Cognitive Effort Research Articles

The Neural Bases of Language Processing During Social and Non-Social Contexts: A fNIRS Study of Autistic and Neurotypical Preschool-Aged Children.

Little is known about how the brains of autistic children process language during real-world "social contexts," despite the fact that challenges with language, communication, and social interaction are core features of Autism Spectrum Disorder (ASD). We investigated the neural bases of language processing during social and non-social contexts in a sample of N=20 autistic and N=20 neurotypical (NT) preschool-aged children, 3 to 6 years old. Functional near-infrared spectroscopy (fNIRS) was used to measure children's brain response to "live language" spoken by a live experimenter during an in-person social context (i.e., book reading), and "recorded language" played via an audio recording during a non-social context (i.e., screen time). We examined within-group and between-group differences in the strength and localization of brain response to live language and recorded language, as well as correlations between children's brain response and language skills measured by the Preschool Language Scales. In the NT group, brain response to live language was greater than brain response to recorded language in the right temporal parietal junction (TPJ). In the ASD group, the strength of brain response did not differ between conditions. The ASD group showed greater brain response to recorded language than the NT group in the right inferior and middle frontal gyrus (IMFG). Across groups, children's language skills were negatively associated with brain response to recorded language in the right IMFG, suggesting that processing recorded language required more cognitive effort for children with lower language skills. Children's language skills were also positively associated with the difference in brain response between conditions in the right TPJ, demonstrating that children who showed a greater difference in brain response to live language versus recorded language had higher language skills. Findings should be considered preliminary until they are replicated in a larger sample. Findings suggest that the brains of NT children, but not autistic children, process language differently during social and non-social contexts. Individual differences in how the brain processes language during social and non-social contexts may help to explain why language skills are so variable across children with and without autism.

Too hard, too easy, or just right? The effects of context on effort and boredom aversion.

Despite people's general desire to avoid cognitive effort, there is a limit to our parsimony: boredom, a state defined by a lack of successful mental engagement, is found to be similarly aversive. The work presented here investigates how context - the alternative tasks present and the environmental context - impacts people's aversion to exerting cognitive effort and avoiding boredom via a demand-selection task. In a population of undergraduate students, we assessed how people's willingness to exert mental effort (in a working memory task) is affected by the presence of an easier alternative (less cognitively demanding) or a boring alternative (doing nothing at all). To manipulate environmental context, we conducted the experiment online, where participants completed the task remotely, and in a controlled laboratory setting. We find people willingly seek out effortful tasks to avoid boredom, despite avoiding high demands when both tasks on offer required some effort. We also find large effects of the participants' environmental context, with preferences for the most demanding task increasing by over 150% in the lab compared to online. These results bear relevance to theories that argue the costs of effort are determined relative to the alternatives available (e.g., opportunity cost theories). Moreover, the results demonstrate that researchers who deliberately (or inadvertently) manipulate effort and boredom must consider the effects context (both choice and environmental) may have on people's behaviour.

Word frequency and cognitive effort in turns-at-talk: turn structure affects processing load in natural conversation.

Frequency distributions are known to widely affect psycholinguistic processes. The effects of word frequency in turns-at-talk, the nucleus of social action in conversation, have, by contrast, been largely neglected. This study probes into this gap by applying corpus-linguistic methods on the conversational component of the British National Corpus (BNC) and the Freiburg Multimodal Interaction Corpus (FreMIC). The latter includes continuous pupil size measures of participants of the recorded conversations, allowing for a systematic investigation of patterns in the contained speech and language on the one hand and their relation to concurrent processing costs they may incur in speakers and recipients on the other hand. We test a first hypothesis in this vein, analyzing whether word frequency distributions within turns-at-talk are correlated with interlocutors' processing effort during the production and reception of these turns. Turns are found to generally show a regular distribution pattern of word frequency, with highly frequent words in turn-initial positions, mid-range frequency words in turn-medial positions, and low-frequency words in turn-final positions. Speakers' pupil size is found to tend to increase during the course of a turn at talk, reaching a climax toward the turn end. Notably, the observed decrease in word frequency within turns is inversely correlated with the observed increase in pupil size in speakers, but not in recipients, with steeper decreases in word frequency going along with steeper increases in pupil size in speakers. We discuss the implications of these findings for theories of speech processing, turn structure, and information packaging. Crucially, we propose that the intensification of processing effort in speakers during a turn at talk is owed to an informational climax, which entails a progression from high-frequency, low-information words through intermediate levels to low-frequency, high-information words. At least in English conversation, interlocutors seem to make use of this pattern as one way to achieve efficiency in conversational interaction, creating a regularly recurring distribution of processing load across speaking turns, which aids smooth turn transitions, content prediction, and effective information transfer.

