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Traditional response inhibition tasks are assumed to capture one’s ability to inhibit a response. This ability, however, requires a reactive process and a proactive process, post-error slowing (PES). Recent evidence shows that Stop-Signal Tasks (SSTs) measure the reactive process, and while the Sustained Attention to Response Task measures overall response inhibition, that measure is confounded by a proactive process. Since the diseases associated with response inhibition deficits often co-occur with symptoms that diminish the capacity for lengthy behavioural testing, and, since it is unknown to which process such decrements can be attributed and where in the brain these processes are generated, rapid and precise measurement of reactive and proactive processes is important. To address these issues, we administered a battery of four response inhibition tasks to healthy young adults (N = 123), two SSTs and two Go/No-Go tasks. In three tasks, we implemented adaptations to allow direct observation of proactive inhibition, reactive inhibition, and overall response inhibition. We introduce a novel cueing procedure to investigate the possibility of a predictive mechanism of proactive inhibition, arguing that slower response times on trials with a higher Stop/No-Go probability indicate predictive proactive inhibition. Based on these findings, we propose a novel demarcation to proactive inhibition: remedial proactive inhibition (PES), and predictive proactive inhibition. Additionally, we provide empirical support for a Bayesian adaptive staircase (Livesey & Livesey, 2016) that allows rapid convergence on estimates of reactive inhibition in SSTs in as few as 20 trials that are robust against potential predictive proactive inhibition confounds.

Parkinson's disease (PD), the second most common neurodegenerative disorder, is characterized by cardinal motor impairments, including akinesia and tremor, as well as by a host of non-motor symptoms, including both autonomic and cognitive dysfunction. PD is associated with a death of nigral dopaminergic neurons, as well as the pathological spread of Lewy bodies, consisting predominantly of the misfolded protein alpha-synuclein. To date, only symptomatic treatments, such as levodopa, are available, and trials aiming to cure the disease, or at least halt its progression, have not been successful. Wong et al. (2019) suggested that the lack of effective therapy against neurodegeneration in PD might be attributed to the fact that the molecular mechanisms standing behind the dopaminergic neuronal vulnerability are still a major scientific challenge. Understanding these molecular mechanisms is critical for developing effective therapy. Thirty-five years ago, Calne and William Langston (1983) raised the question of whether biological or environmental factors precipitate the development of PD. In spite of great advances in technology and medicine, this question still lacks a clear answer. Only 5-15% of PD cases are attributed to a genetic mutation, with the majority of cases classified as idiopathic, which could be linked to exposure to environmental contaminants. Rodent models play a crucial role in understanding the risk factors and pathogenesis of PD. Additionally, well-validated rodent models are critical for driving the preclinical development of clinically translatable treatment options. In this review, we discuss the mechanisms, similarities and differences, as well as advantages and limitations of different neurotoxin-induced rat models of PD. In the second part of this review, we will discuss the potential future of neurotoxin-induced models of PD. Finally, we will briefly demonstrate the crucial role of gene-environment interactions in PD and discuss fusion or dual PD models. We argue that these models have the potential to significantly further our understanding of PD.

This study investigated electroencephalography (EEG) correlates of prediction error during probabilistic learning in pre-adolescents. The detection of prediction errors, the discrepancies between experienced and anticipated outcomes, is thought to be a critical mechanism that drives new learning. Thirty-three typically developing pre-adolescents (mean age = 10.62 years) participated in an associative learning task in which they learned the probabilistic relationships between cues and outcome stimuli in the absence of explicit feedback. We investigated whether three outcome-locked event-related potentials (ERPs) could reflect prediction error processing: the P3, the late positive potential (LPP), and the feedback-related negativity (FRN). All ERP components investigated were sensitive to the magnitude of hypothetical prediction errors that were estimated based on each individual’s learning performance. Higher estimated prediction errors generated larger P3 and LPP components, and a more negative FRN. These findings indicate that pre-adolescents are capable of undergoing probabilistic learning in the absence of explicit feedback, much in the same way as adults, and that prediction error processing is physiologically indexed via the FRN, P3 and LPP following outcome stimuli. Refereed/Peer-reviewed

