Cognitive Science Literature Research Articles

The Antecedents of Transformer Models

Transformer models of language represent a step change in our ability to account for cognitive phenomena. Although the specific architecture that has garnered recent interest is quite young, many of its components have antecedents in the cognitive science literature. In this article, we start by providing an introduction to large language models aimed at a general psychological audience. We then highlight some of the antecedents, including the importance of scale, instance-based memory models, paradigmatic association and systematicity, positional encodings of serial order, and the learning of control processes. This article offers an exploration of the relationship between transformer models and their precursors, showing how they can be understood as a next phase in our understanding of cognitive processes.

Deconstructing emotions in self-control through computational modeling

Positive and negative emotions have a determining role in self-control, a vital aspect of human decision-making, defined as the dilemma between a smaller sooner reward and a larger later reward. Self-control, as an internal conflict between the higher (pre-frontal cortex) and the lower (limbic system) parts of the brain, has already been simulated using the Iterated Prisoner’s Dilemma game with learning in a computational model. However, the concept of emotions, defined as states elicited by positive and negative reinforcers, is absent from the existing self-control model. By increasing and decreasing the values of the reinforcement signals in the Prisoner’s Dilemma payoff matrix in-between the rounds, we simulated the increment or decrement of positive or negative emotions’ intensity and thus the effects of the presence of emotions, rather than the emotions per se. Our results reflect the restorative role of positive emotions on self-control, the necessity of negative emotions for successful self-control and the impairment of self-control due to intense negative emotions. Furthermore, our results reveal the importance of parameters in self-regulation, such as the intensity of emotions and the frequency it changes. In conclusion, we incorporated the effect of emotions in a computational model of self-control, and with our results complying with cognitive science literature, we demonstrated the cognitive adequacy of our model. We anticipate in this way to provide novel approaches for comprehending self-control behaviour, and to contribute to the general attempt of modeling human behaviour.

The Diagrammatic Image Schema Language (DISL)

ABSTRACT Image schemas are mental patterns learned from perceptual experiences capturing conceptual constructions in expressions. In linguistic analysis, their visualizations are often context-dependent without a generalizable structure. Addressing this, we introduce The Diagrammatic Image Schema Language: a formal representation language that systematizes a set of visual combination rules for different conceptual primitives from the cognitive science literature. These primitives are distinguished from a formal point of view to allow for more general application. DISL also contains a logical exchange format in which the diagrams may be made machine-readable. Using DISL, the semantic structure of complex scenarios can be represented and computed.

Reliabilist epistemology meets bounded rationality

Epistemic reliabilism holds that a belief is justified if and only if it is produced by a reliable or truth-conducive process. I argue that reliabilism offers an epistemology for bounded rationality. This latter concept refers to normative and descriptive accounts of real-world reasoning instead of some ideal reasoning. However, as initially formulated, reliabilism involves an absolute, context-independent assessment of rationality that does not do justice to the fact that several processes are reliable in some reasoning environments but not in others, as is widely reported in the cognitive sciences literature. I consider possible solutions to this problem. Resorting to ‘normality reliabilism’, a variant of the theory, is one; but I find it insufficient. Therefore, in addition, I propose to relativise the reliability assessment to reasoning environments. This novel version of reliabilism fits bounded rationality better than the original one does.

The cognitive origin and cultural evolution of taboos in human societies

AbstractWhy do human societies have so many taboos, defined here as culturally prohibited activities? In this article, I offer a naturalistic account of the origin and transmission of taboos from an evolutionary anthropological perspective. Drawing from the extensive literature in cognitive science and cultural evolution, I argue that taboos may arise from our tendency to retrospectively attribute causes to misfortunes due to a deterministic worldview, and the imperfect transmission of taboos often leads to the loss of their original utilitarian rationale, making them resemble mere cultural conventions. While this account does not explain all cultural prohibitions in human societies, it provides valuable insights into the psychological and social mechanisms by which many taboos are generated in a bottom‐up fashion. Towards the end of the article, I offer a few implications on the proposed account of taboos and specify some testable predictions that merit further studies.

Rapid runtime learning by curating small datasets of high-quality items obtained from memory.

