Qualitative Individual Differences Research Articles

Is It Time to Reconsider the ‘Gold Standard’ for Nativelikeness in ERP Studies on Grammatical Processing in a Second Language? A Critical Assessment Based on Qualitative Individual Differences

Abstract In most event-related potential (ERP) studies on the second language (L2) processing, the native speaker (L1) control group’s grand average ERP pattern serves as the ‘gold standard’ that the L2 group has to reach to be labeled ‘native-like’. This relies on the assumption that the grand average is representative of all or most individuals in a group. Recent research, however, has shown that there can be considerable systematic qualitative variability between individuals even in coherent L1 samples, especially in studies on morphosyntactic processing. We discuss how these qualitative individual differences can undermine previous findings from the gold standard paradigm, and critically assess the main ERP components used as markers for nativelike grammatical processing, namely the left-anterior negativity and the P600. We argue that qualitative variation reflects the dynamics characteristic of nativelike grammatical processing and propose a model for experimental designs that can capture these processing dynamics and, thereby, has the potential to provide a more fine-grained understanding of nativelike attainment in an L2.

Are there Reliable Qualitative Individual Differences in Cognition? Probably Not.

Rouder and Haaf (2021, this issue) propose a focus shift toward qualitative individual differences in cognition and present a research toolkit for doing so. In this invited commentary, I will argue that the observation of qualitative individual differences may be no more than an indicator that more theoretical and empirical work needs to be done to identify the mechanisms and abilities underlying these individual differences in directionality. I will then move on to discuss how the toolkit can be used though to investigate one of the currently most central current topics in cognitive differential psychology, that is, the question over the existence of true quantitative individual differences in attentional control experimental effects. I conclude that, while highly valuable, no toolkit can save us from facing the challenging theoretical and conceptual questions in this area.

Do Individual Effects Reflect Quantitative or Qualitative Differences in Cognition?

Rouder and Haaf (2020) posed the important question if there are some individuals whose behavior is not in accordance with well-established experimental effects and whether these individual differences are quantitative or qualitative in nature. In our commentary, we discuss the distinction between quantitative and qualitative individual differences and between individual and average causal effects and come to the conclusion that this is not a new question, but in fact one that has already been discussed by Gordon W. Allport (1937) and Donald B. Rubin (1974, 1978). Moreover, we critically examine their proposed rule of thumb to collect about 100 trials per experimental condition to reliably measure individual differences in typical experimental effects. Based on simulation results, we suggest to not rely on any general rule of thumb, but to use simulation studies and the convenient quid function provided by the authors to make more informed decisions regarding trial numbers for specific experimental designs.

Are There Reliable Qualitative Individual Difference in Cognition?

In this paper we propose a new set of questions that focus on the direction of effects. In almost all studies the direction is important. For example, in a Stroop task we expect responses to incongruent items to be slower than those to congruent ones, and this direction implies one theoretical explanation. Yet, if congruent words are slowed down relative to incongruent words we would have a completely different theoretical explanation. We ask a ‘does everybody’ question, such as, ‘does every individual show a Stroop effect in the same direction?’ Or, ‘does every individual respond faster to loud tones than soft tones?’ If all individuals truly have effects in the same direction that implicate a common theory, we term the differences among them as quantitative individual differences. Conversely, if all individuals truly have effects in different directions that implicate different theories, we term the differences among them as qualitative individual differences. Here, we provide a users guide to the question of whether individual differences are qualitative or quantitative. We discuss theoretical issues, methodological advances, new software for assessment, and, most importantly, how the question impacts theory development in cognitive science. Our hope is that this mode of analysis is a productive tool in researchers’ toolkits.

A Plea for Studying Qualitative Individual Differences by Default.

