Content uploaded by Jana Krivec
Author content
All content in this area was uploaded by Jana Krivec on Jun 02, 2021
Content may be subject to copyright.
A preview of the PDF is not available
Identification and Conceptualization of Procedural Chunks in Chess
Abstract and Figures
When it comes to cognitive architecture and human information processing, chunks are one of the best known and most recognized constructs. Nevertheless, the nature of chunks is still very elusive, especially when it comes to chunks in procedural knowledge. This study deals with basic features of procedural information processing and examines the manifestation of chunks in procedural knowledge. The participants' task was to reconstruct sequences of chess moves. Chess was chosen as an experimental domain, because of its complexity, well-defined rules and standardized measure of chess player strength. From the results we conclude that short-term memory capacity is determined by the combination of the size and amount of procedural chunks recalled to the short-term memory. We have shown that on average, participants with more specialized knowledge operated faster and with larger chunks of procedural information than participants with less specialized knowledge. We have shown that in procedural information processing, the level of expertise and the sorting order of the retrieved information are important factors that influence the amount of procedural chunks retained in the short-term memory. Therefore, the capacity of short-term memory in complex situations cannot be expressed as a simple concept.
Figures - uploaded by Jana Krivec
Author content
All figure content in this area was uploaded by Jana Krivec
Content may be subject to copyright.
... This depth re ects the combination of pattern recognition and search processes during decision-making. Chess players use chunking in their working memory to group related pieces and positions [23][24][25]. Experienced players ...
... Additionally, disadvantageous evaluation shifts may create cognitive dissonance or stress that leads to impaired decision performance, as players are more likely to rely on heuristics rather than utilising more deliberate decision-making strategies [72][73][74]. The moderate impact of constellation rarity on blunder likelihood also reinforced the importance of chunking theory and visual pattern recognition in chess play [23,75]. Uncommon constellations likely diminish the effectiveness of pattern recognition [27,12], limiting players' ability to leverage their accumulated knowledge and experiences. ...
... Pattern familiarity shows a similar trend, with uncommon patterns tending to lead to suboptimal moves and common patterns associated with higher chances of optimal moves. These ndings reinforce the theories of cognitive load [69], attention allocation [71], and pattern recognition in chess expertise [23,75] discussed earlier. They highlight how reduced cognitive strain in familiar, less di cult situations enables more effective information processing and decision-making, while increased cognitive demands in unfamiliar or complex scenarios can lead to errors and suboptimal play. ...
Introduction: Quantifying signals in large, sparse datasets is challenging, as noise and redundant features often obscure informative patterns. Chess middlegames, with their dynamic complexity and endless possibilities, provide a testbed for exploring such challenges. Building on 12 studies that identified three categories of chess complexity—difficulty, optionality, and rarity—we propose a novel method to quantify chess pattern complexity. Methods: A complexity scoring system was developed, integrating centipawn change, variability, engine search depth, and inverse document frequency of constellations to measure difficulty, optionality, and rarity. From a large dataset of chess games, we extracted 106,810 unique 11-ply constellations that appeared at least 20 times across 2,235,823 games. The chess engine was used to evaluate chess decisions when answering on constellations, and a multinomial logistic regression model was employed to predict move quality. Results: The model demonstrated good predictive power with a weighted F1 score of 0.75 and a ROC AUC of 0.82. It correctly classified over 70% of blunders, mistakes, and optimal moves, although it has less success in differentiating inaccuracies from optimal moves. Discussion: Our approach establishes a flexible framework for quantifying complexity in large, sparse datasets, demonstrating applicability beyond chess. By integrating different metrics, we achieve high accuracy in predicting move quality during chess middlegames. Beyond supporting chess education by identifying constellations tied to specific move types, this methodology could be valuable in contexts where understanding the interplay of dynamic patterns and their outcomes is essential for deriving actionable insights.
