Attention, predictive learning, and the inverse base-rate effect: Evidence from event-related potentials

We report the first electrophysiological investigation of the inverse base-rate effect (IBRE), a robust non-rational bias in predictive learning. In the IBRE, participants learn that one pair of symptoms (AB) predicts a frequently occurring disease, whilst an overlapping pair of symptoms (AC) predicts a rarely occurring disease. Participants subsequently infer that BC predicts the rare disease, a non-rational decision made in opposition to the underlying base rates of the two diseases. Error-driven attention theories of learning state that the IBRE occurs because C attracts more attention than B. On the basis of this account we predicted and observed the occurrence of brain potentials associated with visual attention: a posterior Selection Negativity, and a concurrent anterior Selection Positivity, for C vs. B in a post-training test phase. Error-driven attention theories further predict no Selection Negativity, Selection Positivity or IBRE, for control symptoms matched on frequency to B and C, but for which there was no shared symptom (A) during training. These predictions were also confirmed, and this confirmation discounts alternative explanations of the IBRE based on the relative novelty of B and C. Further, we observed higher response accuracy for B alone than for C alone; this dissociation of response accuracy (B>C) from attentional allocation (C>B) discounts the possibility that the observed attentional difference was caused by the difference in response accuracy

Model-free and model-based reward prediction errors in EEG

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world’s structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain

Formal modelling of data integration systems security policies

Data Integration Systems (DIS) are concerned with integrating data from multiple data sources to resolve user queries. Typically, organisations providing data sources specify security policies that impose stringent requirements on the collection, processing, and disclosure of personal and sensitive data. If the security policies were not correctly enforced by the integration component of DIS, the data is exposed to data leakage threats, e.g. unauthorised disclosure or secondary use of the data. SecureDIS is a framework that helps system designers to mitigate data leakage threats during the early phases of DIS development. SecureDIS provides designers with a set of informal guidelines written in natural language to specify and enforce security policies that capture confidentiality, privacy, and trust properties. In this paper, we apply a formal approach to model a DIS with the SecureDIS security policies and verify the correctness and consistency of the model. The model can be used as a basis to perform security policies analysis or automatically generate a Java code to enforce those policies within DIS

Genotoxic capacity of Cd/Se semiconductor quantum dots with differing surface chemistries.

Quantum dots (QD) have unique electronic and optical properties promoting biotechnological advances. However, our understanding of the toxicological structure-activity relationships remains limited. This study aimed to determine the biological impact of varying nanomaterial surface chemistry by assessing the interaction of QD with either a negative (carboxyl), neutral (hexadecylamine; HDA) or positive (amine) polymer coating with human lymphoblastoid TK6 cells. Following QD physico-chemical characterisation, cellular uptake was quantified by optical and electron microscopy. Cytotoxicity was evaluated and genotoxicity was characterised using the micronucleus assay (gross chromosomal damage) and the HPRT forward mutation assay (point mutagenicity). Cellular damage mechanisms were also explored, focusing on oxidative stress and mitochondrial damage. Cell uptake, cytotoxicity and genotoxicity were found to be dependent on QD surface chemistry. Carboxyl-QD demonstrated the smallest agglomerate size and greatest cellular uptake, which correlated with a dose dependent increase in cytotoxicity and genotoxicity. Amine-QD induced minimal cellular damage, while HDA-QD promoted substantial induction of cell death and genotoxicity. However, HDA-QD were not internalised by the cells and the damage they caused was most likely due to free cadmium release caused by QD dissolution. Oxidative stress and induced mitochondrial reactive oxygen species were only partially associated with cytotoxicity and genotoxicity induced by the QD, hence were not the only mechanisms of importance. Colloidal stability, nanoparticle (NP) surface chemistry, cellular uptake levels and the intrinsic characteristics of the NPs are therefore critical parameters impacting genotoxicity induced by QD