I see great potential in the approach proposed by Rouder and Haaf. First, using an example from unethical decision making, I demonstrate that considering quantitative individual differences alone can make us overlook important psychological phenomena that are only visible at the individual level. Thus, the study of quantitative individual differences should, by default, be complemented by investigation of qualitative individual differences. Second, having powerful tools to study qualitative individual differences in cognition has great potential to advance personality science. Recently, personality psychologists are increasingly working towards obtaining a better understanding of the processes that underlie the expression of personality in behavior. The toolbox provided by Rouder and Haaf may add to this research in meaningful ways.

Qualitative Individual Differences are Useful, but Reliability Should be Assessed and Not Assumed.

Rouder and Haaf (2021) propose that studying qualitative individual differences would be a useful tool for researchers. I agree with their central message. I use this commentary to highlight examples from the literature where similar questions have been asked, and how researchers have addressed them with existing tools. I also observe that while the hierarchical Bayesian framework is a useful tool for studying individual differences, it does not relieve us of the requirement to evaluate the forms of reliability that are critical to our research questions.

Qualitative Versus Quantitative Individual Differences in Cognitive Neuroscience.

Individual differences in cognitive performance can be quantitative or qualitative in nature. Accounting for qualitative as well as quantitative individual differences is of importance for cognitive neuroscience, where a central goal is not only to relate brain function to behavior generally, but also to understand and predict individual behavior from neural data. In turn, cognitive neuroscience can help determine the nature of individual differences by revealing the underlying neural mechanisms and uncover qualitative individual differences that are not immediately apparent from behavioral data, enhancing our understanding of why and how people behave the way they do.

Individual variation in the vigor and form of Pavlovian conditioned responses: Analysis of a model system

Pavlovian conditioning results in individual variation in the vigor and form of acquired behaviors. Here, we describe a general-process model of associative learning (HeiDI; How excitation and inhibition determine ideo-motion) that provides an analysis for such variation together with a range of other important group-level phenomena. The model takes as its starting point the idea that pairings of a conditioned stimulus (CS) and an unconditioned stimulus (US) result in the formation of reciprocal associations between their central representations. The asymptotic values of these associations and the rate at which these are reached are held to be influenced by the perceived salience of the CS (αCS) and US (βUS). Importantly, whether this associative knowledge is exhibited in behavior that reflects the properties of the CS (e.g., sign-tracking) or US (e.g., goal-tracking) is also influenced by the relative values of αCS and βUS. In this way, HeiDI provides an analysis for both quantitative and qualitative individual differences generated by Pavlovian conditioning procedures.

HeiDI: A model for Pavlovian learning and performance with reciprocal associations.

Associative treatments of how Pavlovian conditioning affects conditioned behavior are rudimentary: A simple ordinal mapping is held to exist between the strength of an association (V) between a conditioned stimulus (CS) and an unconditioned stimulus (US; i.e., VCS-US) and conditioned behavior in a given experimental preparation. The inadequacy of this simplification is highlighted by recent studies that have taken multiple measures of conditioned behavior: Different measures of conditioned behavior provide the basis for drawing opposite conclusions about VCS-US across individual animals. Here, we develop a simple model involving reciprocal associations between the CS and US (VCS-US and VUS-CS) that simulates these qualitative individual differences in conditioned behavior. The new model, HeiDI (How excitation and inhibition Determine Ideo-motion), enables a broad range of phenomena to be accommodated, which are either beyond the scope of extant models or require them to appeal to additional (learning) processes. It also provides an impetus for new lines of inquiry and generates novel predictions. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Choice patterns reveal qualitative individual differences among discounting of delayed gains, delayed losses, and probabilistic losses.

