stimuli, that they succeeded in a visual reversal learning task, and that they displayed an increase in learning speed across consecutive reversals, suggesting that they are capable of serial reversal learning-set formation with visual cues. The fastest-learning individual completed five reversals within the 80 sessions. The spider monkeys outperformed most other primate and nonprimate mammal species tested so far on this type of cognitive task, including chimpanzees, with regard to their learning speed in both the initial learning task and in the first reversal task, suggesting a high degree of behavioral flexibility and inhibitory control. Our findings support the notion that socio-ecological factors such as dietary specialization and social complexity foster advanced cognitive skills in primates.

The influence of sleep on motor skill consolidation has been a research topic of increasing interest. In this study, we distinguished general skill learning from sequence-specific learning in a probabilistic implicit sequence learning task (alternating serial reaction time) in young and old adults before and after a 12-h offline interval which did or did not contain sleep (p.m.-a.m. and a.m.-p.m. groups, respectively). The results showed that general skill learning, as assessed via overall reaction time, improved offline in both the young and older groups, with the young group improving more than the old. However, the improvement was not sleep-dependent, in that there was no difference between the a.m.-p.m. and p.m.-a.m. groups. We did not find sequence-specific offline improvement in either age group for the a.m.-either p.m. or p.m.-a.m. groups, suggesting that consolidation of this kind of implicit motor sequence learning may not be influenced by sleep.

Transcranial direct current stimulation (tDCS) transiently alters cortical excitability and synaptic plasticity. So far, few studies have investigated the behavioral effects of applying tDCS to the cerebellum. Given the cerebellum’s inhibitory effects on cortical motor areas as well as its role in fine motor control and motor coordination, we investigated whether cerebellar tDCS can modulate response selection processes and motor sequence learning. Seventy-two participants received either cerebellar anodal (excitatory), cathodal (inhibitory), or sham (placebo) tDCS while performing a serial reaction time task (SRTT). To compare acute and long-term effects of stimulation on behavioral performance, participants came back for follow-up testing at 24 h after stimulation. Results indicated no group differences in performance prior to tDCS. During stimulation, tDCS did not affect sequence-specific learning, but anodal as compared to cathodal and sham stimulations did modulate response selection processes. Specifically, anodal tDCS increased response latencies independent of whether a trained or transfer sequence was being performed, although this effect became smaller throughout training. At the 24-h follow-up, the group that previously received anodal tDCS again demonstrated increased response latencies, but only when the previously trained sequence and a transfer sequence had to be performed in the same experimental block. This increased behavioral interference tentatively points to a detrimental effect of anodal cerebellar tDCS on sequence consolidation/retention. These results are consistent with the notion that the cerebellum exerts an inhibitory effect on cortical motor areas, which can impair sequential response selection when this inhibition is strengthened by tDCS.

To survive and reproduce, animals need to behave adaptively by adjusting their behavior to their environment, with learning facilitating some of these processes. Dogs have become a go-to model species in comparative cognition studies, making our understanding of their learning skills paramount at multiple levels, not only with regards to basic research on their cognitive skills and the effects of domestication, but also with applied purposes such as training. In order to tackle these issues, we tested similarly raised wolves and dogs in a serial learning task inspired by Harlow’s “learning set.” In Phase 1 , different pairs of objects were presented to the animals, one of which was baited while the other was not. Both species’ performance gradually improved with each new set of objects, showing that they “learnt to learn,” but no differences were found between the species in their learning speed. In Phase 2 , once subjects had learned the association between one of the objects and the food reward, the contingencies were reversed and the previously unrewarded object of the same pair was now rewarded. Dogs’ performance in this task seemed to be better than wolves’, albeit only when considering just the first session of each reversal, suggesting that the dogs might be more flexible than wolves. Further research (possibly with the aid of refined methods such as computer-based tasks) would help ascertain whether these differences between wolves and dogs are persistent across different learning tasks.

Rationale Impairments in behavioral flexibility lie at the core of anxiety and obsessive-compulsive disorders. Few studies, however, have investigated the neural substrates of natural variation in behavioral flexibility and whether inflexible behavior is linked to anxiety and peripheral markers of stress and monoamine function. Objective The objective of the study was to investigate peripheral and central markers associated with perseverative behavior on a spatial-discrimination serial reversal learning task. Methods Rats were trained on a reversal learning task prior to blood sampling, anxiety assessment, and the behavioral evaluation of selective monoamine oxidase-A (MAO-A) and MAO-B inhibitors, which block the degradation of serotonin (5-HT), dopamine (DA), and noradrenaline (NA). Results Perseveration correlated positively with 5-HT levels in blood plasma and inversely with trait anxiety, as measured on the elevated plus maze. No significant relationships were found between perseveration and the stress hormone corticosterone or the 5-HT precursor tryptophan. Reversal learning was significantly improved by systemic administration of the MAO-A inhibitor moclobemide but not by the MAO-B inhibitor lazabemide. Moclobemide also increased latencies to initiate a new trial following an incorrect response suggesting a possible role in modulating behavioral inhibition to negative feedback. MAO-A but not MAO-B inhibition resulted in pronounced increases in 5-HT and NA content in the orbitofrontal cortex and dorsal raphé nuclei and increased 5-HT and DA content in the basolateral amygdala and dorsomedial striatum. Conclusions These findings indicate that central and peripheral monoaminergic mechanisms underlie inter-individual variation in behavioral flexibility, which overlaps with trait anxiety and depends on functional MAO-A activity.

