Personal relative deprivation and pro-environmental intentions

Personal relative deprivation (PRD; the belief that one is worse off than other people who are similar to oneself) is associated with a reduced willingness to delay gratification, lower prosociality, and increased materialism. These results suggest that PRD may play a role in shaping people’s willingness to act to protect the natural environment. We report 3 studies that investigate a possible link between PRD and pro-environmental intentions (ENV). Study 1 was an exploratory study using a US sample; Studies 2 and 3 were pre-registered replications using UK and US samples, respectively. In each study, participants self-reported PRD and ENV; they also indicated their subjective social status (where they come on a national “ladder” of social class) and reported their income, education, age, and gender/sex. All three studies found a negative correlation between PRD and ENV. However, multiple regression analyses in which ENV was regressed on PRD and all other variables simultaneously indicated that the unique effect of PRD was small and, for Studies 2 and 3, the 95% confidence intervals included zero. No other variable emerged as a clear unique predictor across all three studies. The data suggest that PRD may be associated with reduced intention to act pro-environmentally, but the causal status of this association, and its relationship to other demographic and social-status variables, remains a topic for further research.

Identifying Causal Subsequent Memory Effects

Over 40 years of accumulated research has detailed associations between neuroimaging signals measured during a memory encoding task and later memory performance, across a variety of brain regions, measurement tools, statistical approaches and behavioral tasks. But the interpretation of these Subsequent Memory Effects (SMEs) remains unclear: if the identified signals reflect cognitive and neural mechanisms of memory encoding then the underlying neural activity must be causally related to future memory. However, almost all previous SME analyses do not control for potential confounders of this causal interpretation, such as serial position and item effects. We collect a large fMRI dataset and use a novel experimental design and analysis approach that allows us to statistically adjust for all exogenous confounding variables. We find that, using standard approaches without adjustment, we replicate several univariate and multivariate subsequent memory effects and are able to predict memory performance across people. However, we are unable to identify any signal that reliably predicts subsequent memory after adjusting for confounding variables, bringing into doubt the causal status of these effects. We apply the same approach to subjects' judgments of learning collected during an encoding period, and show that these behavioral measures of encoding quality do predict memory after adjustments, suggesting that it is possible to measure signals at the time of encoding that reflect causal mechanisms but that existing neuroimaging measures may not have the precision and specificity to do so.

MARS: leveraging allelic heterogeneity to increase power of association testing

In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.

Representing uncertainty in the Rescorla-Wagner model: Blocking, the redundancy effect, and outcome base rate

It is generally assumed that the Rescorla and Wagner (1972) model adequately accommodates the full results of simple cue competition experiments in humans (e.g. Dickinson et al., 1984), while the Bush and Mosteller (1951) model cannot. We present simulations that demonstrate this assumption is wrong in at least some circumstances. The Rescorla-Wagner model, as usually applied, fits the full results of a simple forward cue-competition experiment no better than the Bush-Mosteller model. Additionally, we present a novel finding, where letting the associative strength of all cues start at an intermediate value (rather than zero), allows this modified model to provide a better account of the experimental data than the (equivalently modified) Bush-Mosteller model. This modification also allows the Rescorla-Wagner model to account for a redundancy effect experiment (Uengoer et al., 2013); something that the unmodified model is not able to do. Furthermore, the modified Rescorla-Wagner model can accommodate the effect of varying the proportion of trials on which the outcome occurs (i.e. the base rate) on the redundancy effect (Jones et al., 2019). Interestingly, the initial associative strength of cues varies in line with the outcome base rate. We propose that this modification provides a simple way of mathematically representing uncertainty about the causal status of novel cues within the confines of the Rescorla-Wagner model. The theoretical implications of this modification are discussed. We also briefly introduce free and open resources to support formal modelling in associative learning. Keywords: associative learning, prediction error, uncertainty, modelling, blocking, redundancy effect, open science.

Representing uncertainty in the Rescorla-Wagner model: blocking, the redundancy effect, and outcome base rate

It is generally assumed that the Rescorla and Wagner (1972) model adequately accommodates the full results of simple cue competition experiments in humans (e.g. Dickinson et al., 1984), while the Bush and Mosteller (1951) model cannot. We present simulations that demonstrate this assumption is wrong in at least some circumstances. The Rescorla-Wagner model, as usually applied, fits the full results of a simple forward cue-competition experiment no better than the Bush-Mosteller model. Additionally, we present a novel finding, where letting the associative strength of all cues start at an intermediate value (rather than zero), allows this modified model to provide a better account of the experimental data than the (equivalently modified) Bush-Mosteller model. This modification also allows the Rescorla-Wagner model to account for a redundancy effect experiment (Uengoer et al., 2013); something that the unmodified model is not able to do. Furthermore, the modified Rescorla-Wagner model can accommodate the effect of varying the proportion of trials on which the outcome occurs (i.e. the base rate) on the redundancy effect (Jones et al., 2019). Interestingly, the initial associative strength of cues varies in line with the outcome base rate. We propose that this modification provides a simple way of mathematically representing uncertainty about the causal status of novel cues within the confines of the Rescorla-Wagner model. The theoretical implications of this modification are discussed. We also briefly introduce free and open resources to support formal modelling in associative learning.