A new probabilistic losses questionnaire as well as Kirby's delayed gains questionnaire and a previously developed delayed losses questionnaire were administered to a large online sample. Almost all participants showed the positive discounting choice pattern expected on the Kirby questionnaire, decreasing their choice of a delayed gain as time to its receipt increased. In contrast, approximately 15% of the participants showed negative discounting on the delayed losses questionnaire and/or the probabilistic losses questionnaire, decreasing their choice of an immediate loss as time to a delayed loss decreased and/or decreasing their choice of a certain loss as likelihood of the probabilistic loss increased. Mixture model analysis confirmed the existence of these negative discounting subgroups. The inconsistent findings observed in previous research involving delayed/probabilistic losses may be due to differences in the proportion of negative discounters who participated in previous studies. Further research is needed to determine how negative discounting of delayed and probabilistic losses manifests itself in everyday decisions. It should be noted that the presence of individuals who show atypical choice patterns when losses are involved may pose challenges for efforts to modify discounting in order to ameliorate behavioral problems, especially because many such problems concern choices that have negative consequences, often delayed and/or probabilistic.

Minimizing Precision-Weighted Sensory Prediction Errors via Memory Formation and Switching in Motor Adaptation

Humans predict the sensory consequences of motor commands by learning internal models of the body and of environment perturbations. When facing a sensory prediction error, should we attribute this error to a change in our body, and update the body internal model, or to a change in the environment? In the latter case, should we update an existing perturbation model or create a new model? Here, we propose that a decision-making process compares the models' prediction errors, weighted by their precisions, to select and update either the body model or an existing perturbation model. When no model can predict a perturbation, a new perturbation model is created and selected. When a model is selected, both the prediction's mean estimate and uncertainty are updated to minimize future prediction errors and to increase the precision of the predictions. Results from computer simulations, which we verified in an arm visuomotor adaptation experiment with subjects of both sexes, account for short aftereffects and large savings after adaptation to large, but not small, perturbations. Results also clarify previous data in the absence of errors (error-clamp): motor memories show an initial lack of decay after a large perturbation, but gradual decay after a small perturbation. Finally, qualitative individual differences in adaptation were explained by subjects selecting and updating either the body model or a perturbation model. Our results suggest that motor adaptation belongs to a general class of learning according to which memories are created when no existing memories can predict sensory data accurately and precisely.SIGNIFICANCE STATEMENT When movements are followed by unexpected outcomes, such as following the introduction of a visuomotor or a force field perturbation, or the sudden removal of such perturbations, it is unclear whether the CNS updates existing memories or creates new memories. Here, we propose a novel model of adaptation, and investigate, via computer simulations and behavioral experiments, how the amplitude and schedule of the perturbation, as well as the characteristics of the learner, lead to the selection and update of existing memories or the creation of new memories. Our results provide insights into a number of puzzling and contradictory motor adaptation data, as well as into qualitative individual differences in adaptation.

Specific patterns of brain alterations underlie distinct clinical profiles in Huntington's disease.

Huntington's disease (HD) is a genetic neurodegenerative disease which involves a triad of motor, cognitive and psychiatric disturbances. However, there is great variability in the prominence of each type of symptom across individuals. The neurobiological basis of such variability remains poorly understood but would be crucial for better tailored treatments. Multivariate multimodal neuroimaging approaches have been successful in disentangling these profiles in other disorders. Thus we applied for the first time such approach to HD. We studied the relationship between HD symptom domains and multimodal measures sensitive to grey and white matter structural alterations. Forty-three HD gene carriers (23 manifest and 20 premanifest individuals) were scanned and underwent behavioural assessments evaluating motor, cognitive and psychiatric domains. We conducted a multimodal analysis integrating different structural neuroimaging modalities measuring grey matter volume, cortical thickness and white matter diffusion indices – fractional anisotropy and radial diffusivity. All neuroimaging measures were entered into a linked independent component analysis in order to obtain multimodal components reflecting common inter-subject variation across imaging modalities. The relationship between multimodal neuroimaging independent components and behavioural measures was analysed using multiple linear regression. We found that cognitive and motor symptoms shared a common neurobiological basis, whereas the psychiatric domain presented a differentiated neural signature. Behavioural measures of different symptom domains correlated with different neuroimaging components, both the brain regions involved and the neuroimaging modalities most prominently associated with each type of symptom showing differences. More severe cognitive and motor signs together were associated with a multimodal component consisting in a pattern of reduced grey matter, cortical thickness and white matter integrity in cognitive and motor related networks. In contrast, depressive symptoms were associated with a component mainly characterised by reduced cortical thickness pattern in limbic and paralimbic regions. In conclusion, using a multivariate multimodal approach we were able to disentangle the neurobiological substrates of two distinct symptom profiles in HD: one characterised by cognitive and motor features dissociated from a psychiatric profile. These results open a new view on a disease classically considered as a uniform entity and initiates a new avenue for further research considering these qualitative individual differences.

