Psychology Within and Without the State

Psychological research in small-scale societies is crucial for what it stands to tell us about human psychological diversity. However, people in these communities, typically Indigenous communities in the global South, have been underrepresented and sometimes misrepresented in psychological research. Here I discuss the promises and pitfalls of psychological research in these communities, reviewing why they have been of interest to social scientists and how cross-cultural comparisons have been used to test psychological hypotheses. I consider factors that may be undertheorized in our research, such as political and economic marginalization, and how these might influence our data and conclusions. I argue that more just and accurate representation of people from small-scale communities around the world will provide us with a fuller picture of human psychological similarity and diversity, and it will help us to better understand how this diversity is shaped by historical and social processes. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Feature similarity is non-linearly related to attentional selection: evidence from visual search and sustained attention tasks

While many theories of attention highlight the importance of similarity between target and distractor items for selection, few studies have directly quantified the function underlying this relationship. Across two commonly used tasks—visual search and sustained attention—we investigated how target-distractor similarity impacts feature-based attentional selection, in particular asking whether stimulus-based or psychological similarity better explains performance. We found that both similarity measures were non-linearly related to task performance, although psychological similarity explained a big portion of the non-linearities observed in the data, suggesting that measures of psychological similarity are more appropriate when studying effects of target-distractor similarities. Importantly, we found comparable patterns of performance in both visual search and sustained feature-based attention tasks, with performance (RTs and d’, respectively) plateauing at medium target-distractor distances and exponential functions capturing the relationship between stimulus-based and psychological similarity and performance well. In contrast, visual search efficiency, as measured by search slopes, was affected by only a narrow range of similarity levels (10-20°). These findings place novel constraints on models of selective attention and emphasize the importance of considering the similarity structure of the feature space. Broadly, the non-linear effects of similarity on attention are consistent with accounts that propose attention exaggerates the distance between competing representations, possibly through enhancement of off-tuned neurons.

People are Insensitive to Within-Category Feature Correlations in Categorization

This work compares two types of psychological similarity in categorization. Similarity is a central component of categorization theories. Exemplar theories, for instance, assume that people categorize new exemplars based on their similarity to previous category members. Traditionally, the underlying psychological similarity is based on the sum of two exemplars' squared feature value differences (Euclidean similarity). The Euclidean similarity, however, ignores the distribution of exemplars within categories by assuming uncorrelated features within categories. The Mahalanobis similarity, in turn, extends the Euclidean similarity by accounting for within-category feature correlations. Results from machine learning have shown that in categorization problems involving correlated features within categories, the Mahalanobis similarity can outperform the Euclidean similarity. On the empirical side, results from psychology indicate that people can be sensitive to within-category feature correlations: Some findings suggest a general sensitivity for within-category feature correlations, yet others have argued that this sensitivity depends on the category structure, task format, and amount of training. The present work rigorously tested the correlation-insensitive Euclidean similarity against the correlation-sensitive Mahalanobis similarity to investigate if people use within-category feature correlations in categorization.

Generalizing Psychological Similarity Spaces to Unseen Stimuli

The cognitive framework of conceptual spaces proposes to represent concepts as regions in psychological similarity spaces. These similarity spaces are typically obtained through multidimensional scaling (MDS), which converts human dissimilarity ratings for a fixed set of stimuli into a spatial representation. One can distinguish metric MDS (which assumes that the dissimilarity ratings are interval or ratio scaled) from nonmetric MDS (which only assumes an ordinal scale). In our first study, we show that despite its additional assumptions, metric MDS does not necessarily yield better solutions than nonmetric MDS. In this chapter, we furthermore propose to learn a mapping from raw stimuli into the similarity space using artificial neural networks (ANNs) in order to generalize the similarity space to unseen inputs. In our second study, we show that a linear regression from the activation vectors of a convolutional ANN to similarity spaces obtained by MDS can be successful and that the results are sensitive to the number of dimensions of the similarity space.

