Computational Cognitive Model Research Articles

Future Interdisciplinary Combination of AI Technologies and Psychology

The paper aims to address several interdisciplinary and multidisciplinary research issues at the confluence of psychology with the field of artificial intelligence technologies, namely how the integration of big data analytics, advancements in human-computer interaction (HCI), and innovations in brain-computer interfaces (BCIs), can transform psychological research and practice. The paper explores the historical foundations and contemporary developments in the interdisciplinary integration of AI technologies with psychological research and practice. Beginning with early computational models of cognition, the discussion highlights the evolution of AI applications in psychological analysis, including machine learning, natural language processing (NLP), HCI and BCIs. These technologies have not only enhanced the scalability and personalization of mental health care but have also introduced new methods for real-time feedback in therapeutic settings. This paper provides a wide examination of how these interdisciplinary efforts can complement and advance both fields, encouragement mutual development and innovation.Article’s history: Received 1st of September, 2023; Received in revised form 20th of September, 2023; Accepted 21st of October, 2023; Published as article in Volume I, Issue 1, 2023.Copyright© 2023. The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.Keywords: artificial intelligence, big data, brain-computer interface, human computer interaction.

Understanding the complexity of computational models through optimization and sloppy parameter analyses: The case of the Connectionist Dual-Process Model

A major strength of computational cognitive models is their capacity to accurately predict empirical data. However, challenges in understanding how complex models work and the risk of overfitting have often been addressed by trading off predictive accuracy with model simplification. Here, we introduce state-of-the-art model analysis techniques to show how a large number of parameters in a cognitive model can be reduced into a smaller set that is simpler to understand and can be used to make more constrained predictions with. As a test case, we created different versions of the Connectionist Dual-Process model (CDP) of reading aloud whose parameters were optimized on seven different databases. The results showed that CDP was not overfit and could predict a large amount of variance across those databases. Indeed, the quantitative performance of CDP was higher than that of previous models in this area. Moreover, sloppy parameter analysis, a mathematical technique used to quantify the effects of different parameters on model performance, revealed that many of the parameters in CDP have very little effect on its performance. This shows that the dynamics of CDP are much simpler than its relatively large number of parameters might suggest. Overall, our study shows that cognitive models with large numbers of parameters do not necessarily overfit the empirical data and that understanding the behavior of complex models is more tractable using appropriate mathematical tools. The same techniques could be applied to many different complex cognitive models whenever appropriate datasets for model optimization exist.

Psychological value theory: A computational cognitive model of charitable giving

Charitable giving involves a complex economic and social decision because the giver expends resources for goods or services they will never receive. Although psychologists have identified numerous factors that influence charitable giving, there currently exists no unifying computational model of charitable choice. Here, we submit one such model, based within the strictures of Psychological Value Theory (PVT). In four experiments, we assess whether charitable giving is driven by the perceived Psychological Value of the recipient. Across all four experiments, we simultaneously predict response choice and response time with high accuracy. In a fifth experiment, we show that PVT predicts charitable giving more accurately than an account based on competence and warmth. PVT accurately predicts which charity a respondent will choose to donate to and separately, whether a respondent will choose to donate at all. PVT models the cognitive processes underlying charitable donations and it provides a computational framework for integrating known influences on charitable giving. For example, we show that in-group preference influences charitable giving by changing the Psychological Values of the options, rather than by bringing about a response bias toward the in-group.

A novel similarity measurement for triangular cloud models based on dual consideration of shape and distance.

It is important to be able to measure the similarity between two uncertain concepts for many real-life AI applications, such as image retrieval, collaborative filtering, risk assessment, and data clustering. Cloud models are important cognitive computing models that show promise in measuring the similarity of uncertain concepts. Here, we aim to address the shortcomings of existing cloud model similarity measurement algorithms, such as poor discrimination ability and unstable measurement results. We propose an EPTCM algorithm based on the triangular fuzzy number EW-type closeness and cloud drop variance, considering the shape and distance similarities of existing cloud models. The experimental results show that the EPTCM algorithm has good recognition and classification accuracy and is more accurate than the existing Likeness comparing method (LICM), overlap-based expectation curve (OECM), fuzzy distance-based similarity (FDCM) and multidimensional similarity cloud model (MSCM) methods. The experimental results also demonstrate that the EPTCM algorithm has successfully overcome the shortcomings of existing algorithms. In summary, the EPTCM method proposed here is effective and feasible to implement.