On the “Strength” of Behavior

The place of the concept of response strength in a natural science of behavior has been the subject of much debate. This article reconsiders the concept of response strength for reasons linked to the foundations of a natural science of behavior. The notion of response strength is implicit in many radical behaviorists’ work. Palmer (2009) makes it explicit by applying the response strength concept to three levels: (1) overt behavior, (2) covert behavior, and (3) latent or potential behavior. We argue that the concept of response strength is superfluous in general, and an explication of the notion of giving causal status to nonobservable events like latent behavior or response strength is harmful to a scientific endeavor. Interpreting EEG recordings as indicators of changes in response strength runs the risk of reducing behavior to underlying mechanisms, regardless of whether such suggestions are accompanied by behavioral observations. Many radical behaviorists understand behavior as a discrete unit, inviting conceptual mistakes reflected in the notion of response strength. A molar view is suggested as an alternative that accounts for the temporally extended nature of behavior and avoids the perils of a response-strength based approach.

The role of causal structure in implicit cognition

Distinguishing between mere association and causation is crucial for successful interactions with the environment: Only causal, but not merely associated, stimuli allow humans to produce rewards and avoid punishments by intervening on causal systems. Accordingly, prior research has demonstrated that explicit (controlled) cognition represents causal relationships above and beyond mere association; however, it is unclear whether this difference is also reflected by implicit (automatic) cognition. In the present studies, participants (total N = 2570) observed causal events during which two stimuli were equally associated with positive or negative outcomes but only one of them was causally responsible for these outcomes. Across 5 paradigms, differences in causal status were consistently reflected not only by explicit measures of evaluation (Likert scales; Cohen’s d = .27, BF10 > 10^37) but also by their implicit counterparts (Implicit Association Tests; Cohen’s d = .22, BF10 > 10^24). This result emerged in both within-participant and between-participant designs and irrespective of whether exposure to the causal events was preceded by detailed verbal instructions or not. Moreover, the effect was sensitive to the valence of the outcome, with causal responsibility for positive events resulting in stronger positive evaluations and causal responsibility for negative events in stronger negative evaluations than mere association with such events. Overall, contrary to most dual-process accounts, these findings suggest that implicit cognition can encode causal relationships and thereby contribute to adaptive decision making.

Inferential Internalism and the Causal Status Effect

To justify inductive inference and vanquish classical skepticisms about human memory, external world realism, etc., Richard Fumerton proposes his “inferential internalism,” an epistemology whereby humans ‘see’ by Russellian acquaintance Keynesian probable relations (PRs) between propositions. PRs are a priori necessary relations of logical probability, akin to but not reducible to logical entailments, such that perceiving a PR between one’s evidence E and proposition P of unknown truth value justifies rational belief in P to an objective degree. A recent critic of inferential internalism is Alan Rhoda, who questions its psychological plausibility. Rhoda argues that in order to see necessary relations between propositions E and P, one would need acquaintance with too many propositions at once, since our evidence E is often complex. In this paper, I criticize Rhoda’s implausibility objection as too quick. Referencing the causal status effect (CSE) from psychology, I argue that some of the complex features of evidence E contribute to our type-categorizing it as E-type, and thus we do not need to ‘see’ all of the complex features when we see the PR between E and P. My argument leaves unchanged Fumerton’s justificatory role for the PR, but enhances its psychological plausibility.

Compulsory Schooling and Returns to Education: A Re-Examination

This paper re-examines the instrumental variable (IV) approach to estimating returns to education by use of compulsory school law (CSL) in the US. We show that the IV-approach amounts to a change in model specification by changing the causal status of the variable of interest. From this perspective, the IV-OLS (ordinary least square) choice becomes a model selection issue between non-nested models and is hence testable using cross validation methods. It also enables us to unravel several logic flaws in the conceptualisation of IV-based models. Using the causal chain model specification approach, we overcome these flaws by carefully distinguishing returns to education from the treatment effect of CSL. We find relatively robust estimates for the first effect, while estimates for the second effect are hindered by measurement errors in the CSL indicators. We find reassurance of our approach from fundamental theories in statistical learning.