Latent variable mixture models in research on learning and individual differences

Herein, we provide an introduction to the special issue “Latent variable mixture models in research on learning and individual differences”. Latent variable mixture models are argued to be a powerful tool for capturing non-linear and qualitative individual differences in learners' knowledge, characteristics, and development. The current special issue provides an overview of the use of these analytical tools in investigations of learning and individual differences by presenting a wide-range of empirical studies utilizing the methods. A practical non-technical introduction and discussion are also included in the special issue.

Learning to solve figural matrix analogies: The paths children take

Analogical reasoning is essential for acquiring and integrating new knowledge and skills. Although much research has focused on this important skill, children's paths from non-analogical to analogical reasoning remain unclear. In this study, 388 children (ages 4–10 years) solved a series of figural analogies within a pretest-training-posttest design, with training comprising either multiple tries (N = 196) or tutoring feedback (N = 192). Working memory tasks were also administered. Latent transition analyses identified five phases with qualitative individual differences in children's analogy solving: duplication, idiosyncratic, beginner analogical, intermediate analogical and advanced analogical reasoning. Children's paths through these phases were not sequential; there was great variability between children and how they progressed through these phases. Working memory was related to children's reasoning phase at pretest, but not to their rate and path of change. Age and the type of feedback received during training were the clearest indicators of children's learning paths and rates of change.

441. New Learning or Unlearning: Computational Heterogeneity in Extinction Predicts the Recovery of Threat Responses

Anxiety disorders are characterized by persistent and debilitating fear. Exposure therapies, based on the principles of extinction learning, are effective for many. However, for some, extinguished threat responses reemerge, a phenomenon that compromises the efficacy of such therapies. Here, we propose that this heterogeneity might stem from qualitative individual differences in the nature of extinction learning that can be captured by a computational process model.

The origins of individual differences in how learning is expressed in rats: A general-process perspective.

Laboratory rats can exhibit marked, qualitative individual differences in the form of acquired behaviors. For example, when exposed to a signal-reinforcer relationship some rats show marked and consistent changes in sign-tracking (interacting with the signal; e.g., a lever) and others show marked and consistent changes in goal-tracking (interacting with the location of the predicted reinforcer; e.g., the food well). Here, stable individual differences in rats’ sign-tracking and goal-tracking emerged over the course of training, but these differences did not generalize across different signal-reinforcer relationships (Experiment 1). This selectivity suggests that individual differences in sign- and goal-tracking reflect differences in the value placed on individual reinforcers. Two findings provide direct support for this interpretation: the palatability of a reinforcer (as measured by an analysis of lick-cluster size) was positively correlated with goal-tracking (and negatively correlated with sign-tracking); and sating rats with a reinforcer affected goal-tracking but not sign-tracking (Experiment 2). These results indicate that the observed individual differences in sign- and goal-tracking behavior arise from the interaction between the palatability or value of the reinforcer and processes of association as opposed to dispositional differences (e.g., in sensory processes, “temperament,” or response repertoire).