Expertise increases planning depth in human gameplay

A hallmark of human intelligence is the ability to plan multiple steps into the future1,2. Despite decades of research3-5, it is still debated whether skilled decision-makers plan more steps ahead than novices6-8. Traditionally, the study of expertise in planning has used board games such as chess, but the complexity of these games poses a barrier to quantitative estimates of planning depth. Conversely, common planning tasks in cognitive science often have a lower complexity9,10 and impose a ceiling for the depth to which any player can plan. Here we investigate expertise in a complex board game that offers ample opportunity for skilled players to plan deeply. We use model fitting methods to show that human behaviour can be captured using a computational cognitive model based on heuristic search. To validate this model, we predict human choices, response times and eye movements. We also perform a Turing test and a reconstruction experiment. Using the model, we find robust evidence for increased planning depth with expertise in both laboratory and large-scale mobile data. Experts memorize and reconstruct board features more accurately. Using complex tasks combined with precise behavioural modelling might expand our understanding of human planning and help to bridge the gap with progress in artificial intelligence.

Gender-inclusive language as a Rational Speech Act in Spanish

Amidst social changes in gendered language use, there is pushback from institutions such as the Spanish Royal Academy, which claims that the use of the generic masculine (e.g., bomberos ‘firemen’) in describing a mixed-gender group is equally inclusive of both men and women (Bosque 2012). By contrast, speakers of Spanish have increasingly adopted gender-inclusive alternatives to the generic masculine (e.g., bomberos o bomberas; Bengoechea 2015). Across two behavioral tasks, we investigated whether gender-inclusive forms actually lead to more inclusive interpretations. We found that the use of the inclusive form (by contrast to the generic masculine) indeed yields more inclusive interpretations, increasing the inferred femaleness of stereotypically male professions, but also decreasing the inferred femaleness of stereotypically female professions. In an attempt to explain the reasoning that delivers inclusive interpretations, we developed a computational cognitive model of the reasoning process. Our model treats the phenomenon as an instance of a markedness implicature: speakers use the longer, inclusive form to guide listeners away from their prior expectations. This work highlights the need for further research into the use of gender-inclusive language cross-linguistically, as well as for pushback against prescriptive institutions perpetuating stereotypes.

An individual differences perspective on pragmatic abilities in the preschool years.

Pragmatic abilities are fundamental to successful language use and learning. Individual differences studies contribute to understanding the psychological processes involved in pragmatic reasoning. Small sample sizes, insufficient measurement tools, and a lack of theoretical precision have hindered progress, however. Three studies addressed these challenges in three- to 5-year-old German-speaking children (N=228, 121 female). Studies 1 and 2 assessed the psychometric properties of six pragmatics tasks. Study 3 investigated relations among pragmatics tasks and between pragmatics and other cognitive abilities. The tasks were found to measure stable variation between individuals. Via a computational cognitive model, individual differences were traced back to a latent pragmatics construct. This presents the basis for understanding the relations between pragmatics and other cognitive abilities. RESEARCH HIGHLIGHTS: Individual differences in pragmatic abilities are important to understanding variation in language development. Research in this domain lacks a precise theoretical framework and psychometrically high-quality measures. We present six tasks capturing a wide range of pragmatic abilities with excellent re-test reliability. We use a computational cognitive model to provide a substantive theory of individual differences in pragmatic abilities.

Predicting openness to experience via a multiplex cognitive network approach

Openness to Experience is most strongly related to aspects of high-level cognition, such as creativity. Yet, the role of cognitive capacities in Openness is still far from understood. We examine how individuals search their memory predicts levels of Openness. Participants (N = 163) had one minute to generate synonyms to the word hot, which operationalizes mental navigation through a multidimensional representation of the mental lexicon – a cognitive multiplex network. We find high accuracy in low- and high- Openness group classification, and good prediction of individual differences in Openness. These results support the use of computational cognitive modelling for the study of personality traits. Further, our results suggest that people high in Openness engage in a distinct style of cognitive search.