Efficient learning requires allocating limited attentional resources to meaningful stimuli and away from irrelevant stimuli. This prioritization may occur via covert attention, evident in the activity of the visual cortex. We used steady-state visual evoked potentials (SSVEPs) to assess whether associability-driven changes in stimulus processing were evident in visuocortical responses. Participants were trained on a learned-predictiveness protocol, whereby one stimulus on each trial accurately predicted the correct response for that trial, and the other was irrelevant. In a second phase the task was arranged so that all cues were objectively predictive. Participants’ overt attention (eye gaze) was affected by each cue’s reinforcement history, as was their covert attention (SSVEP responses). These biases persisted into Phase 2 when all stimuli were objectively predictive, thereby demonstrating that learned attentional processes are evident in basic sensory processing, and exert an effect on covert attention above and beyond the effects of overt gaze bias.

Anxiety disorders are characterised by the perception of fear and threat in the presence of stimuli that are neutral or ambiguous. Attempts in previous research to explain the relationship between anxiety and fear learning have been inconsistent, possibly due to the influence of an unmeasured mechanism that mediates the relationship between them. Working memory capacity has been suggested as one such mechanism. The current study investigated the influence of anxiety-based individual differences upon associative fear learning, while accounting for individual differences in working memory. We hypothesised that individuals high in both anxiety and working memory would show unimpaired fear learning whereas individuals high in anxiety and low in working memory would exhibit dysfunctional fear learning. Sixty participants completed a battery of anxiety and working memory tests, as well as a fear conditioning experiment that tested for blocking, conditioned inhibition and fear discrimination. We found that anxious individuals were more likely to show impaired fear discrimination only if they also had a low working memory capacity. Furthermore, anxiety was particularly associated with poorer learning about safety cues. Such relationships were not observed for blocking and conditioned inhibition. These results suggest that the relationship between anxiety and fear learning is complex and warrants further investigation of the potential mediating role of higher-order cognitive faculties.

The ability to inhibit a prepared emotional or motor action is difficult but critical to everyday functioning. It is well-established that response inhibition relies on the dopaminergic system in the basal ganglia. However, response inhibition is often measured imprecisely due to a process which slows our responses and increases subsequent inhibition success known as proactive inhibition. As the role of the dopamine system in proactive inhibition is unclear, we investigated the contribution of dopaminergic genes to proactive inhibition. We operationalised proactive inhibition as slower responses after failures to inhibit a response in a Go/No-Go paradigm and investigated its relationship to rs686/A at DRD1 (associated with increased gene expression) and rs1800497/T at DRD2 (associated with reduced D2 receptor availability). Even though our sample (N = 264) was relatively young (18-40 years), we found that proactive inhibition improves the ability to withhold erroneous responses in older participants (p = 0.002) and those with lower fluid intelligence scores (p

Perceptions of the effectiveness of a moderate probabilistic cause are influenced by the presence of stronger alternative causes. One important idea is that this influence occurs because the strong cause renders the weaker one statistically redundant. Alternatively, the causes might be contrasted to each other, so the stronger cause may simply overpower perceptions of the weaker one. Causes may have the same polarity (e.g., two generative/excitatory causes or two preventive/inhibitory causes) or be of opposite polarity (e.g., a generative cause versus a preventive or inhibitory cause). Previously, we found that the presence of a stronger redundant alternative cause of the same polarity reduces causal judgements of the moderate cause (i.e., blocking occurs) but a stronger cause of the opposite polarity enhances judgements of the moderate cause (i.e., enhancement). Experiments 1 and 2 further explored these cue competition effects with redundant and non-redundant alternative causes (i.e., correlated versus independent alternatives). We generally found that blocking and enhancement occur with both redundant and non-redundant alternative causes. This is inconsistent with an information processing view of cue competition that relies on statistical redundancy to account for blocking. Although these results are inconsistent with a redundancy information processing account of cue competition and are consistent with our earlier contrast account, we demonstrate here that a simple associative model can account for the sometimes apparently contradictory effects of cue competition.