Effects of Grammatical Gender on Gender Inferences: Experimental Evidence From Italian Common Gender Nouns

Recent psycholinguistic research has focused on how different grammatical gender marking strategies affect people's mental representation of referents’ gender. Such works particularly explored how explicitly encoded linguistic elements, such as grammatical gender markers, may drive the inferential process as attentional clues. Results of reading comprehension tasks in French and German have shown that the explicit encoding of masculine gender in plural forms of role nouns often leads to a male bias, a specific masculine inference corresponding to the grammatical gender clue, even when the masculine form was intended as generic, thus including women and men. Moreover, comparing generic masculine forms with gender-fair alternatives revealed that the latter significantly reduce this male bias. The present study examines the impact of three gender marking strategies on the construction of generic mental representations. Indeed, the experiment tested generic masculine against two gender-fair forms (split masculine/feminine forms and ambiguous syntactic reformulations) among 38 Italian speakers. No significant effect was found in generating a generic mental representation through form manipulation. However, ambiguous syntactic reformulation realised by presenting target nouns from the Italian common gender noun class and through the neutralisation of determiners’ gender, increased the probability of a male-specific inference. Additionally, a keen interest in gender-fair language topics was linked to longer reaction times, indicating a higher cognitive effort during the inference process.

Using memes and emoji-scales in a web survey: experimental assessment of consequences for multimodal cognitive effort and data quality

ABSTRACT Decreasing web survey participation rates lead to higher importance of ways to increase respondents’ motivation to participate and gamification is seen as one of possible ways to do so. However, there is no clear understanding on how it affects respondents’ cognitive load. This study investigates whether gamification reduces cognitive load among 18–25-year-old survey respondents. A laboratory experiment was conducted with 128 randomly assigned students to either a non-gamified or gamified web survey design. Gamification was implemented using emojis for ordinal scales and motivating memes between sets of questions. Cognitive load was measured through paradata, subjective evaluation, and neurophysiological indicator (pupil diameter dynamics). Results show higher cognitive effort for the gamification group based on pupil diameter dynamics, but subjective evaluation, mouse movements, and completion times show increased cognitive effort for the non-gamified group. Attention was higher for the gamification group, and data quality did not differ. Therefore, gamification may increase cognitive load, but it also increases motivation and engagement, leading to similar data quality compared to non-gamified web surveys.

Bringing the field into the lab: a novel virtual reality outdoor march simulator for evaluating cognitive and physical performance

Soldiers, athletes, and rescue personnel must often maintain cognitive focus while performing intense, prolonged, and physically demanding activities. The simultaneous activation of cognitive and physical functions can disrupt their performance reciprocally. In the current study, we developed and demonstrated the feasibility of a virtual reality (VR)-based experimental protocol that enables rigorous exploration of the effects of prolonged physical and cognitive efforts. A battery of established neurocognitive tests was used to compare novel cognitive tasks to simulated loaded marches. We simulated a 10-km loaded march in our virtual reality environment, with or without integrated cognitive tasks (VR-COG). During three experimental visits, participants were evaluated pre- and post-activity, including the Color Trail Test (CTT), the Synthetic Work Environment (SYNWIN) battery for assessing multitasking, and physical tests (i.e., time to exhaustion). Results show that Strong or moderate correlations (r ≥ 0.58, p ≤ 0.05) were found between VR-COG scores and scores on the cognitive tests. Both the SYNWIN and CTT showed no condition effects but significant time effects, indicating better performance in the post-activity assessment than in the pre-activity assessment. This novel protocol can contribute to our understanding of physical-cognitive interactions, since virtual environments are ideal for studying high performance professional activity in realistic but controlled settings.

Emotion regulation training for adolescents with major depression: Results from a randomized controlled trial.

