162,458 research outputs found

    Influence of Cognitive Functioning on Age-Related Performance Declines in Visuospatial Sequence Learning

    Get PDF
    Objectives: The aim of this study was to investigate how age-related performance differences in a visuospatial sequence learning task relate to age-related declines in cognitive functioning. Method: Cognitive functioning of 18 younger and 18 older participants was assessed using a standardized test battery. Participants then undertook a perceptual visuospatial sequence learning task. Various relationships between sequence learning and participants’ cognitive functioning were examined through correlation and factor analysis. Results: Older participants exhibited significantly lower performance than their younger counterparts in the sequence learning task as well as in multiple cognitive functions. Factor analysis revealed two independent subsets of cognitive functions associated with performance in the sequence learning task, related to either the processing and storage of sequence information (first subset) or problem solving (second subset). Age-related declines were only found for the first subset of cognitive functions, which also explained a significant degree of the performance differences in the sequence learning task between age-groups. Discussion: The results suggest that age-related performance differences in perceptual visuospatial sequence learning can be explained by declines in the ability to process and store sequence information in older adults, while a set of cognitive functions related to problem solving mediates performance differences independent of age

    Normative computations, uncertainty biases, and lifespan differences

    Get PDF
    Learning often takes place in environments with uncertainty about current and future outcomes. To behave adaptively in these circumstances, people need to learn beliefs from past experiences, based on which they can predict future outcomes. In my dissertation, I examine: (1) Normative computations that should determine learning under uncertainty. (2) Uncertainty biases that lead to deviations from normative learning. (3) Age-related differences in learning under uncertainty that are characteristic across the lifespan. Here, the term normative computations from the field of computational neuroscience refers to computations that provide an optimal solution to a learning and decision-making problem. My dissertation studies draw on computational models that implement normative computations and formally define uncertainty. Based on these models, the studies systematically investigated to what degree younger adults and people across the lifespan consider uncertainty when learning from their experiences. I begin by illustrating that adaptive behavior consists of several related steps, including a representation of the environment, decision making, and learning (Introduction). Based on this, I present a framework that decomposes uncertainty into three forms: perceptual uncertainty, expected uncertainty, and unexpected uncertainty (Normative computations). Perceptual uncertainty is related to sensory information processing, expected uncertainty arises from outcome variability, and unexpected uncertainty is the consequence of changes in the environment. For each form, I describe how individuals should learn under uncertainty based on normative computations. I then show that biases, that is, deviations from a normative consideration of uncertainty, are characteristic of human learning behavior (Uncertainty biases). Finally, I motivate why capturing these biases in computational models of cognition can improve our understanding of age-related lifespan differences in learning under uncertainty (Lifespan differences). The first dissertation study (Bruckner et al., 2020a) examined which normative computations should guide learning under perceptual uncertainty, to which degree humans regulate learning accordingly, and how past perceptual choices bias this process. The second study (Nassar et al., 2016) investigated expected and unexpected uncertainty in younger and older adults, particularly how biases in the consideration of uncertainty explain age-related learning differences. The third study (Bruckner et al., 2020b) built upon this and examined the role of simplified learning strategies across the lifespan. Finally, the fourth study (Van den Bos et al., 2018) was an opinion