... However, we also argue that consumers who have purchased products from the same product group in the past (i.e., those with product domain knowledge) would not need the same exposure time to post helpful reviews. Past studies suggest that users with prior experience possess chunks that represent functional units (Chase & Simon, 1973;Krivec et al., 2021;Larkin et al., 1980). Chunks are stimulus patterns or configurations that experienced users can recognize (Newell & Simon, 1972). ...
... Expert knowledge is chunked so as to include more procedural knowledge and expertise about the conditions of applicability (Chi et al., 1982). Krivec et al. (2021) found that for the reconstruction of chess moves, highskilled chess players are significantly more accurate than low-skilled players; the speed of information recall is also markedly higher for the high-skilled group. They concluded that users with more specialized knowledge operate faster with larger chunks of procedural information than those with less knowledge. ...
... The basic procedures of building a tent could be i) unpacking the tent supplies, ii) laying down a ground cloth, iii) insetting the tent poles through the frame, iv) raising the tent, v) hammering in the tent pegs, vi) setting up the rain-fly, and vii) testing the firmness of the tent in different weather conditions. Expert users will possess compound procedural chunks containing one or more steps (Krivec et al., 2021). For example, a compound chunk could consist of steps iii), iv), and v). ...
Many online sellers send a review request only a few days after product delivery to gather customer reviews. Yet, the value of this strategy is questionable because buyers with short product exposure are unlikely to have enough time to inspect the product thoroughly and thus may not offer valuable evaluations. We address this question by examining the influence of consumers’ product exposure on the helpfulness of their reviews. Our findings suggest that those with a longer product exposure tend to produce more helpful posts. The subsequent topic modeling analyses reveal that reviewers’ assessments of product utilitarian aspects increase with product exposure. Such information is perceived as less subjective and contains more discussions on product functionality. Lastly, we found that users with prior product domain knowledge do not need a long exposure to produce helpful reviews. Businesses with an urgent need to gain reviews may target them as a priority.
... reduce the cognitive load on WM (Krivec et al., 2021;Thalmann et al., 2019). It may also offer a 134 learning mechanism by which hierarchically organised cultural tokens as described above are 135 acquired and consolidated. ...
To what extent do the cognitive capacities of learners shape the dynamics of cultural evolution and gene-culture coevolution? To begin to address some of these questions, an individual-based model was built, incorporating the cognitive dynamics of working memory, long-term memory and learning strategies—imitation and learning by induction. By representing cultural values as hierarchically arranged sequences of unit token values, this work showed how the emergence of complex cultural representations depends on chunking, becomes constrained by working memory and is spatially limited by learning distance. Cultural token variants diverged into spatially clustered lineages with agent similarity scaling relative to distance, demonstrating the emergence of cultural systems built upon shared cultural variants. Moreover, by simulating a process of inductive learning, agents converged upon certain cultural primitives and complex tokens, thereby marrying aspects of cultural attractor theory with cumulative cultural evolution. The model offers some computational insight into the roles of imitation and inductive learning across the lifespan, showing that imitation occurred most in the first iterations of an agent’s lifespan and inductive inference throughout the adult lifespan, thereby shedding light on the proximal effects of cognitive capacities in the debate between replication and reconstruction of cultural values.
... In the chess scenario, chess experts could segment large amounts of information about the chess game into small chunks, and further condense it into larger chunks (concatenation) based on the numerous chess games they have played. In particular, during chess game, chunk memory helps chess players to automatically recall memories of perceptual patterns and separates chunks into procedural chunks (segmentation), allowing the experts to manifest faster perception of different situations and produce more accurate decision-making than regular players (Gong et al., 2015;Krivec et al., 2021;Krivec et al., 2009). By contrast, without su cient chess experience, novice players are more inclined to use iterative strategies to predict their actions or the mental state of other players (Powell et al., 2017), which needs more cognitive effort and time demanded by the decision-making. ...