Parkinson’s disease (PD) is a chronic, progressive neurodegenerative disorder, characterized by a loss of dopaminergic neurons in the substantia nigra pars compacta. Given that dopamine is critically involved in learning and other cognitive processes, such as working memory, dopamine loss in PD has been linked both to learning abnormalities and to cognitive dysfunction more generally in the disease. It is unclear, however, whether avoidance behavior is impacted in PD. This is significant, as this type of instrumental behavior plays an important role in both decision-making and emotional (dys)function. Consequently, the aim of the present study was to examine avoidance learning and operant extinction in PD using a computer-based task. On this task, participants control a spaceship and attempt to shoot an enemy spaceship to gain points. They also learn to hide in safe areas to protect from (i.e., avoid) aversive events (on-screen explosions and point loss). The results showed that patients with PD (N = 25) acquired an avoidance response during aversive periods to the same extent as healthy age- and education-matched controls (N = 19); however, patients demonstrated greater hiding during safe periods not associated with aversive events, which could represent maladaptive generalization of the avoidance response. Furthermore, this impairment was more pronounced during the extinction phase, and in patients who reported higher levels of depression. These results demonstrate for the first time that PD is associated with maladaptive avoidance patterns, which could possibly contribute to the emergence of depression in the disease.

Neuroscientists continue to grapple with the conundrum of compulsive drug use. Why do compulsions to seek addictive drugs persist in the face of diminishing pleasure and escalating negative consequences? This chapter surveys several influential theories which look to learning and motivation processes to address this question. Discussed in turn are the possibilities that compulsions arise from: (1) maladaptive prediction error processing, (2) heightened incentive salience via sensitization of drugs and drug-associated cues, (3) the aberrant emergence of habitual control at the expense of goal-directed behavior, (4) Pavlovian instrumental transfer effects and/or (5) a lack of sensitivity to punishment. In each instance we present behavioral evidence derived primarily from Pavlovian and instrumental conditioning experiments, as well as neurobiological evidence derived from manipulation and measurement of the mesolimbic dopamine system. We note limitations and outstanding questions and encourage an integrative approach, whereby computational modeling techniques are employed to combine and compare theoretical assumptions.

Performing sequences of movements is a ubiquitous skill that involves dopamine transmission. However, it is unclear which components of the dopamine system contribute to which aspects of motor sequence learning. Here we used a genetic approach to investigate the relationship between different components of the dopamine system and specific aspects of sequence learning in humans. In particular, we investigated variations in genes that code for the catechol-O-methyltransferase (COMT) enzyme, the dopamine transporter (DAT) and dopamine D1 and D2 receptors (DRD1 and DRD2). COMT and the DAT regulate dopamine availability in the prefrontal cortex and the striatum, respectively, two key regions recruited during learning, whereas dopamine D1 and D2 receptors are thought to be involved in long-term potentiation and depression, respectively. We show that polymorphisms in the COMT, DRD1 and DRD2 genes differentially affect behavioral performance on a sequence learning task in 161 Caucasian participants. The DRD1 polymorphism predicted the ability to learn new sequences, the DRD2 polymorphism predicted the ability to perform a previously learnt sequence after performing interfering random movements, whereas the COMT polymorphism predicted the ability to switch flexibly between two sequences. We used computer simulations to explore potential mechanisms underlying these effects, which revealed that the DRD1 and DRD2 effects are possibly related to neuroplasticity. Our prediction-error algorithm estimated faster rates of connection strengthening in genotype groups with presumably higher D1 receptor densities, and faster rates of connection weakening in genotype groups with presumably higher D2 receptor densities. Consistent with current dopamine theories, these simulations suggest that D1-mediated neuroplasticity contributes to learning to select appropriate actions, whereas D2-mediated neuroplasticity is involved in learning to inhibit incorrect action plans. However, the learning algorithm did not account for the COMT effect, suggesting that prefrontal dopamine availability might affect sequence switching via other, non-learning, mechanisms. These findings provide insight into the function of the dopamine system, which is relevant to the development of treatments for disorders such as Parkinson’s disease. Our results suggest that treatments targeting dopamine D1 receptors may improve learning of novel sequences, whereas those targeting dopamine D2 receptors may improve the ability to initiate previously learned sequences of movements.