Cognitive markers for efficacy of neurofeedback for attention-deficit hyperactivity disorder – personalized medicine using computational psychiatry in a randomized clinical trial

ABSTRACT Background Exploring whether cognitive components (identified by baseline cognitive testing and computational modeling) moderate clinical outcome of neurofeedback (NF) for attention-deficit hyperactivity disorder (ADHD). Method 142 children (aged 7–10) with ADHD were randomly assigned to either NF (n = 84) or control treatment (n = 58) in a double-blind clinical trial (NCT02251743). The NF group received live, self-controlled downtraining of electroencephalographic theta/beta ratio power. The control group received identical-appearing reinforcement from prerecorded electroencephalograms from other children. 133 (78 NF, 55 control) children had cognitive processing measured at baseline with the Integrated Visual and Auditory Continuous Performance Test (IVA2-CPT) and were included in this analysis. A diffusion decision model applied to the IVA2-CPT data quantified two latent cognitive components deficient in ADHD: drift rate and drift bias, indexing efficiency and context sensitivity of cognitive processes involving information integration. We explored whether these cognitive components moderated the improvement in parent- and teacher-rated inattention symptoms from baseline to treatment end (primary clinical outcome). Results Baseline cognitive components reflecting information integration (drift rate, drift bias) moderated the improvement in inattention due to NF vs. control treatment (p = 0.006). Specifically, those with either the most or least severe deficits in these components showed more improvement in parent- and teacher-rated inattention when assigned to NF (Cohen’s d = 0.59) than when assigned to control (Cohen’s d = −0.21). Conclusions Pre-treatment cognitive testing with computational modeling identified children who benefitted more from neurofeedback than control treatment for ADHD.

Evidence accumulation modelling in the wild: understanding safety-critical decisions.

Evidence accumulation models (EAMs) are a class of computational cognitive model used to understand the latent cognitive processes that underlie human decisions and response times (RTs). They have seen widespread application in cognitive psychology and neuroscience. However, historically, the application of these models was limited to simple decision tasks. Recently, researchers have applied these models to gain insight into the cognitive processes that underlie observed behaviour in applied domains, such as air-traffic control (ATC), driving, forensic and medical image discrimination, and maritime surveillance. Here, we discuss how this modelling approach helps researchers understand how the cognitive system adapts to task demands and interventions, such as task automation. We also discuss future directions and argue for wider adoption of cognitive modelling in Human Factors research.

A computational cognitive model of behaviors and decisions that modulate pandemic transmission: Expectancy-value, attitudes, self-efficacy, and motivational intensity.

We present a computational cognitive model that incorporates and formalizes aspects of theories of individual-level behavior change and present simulations of COVID-19 behavioral response that modulates transmission rates. This formalization includes addressing the psychological constructs of attitudes, self-efficacy, and motivational intensity. The model yields signature phenomena that appear in the oscillating dynamics of mask wearing and the effective reproduction number, as well as the overall increase of rates of mask-wearing in response to awareness of an ongoing pandemic.

Cathectic Mechanisms of Culture. Part 1

The first part of the article introduces a new research program that focuses on the constitutive role of emotions, affect, and their intensity in meaning-making. It opposes the existing broad tradition that effectively posits culture as information and sees cultural processes as coding, transferring, and processing information. I suggest that such a “cybernetic” paradigm of culture stems from the implicit or explicit domination of the computational models of cognition in sociology. However, current progress in cognitive neuroscience and its theoretical comprehension makes these models inadequate. The ongoing shift to distributed models of cognition calls for an adjustment of the concept of culture regarding its “vertical”, emotional dimension. Instead of seeing emotions as an “amplifier” of pre-existing cultural meanings or as a “fuel” of social processes, we should see emotions as an ingredient of an emergent synthesis that creates culture. The first part of the article introduces a historical-sociological reconstruction of theories recognizing an integral emotional dimension of culture; this is opposed to the entire sub-discipline of the sociology of emotions that broadly describes the mutual influences between social life and emotions as a human psychic trait. In the context of the ongoing debates about a cognitive turn in sociology, this historical-sociological reconstruction allows me to build a Durkheimian theory of cathectic mechanisms of culture based on the re-interpretation of the Freudian concept of cathexis. The article also contains the basic principles and implications of the sociological theory of cathexis.