paper on how applying computational cognitive models advances our understanding of age-related lifespan differences in learning and decision making. In the following, I briefly summarize the results of the dissertation studies mentioned above. In Bruckner et al. (2020a), we showed that perceptual uncertainty often corrupts learning because of misinterpreted perceptual information. Learning behavior under perceptual uncertainty should be more cautious than in perceptually clear situations to avoid such misinterpretations. We found that humans consider perceptual uncertainty during learning. However, we also identified learning biases driven by previous perceptual choices, which led to a less cautious regulation of learning. In Nassar et al. (2016), our results suggested that age-related learning differences are related to the adjustment to expected uncertainty. In particular, we found that older adults (60 to 80 years) exhibit a bias to underestimate uncertainty about their beliefs compared to younger adults (20 to 30 years). This form of uncertainty underestimation leads to less flexible learning behavior compared to younger adults. In Bruckner et al. (2020b), we found that age-related impairments in learning under uncertainty often arise because children (7 to 11 years) and older adults resort to simplified learning strategies that lead to more repetitive responding (perseveration) and stronger environmental influences on behavior (environmental control) compared to younger adults. Finally, in Van den Bos et al. (2018), we argued that computational cognitive models are an essential tool to gain a better understanding of age-related learning and decision-making differences. In particular, we illustrated both promises of the application of computational models to study age-related behavioral differences (concerning risk-taking, strategy selection, and reinforcement learning) and potential pitfalls. After discussing the implications of these studies (General discussion and future directions), I propose a cognitive model of learning under uncertainty based on the new insights of my studies and previous work in the literature (Uncertainty in the cycle of adaptive behavior). In summary, the dissertation highlights that learning is a dynamic process that is influenced by multiple forms of uncertainty. People take uncertainty into account during learning but show inherent uncertainty biases that substantially change across the lifespan.Adaptives Verhalten verlangt eine ständige Verarbeitung von neuen Ereignissen sowie eine Reaktion auf diese. In der Psychologie und den Neurowissenschaften wird dies als Lern- und Entscheidungsprozess bezeichnet. Solche Prozesse finden in der Regel in Situationen statt, in denen Unsicherheit über aktuelle und zukünftige Ereignisse herrscht. Um sich in derartigen Situationen erfolgreich zurechtfinden zu können, muss man aus den Erfahrungen der Vergangenheit Vorhersagen über zukünftige Ereignisse ableiten. Die Dissertation behandelt folgende Themen: (1) Normative Berechnungen, die dem Lernen unter Unsicherheit zugrunde liegen sollten. (2) Verzerrungen, die bei der Berücksichtigung von Unsicherheit zu Abweichungen vom normativen Lernen führen. (3) Altersrelatierte Unterschiede über die Lebensspanne, die beim Lernen unter Unsicherheit charakteristisch sind. Der Begriff normative Berechnungen aus dem Forschungsfeld Computational Neuroscience bezieht sich in dieser Dissertation auf Berechnungen, die zu einer optimalen Lösung eines Lern- und Entscheidungsproblems führen. Meine Dissertationsstudien basieren auf Computermodellen, die normative Berechnungen implementieren und Unsicherheit formal definieren. Anhand dieser Modelle wird systematisch untersucht, inwieweit Menschen im jüngeren Erwachsenenalter und über die Lebensspanne Unsicherheit berücksichtigen, um aus ihren Erfahrungen zu lernen. Zu Beginn der Dissertation wird demonstriert, dass adaptives Verhalten aus mehreren Schritten besteht, von der Repräsentation der Umgebung über die Entscheidungsfindung bis hin zu Lernprozessen (Introduction). Auf dieser Grundlage stelle ich zunächst ein Modell vor, das Unsicherheit in drei Formen unterteilt: Perzeptuelle Unsicherheit, erwartete Unsicherheit und unerwartete Unsicherheit (Normative computations). Perzeptuelle Unsicherheit hängt mit der Verarbeitung sensorischer Informationen zusammen, erwartete Unsicherheit ergibt sich aus der Variabilität von Ereignissen