Can compression take place in working memory without a central contribution of long-term memory?

Information is easier to remember when it is recognized as structured. One explanation for this benefit is that people represent structured information in a compressed form, thus reducing memory load. However, the contribution of long-term memory and working memory to compression are not yet disentangled. Previous work has mostly produced evidence that long-term memory is the main source of compression. In the present work, we reveal two signatures of compression in working memory using a large-scale naturalistic data set from a science museum. Analyzing data from more than 32,000 memory trials, in which people attempted to recall briefly displayed sequences of colors, we examined how the estimated compressibility of each sequence predicted memory performance. Besides finding that compressibility predicted memory performance, we found that greater compressibility of early subsections of sequences predicted better memory for later subsections, and that mis-recalled sequences were simpler than the originals. These findings suggest that (1) more compressibility reduces memory load, leaving space for additional information; (2) memory errors are not random and instead reflect compression gone awry. Together, these findings suggest that compression can take place in working memory. This may enable efficient storage on the spot without direct contributions from long-term memory. However, we also discuss ways long-term memory could explain our findings.

Human behavioral complexity peaks at age 25.

Random Item Generation tasks (RIG) are commonly used to assess high cognitive abilities such as inhibition or sustained attention. They also draw upon our approximate sense of complexity. A detrimental effect of aging on pseudo-random productions has been demonstrated for some tasks, but little is as yet known about the developmental curve of cognitive complexity over the lifespan. We investigate the complexity trajectory across the lifespan of human responses to five common RIG tasks, using a large sample (n = 3429). Our main finding is that the developmental curve of the estimated algorithmic complexity of responses is similar to what may be expected of a measure of higher cognitive abilities, with a performance peak around 25 and a decline starting around 60, suggesting that RIG tasks yield good estimates of such cognitive abilities. Our study illustrates that very short strings of, i.e., 10 items, are sufficient to have their complexity reliably estimated and to allow the documentation of an age-dependent decline in the approximate sense of complexity.

Algorithmic complexity for psychology: a user-friendly implementation of the coding theorem method.

Kolmogorov-Chaitin complexity has long been believed to be impossible to approximate when it comes to short sequences (e.g. of length 5-50). However, with the newly developed coding theorem method the complexity of strings of length 2-11 can now be numerically estimated. We present the theoretical basis of algorithmic complexity for short strings (ACSS) and describe an R-package providing functions based on ACSS that will cover psychologists' needs and improve upon previous methods in three ways: (1) ACSS is now available not only for binary strings, but for strings based on up to 9 different symbols, (2) ACSS no longer requires time-consuming computing, and (3) a new approach based on ACSS gives access to an estimation of the complexity of strings of any length. Finally, three illustrative examples show how these tools can be applied to psychology.

Nothing Happens by Accident, or Does It? A Low Prior for Randomness Does Not Explain Belief in Conspiracy Theories.

Belief in conspiracy theories has often been associated with a biased perception of randomness, akin to a nothing-happens-by-accident heuristic. Indeed, a low prior for randomness (i.e., believing that randomness is a priori unlikely) could plausibly explain the tendency to believe that a planned deception lies behind many events, as well as the tendency to perceive meaningful information in scattered and irrelevant details; both of these tendencies are traits diagnostic of conspiracist ideation. In three studies, we investigated this hypothesis and failed to find the predicted association between low prior for randomness and conspiracist ideation, even when randomness was explicitly opposed to malevolent human intervention. Conspiracy believers' and nonbelievers' perceptions of randomness were not only indistinguishable from each other but also accurate compared with the normative view arising from the algorithmic information framework. Thus, the motto "nothing happens by accident," taken at face value, does not explain belief in conspiracy theories.

Algorithmic complexity for short binary strings applied to psychology: a primer.

As human randomness production has come to be more closely studied and used to assess executive functions (especially inhibition), many normative measures for assessing the degree to which a sequence is randomlike have been suggested. However, each of these measures focuses on one feature of randomness, leading researchers to have to use multiple measures. Although algorithmic complexity has been suggested as a means for overcoming this inconvenience, it has never been used, because standard Kolmogorov complexity is inapplicable to short strings (e.g., of length l ≤ 50), due to both computational and theoretical limitations. Here, we describe a novel technique (the coding theorem method) based on the calculation of a universal distribution, which yields an objective and universal measure of algorithmic complexity for short strings that approximates Kolmogorov-Chaitin complexity.