Difficulties in emotion regulation (ER) are thought to contribute to the development and maintenance of major depression (MD) in adolescents. In healthy adults, a task-based training of ER has previously proven effective to reduce stress, but no such studies are available for MD. It is also unclear whether findings can be generalized onto adolescent populations. The final sample consisted of n = 70 adolescents with MD, who were randomized to a task-based ER training (n = 36) or a control training (n = 34). Across four sessions, the ER group was trained to downregulate negative affect to negative images via reappraisal, while the control group was instructed to attend the images. Rumination, stress-, and affect-related measures were assessed as primary outcomes, behavioral and neurophysiological responses (late positive potential, LPP), as secondary outcomes. The trial was preregistered at clinicaltrials.gov (NCT03957850). While there was no significant differential effect of the ER training on primary outcomes, we found small to moderate effects on rumination in the ER group, but not the control group. During reappraisal (compared to attend), the ER group showed an unexpected increase of the LPP during the first, but not during later training sessions. Although replication in large, multicenter trials is needed, our findings on effect sizes suggest that ER training might be promising to decrease rumination in adolescent MD. The LPP increase at the first session may represent cognitive effort, which was successfully reduced over the sessions. Future studies should research whether training effects transfer to daily life and are durable over a longer time period. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Examining the Effect of Collaboration Effort, Input Method, and Age on Driver–Automation Collaboration in Unstructured Driving Environments

Both drivers and the current state of advanced driver-assistance systems (ADASs) are imperfect, but their collaboration as a team can compensate for individual limitations. Unstructured driving environments (e.g., parking lots and off-road trails) entail greater challenges for an ADAS and require driver–automation collaboration. However, few studies describe the factors shaping drivers’ subjective experience of collaboration with driving automation in unstructured environments. In this study, we examined the effects of collaboration effort and input method on drivers’ subjective experience, including mental workload, trust, and perceived usability. In addition, we examined how collaboration is experienced differently across different age groups. In this driving simulation study, participants collaborated with a hypothetical ADAS to navigate two unstructured driving environments. The results indicate that collaboration effort and input method did not directly affect drivers’ collaboration experience. However, examining the effect of collaboration effort by driver age suggests that older drivers (55 years old and over) experienced higher mental workload and gave lower usability ratings to the collaboration method than younger drivers. These results imply that older drivers may experience more challenging collaboration than younger drivers when they must devote substantial cognitive effort to collaborate with automation. The findings suggest designing driver–automation collaboration features to reduce cognitive effort for older drivers navigating unstructured driving environments. Moreover, future research must examine drivers’ collaboration experiences based on their unique situations, shaped by the interplay between situational properties (e.g., task demands) and driver characteristics (e.g., age) rather than relying on a limited understanding of individual factors.

PURPOSE AND ADVANTAGES OF CHATBOTS

Digital technologies, in particular chatbots, are becoming a key element of interaction between companies (brands) and consumers, performing various functions, replacing a person and imitating his behavior. They find new functional areas related to the current activities of the organization, acquire new competencies that affect the level of market flexibility of the business and its operational efficiency. Chatbots can guide shoppers through transactional steps, saving them cognitive effort and time, and become an asset that differentiates a company or brand from its competitors. The authors believe that the personalized diversification of chatbots across economic sectors and market sectors will increase the level of maturity of companies and brands in the digital environment, aimed at public recognition and universal socialization. Machine learning gives customer service chatbots the ability to detect emotions and also emotionally engage with customers just like human agents do. The development and analysis of intelligent software and hardware, called intelligent agents, are becoming more and more integrated into everyday life. Intelligent agents perform a variety of tasks, from simple to complex operations. Chatbot, which is a typical example of an artificial intelligence system; communicates with users through messaging apps, websites, mobile apps or phone. Chatbots are one of the simplest and most common examples of human-computer interaction (HCI). In chatbot software, the process begins with understanding the offer sent by the user. The sentence submitted by the user is analyzed using natural language processing (NLP) to understand what the user means. Thus, as a result of analyzing the proposal, artificial intelligence makes a decision like a person and transmits to the user the most appropriate answer to the question. Some chatbots powered by machine learning learn more and become smarter as they interact with users. Below are some chatbot software concepts: Chatbots, which are one of the tools that can be preferred to quickly communicate with users, save time. Chatbots, which are becoming increasingly popular today, are coming to the fore in business activities. In this context, the use of chatbots in marketing and customer relations is growing every day. With the help of chatbots, it becomes much easier to reach a large customer base. Keywords: dialog box, artificial intelligence, communications, consumers, chatbots.