We propose the "runtime learning" hypothesis which states that people quickly learn to perform unfamiliar tasks as the tasks arise by using task-relevant instances of concepts stored in memory during mental training. To make learning rapid, the hypothesis claims that only a few class instances are used, but these instances are especially valuable for training. The paper motivates the hypothesis by describing related ideas from the cognitive science and machine learning literatures. Using computer simulation, we show that deep neural networks (DNNs) can learn effectively from small, curated training sets, and that valuable training items tend to lie toward the centers of data item clusters in an abstract feature space. In a series of three behavioral experiments, we show that people can also learn effectively from small, curated training sets. Critically, we find that participant reaction times and fitted drift rates are best accounted for by the confidences of DNNs trained on small datasets of highly valuable items. We conclude that the runtime learning hypothesis is a novel conjecture about the relationship between learning and memory with the potential for explaining a wide variety of cognitive phenomena.

An information-theoretic approach to the typology of spatial demonstratives

We explore systems of spatial deictic words (such as ‘here’ and ‘there’) from the perspective of communicative efficiency using typological data from over 200 languages Nintemann et al. (2020). We argue from an information-theoretic perspective that spatial deictic systems balance informativity and complexity in the sense of the Information Bottleneck (Zaslavsky et al., (2018). We find that under an appropriate choice of cost function and need probability over meanings, among all the 21,146 theoretically possible spatial deictic systems, those adopted by real languages lie near an efficient frontier of informativity and complexity. Moreover, we find that the conditions that the need probability and the cost function need to satisfy for this result are consistent with the cognitive science literature on spatial cognition, especially regarding the source–goal asymmetry. We further show that the typological data are better explained by introducing a notion of consistency into the Information Bottleneck framework, which is jointly optimized along with informativity and complexity.

Early adversity and the development of explore-exploit tradeoffs.

Childhood adversity can have wide-ranging and long-lasting effects on later life. But what are the mechanisms that are responsible for these effects? This article brings together the cognitive science literature on explore-exploit tradeoffs, the empirical literature on early adversity, and the literature in evolutionary biology on 'life history' to explain how early experience influences later life. We propose one potential mechanism: early experiences influence 'hyperparameters' that determine the balance between exploration and exploitation. Adversity might accelerate a shift from exploration to exploitation, with broad and enduring effects on the adult brain and mind. These effects may be produced by life-history adaptations that use early experience to tailor development and learning to the likely future states of an organism and its environment.

Predictive policing and algorithmic fairness

This paper examines racial discrimination and algorithmic bias in predictive policing algorithms (PPAs), an emerging technology designed to predict threats and suggest solutions in law enforcement. We first describe what discrimination is in a case study of Chicago’s PPA. We then explain their causes with Broadbent’s contrastive model of causation and causal diagrams. Based on the cognitive science literature, we also explain why fairness is not an objective truth discoverable in laboratories but has context-sensitive social meanings that need to be negotiated through democratic processes. With the above analysis, we next predict why some recommendations given in the bias reduction literature are not as effective as expected. Unlike the cliché highlighting equal participation for all stakeholders in predictive policing, we emphasize power structures to avoid hermeneutical lacunae. Finally, we aim to control PPA discrimination by proposing a governance solution—a framework of a social safety net.

Odontocetes (‘Toothed Whales’): Cognitive Science and Moral Standing – Are Dolphins Persons?

Abstract Odontocetes, or ‘toothed whales’, have a complex brain structure and possess rationality, self-awareness, sociability and culture. Cognitive science and modern theories of personhood challenge the notion that humans alone are moral persons. This paper reviews evidence from the cognitive science literature relevant to moral personhood in bottlenose dolphins, orcas, and beluga whales. It applies theories of personhood of Peter Singer, David DeGrazia, and Steven Wise, and finds that odontocetes fulfil criteria to be granted at least borderline personhood. The legal implications of attributing personhood to dolphins remains uncertain. Recognition of dolphin personhood may lead to fundamental legal rights against capture, captivity, and killing; alternatively, the courts may continue to restrict legal personhood and associated protections to human beings. Finally, despite the major influence of personhood on morality and law in the West, the biologically more widespread quality of sentience is sufficient for greater moral considerability and legal protections for nonhuman species.

Bringing cumulative technological culture beyond copying versus reasoning.

The dominant view of cumulative technological culture suggests that high-fidelity transmission rests upon a high-fidelity copying ability, which allows individuals to reproduce the tool-use actions performed by others without needing to understand them (i.e., without causal reasoning). The opposition between copying versus reasoning is well accepted but with little supporting evidence. In this article, we investigate this distinction by examining the cognitive science literature on tool use. Evidence indicates that the ability to reproduce others' tool-use actions requires causal understanding, which questions the copying versus reasoning distinction and the cognitive reality of the so-called copying ability. We conclude that new insights might be gained by considering causal understanding as a key driver of cumulative technological culture.