Are economic conditions related to nonnormative life satisfaction development? Evaluating the relative impact of economic conditions, personality, and subjective health

AbstractThis study examined the relative contribution of psychological and socioeconomic resources to explaining qualitative individual differences in life satisfaction development. We used growth mixture modeling and a cohort‐sequential design to investigate life satisfaction development from ages 25 to 65, in a nationally representative panel (the Socioeconomic Panel). Eighty‐three percent of the participants experienced stability in life satisfaction. In a subgroup of individuals (10%), life satisfaction declined. This subgroup lived under less favorable economic conditions and reported downward moves on an index of socioeconomic status. In another subgroup (7%), life satisfaction was low at age 25 and increased up to age 65. This group was also socioeconomically disadvantaged but scored higher on adaptive personality traits and experienced upward social mobility. Generally, personality traits explained level differences in life satisfaction better than economic conditions. However, economic conditions explained nonnormative life satisfaction development better than generalized control beliefs and the Big Five traits.

Individual differences in learning predict the return of fear.

Using a laboratory analogue of learned fear (Pavlovian fear conditioning), we show that there is substantial heterogeneity across individuals in spontaneous recovery of fear following extinction training. We propose that this heterogeneity might stem from qualitative individual differences in the nature of extinction learning. Whereas some individuals tend to form a new memory during extinction, leaving their fear memory intact, others update the original threat association with new safety information, effectively unlearning the fear memory. We formalize this account in a computational model of fear learning and show that individuals who, according to the model, are more likely to form new extinction memories tend to show greater spontaneous recovery compared to individuals who appear to only update a single memory. This qualitative variation in fear and extinction learning may have important implications for understanding vulnerability and resilience to fear-related psychiatric disorders.

The probability of iterated conditionals.

Iterated conditionals of the form If p, then if q, r are an important topic in philosophical logic. In recent years, psychologists have gained much knowledge about how people understand simple conditionals, but there are virtually no published psychological studies of iterated conditionals. This paper presents experimental evidence from a study comparing the iterated form, If p, then if q, r with the "imported," noniterated form, If p and q, then r, using a probability evaluation task and a truth-table task, and taking into account qualitative individual differences. This allows us to critically contrast philosophical and psychological approaches that make diverging predictions regarding the interpretation of these forms. The results strongly support the probabilistic Adams conditional and the "new paradigm" that takes this conditional as a starting point.

Potential Correlation between Tumor Aggressiveness and Protein Expression Patterns of Nipple Aspirate Fluid (NAF) Revealed by Gel-Based Proteomic Analysis

Breast cancer is the leading cause of cancer related deaths in women. Most breast cancers stem from mammary ductal cells that secrete nipple aspirate fluid (NAF), a biological sample that contains proteins associated with the tumor microenvironment. In this study, NAF samples from both breasts of 7 Brazilian patients with unilateral breast cancer were analyzed. These samples were systematically compared using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and two-dimensional fluorescence difference gel electrophoresis (2D-DIGE); substantial qualitative individual differences were observed. In general, when NAF samples were compared from both breasts within the same patient their electrophoretic patterns were very similar, regardless of their cancer status. A comparison of all patients identified 2 main NAF protein profiles. The HomEP, homogeneous expression profile, was characterized by typical SDS-PAGE and 2D-DIGE protein patterns that were observed in patients with a good breast cancer prognosis and were similar to previous Type I NAF classifications that used one-dimensional electrophoresis. The HetEP, heterogeneous expression profile, was characterized by distinct protein patterns that have not been reported in previous studies and have been primarily observed in breast cancer patients with a poor prognosis. The NAF samples were rich in metal-dependent proteolytic enzymes, as visualized by SDS-PAGE zymography. They varied qualitatively with respect to their gelatinolytic band distribution. However, there were no correlations between these characteristics and the pathologic features of these tumors. A comparative analysis of NAF samples taken from each breast in a single patient showed conserved zymographic patterns. In conclusion, the present study highlights important distinctions in the protein content of individual NAF samples and provides insight into the composition of the tumor microenvironment. These data reinforce breast cancer as a heterogeneous disease with a diverse natural history, which is becoming increasingly evident through other recent studies.