Similarity Judgments Predict N400 Amplitude Differences between Taxonomic Category Members and Thematic Associates

Human similarity judgments do not reliably conform to the predictions of leading theories of psychological similarity. Evidence from the triad similarity judgment task shows that people often identify thematic associates like DOG and BONE as more similar than taxonomic category members like DOG and CAT, even though thematic associates lack the type of featural or relational similarity that is foundational to theories of psychological similarity. This specific failure to predict human behavior has been addressed as a consequence of education and other individual differences, an artifact of the triad similarity judgment paradigm, or a shortcoming in psychological accounts of similarity. We investigated the judged similarity of semantically-related concepts (taxonomic category members and thematic associates) as it relates to other task-independent measures of semantic knowledge and access. Participants were assessed on reading and language ability, then event-related potentials (ERPs) were collected during a passive, sequential word reading task that presented pseudowords and taxonomically-related, thematically-related, and unrelated word sequences, and, finally, similarity judgments were collected with the classic two-alternative forced-choice triad task. The results uncovered a correspondence between ERP amplitude and triad-based similarity judgments---similarity judgment behavior reliably predicts ERP amplitude during passive word reading, absent of any instruction to consider similarity. It was also found that individual differences in reading and language ability independently predicted ERP amplitude. This evidence suggests that similarity judgments are driven by reliable patterns of thought that are not solely rooted in the interpretation of task goals or reading and language ability.

Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation

In neural decoding, there has been a growing interest in machine learning on whole-brain functional magnetic resonance imaging (fMRI). However, the size discrepancy between the feature space and the training set poses serious challenges. Simply increasing the number of training examples is infeasible and costly. In this paper, we proposed a domain adaptation framework for whole-brain fMRI (DawfMRI) to improve whole-brain neural decoding on target data leveraging pre-existing source data. DawfMRI consists of three steps: 1) feature extraction from whole-brain fMRI, 2) source and target feature adaptation, and 3) source and target classifier adaptation. We evaluated its eight possible variations, including two non-adaptation and six adaptation algorithms, using a collection of seven task-based fMRI datasets (129 unique subjects and 11 cognitive tasks in total) from the OpenNeuro project. The results demonstrated that appropriate source domain can help improve neural decoding accuracy for challenging classification tasks. The best-case improvement is 8.94% (from 78.64% to 87.58%). Moreover, we discovered a plausible relationship between psychological similarity and adaptation effectiveness. Finally, visualizing and interpreting voxel weights showed that the adaptation can provide additional insights into neural decoding.

Similarity is as Similarity Does? A Critical Inquiry into the Effect of Thematic Association on Similarity

Leading theories of psychological similarity are based on the degree of match in semantic content between compared cases (i.e., shared features, low dimensional distance, alignable relations). Broader forms of semantic relatedness such as the degree of association between cases (e.g., egg and spatula) are generally not considered to contribute to similarity judgments. However, empirical work has demonstrated a behavioral tendency to choose associated pairs over proximal pairs (i.e., high semantic content overlap) in similarity judgement tasks. As a result, dual-process models have been proposed that posit thematic integration in addition to content match as component processes of similarity. The present experiments investigate the thematic association effect in similarity in order to more clearly determine whether such a theoretical redirection is warranted. An alternative viewpoint is that confusion between similarity and association is the cause of the reported thematic bias. Experiment 1 introduces a modified similarity judgement task and addresses the impact of task instructions as a potential causal factor underlying the thematic association effect on similarity. Experiment 2 specifically compares the novel similarity task to a traditional two-alternative, forced choice triad task. Experiment 3 addresses the possibility of bias in the stimulus sets used in Experiments 1 and 2. Across the experiments we find association-based responding to be much less prevalent than in previous demonstrations: the traditional finding of a thematic preference only occurred when participants were specifically asked to select based on associativity (“goes with”). Modifications to conventional methodology that minimize biasing factors clearly attenuate the effect of association on similarity. We interpret these findings as evidence that that the thematic association effect derives from intrusions on psychological similarity, not from an additional component intrinsic to psychological similarity.