High intelligence is not associated with a greater propensity for mental health disorders.

BACKGROUND: Studies reporting that highly intelligent individuals have more mental health disorders often have sampling bias, no or inadequate control groups, or insufficient sample size. We addressed these caveats by examining the difference in the prevalence of mental health disorders between individuals with high and average general intelligence (g-factor) in the UK Biobank. METHODS: Participants with g-factor scores standardized relative to the same-age UK population, were divided into two groups: a high g-factor group (g-factor 2 SD above the UK mean; N = 16,137) and an average g-factor group (g-factor within 2 SD of the UK mean; N = 236,273). Using self-report questionnaires and medical diagnoses, we examined group differences in the prevalence of 32 phenotypes, including mental health disorders, trauma, allergies, and other traits. RESULTS: High and average g-factor groups differed across 15/32 phenotypes and did not depend on sex and/or age. Individuals with high g-factors had less general anxiety (odds ratio [OR] = 0.69, 95% CI [0.64;0.74]) and post-traumatic stress disorder (PTSD; OR = 0.67, 95 %CI [0.61;0.74]), were less neurotic (ß = -0.12, 95% CI [-0.15;-0.10]), less socially isolated (OR = 0.85, 95% CI [0.80;0.90]), and were less likely to have experienced childhood stressors and abuse, adulthood stressors, or catastrophic trauma (OR = 0.69-0.90). However, they generally had more allergies (e.g., eczema; OR = 1.13-1.33). CONCLUSIONS: The present study provides robust evidence that highly intelligent individuals do not have more mental health disorders than the average population. High intelligence even appears as a protective factor for general anxiety and PTSD.

Pseudoexpertise: A Conceptual and Theoretical Analysis.

Some people publicly pretend to be experts while not being ones. They are pseudoexperts, and their presence seems to be ubiquitous in the current cultural landscape. This manuscript explores the nature and mechanisms of pseudoexpertise. We first provide a conceptual analysis of pseudoexperts based on prototypical cases of pseudoexpertise and recent philosophical work on the concept of expertise. This allows us to propose a definition that captures real-world cases of pseudoexpertise, distinguishes it from related but different concepts such as pseudoscience, and highlights what is wrong with pseudoexpertise. Next, based on this conceptual analysis, we propose a framework for further research on pseudoexpertise, built on relevant empirical and theoretical approaches to cultural cognition. We provide exploratory answers to three questions: why is there pseudoexpertise at all; how can pseudoexperts be successful despite not being experts; and what becomes of pseudoexperts in the long run. Together, these conceptual and theoretical approaches to pseudoexpertise draw a preliminary framework from which to approach the very troubling problem posed by persons usurping the capacities and reputations of genuine experts.

Working memory complex span tasks and fluid intelligence: Does the positional structure of the task matter?

The complex span task used to evaluate working memory (WM) capacity has been considered to be the most predictive task of fluid intelligence. However, the structure of the complex span tasks varies from one study to another, and it has not been questioned yet whether these variants could influence the predictive power of these tasks. Previous studies have typically used either structures based on alternating processing-storage patterns or alternating storage-processing patterns. We present one experiment in which the participants were submitted to both the processing-storage vs. storage-processing types. After completing both types of complex span tasks, the participants performed a reasoning test (Matrix Reasoning of the Wechsler Adult Intelligence Scale - WAIS-IV). The results showed a significant difference in the WM spans between the two conditions, with higher spans observed in the processing-storage alternating structure, and different serial position curves. However, the correlations showed that both types of tasks remained equally predictive of performance in the reasoning test. These results are discussed in regard to the time-based resource-sharing model.

Amount of Learning and Signal Stability Modulate Emergence of Structure and Iconicity in Novel Signaling Systems.