Previous experience with discrimination problems that can only be solved by learning about stimulus configurations enhances performance on new configural discriminations. Some of these effects can be explained by a shift toward increased configural processing (learning about combinations of cues rather than about individual elements), or by a tendency to generalize a learned rule to a new training set. We investigated whether fluid abilities influence the extent that previous experience with configural discriminations improves performance on subsequent discriminations. In Experiments 1 and 2 we used patterning discriminations that could be solved by applying a simple rule, whereas in Experiment 3 we used biconditional discriminations that could not be solved using a rule. Fluid abilities predicted the improvement on the second training set in all experiments, including Experiment 3 in which rule-based generalization could not explain the improvement on the second discrimination. This supports the idea that fluid abilities contribute to performance by inducing a shift toward configural processing rather than rule-based generalization. However, fluid abilities also predicted performance on a rule-based transfer test in Experiment 2. Taken together, these results suggest that fluid abilities contribute to both a flexible shift toward configural processing and to rule-based generalization.

Learning is one of our most adaptive abilities, allowing us to adjust our expectations about future events. Aberrant learning processes may underlie disorders such as anxiety, motivating the search for the neural mechanisms that underpin learning. Animal studies have shown that the neurotransmitter GABA is required for the computation of prediction errors, the mismatches between anticipated and experienced outcomes, which drive new learning. Given that evidence from human studies is lacking, we sought to determine whether these findings extend to humans. Here, in two samples of Caucasian individuals, we investigated whether genetically determined individual differences in GABA neurotransmission predict the P3 event-related potential, an EEG component known to reflect prediction error processing. Consistent with the results of animal studies, we show that a weighted genetic risk score computed from the number of GABRB2 rs1816072 A alleles (associated with increased expression of the GABAA receptor β2 subunit gene) and the number of ErbB4 rs7598440 T alleles (associated with increased GABA concentration) predicts optimal prediction error processing during aversive classical conditioning with both visual (Experiment 1, N = 90; p = .010) and auditory (Experiment 2; N = 92; p = .031) unconditioned stimuli. Our finding that optimal processing of aversive prediction errors is reduced in individuals genetically predisposed towards decreased GABA neurotransmission suggests a potential mechanism linking GABA and anxiety. Specifically, reduced GABA signalling via GABAA receptors could result in aberrant learning from aversive experiences and vulnerability to anxiety disorders.

The Serial Reaction Time Task (SRTT) is thought to assess implicit learning, which seems to be preserved with age. However, the reaction time (RT) measures employed on implicit-like tasks might be too unreliable to detect individual differences. We investigated whether RT-based measures mask age effects by comparing the performance of 43 younger and 35 older adults on SRTT and an explicit-like Predictive Sequence Learning Task (PSLT). RT-based measures (difference scores and a ratio) were collected for both tasks, and accuracy was additionally measured for PSLT. We also measured fluid abilities. The RT-difference scores indicated preserved SRTT and PSLT performance with age and did not correlate with fluid abilities, while ratio RT and the accuracy-based measures indicated age-related decline and correlated with fluid abilities. Therefore, RT-difference scores might mask individual differences, which compromises the interpretation of previous studies using SRTT.