Feature- versus rule-based generalization in rats, pigeons and humans

Humans can spontaneously create rules that allow them to efficiently generalize what they have learned to novel situations. An enduring question is whether rule-based generalization is uniquely human or whether other animals can also abstract rules and apply them to novel situations. In recent years, there have been a number of high-profile claims that animals such as rats can learn rules. Most of those claims are quite weak because it is possible to demonstrate that simple associative systems (which do not learn rules) can account for the behavior in those tasks. Using a procedure that allows us to clearly distinguish feature-based from rule-based generalization (the Shanks-Darby procedure), we demonstrate that adult humans show rule-based generalization in this task, while generalization in rats and pigeons was based on featural overlap between stimuli. In brief, when learning that a stimulus made of two components ("AB") predicts a different outcome than its elements ("A" and "B"), people spontaneously abstract an opposites rule and apply it to new stimuli (e.g., knowing that "C" and "D" predict one outcome, they will predict that "CD" predicts the opposite outcome). Rats and pigeons show the reverse behavior-they generalize what they have learned, but on the basis of similarity (e.g., "CD" is similar to "C" and "D", so the same outcome is predicted for the compound stimulus as for the components). Genuinely rule-based behavior is observed in humans, but not in rats and pigeons, in the current procedure

Similarities and differences: Comment on Chan et al.

Spicer et al. (2020) reported a series of causal learning experiments in which participants appeared to learn most readily about cues when they were not certain of their causal status, and proposed that their results were a consequence of participants’ use of “theory protection”. In the present issue Chan et al. present an alternative view, using a modification of Rescorla and Wagner’s (1972) influential model of learning. Although the explanation offered by Chan et al. appears very different to that suggested by Spicer et al., there are conceptual commonalities. Here we briefly discuss the similarities and differences of the two approaches, and agree with Chan et al.’s proposal that the best way to advance the debate will be to test situations in which the two theories make differing predictions

Cell type-dependent changes in CdSe/ZnS quantum dot uptake and toxic endpoints.

Toxicity of nanoparticles (NPs) is often correlated with the physicochemical characteristics of the materials. However, some discrepancies are noted in in-vitro studies on quantum dots (QDs) with similar physicochemical properties. This is partly related to variations in cell type. In this study, we show that epithelial (BEAS-2B), fibroblast (HFF-1), and lymphoblastoid (TK6) cells show different biological responses following exposure to QDs. These cells represented the 3 main portals of NP exposure: bronchial, skin, and circulatory. The uptake and toxicity of negatively and positively charged CdSe:ZnS QDs of the same core size but with different surface chemistries (carboxyl or amine polymer coatings) were investigated in full and reduced serum containing media following 1 and 3 cell cycles. Following thorough physicochemical characterization, cellular uptake, cytotoxicity, and gross chromosomal damage were measured. Cellular damage mechanisms in the form of reactive oxygen species and the expression of inflammatory cytokines IL-8 and TNF-α were assessed. QDs uptake and toxicity significantly varied in the different cell lines. BEAS-2B cells demonstrated the highest level of QDs uptake yet displayed a strong resilience with minimal genotoxicity following exposure to these NPs. In contrast, HFF-1 and TK6 cells were more susceptible to toxicity and genotoxicity, respectively, as a result of exposure to QDs. Thus, this study demonstrates that in addition to nanomaterial physicochemical characterization, a clear understanding of cell type-dependent variation in uptake coupled to the inherently different capacities of the cell types to cope with exposure to these exogenous materials are all required to predict genotoxicity

Negative mood reverses devaluation of goal-directed drug-seeking favouring an incentive learning account of drug dependence.

BACKGROUND: Two theories explain how negative mood primes smoking behaviour. The stimulus–response (S-R) account argues that in the negative mood state, smoking is experienced as more reinforcing, establishing a direct (automatic) association between the negative mood state and smoking behaviour. By contrast, the incentive learning account argues that in the negative mood state smoking is expected to be more reinforcing, which integrates with instrumental knowledge of the response required to produce that outcome. OBJECTIVES: One differential prediction is that whereas the incentive learning account anticipates that negative mood induction could augment a novel tobacco-seeking response in an extinction test, the S-R account could not explain this effect because the extinction test prevents S-R learning by omitting experience of the reinforcer. METHODS: To test this, overnight-deprived daily smokers (n = 44) acquired two instrumental responses for tobacco and chocolate points, respectively, before smoking to satiety. Half then received negative mood induction to raise the expected value of tobacco, opposing satiety, whilst the remainder received positive mood induction. Finally, a choice between tobacco and chocolate was measured in extinction to test whether negative mood could augment tobacco choice, opposing satiety, in the absence of direct experience of tobacco reinforcement. RESULTS: Negative mood induction not only abolished the devaluation of tobacco choice, but participants with a significant increase in negative mood increased their tobacco choice in extinction, despite satiety. CONCLUSIONS: These findings suggest that negative mood augments drug-seeking by raising the expected value of the drug through incentive learning, rather than through automatic S-R control