Testing three coping strategies for time pressure in categorizations and similarity judgments

This article compares three psychological mechanisms to make multi-attribute inferences under time pressure in the domains of categorization and similarity judgments. Specifically, we test if people under time pressure attend to fewer object features (attention focus), if they respond less precisely (lower choice sensitivity), or if they simplify a psychological similarity function (simplified similarity). The simpler psychological similarity considers the number of matching features but ignores the actual feature value differences. We conducted three experiments (two of them preregistered) in which we manipulated time pressure: one was a categorization task, which was designed based on optimal experimental design principles, and the other two involved a similarity judgment task. Computational cognitive modeling following an exemplar-similarity framework showed that the behavior of most participants under time pressure is in line with a lower choice sensitivity, this means less precise response selection, especially when people make similarity judgments. We find that the variability of participants’ behavior increases with time pressure, to a point where participants are unlikely to make inferences anymore but instead start choosing readily available response options repeatedly. These findings are consistent with related research in other cognitive domains, such as risky choices, and add to growing evidence that time pressure and other forms of cognitive load do not necessarily alter core cognitive processes themselves but rather affect the precision of response selection.

A Developmental Approach for Training Deep Belief Networks

Deep belief networks (DBNs) are stochastic neural networks that can extract rich internal representations of the environment from the sensory data. DBNs had a catalytic effect in triggering the deep learning revolution, demonstrating for the very first time the feasibility of unsupervised learning in networks with many layers of hidden neurons. These hierarchical architectures incorporate plausible biological and cognitive properties, making them particularly appealing as computational models of human perception and cognition. However, learning in DBNs is usually carried out in a greedy, layer-wise fashion, which does not allow to simulate the holistic maturation of cortical circuits and prevents from modeling cognitive development. Here we present iDBN, an iterative learning algorithm for DBNs that allows to jointly update the connection weights across all layers of the model. We evaluate the proposed iterative algorithm on two different sets of visual stimuli, measuring the generative capabilities of the learned model and its potential to support supervised downstream tasks. We also track network development in terms of graph theoretical properties and investigate the potential extension of iDBN to continual learning scenarios. DBNs trained using our iterative approach achieve a final performance comparable to that of the greedy counterparts, at the same time allowing to accurately analyze the gradual development of internal representations in the deep network and the progressive improvement in task performance. Our work paves the way to the use of iDBN for modeling neurocognitive development.

Modeling Individual Differences in Children's Information Integration During Pragmatic Word Learning.

Pragmatics is foundational to language use and learning. Computational cognitive models have been successfully used to predict pragmatic phenomena in adults and children - on an aggregate level. It is unclear if they can be used to predict behavior on an individual level. We address this question in children (N = 60, 3- to 5-year-olds), taking advantage of recent work on pragmatic cue integration. In Part 1, we use data from four independent tasks to estimate child-specific sensitivity parameters to three information sources: semantic knowledge, expectations about speaker informativeness, and sensitivity to common ground. In Part 2, we use these parameters to generate participant-specific trial-by-trial predictions for a new task that jointly manipulated all three information sources. The model accurately predicted children's behavior in the majority of trials. This work advances a substantive theory of individual differences in which the primary locus of developmental variation is sensitivity to individual information sources.

Integrated Modeling of Fatigue Impacts on C-17 Approach and Landing Performance

ABSTRACT Objective The current effort develops an initial integrated model of fatigue in the context of C-17 approach and landing operations. Specifically, we integrate a biomathematical fatigue model with a task network model to estimate pilot performance degradation. Background Fatigue risk management is a critical process in aviation and flight deck operations, given its dramatic impact on aviation safety and pilot health. Biomathematical fatigue models are useful tools in several aviation fatigue risk management programs that can be embedded in electronic device applications. However, these tools are limited in terms of identifying specific performance outcomes affected by fatigue, as well as individualizing fatigue estimates to individual pilots. Integrating computational cognitive models and biomathematical fatigue models can help address these issues. Methods Forty-four aircrew members completed the study with 10 performing 33 landings with sets of corresponding actigraph data and C-17 performance metrics. We developed a task network model of C-17 approach and landing operations and integrated biomathematical fatigue model predictions based on actigraph data from the aircrew. We then compared predictions from this integrated model with C-17 performance metrics from the missions. Results We successfully predicted delays in setting flaps, landing gear, and engaging the speed brake. Conclusion After further development and validation, this integrated model can be implemented within an application to provide real-time information on pilot fatigue and expected performance on specific aircraft operations.

A Computational Cognitive Model of Driver Response Time for Scheduled Freeway Exiting Takeovers in Conditionally Automated Vehicles.