The Serial Reaction Time Task (SRTT) was designed to measure motor sequence learning and is widely used in many fields in cognitive science and neuroscience. However, the common performance measures derived from SRTT – reaction time (RT) difference scores – may not provide valid measures of sequence learning. This is because RT-difference scores may be subject to floor effects and otherwise not sufficiently reflective of learning. A ratio RT measure might minimise floor effects. Furthermore, measures derived from predictive accuracy may provide a better assessment of sequence learning. Accordingly, we developed a Predictive Sequence Learning Task (PSLT) in which performance can be assessed via both RT and predictive accuracy. We compared performance of N = 99 adults on SRTT and PSLT in a within-subjects design and also measured fluid abilities. The RT-difference scores on both tasks were generally not related to fluid abilities, replicating previous findings. In contrast, a ratio RT measure on SRTT and PSLT and accuracy measures on PSLT were related to fluid abilities. The accuracy measures also indicated an age-related decline in performance on PSLT. The current patterns of results were thus inconsistent across different measures on the same tasks, and we demonstrate that this discrepancy is potentially due to floor effects on the RT difference scores. This may limit the potential of SRTT to measure sequence learning and we argue that PSLT accuracy measures could provide a more accurate reflection of learning ability.

A theory or model of cause such as Cheng's power ( p) allows people to predict the effectiveness of a cause in a different causal context from the one in which they observed its actions. Liljeholm and Cheng demonstrated that people could detect differences in the effectiveness of the cause when causal power varied across contexts of different outcome base rates, but that they did not detect similar changes when only the cause–outcome contingency, ∆ p, but not power, varied. However, their procedure allowed participants to simplify the causal scenarios and consider only a subsample of observations with a base rate of zero. This confounds p, ∆ p, and the probability of an outcome (O) given a cause (C), P(O|C). Furthermore, the contingencies that they used confounded p and P(O|C) in the overall sample. Following the work of Liljeholm and Cheng, we examined whether causal induction in a wider range of situations follows the principles suggested by Cheng. Experiments 1a and 1b compared the procedure used by Liljeholm and Cheng with one that did not allow the sample of observations to be simplified. Experiments 2a and 2b compared the same two procedures using contingencies that controlled for P(O|C). The results indicated that, if the possibility of converting all contexts to a zero base rate situation was avoided, people were sensitive to changes in P(O|C), p, and ∆ p when each of these was varied. This is inconsistent with Liljeholm and Cheng's conclusion that people detect only changes in p. These results question the idea that people naturally extract the metric or model of cause from their observation of stochastic events and then, reasonably exclusively, use this theory of a causal mechanism, or for that matter any simple normative theory, to generalize their experience to alternative contexts.

We tested whether preventive and generative reasoning processes are symmetrical by keeping the training and testing of preventive (inhibitory) and generative (excitatory) causal cues as similar as possible. In Experiment 1, we extinguished excitors and inhibitors in a blocking design, in which each extinguished cause was presented in compound with a novel cause, with the same outcome occurring following the compound and following the novel cause alone. With this novel extinction procedure, the inhibitory cues seemed more likely to lose their properties than the excitatory cues. In Experiment 2, we investigated blocking of excitatory and inhibitory causes and found similar blocking effects. Taken together, these results suggest that acquisition of excitation and inhibition is similar, but that inhibition is more liable to extinguish with our extinction procedure. In addition, we used a variable outcome, and this enabled us to test the predictions of an inferential reasoning account about what happens when the outcome level is at its minimum or maximum (De Houwer, Beckers, & Glautier, 2002). We discuss the predictions of this inferential account, Rescorla and Wagner's (1972) model, and a connectionist model—the auto-associator.

We agree with Jones & Love (J&L) that much of Bayesian modeling has taken a fundamentalist approach to cognition; but we do not believe in the potential of Bayesianism to provide insights into psychological processes. We discuss the advantages of associative explanations over Bayesian approaches to causal induction, and argue that Bayesian models have added little to our understanding of human causal reasoning.

The propositional or rationalist Bayesian approach to learning is contrasted with an interpretation of causal learning in associative terms. A review of the development of the use of rational causal models in the psychology of learning is discussed concluding with the presentation of three areas of research related to cause-effect learning. We explain how rational context choices, a selective association effect (i.e., blocking of inhibition) as well as causal structure can all emerge from processes that can be modeled using elements of standard associative theory. We present the auto-associator (e.g., Baetu & Baker, 2009) as one such simple account of causal structure.