Perhaps an Other Time

In this article it is argued that conceptions of time have important cognitive and behavioural effects on historical agents, and that in ancient China at least one such conception tied fundamentally with the traditional Chinese calendar, the Stems and Branches system, is significantly different than the worldwide dominant modern conception of time in ways that deserve wider acknowledgement and exploration. The article relies on cognitive science literature, Takayama’s method of uncovering ancient cognition, and Bradd Shore’s Cultural Models Theory, to make its case. By examining the underlying qualitative and calculative structures of the calendar(s) in use by the humans we study, we can begin to see just how potentially different these views of time were and are in ways so fundamental to being in the world as to warrant new (re)considerations of historical actors cognizing about in and about their respective conceptual frameworks of time and the behaviours they engage in as a consequence.

Next-Generation Fraction Intervention and the Long-Term Advantage of Interleaved Instruction

This study's first purpose was to investigate effects of a fourth- and fifth-grade “next-generation” fraction intervention, which included six enhancements over a previously validated fraction intervention, designed to address career- and college-readiness standards. The study's second purpose was to assess effects of the next-generation fraction intervention at follow-up, 1 year after intervention ended. The third purpose was to isolate the effects of one of the six intervention enhancements: interleaved fraction calculations instruction. Students with intensive intervention needs were randomized to next-generation fraction intervention (Super Solvers [SSINT]) with blocked calculations instruction (SSINT_B), SSINT with interleaved calculations instruction (SSINT_I), and control. On a mix of proximal and transfer outcomes, SSINT (across conditions) produced strong, significant effects over control at posttest. At follow-up, effect sizes were weaker but remained significant on calculations: g = 1.22. On other measures, follow-up g was 0.39 to 0.58. The effect of SSINT_I over SSINT_B, although not significant at posttest ( g = 0.28), was statistically significant and large at follow-up ( g = 0.65), in line with the cognitive science literature showing long-term advantages for interleaved instruction. Results suggest next-generation fraction intervention efficacy for intensive-needs students and the importance of interleaved instruction.

An Open-Source Cognitive Test Battery to Assess Human Attention and Memory.

Cognitive test batteries are widely used in diverse research fields, such as cognitive training, cognitive disorder assessment, or brain mechanism understanding. Although they need flexibility according to their usage objectives, most test batteries are not available as open-source software and are not be tuned by researchers in detail. The present study introduces an open-source cognitive test battery to assess attention and memory, using a javascript library, p5.js. Because of the ubiquitous nature of dynamic attention in our daily lives, it is crucial to have tools for its assessment or training. For that purpose, our test battery includes seven cognitive tasks (multiple-objects tracking, enumeration, go/no-go, load-induced blindness, task-switching, working memory, and memorability), common in cognitive science literature. By using the test battery, we conducted an online experiment to collect the benchmark data. Results conducted on 2 separate days showed the high cross-day reliability. Specifically, the task performance did not largely change with the different days. Besides, our test battery captures diverse individual differences and can evaluate them based on the cognitive factors extracted from latent factor analysis. Since we share our source code as open-source software, users can expand and manipulate experimental conditions flexibly. Our test battery is also flexible in terms of the experimental environment, i.e., it is possible to experiment either online or in a laboratory environment.

Making it abstract, making it contestable: politicization at the intersection of political and cognitive science

The notion of politicization has been often assimilated to that of partisanship, especially in political and social sciences. However, these accounts underestimate more fine-grained, and yet pivotal, aspects at stake in processes of politicization. In addition, they overlook cognitive mechanisms underlying politicizing practices. Here, we propose an integrated approach to politicization relying on recent insights from both social and political sciences, as well as cognitive science. We outline two key facets of politicization, that we call partial indetermination and contestability, and we show how these can be accounted for by appealing to recent literature in cognitive science concerned with abstract conceptual knowledge. We suggest that politicizing a concept often implies making its more abstract components more salient, hence legitimating its contestable character. Finally, we provide preliminary suggestions to test our proposal, using the concept of gender as case study.

Applying cognitive principles to model-finding output: the positive value of negative information

Model-finders, such as SAT/SMT-solvers and Alloy, are used widely both directly and embedded in domain-specific tools. They support both conventional verification and, unlike other verification tools, property-free exploration. To do this effectively, they must produce output that helps users with these tasks. Unfortunately, the output of model-finders has seen relatively little rigorous human-factors study. Conventionally, these tools tend to show one satisfying instance at a time. Drawing inspiration from the cognitive science literature, we investigate two aspects of model-finder output: how many instances to show at once, and whether all instances must actually satisfy the input constraints. Using both controlled studies and open-ended talk-alouds, we show that there is benefit to showing negative instances in certain settings; the impact of multiple instances is less clear. Our work is a first step in a theoretically grounded approach to understanding how users engage cognitively with model-finder output, and how those tools might better support users in doing so.