Iterated language learning experiments that explore the emergence of linguistic structure in the laboratory vary considerably in methodological implementation, limiting the generalizability of findings. Most studies also restrict themselves to exploring the emergence of combinatorial and compositional structure in isolation. Here, we use a novel signal space comprising binary auditory and visual sequences and manipulate the amount of learning and temporal stability of these signals. Participants had to learn signals for meanings differing in size, shape, and brightness; their productions in the test phase were transmitted to the next participant. Across transmission chains of 10 generations each, Experiment 1 varied how much learning of auditory signals took place, and Experiment 2 varied temporal stability of visual signals. We found that combinatorial structure emerged only for auditory signals, and iconicity emerged when the amount of learning was reduced, as an opportunity for rote-memorization hampers the exploration of the iconic affordances of the signal space. In addition, compositionality followed an inverted u-shaped trajectory raising across several generations before declining again toward the end of the transmission chains. This suggests that detection of systematic form-meaning linkages requires stable combinatorial units that can guide learners toward the structural properties of signals, but these combinatorial units had not yet emerged in these unfamiliar systems. Our findings underscore the importance of systematically manipulating training conditions and signal characteristics in iterated language learning experiments to study the interactions between the emergence of iconicity, combinatorial and compositional structure in novel signaling systems.

Mathematical transcription of the 'time-based resource sharing' theory of working memory.

The time-based resource sharing (TBRS) model is a prominent model of working memory that is both predictive and simple. TBRS is a mainstream decay-based model and the most susceptible to competition with interference-based models. A connectionist implementation of TBRS, TBRS*, has recently been developed. However, TBRS* is an enriched version of TBRS, making it difficult to test general characteristics resulting from TBRS assumptions. Here, we describe a novel model, TBRS2, built to be more transparent and simple than TBRS*. TBRS2 is minimalist and allows only a few parameters. It is a straightforward mathematical transcription of TBRS that focuses exclusively on the activation level of memory items as a function of time. Its simplicity makes it possible to derive several theorems from the original TBRS and allows several variants of the refresh process to be tested without relying on particular architectures.

Creationism and conspiracism share a common teleological bias.

Teleological thinking - the attribution of purpose and a final cause to natural events and entities - has long been identified as a cognitive hindrance to the acceptance of evolution, yet its association to beliefs other than creationism has not been investigated. Here, we show that conspiracism - the proneness to explain socio-historical events in terms of secret and malevolent conspiracies - is also associated to a teleological bias. Across three correlational studies (N > 2000), we found robust evidence of a teleological link between conspiracism and creationism, which was partly independent from religion, politics, age, education, agency detection, analytical thinking and perception of randomness. As a resilient 'default' component of early cognition, teleological thinking is thus associated with creationist as well as conspiracist beliefs, which both entail the distant and hidden involvement of a purposeful and final cause to explain complex worldly events.

Compression in Working Memory and Its Relationship With Fluid Intelligence.

Working memory has been shown to be strongly related to fluid intelligence; however, our goal is to shed further light on the process of information compression in working memory as a determining factor of fluid intelligence. Our main hypothesis was that compression in working memory is an excellent indicator for studying the relationship between working-memory capacity and fluid intelligence because both depend on the optimization of storage capacity. Compressibility of memoranda was estimated using an algorithmic complexity metric. The results showed that compressibility can be used to predict working-memory performance and that fluid intelligence is well predicted by the ability to compress information. We conclude that the ability to compress information in working memory is the reason why both manipulation and retention of information are linked to intelligence. This result offers a new concept of intelligence based on the idea that compression and intelligence are equivalent problems.

A preference for some types of complexity comment on "perceived beauty of random texture patterns: A preference for complexity".

In two experiments, Friedenberg and Liby (2016) studied how a diversity of complexity estimates such as density, number of blocks, GIF compression rate and edge length impact the perception of beauty of semi-random two-dimensional patterns. They concluded that aesthetics ratings are positively linked with GIF compression metrics and edge length, but not with the number of blocks. They also found an inverse U-shaped link between aesthetic judgments and density. These mixed results originate in the variety of metrics used to estimate what is loosely called "complexity" in psychology and indeed refers to conflicting notions. Here, we reanalyze their data adding two more conventional and normative mathematical measures of complexity: entropy and algorithmic complexity. We show that their results can be interpreted as an aesthetic preference for low redundancy, balanced patterns and "crooked" figures, but not for high algorithmic complexity. We conclude that participants tend to have a preference for some types of complexity, but not for all. These findings may help understand divergent results in the study of perceived beauty and complexity, and illustrate the need to specify the notion of complexity used in psychology. The field would certainly benefit from a precise taxonomy of complexity measures.

Generalized Benford's Law as a Lie Detector.