Two experiments investigated extinction and blocking of a conditioned inhibitor in a human contingency learning task. Lotz and Lachnit (2009) and Melchers, Wolff, and Lachnit (2006) reported extinction of inhibition only when participants experienced outcome levels lower than those used in training. In Experiment 1, which used more neutral instructions than the previously mentioned studies, we found that extinction of inhibition occurred, whether or not participants experienced lower outcome levels. In Experiment 2, we applied this outcome manipulation to blocking of a conditioned inhibitor. We found blocking of inhibition both when participants had experience with lower outcomes and when they did not. The results of our two experiments are consistent with Rescorla and Wagner’s (1972) associative model, and inconsistent with an inferential account of causal learning (De Houwer, Beckers, & Vandorpe, 2005).

In two experiments, we studied the strategies that people use to discover causal relationships. According to inferential approaches to causal discovery, if people attempt to discover the power of a cause, then they should naturally select the most informative and unambiguous context. For generative causes this would be a context with a low base rate of effects generated by other causes and for preventive causes a context with a high base rate. In the following experiments, we used probabilistic and/or deterministic target causes and contexts. In each experiment, participants observed several contexts in which the effect occurred with different probabilities. After this training, the participants were presented with different target causes whose causal status was unknown. In order to discover the influence of each cause, participants were allowed, on each trial, to choose the context in which the cause would be tested. As expected by inferential theories, the participants preferred to test generative causes in low base rate contexts and preventative causes in high base rate contexts. The participants, however, persisted in choosing the less informative contexts on a substantial minority of trials long after they had discovered the power of the cause. We discuss the matching law from operant conditioning as an alternative explanation of the findings.

Three experiments investigated the way participants construct causal chains from experience with the individual links that make up those chains. Participants were presented with contingency information about the relationship between events A and B, as well as events B and C, using trial-by-trial presentations. The A-B and B-C contingencies could be positive, negative, or zero. Although participants had never experienced A and C together, A-C ratings were a multiplicative function of the A-B and B-C contingencies. These findings can be generated by an auto-associator using the delta rule. This explanation is also useful for understanding sensory preconditioning and second-order conditioning.

A strong positive predictor of an outcome modulates the causal judgments of a moderate predictor. To study the empirical basis of this modulation, we compared treatments with one and with two strong competing (i.e., modulating) causes. This allowed us to vary the frequency of outcome occurrences or effects paired with the predictors. We investigated causal competition between positive predictors (those signaling the occurrence of the outcome), between negative predictors (those signaling the absence of the outcome) and between predictors of opposite polarity (positive and negative). The results are consistent with a contrast rather than a reduced associative strength or conditional contingency account, because a strong predictor of opposite polarity enhances rather than reduces causal estimates of moderate predictors. In addition, we found competition effects when the strong predictor predicted fewer outcome occurrences than the moderate predictor, thus implying that cue competition is, at least sometimes, a consequence of contingency rather than total cue-outcome pairings.

Mitchell et al.'s claim, that their propositional theory is a single-process theory, is illusory because they relegate some learning to a secondary memory process. This renders the single-process theory untestable. The propositional account is not a process theory of learning, but rather, a heuristic that has led to interesting research.

The relative validity effect (Wagner, Logan, Haberlandt,Price, 1968) demonstrated that a strong cue or cause reduces responding to, or judgments of, a weaker cue or cause. We report two experiments with human subjects using relative validity preparations in which we investigate one- and two-cue competition effects. Previously, we investigated the effect using instrumental and Pavlovian conditioning preparations with rats. In the first experiment, we used a procedure analogous to the animal preparations. In the second experiment, we used a different probabilistic procedure. The results with humans and rats are very similar. In each species we find similar interference with processing the moderate predictor with one or with two strong competitors. These results are not well predicted by most associative models.