und unerwartete Unsicherheit ist die Folge von Veränderungen in der Umgebung. Für jede dieser drei Formen beschreibe ich, wie Unsicherheit beim Lernen aufgrund von normativen Berechnungen berücksichtigt werden sollte. Danach zeige ich, dass Verzerrungen, also Abweichungen von den normativen Berechnungen, durch die man sich an Unsicherheit anpasst, charakteristisch für menschliches Lernen sind (Uncertainty biases). Abschließend erfolgt eine Darstellung, die verdeutlicht, warum die Erfassung dieser Verzerrungen mit Computermodellen nützlich ist, um altersrelatierte Unterschiede über die Lebensspanne beim Lernen unter Unsicherheit besser verstehen zu können (Lifespan differences). In der ersten Dissertationsstudie (Bruckner et al., 2020a) wurde untersucht, welche normativen Berechnungen beim Lernen unter perzeptueller Unsicherheit wichtig sind, in welchem Maße jüngere Erwachsene dementsprechend lernen und wie dieser Prozess durch vorherige perzeptuelle Entscheidungen verzerrt wird. In der zweiten Studie (Nassar et al., 2016) wurde Lernen unter erwarteter und unerwarteter Unsicherheit bei jüngeren und älteren Erwachsenen untersucht. Insbesondere wurde hier erforscht, inwiefern Verzerrungen bei der Berücksichtigung dieser Unsicherheiten altersrelatierte Lernunterschiede erklären. Die dritte Studie (Bruckner et al., 2020b) hat darauf aufgebaut und speziell bei Kindern und älteren Erwachsenen untersucht, inwiefern sie auf vereinfachte Lernstrategien zurückgreifen und auf normative Berechnungen verzichten. Die vierte Studie (Van den Bos et al., 2018) hat schließlich beschrieben, wie Computermodelle die Erforschung altersrelatierter Lernunterschiede über die Lebensspanne unterstützen können. Die Ergebnisse der oben genannten Studien werden im Folgenden kurz zusammengefasst. In Bruckner et al. (2020a) konnten wir zeigen, dass perzeptuelle Unsicherheit beim Lernen zu vorschnellen Schlussfolgerungen auf Basis von Fehlinterpretationen einer Wahrnehmung führen kann. Um vorschnelle Schlussfolgerungen zu vermeiden, sollte man sich beim Lernen unter perzeptueller Unsicherheit vorsichtiger verhalten als in perzeptuell eindeutigen Situationen. Wir fanden in dieser Studie heraus, dass Menschen perzeptuelle Unsicherheit beim Lernen berücksichtigen. Zusätzlich stellten wir allerdings eine Verzerrung bei der Berücksichtigung perzeptueller Unsicherheit aufgrund von früheren perzeptuellen Entscheidungen beim Lernen fest, die wiederum zu einer weniger vorsichtigen Anpassung des Lernverhaltens führt. In Nassar et al. (2016) fanden wir Hinweise darauf, dass altersrelatierte Lernunterschiede mit Verzerrungen bei der Anpassung an erwartete Unsicherheit zusammenhängen. Insbesondere stellten wir fest, dass ältere Erwachsene (60 bis 80 Jahre) dazu neigen, die Unsicherheit über ihre Erwartungen im Vergleich zu jüngeren Erwachsenen (20 bis 30 Jahre) zu unterschätzen. Diese Form der Unsicherheitsunterschätzung führt zu einem weniger flexiblen Lernverhalten im Vergleich zu jüngeren Erwachsenen. In Bruckner et al. (2020b) wurde gezeigt, dass altersrelatierte Unterschiede beim Lernen unter Unsicherheit damit zusammenhängen, dass Kinder (7 bis 11 Jahre) und ältere Erwachsene häufig auf vereinfachte Lernstrategien zurückgreifen, was dazu führt, dass Verhalten wiederholt (Perseveration) oder stärker durch die Umgebung beeinflusst wird (externe Kontrolle). Abschließend wurde in Van den Bos et al. (2018) argumentiert, dass Computermodellierung eine wichtige Methode ist, um altersrelatierte Unterschiede beim Lernen und in der Entscheidungsfindung besser zu verstehen. Hier wurden sowohl die Vorteile der Anwendung von Computermodellen zur Erforschung altersrelatierter Verhaltensunterschiede (in Bezug auf Risikobereitschaft, Strategieauswahl und Verstärkungslernen) als auch potenzielle Fallstricke aufgezeigt. Nach der Diskussion der Dissertationsprojekte (General discussion and future directions) stelle ich ein kognitives Modell zum Lernen unter Unsicherheit vor, das auf den neuen Erkenntnissen meiner Studien und früheren Arbeiten aus der Literatur basiert (Uncertainty in the cycle of adaptive behavior). Zusammenfassend legt meine Dissertation dar, dass Lernen ein dynamischer Prozess ist, der von vielfältigen Formen der Unsicherheit beeinflusst wird. Menschen berücksichtigen ihre Unsicherheit beim Lernen, weisen aber charakteristische Unsicherheitsverzerrungen auf, die sich im Laufe der Lebensspanne erheblich verändern