No strong support for a Dunning–Kruger effect in creativity: analyses of self-assessment in absolute and relative terms

Competencies related to the evaluation of own cognitive processes, called metacognitive monitoring, are crucial as they help decide whether to persist in or desist from cognitive efforts. One of the most well-known phenomena in this context—the Dunning–Kruger effect—is that less skilled people tend to overestimate their performance. This effect has been reported for various kinds of performance including creativity. More recently, however, it has been suggested that this phenomenon could be a statistical artifact caused by the better-than-average effect and by regression toward the mean. Therefore, we examined the Dunning–Kruger effect in the context of creative thinking performance (i.e., divergent thinking ability) across two studies (Study 1: N = 425; Study 2: N = 317) and applied the classical quartile-based analysis as well as newly recommended, advanced statistical approaches: the Glejser test of heteroscedasticity and nonlinear quadratic regression. We found that the results indeed depended on the employed statistical method: While classical analyses supported the Dunning–Kruger effect across all conditions, it was not consistently supported by the more advanced statistical methods. These findings are in line with recent work challenging certain assumptions of the Dunning–Kruger effect and we discuss factors that undermine accurate self-assessments, especially in the context of creative performance.

P.001 Planning & decision-making in obsessive-compulsive disorder (OCD) through the lens of ERP: a comparative analysis

Background: This study aimed to investigate the effect of impulsivity on the planning & decision-making of individuals with OCD compared to a control group, focusing on amplitude and latency during the Tower of London (TOL) task. Methods: A sample of a total of 76 (dominantly right-handed & aged between 18-30 yrs) participated. Participants with OCD were assessed with the Y-BOCS & symptom checklist, BIS-11, and the HCs were screened with the GHQ-12. ERP components were measured by using TOL on E-prime 3.0. The amplitude and latency along with the spectral power for each problem-solving task were measured and analyzed. Results: Statistically significant differences were found in the Latency variable in the left frontal area of the brain, indicating distinctive latency patterns in individuals with OCD compared to controls. No statistically significant differences were observed in amplitude or latency for other move sequences. High spectral activity was detected in individuals with OCD for an extended period. Conclusions: Individuals with OCD exhibit higher activity indicative of ambivalence during decision-making which indicates that to overcome impulsive urges, thus they need to put more cognitive effort to maintain the same outcomes. To maintain error-free results obsessive & compulsive behaviors are a necessary evil.

Calculating Care: Working Out Ways Through (Economic) Insecurity at a Neighbourhood Market in Ouagadougou, Burkina Faso

ABSTRACT Drawing on twelve months of ethnographic fieldwork, this article traces the everyday practices of vendors at Marché Collé, a neighbourhood market in Ouagadougou, Burkina Faso, as they calculated their way through uncertainty. As intensifying political and economic insecurity refracted into their businesses and moral worlds, the emotional and cognitive efforts of their calculative practices became heightened and more visible, illuminating what they cared about, and reflecting their changing scope of agency in exercising this care. The experiences and expertise of Marché Collé’s vendors illustrate a form of work underexplored in academic work thus far: the emotional and cognitive practices through which people direct their labour and deploy their resources to meet needs and aspirations and mitigate threats. I call this labour ‘calculating care’ and argue that the complexity and amount of this work someone must do corresponds to the (under)valuation of their labour, and body, through socially constructed inequalities.

Pupil dilation reflects effortful action invigoration in overcoming aversive Pavlovian biases

“Pavlovian” or “motivational” biases describe the phenomenon that the valence of prospective outcomes modulates action invigoration: Reward prospect invigorates action, whereas punishment prospect suppresses it. The adaptive role of these biases in decision-making is still unclear. One idea is that they constitute a fast-and-frugal decision strategy in situations characterized by high arousal, e.g., in presence of a predator, which demand a quick response. In this pre-registered study (N = 35), we tested whether such a situation—induced via subliminally presented angry versus neutral faces—leads to increased reliance on Pavlovian biases. We measured trial-by-trial arousal by tracking pupil diameter while participants performed an orthogonalized Motivational Go/NoGo Task. Pavlovian biases were present in responses, reaction times, and even gaze, with lower gaze dispersion under aversive cues reflecting “freezing of gaze.” The subliminally presented faces did not affect responses, reaction times, or pupil diameter, suggesting that the arousal manipulation was ineffective. However, pupil dilations reflected facets of bias suppression, specifically the physical (but not cognitive) effort needed to overcome aversive inhibition: Particularly strong and sustained dilations occurred when participants managed to perform Go responses to aversive cues. Conversely, no such dilations occurred when they managed to inhibit responses to Win cues. These results suggest that pupil diameter does not reflect response conflict per se nor the inhibition of prepotent responses, but specifically effortful action invigoration as needed to overcome aversive inhibition. We discuss our results in the context of the “value of work” theory of striatal dopamine.