The first significant (leftmost nonzero) digit of seemingly random numbers often appears to conform to a logarithmic distribution, with more 1s than 2s, more 2s than 3s, and so forth, a phenomenon known as Benford's law. When humans try to produce random numbers, they often fail to conform to this distribution. This feature grounds the so-called Benford analysis, aiming at detecting fabricated data. A generalized Benford's law (GBL), extending the classical Benford's law, has been defined recently. In two studies, we provide some empirical support for the generalized Benford analysis, broadening the classical Benford analysis. We also conclude that familiarity with the numerical domain involved as well as cognitive effort only have a mild effect on the method's accuracy and can hardly explain the positive results provided here.

Developmental Abilities to Form Chunks in Immediate Memory and Its Non-Relationship to Span Development.

Both adults and children -by the time they are 2-3 years old- have a general ability to recode information to increase memory efficiency. This paper aims to evaluate the ability of untrained children aged 6-10 years old to deploy such a recoding process in immediate memory. A large sample of 374 children were given a task of immediate serial report based on SIMON®, a classic memory game made of four colored buttons (red, green, yellow, blue) requiring players to reproduce a sequence of colors within which repetitions eventually occur. It was hypothesized that a primitive ability across all ages (since theoretically already available in toddlers) to detect redundancies allows the span to increase whenever information can be recoded on the fly. The chunkable condition prompted the formation of chunks based on the perceived structure of color repetition within to-be-recalled sequences of colors. Our result shows a similar linear improvement of memory span with age for both chunkable and non-chunkable conditions. The amount of information retained in immediate memory systematically increased for the groupable sequences across all age groups, independently of the average age-group span that was measured on sequences that contained fewer repetitions. This result shows that chunking gives young children an equal benefit as older children. We discuss the role of recoding in the expansion of capacity in immediate memory and the potential role of data compression in the formation of chunks in long-term memory.

Structure emerges faster during cultural transmission in children than in adults.

How does children's limited processing capacity affect cultural transmission of complex information? We show that over the course of iterated reproduction of two-dimensional random dot patterns transmission accuracy increased to a similar extent in 5- to 8-year-old children and adults whereas algorithmic complexity decreased faster in children. Thus, children require more structure to render complex inputs learnable. In line with the Less-Is-More hypothesis, we interpret this as evidence that children's processing limitations affecting working memory capacity and executive control constrain the ability to represent and generate complexity, which, in turn, facilitates emergence of structure. This underscores the importance of investigating the role of children in the transmission of complex cultural traits.

The equiprobability bias from a mathematical and psychological perspective.

The equiprobability bias (EB) is a tendency to believe that every process in which randomness is involved corresponds to a fair distribution, with equal probabilities for any possible outcome. The EB is known to affect both children and adults, and to increase with probability education. Because it results in probability errors resistant to pedagogical interventions, it has been described as a deep misconception about randomness: the erroneous belief that randomness implies uniformity. In the present paper, we show that the EB is actually not the result of a conceptual error about the definition of randomness. On the contrary, the mathematical theory of randomness does imply uniformity. However, the EB is still a bias, because people tend to assume uniformity even in the case of events that are not random. The pervasiveness of the EB reveals a paradox: The combination of random processes is not necessarily random. The link between the EB and this paradox is discussed, and suggestions are made regarding educational design to overcome difficulties encountered by students as a consequence of the EB.

Calculating Kolmogorov complexity from the output frequency distributions of small Turing machines.

Drawing on various notions from theoretical computer science, we present a novel numerical approach, motivated by the notion of algorithmic probability, to the problem of approximating the Kolmogorov-Chaitin complexity of short strings. The method is an alternative to the traditional lossless compression algorithms, which it may complement, the two being serviceable for different string lengths. We provide a thorough analysis for all Σ(n=1)(11) 2(n) binary strings of length n<12 and for most strings of length 12≤n≤16 by running all ~2.5 x 10(13) Turing machines with 5 states and 2 symbols (8 x 22(9) with reduction techniques) using the most standard formalism of Turing machines, used in for example the Busy Beaver problem. We address the question of stability and error estimation, the sensitivity of the continued application of the method for wider coverage and better accuracy, and provide statistical evidence suggesting robustness. As with compression algorithms, this work promises to deliver a range of applications, and to provide insight into the question of complexity calculation of finite (and short) strings. Additional material can be found at the Algorithmic Nature Group website at http://www.algorithmicnature.org. An Online Algorithmic Complexity Calculator implementing this technique and making the data available to the research community is accessible at http://www.complexitycalculator.com.