This study develops a computational model to predict drivers' response time and understand the underlying cognitive mechanism for freeway exiting takeovers in conditionally automated vehicles (AVs). Previous research has modeled drivers' takeover response time in emergency scenarios that demand a quick response. However, existing models may not be applicable for scheduled, non-time-critical takeovers as drivers take longer to resume control when there is no time pressure. A model of driver response time in non-time-critical takeovers is lacking. A computational cognitive model of driver takeover response time is developed based on Queuing Network-Model Human Processor (QN-MHP) architecture. The model quantifies gaze redirection in response to takeover request (ToR), task prioritization, driver situation awareness, and driver trust to address the complexities of drivers' takeover strategies when sufficient time budget exists. Experimental data of a preliminary driving simulator study were used to validate the model. The model accounted for 97% of the experimental takeover response time for freeway exiting. The current model can successfully predict drivers' response time for scheduled, non-time-critical freeway exiting takeovers in conditionally AVs. This model can be applied to the human-machine interface design with respect to ToR lead time for enhancing safe freeway exiting takeovers in conditionally AVs. It also provides a foundation for future modeling work towards an integrated driver model of freeway exiting takeover performance.

A Smart Model of Imaginal Perspective Taking.

The ability to judge spatial relations from perspectives that differ from one's current body orientation and location is important for many everyday activities. Despite considerable research on imaginal perspective taking, however, detailed computational accounts of the processes involved in this ability are missing. In this contribution, I introduce Smart (Spatial Memory Access by Reference Frame SelecTion) as a computational cognitive model of imaginal perspective taking processes. In assuming that imaginal perspective taking is governed by reference frame selection for memory access and subsequent motor activation, Smart is able to explain and simulate key findings on human imaginal perspective taking. In addition to providing novel insight into the mechanisms underlying imaginal perspective taking, Smart also has several implications for our view on spatial memory, more generally. In particular, Smart supports the idea that enduring spatial representations are essentially orientation-free and that spatial representations are best viewed as flexible combinations of representation structures and referenceframes.

The interpretation of computational model parameters depends on the context.

Reinforcement Learning (RL) models have revolutionized the cognitive and brain sciences, promising to explain behavior from simple conditioning to complex problem solving, to shed light on developmental and individual differences, and to anchor cognitive processes in specific brain mechanisms. However, the RL literature increasingly reveals contradictory results, which might cast doubt on these claims. We hypothesized that many contradictions arise from two commonly-held assumptions about computational model parameters that are actually often invalid: That parameters generalize between contexts (e.g. tasks, models) and that they capture interpretable (i.e. unique, distinctive) neurocognitive processes. To test this, we asked 291 participants aged 8-30 years to complete three learning tasks in one experimental session, and fitted RL models to each. We found that some parameters (exploration / decision noise) showed significant generalization: they followed similar developmental trajectories, and were reciprocally predictive between tasks. Still, generalization was significantly below the methodological ceiling. Furthermore, other parameters (learning rates, forgetting) did not show evidence of generalization, and sometimes even opposite developmental trajectories. Interpretability was low for all parameters. We conclude that the systematic study of context factors (e.g. reward stochasticity; task volatility) will be necessary to enhance the generalizability and interpretability of computational cognitive models.

Correspondence between cognitive and neural representations for phonology, orthography, and semantics in supramarginal compared to angular gyrus.

The angular and supramarginal gyri (AG and SMG) together constitute the inferior parietal lobule (IPL) and have been associated with cognitive functions that support reading. How those functions are distributed across the AG and SMG is a matter of debate, the resolution of which is hampered by inconsistencies across stereotactic atlases provided by the major brain image analysis software packages. Schematic results from automated meta-analyses suggest primarily semantic (word meaning) processing in the left AG, with more spatial overlap among phonological (auditory word form), orthographic (visual word form), and semantic processing in the left SMG. To systematically test for correspondence between patterns of neural activation and phonological, orthographic, and semantic representations, we re-analyze a functional magnetic resonance imaging data set of participants reading aloud 465 words. Using representational similarity analysis, we test the hypothesis that within cytoarchitecture-defined subregions of the IPL, phonological representations are primarily associated with the SMG, while semantic representations are primarily associated with the AG. To the extent that orthographic representations can be de-correlated from phonological representations, they will be associated with cortex peripheral to the IPL, such as the intraparietal sulcus. Results largely confirmed these hypotheses, with some nuanced exceptions, which we discuss in terms of neurally inspired computational cognitive models of reading that learn mappings among distributed representations for orthography, phonology, and semantics. De-correlating constituent representations making up complex cognitive processes, such as reading, by careful selection of stimuli, representational formats, and analysis techniques, are promising approaches for bringing additional clarity to brain structure-function relationships.