The rapid serial visual presentation (RSVP) task and continuous performance tasks (CPT) are used to assess attentional impairments in patients with psychiatric and neurological conditions. This study developed a novel touchscreen task for rats based on the structure of a human RSVP task and used pharmacological manipulations to investigate their effects on different performance measures. Normal animals were trained to respond to a target image and withhold responding to distractor images presented within a continuous sequence. In a second version of the task, a false-alarm image was included, so performance could be assessed relative to two types of nontarget distractors. The effects of acute administration of stimulant and nonstimulant treatments for ADHD (amphetamine and atomoxetine) were tested in both tasks. Methylphenidate, ketamine, and nicotine were tested in the first task only. Amphetamine made animals more impulsive and decreased overall accuracy but increased accuracy when the target was presented early in the image sequence. Atomoxetine improved accuracy overall with a specific reduction in false-alarm responses and a shift in the attentional curve reflecting improved accuracy for targets later in the image sequence. However, atomoxetine also slowed responding and increased omissions. Ketamine, nicotine, and methylphenidate had no specific effects at the doses tested. These results suggest that stimulant versus nonstimulant treatments have different effects on attention and impulsive behaviour in this rat version of an RSVP task. These results also suggest that RSVP-like tasks have the potential to be used to study attention in rodents.

The present experiment was designed to enhance our understanding of how response effects with varying amounts of useful information influence implicit sequence learning. We recorded event-related brain potentials, while participants performed a modified version of the serial reaction time task (SRTT). In this task, participants have to press one of four keys corresponding to four letters on a computer screen. Unknown to participants, in some parts of the experimental blocks, the stimuli appear in a repetitive (structured) deterministic sequence, whereas in other parts, stimuli were determined randomly. Four groups of participants differing in the presentation of tones after each response performed the SRTT. In the no tone group, no tones were presented after a response. The other three groups differed with respect to the melody generated by the key presses: in the unmelodic group, one out of four different tones was chosen randomly and presented immediately after a response. In the consistent melody group, the press of a response key always resulted in the production of the same tone, resulting in a repetitive melody during structured parts of the sequence (consistent redundant effect). In the inconsistent melody group, the “melody” produced in the sequenced parts of the blocks was identical to the consistent melody group, but the same response could produce two different tones depending on the actual position in the stimulus sequence. Thus, during structured sequences, subjects heard the same melody as in the consistent melody group, but every key press could be followed by one out of two different tones. To disentangle effects of sequence awareness from our experimental manipulations, all analyses were restricted to implicit learners. All four groups showed sequence learning, but to a different degree: in general, every kind of tone improved sequence learning relative to the no tone group. However, unmelodic tones were less beneficial for learning than tones forming a melody. Tones mapped consistently to response keys improved learning faster than tones producing the same melody, but not mapped consistently to keys. However, at the end of the learning phase, the two melody groups did not differ in the amount of sequence learning. The error-related negativity (ERN) increased with sequence learning (larger ERN at the end of the experiment for trials following the sequence compared to random trials) and this effect was more pronounced for the groups that showed more learning. These findings indicate that response effects containing useful information foster sequence learning even if the same response can produce different effects. Furthermore, we replicated earlier results showing that the importance of an error with respect to the task at hand modulates the activity of the human performance monitoring system.

Understanding how organisms make transitive inferences is critical to understanding their general ability to learn serial relationships. In this context, transitive inference (TI) can be understood as a specific heuristic that applies broadly to many different serial learning tasks, which have been the focus of hundreds of studies involving dozens of species. In the present study, monkeys learned the order of 7-item lists of photographic stimuli by trial and error, and were then tested on “derived” lists. These derived test lists combined stimuli from multiple training lists in ambiguous ways, sometimes changing their order relative to training. We found that subjects displayed strong preferences when presented with novel test pairs, even when those pairs were drawn from different training lists. These preferences were helpful when test pairs had an ordering congruent with their ranks during training, but yielded consistently below-chance performance when pairs had an incongruent order relative to training. This behavior can be explained by the joint contributions of transitive inference and another heuristic that we refer to as “positional inference.” Positional inferences play a complementary role to transitive inferences in facilitating choices between novel pairs of stimuli. The theoretical framework that best explains both transitive and positional inferences is a spatial model that represents both the position of each stimulus and its uncertainty. A computational implementation of this framework yields accurate predictions about both correct responses and errors on derived lists.