Previous studies on the chess game demonstrated that chess experts strongly rely on the activation of memory chunks to manifest accurate decision-making. Although the chunk memory might be affected by temporal constraints, it is unclear why the performance of chess experts is not significantly dropped under time pressure. In this study, the underlying cognitive neural mechanism was carefully inspected by accessing the chess game performance between 20 local experienced and 20 inexperienced chess players with 1-minute and 5-minute time constraints. In addition, functional near-infrared spectroscopy (fNIRS) recordings were carried out for each individual from the two groups while playing a 1-minute or 5-minute chess game. It was discovered that under temporal constraints, players exhibited different patterns of functional connectivity in frontal-parietal regions, suggesting that temporal stress can enhance segmentation processes in chess games. In particular, the experienced group exhibited significantly enhanced functional connectivity networks under time pressure including the dorsolateral prefrontal cortex, inferior frontal gyrus, supramarginal gyrus, and postcentral gyrus, which demonstrated the important role of the segmentation process for experienced players under time pressure. Our study found that experienced players were able to enhance recall, reorganize and integrate chunks to improve chess performance under time pressure.
The primary purpose of this study was to determine reading strategy differences in completing a notation memorization task between three ensemble music traditions: jazz, choral, and band. The secondary purpose of this study was to assess possible relationships between ensemble affiliation, memorization strategies, and participants’ memorization accuracy. Participants ( N = 81) had 75 s to memorize short melody while studying silently, singing, or playing on a keyboard. Participants then notated the each melody using staff notation. After completion of the task, participants ranked reading strategies employed. A Kruskal–Wallis test revealed participants in the three ensemble groups approached the memorization task using significantly different cognitive strategies: the choral group used solfege labeling more than the jazz group, the choral group used whole repetition more than the band group, and the jazz group used harmonic analysis more than the choral group. A Spearman rank correlation revealed significant relationships between stated strategies and memorization accuracy. A significant positive relationship was found between identification of patterns and accuracy on the memorization task. Group differences reveal how the forms of music literacy constructed through different ensemble affiliations may create different approaches to understanding staff notation.
To account for the large demands on working memory during text comprehension and expert performance, the traditional models of working memory involving temporary storage must be extended to include working memory based on storage in long-term memory. In the proposed theoretical framework cognitive processes are viewed as a sequence of stable states representing end products of processing. In skilled activities, acquired memory skills allow these end products to be stored in long-term memory and kept directly accessible by means of retrieval cues in short-term memory, as proposed by skilled memory theory. These theoretical claims are supported by a review of evidence on memory in text comprehension and expert performance in such domains as mental calculation, medical diagnosis, and chess.
We investigate experts' ability to assess the difficulty of a mental task for a human. The final aim is to find formalized measures of difficulty that could be used in automated assessment of the difficulty of a task. In experiments with tactical chess problems, the experts' estimations of difficulty are compared to the statistic-based difficulty ratings on the Chess Tempo website. In an eye tracking experiment, the subjects' solutions to chess problems and the moves that they considered are analyzed. Performance data (time and accuracy) are used as indicators of subjectively perceived difficulty. We also aim to identify the attributes of tactical positions that affect the difficulty of the problem. Understanding the connection between players' estimation of difficulty and the properties of the search trees of variations considered is essential, but not sufficient, for modeling the difficulty of tactical problems. Our findings include that (a) assessing difficulty is also very difficult for human experts, and (b) algorithms designed to estimate difficulty should interpret the complexity of a game tree in the light of knowledge-based patterns that human players are able to detect in a chess problem.