Public procurement in Malaysian local authorities: Antecedents of procedural rationality in decision making

The Malaysian public sector is oversaturated with comprehensive procurement procedures. These measures aim to deter unwarranted behaviours from public officers. Nevertheless, there have been recurring irregularities over the past decade in Malaysian local authorities, including work delays, non-compliance with regulations, wasteful purchasing, substandard workmanship etc. This study aimed to investigate factors undermining rationality in public procurement decision making from the cognitive perspective. The cognitive and behavioural science literature was reviewed systematically, focusing on procedural rationality to develop a predictive model of procurement irregularities. This research adopted a quantitative approach. A total of 289 datasets were collected from the procurement officers of Malaysian local authorities and analysed using the Partial Least Square Structural Equation Modelling technique. The empirical findings showed that work experience, prior knowledge, and accountability correlate directly with procedural rationality in procurement decision-making, which would impact the procurement outcome. The research offered insights into the decision-making behaviour of procurement officers from the cognitive psychology perspective. From the managerial standpoint, public procurement procedures should incorporate the elements of accountability, experience, and prior knowledge as part of the quality assurance and control measures.

DEVS-Based Building Blocks and Architectural Patterns for Intelligent Hybrid Cyberphysical System Design

The DEVS formalism has been recognized to support generic open architectures that allow incorporating multiple engineering domains within integrated simulation models. What is missing for accelerated adoption of DEVS-based methodology for intelligent cyberphysical system design is a set of building blocks and architectural patterns that can be replicated and reused in system development. As a start in this direction, this paper offers a notional architecture for intelligent hybrid cyberphysical system design and proceeds to focus on the decision layer to consider DEVS models for basic behaviors such as choice of alternatives, perception of temporal event relations, and recognition and generation of finite state languages cast into DEVS time segments. We proceed to describe a methodology to define DEVS-based building blocks and architectural patterns for design of systems employing fast, frugal, and accurate heuristics. We identify some elements of this kind and establish their status as minimal realizations of their defined behaviors. As minimal realizations such designs must ipso facto underlie any implementation of the same cognitive behaviors. We discuss architectures drawn from the cognitive science literature to show that the fundamental elements drawn from the fast, frugal, and accurate paradigm provide insights into intelligent hybrid cyberphysical system design. We close with open questions and research needed to confirm the proposed concepts.

Aircraft Assembly Snags: Human Errors or Lack of Production Design?

To err is an intrinsic human trait, which means that human errors, at some point, are inevitable. Business improvement tools and practices neglect to deal with the root causes of human error; hence, they ignore certain design considerations that could possibly prevent or minimise such errors from occurring. Recognising this gap, this paper seeks to conceptualise a model that incorporates cognitive science literature based on a mistake-proofing concept, thereby offering a deeper, more profound level of human error analysis. An exploratory case study involving an aerospace assembly line was conducted to gain insights into the model developed. The findings of the case study revealed four different causes of human errors, as follows: (i) description similarity error, (ii) capture errors, (iii) memory lapse errors, and (iv) interruptions. Based on this analysis, error-proofing measures have been proposed accordingly. This paper lays the foundation for future work on the psychology behind human errors in the aerospace industry and highlights the importance of understanding human errors to avoid quality issues and rework in production settings, where labour input is of paramount importance.

Automatic Creativity Measurement in Scratch Programs Across Modalities

Promoting creativity is considered an important goal of education, but creativity is notoriously hard to measure.In this paper, we make the journey fromdefining a formal measure of creativity that is efficientlycomputable to applying the measure in a practical domain. The measure is general and relies on coretheoretical concepts in creativity theory, namely fluency, flexibility, and originality, integratingwith prior cognitive science literature. We adapted the general measure for projects in the popular visual programming language Scratch.We designed a machine learning model for predicting the creativity of Scratch projects, trained and evaluated on human expert creativity assessments in an extensive user study. Our results show that opinions about creativity in Scratch varied widely across experts. The automatic creativity assessment aligned with the assessment of the human experts more than the experts agreed with each other. This is a first step in providing computational models for measuring creativity that can be applied to educational technologies, and to scale up the benefit of creativity education in schools.