Trying Harder: How Cognitive Effort Sculpts Neural Representations during Working Memory.

While the exertion of mental effort improves performance on cognitive tasks, the neural mechanisms by which motivational factors impact cognition remain unknown. Here, we used fMRI to test how changes in cognitive effort, induced by changes in task difficulty, impact neural representations of working memory (WM). Participants (both sexes) were precued whether WM difficulty would be hard or easy. We hypothesized that hard trials demanded more effort as a later decision required finer mnemonic precision. Behaviorally, pupil size was larger and response times were slower on hard compared with easy trials suggesting our manipulation of effort succeeded. Neurally, we observed robust persistent activity during delay periods in the prefrontal cortex (PFC), especially during hard trials. Yet, details of the memoranda could not be decoded from patterns in prefrontal activity. In the patterns of activity in the visual cortex, however, we found strong decoding of memorized targets, where accuracy was higher on hard trials. To potentially link these across-region effects, we hypothesized that effort, carried by persistent activity in the PFC, impacts the quality of WM representations encoded in the visual cortex. Indeed, we found that the amplitude of delay period activity in the frontal cortex predicted decoded accuracy in the visual cortex on a trial-wise basis. These results indicate that effort-related feedback signals sculpt population activity in the visual cortex, improving mnemonic fidelity.

Talent development in female soccer: Developmental activities of professional players in England

ABSTRACT The activities soccer players engage in during their formative years are thought to significantly contribute to the acquisition of expert performance. Whilst this area has seen great interest in male players, there has been little research in females. The study examined developmental activities engaged in by professional female soccer players in England. 56 female soccer players that had either progressed to professional status in adulthood (professional), or did not (ex-academy), completed the Participant History Questionnaire. Professional players started engaging in soccer at an earlier age than their ex-academy counterparts, resulting in greater engagement in practice and play during childhood. During adolescence, professional players engaged in higher amounts of practice than ex-academy players. Engagement in competition and practice was rated as high in physical and cognitive effort by all, yet ex-academy players reported higher levels of physical effort during early adolescence, and cognitive effort during late adolescence. Findings provide an illustration of the talent pathways of professional female soccer players in England and may inform future talent development systems. Large interindividual variation in soccer-specific and other-sport activity data highlight the importance of further understanding the environments of individual soccer nations and their potential impact on the talent identification and development processes.

Assembly complexity and physiological response in human-robot collaboration: Insights from a preliminary experimental analysis

Industry 5.0 paradigm has renewed interest in the human sphere, emphasizing the importance of workers’ well-being in manufacturing activities. In such context, collaborative robotics originated as a technology to support humans in tiring and repetitive tasks. This study investigates the effects of assembly complexity in Human-Robot collaboration using physiological indicators of cognitive effort. In a series of experiments, participants performed assembly processes of different products with varying complexity, in two modalities: manually and with cobot assistance. Physiological measures, including skin conductance, heart rate variability and eye-tracking metrics were collected. The analysis of physiological signals showed trends suggesting the impact of assembly complexity and cobot support. One key finding of the study is that a single physiological signal usually may not provide a complete understanding of cognitive load. Therefore, a holistic approach should be followed. This approach highlighted the importance of considering multiple measures simultaneously to accurately assess workers’ well-being in industrial environments.

A predictor-informed multi-subject bayesian approach for dynamic functional connectivity.

Dynamic functional connectivity investigates how the interactions among brain regions vary over the course of an fMRI experiment. Such transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive effort. In this paper, we develop a multi-subject Bayesian framework where the estimation of dynamic functional networks is informed by time-varying exogenous physiological covariates that are simultaneously recorded in each subject during the fMRI experiment. More specifically, we consider a dynamic Gaussian graphical model approach where a non-homogeneous hidden Markov model is employed to classify the fMRI time series into latent neurological states. We assume the state-transition probabilities to vary over time and across subjects as a function of the underlying covariates, allowing for the estimation of recurrent connectivity patterns and the sharing of networks among the subjects. We further assume sparsity in the network structures via shrinkage priors, and achieve edge selection in the estimated graph structures by introducing a multi-comparison procedure for shrinkage-based inferences with Bayesian false discovery rate control. We evaluate the performances of our method vs alternative approaches on synthetic data. We apply our modeling framework on a resting-state experiment where fMRI data have been collected concurrently with pupillometry measurements, as a proxy of cognitive processing, and assess the heterogeneity of the effects of changes in pupil dilation on the subjects' propensity to change connectivity states. The heterogeneity of state occupancy across subjects provides an understanding of the relationship between increased pupil dilation and transitions toward different cognitive states.