Chunking is the recoding of smaller units of information into larger, familiar units. Chunking is often assumed to help bypassing the limited capacity of working memory (WM). We investigate how chunks are used in WM tasks, addressing three questions: (a) Does chunking reduce the load on WM? Across four experiments chunking benefits were found not only for recall of the chunked but also of other not-chunked information concurrently held in WM, supporting the assumption that chunking reduces load. (b) Is the chunking benefit independent of chunk size? The chunking benefit was independent of chunk size only if the chunks were composed of unique elements, so that each chunk could be replaced by its first element (Experiment 1), but not when several chunks consisted of overlapping sets of elements, disabling this replacement strategy (Experiments 2 and 3). The chunk-size effect is not due to differences in rehearsal duration as it persisted when participants were required to perform articulatory suppression (Experiment 3). Hence, WM capacity is not limited to a fixed number of chunks regardless of their size. (c) Does the chunking benefit depend on the serial position of the chunk? Chunks in early list positions improved recall of other, not-chunked material, but chunks at the end of the list did not. We conclude that a chunk reduces the load on WM via retrieval of a compact chunk representation from long-term memory that replaces the representations of individual elements of the chunk. This frees up capacity for subsequently encoded material. (PsycINFO Database Record
Experts’ remarkable ability to recall meaningful
domain-specific material is a classic result in cognitive psychology.
Influential explanations for this ability have focused on the
acquisition of high-level structures (e.g., schemata) or experts’
capability to process information holistically. However, research
on chess players suggests that experts maintain some reliable
memory advantage over novices when random stimuli (e.g.,
shuffled chess positions) are presented. This skill effect cannot
be explained by theories emphasizing high-level memory structures
or holistic processing of stimuli, because random material
does not contain large structures nor wholes. By contrast, theories
hypothesizing the presence of small memory structures—such as
chunks—predict this outcome, because some chunks still occur
by chance in the stimuli, even after randomization. The current
meta-analysis assessed the correlation between level of expertise
and recall of random material in diverse domains. The overall
correlation was moderate but statistically significant
(r = .41; p < .001 ), and the effect was observed in nearly
every study. This outcome suggests that experts partly base their
superiority on a vaster amount of small memory structures, in
addition to high-level structures or holistic processing.
We investigate the question of automatic prediction of task difficulty for humans, of problems that are typically solved through informed search. Our experimental domain is the game of chess. We analyse experimental data from human chess players solving tactical chess problems. The players also estimated the difficulty of these problems. We carried out an experiment with an approach to automatically estimate the difficulty of problems in this domain. The idea of this approach is to use the properties of a “meaningful search tree” to learn to estimate the difficulty of example problems. The construct of a meaningful search tree is an attempt at approximating problem solving by human experts. The learned difficulty classifier was applied to our experimental problems, and the resulting difficulty estimates matched well with the measured difficulties on the Chess Tempo website, and also with the average difficulty perceived by the players.
The main research question of this study is how the processing of information relates to different contextual characteristics. More specifically, how the context is associated with efficiency of information processing (success and speed), size of chunks, speed of chunk processing and the recall of a chunk. The research domain was the game of chess. The efficiency of information processing and the chunk characteristics were defined with the reconstruction of sequences of chess moves. Context variables were defined using a slightly adapted chess program. Variables on information dispersion, deviation, complexity and positivity were extracted in each chess position. Overall, the results showed that higher dispersion and complexity and lower positivity of information in a context lead to less efficient information processing. The results support the assumptions of the cognitive load theory about the negative effects of external factors burden on information processing and working memory. Our results also support the ACT-R theory, which suggests that more frequent information has a higher activation level and can therefore be retrieved more easily and quickly. The results are also congruent with the positivity effect, which proposes that it is easier to remember positive information than negative information. The findings of our study can be beneficial for the development of intelligent tutoring systems and the design of human–computer interaction systems.
The growing use of computer-like tablets and PCs in educational settings is enabling more students to study online courses featuring computer-aided tests. Preparing these tests imposes a large burden on teachers who have to prepare a large number of questions because they cannot reuse the same questions many times as students can easily memorize their solutions and share them with other students, which degrades test reliability. Another burden is appropriately setting the level of question difficulty to ensure test discriminability. Using magic square puzzles as examples of mathematical questions, we developed a method for automatically preparing puzzles with appropriate levels of difficulty. We used crowdsourcing to collect answers to sample questions to evaluate their difficulty. Item response theory was used to evaluate the difficulty of the questions from crowdworkers’ answers. Deep learning was then used to build a model for predicting the difficulty of new questions.