“Use” as a Conscious Thought: Towards a Theory of “Use” in Autonomous Things

The way users perceive and use information systems artefacts has been mainly studied from the notion of behavioral beliefs, deliberate cognitive efforts, and physical actions performed by human actors to produce certain outcomes. The next generation of information systems, however, can sense, respond, and adapt to environments without necessitating similar cognitive efforts, physical contact, or explicit instructions to operate. Therefore, by leveraging theories of consciousness and technology use, this research aims to advance an alternative understanding of the "use" associated with the next generation of IS artefacts that do not require deliberate cognitive efforts, physical manipulation, or explicit instructions to yield outcomes. The theory and proposed model were refined and validated through the burst detection technique, IS expert involvement (n=10), a pilot study (n=130), and end-user surveys (n= 119). Structural equating modelling techniques were employed to test the theory. We show that unlike the manually operated IS artefacts, the “use” of a fully autonomous artefact is a conscious thought rather than a physical activity of operating a system to produce certain outcomes. We argue that, unlike the traditional notions of use associated with manually operated technologies, conscious use is not characterized solely by behavioral beliefs stemming from logical and reflective cognitive and physical efforts (e.g., effort expectancy). We propose the notion of conscious use within the context of fully autonomous entities and empirically validate its measure. Additionally, we offer recommendations for future research directions in this area. The conceptualization of this new theory for fully autonomous IS artefacts adds significant academic value to the literature given the convergence of AI-based machine learning systems and cognitive computing systems.

Reappraisal and mindset interventions on pressurised esport performance

AbstractThis study investigated the impact of two low‐resource interventions on esport competitors' responses to pressure, focusing primarily on state anxiety, challenge and threat appraisals, and action performance. In Experiment 1, a single‐session arousal reappraisal intervention demonstrated a significant influence on high‐pressure esports performance. Notably, it not only prompted a shift in appraisals from threat to challenge but also reduced cognitive and somatic anxiety, increased quiet eye durations and heightened cognitive effort. In Experiment 2, a preliminary assessment of the Mindset‐Reappraisal intervention was undertaken, demonstrating enhancements in esport action performance, including accelerated completion times and heightened shooting accuracy. This intervention cultivated a stress‐enhancing mindset, lowered cognitive and somatic anxiety, fostered challenge appraisals and underscores the practical efficacy of cost‐effective interventions within the specific context of our performance task. Effective management of arousal levels and educating esports competitors on stress mindset implications may improve performance under pressure, offering valuable insights for esports performance psychology. These findings suggest opportunities for refining pressure‐response strategies, paving the way for comprehensive approaches to optimise performance in competitive esports.

Speed-accuracy trade-offs in best-of-n collective decision making through heterogeneous mean-field modeling.

To succeed in their objectives, groups of individuals must be able to make quick and accurate collective decisions on the best option among a set of alternatives with different qualities. Group-living animals aim to do that all the time. Plants and fungi are thought to do so too. Swarms of autonomous robots can also be programed to make best-of-n decisions for solving tasks collaboratively. Ultimately, humans critically need it and so many times they should be better at it! Thanks to their mathematical tractability, simple models like the voter model and the local majority rule model have proven useful to describe the dynamics of such collective decision-making processes. To reach a consensus, individuals change their opinion by interacting with neighbors in their social network. At least among animals and robots, options with a better quality are exchanged more often and therefore spread faster than lower-quality options, leading to the collective selection of the best option. With our work, we study the impact of individuals making errors in pooling others' opinions caused, for example, by the need to reduce the cognitive load. Our analysis is grounded on the introduction of a model that generalizes the two existing models (local majority rule and voter model), showing a speed-accuracy trade-off regulated by the cognitive effort of individuals. We also investigate the impact of the interaction network topology on the collective dynamics. To do so, we extend our model and, by using the heterogeneous mean-field approach, we show the presence of another speed-accuracy trade-off regulated by network connectivity. An interesting result is that reduced network connectivity corresponds to an increase in collective decision accuracy.