    Modeling age-related differences in immediate memory using SIMPLE

    Get PDF
    In the SIMPLE model (Scale Invariant Memory and Perceptual Learning), performance on memory tasks is determined by the locations of items in multidimensional space, and better performance is associated with having fewer close neighbors. Unlike most previous simulations with SIMPLE, the ones reported here used measured, rather than assumed, dimensional values. The data to be modeled come from an experiment in which younger and older adults recalled lists of acoustically confusable and nonconfusable items. A multidimensional scaling solution based on the memory confusions was obtained. SIMPLE accounted for the overall difference in performance both between the two age groups and, within each age group, the overall difference between acoustically confusable and nonconfusable items in terms of the MDS coordinates. Moreover, the model accounted for the serial position functions and error gradients. Finally, the generality of the model’s account was examined by fitting data from an already published study. The data and the modeling support the hypothesis that older adults’ memory may be worse, in part, because of altered representations due to age-related auditory perceptual deficits

    Beyond naming patterns in children with WFDs: definitions for nouns and verbs

    Get PDF
    Children who experience difficulties in naming are described as having word finding difficulties (WFDs). In the present study 31 children with WFDs were identified through a wider survey of educational provision for those with language and communication difficulties. The children were included if they were between 6;4-7;10 years, had normal non-verbal intelligence, no major articulation difficulties and had WFDs as diagnosed by standard scores below 75 on Test of Word Finding Difficulties (TWF, German, 1989). Three control groups were identified who were matched on: chronological age (N = 31), naming age (N = 31) and level of receptive grammar (N = 31). Children?s accuracy of naming and latency to name were assessed for pictures of objects and actions. Children were asked to define the object and action terms at a later point. Children with WFDs were significantly less accurate in naming than their age matched peers but equivalent to that of the language matched peers. The group of children with WFDs were the slowest to accurately name all sets of items. All groups of children were less accurate in the provision of definitions for action terms than object terms. Overall the children with WFDs provided fewer accurate definitions than their chronological age matched peers. The nature of the children?s definitions indicated that they also differed from their language-matched peers. Particular difficulties were noted in the provision of semantic categorisation information. A range of standardised language assessments did not account for these difficulties. The findings are discussed in relation to the idea that WFDs are caused by impoverished semantic representations

    Social re-orientation and brain development: An expanded and updated view.

    Get PDF
    Social development has been the focus of a great deal of neuroscience based research over the past decade. In this review, we focus on providing a framework for understanding how changes in facets of social development may correspond with changes in brain function. We argue that (1) distinct phases of social behavior emerge based on whether the organizing social force is the mother, peer play, peer integration, or romantic intimacy; (2) each phase is marked by a high degree of affect-driven motivation that elicits a distinct response in subcortical structures; (3) activity generated by these structures interacts with circuits in prefrontal cortex that guide executive functions, and occipital and temporal lobe circuits, which generate specific sensory and perceptual social representations. We propose that the direction, magnitude and duration of interaction among these affective, executive, and perceptual systems may relate to distinct sensitive periods across development that contribute to establishing long-term patterns of brain function and behavior

    Training methods for facial image comparison: a literature review

    Get PDF
    This literature review was commissioned to explore the psychological literature relating to facial image comparison with a particular emphasis on whether individuals can be trained to improve performance on this task. Surprisingly few studies have addressed this question directly. As a consequence, this review has been extended to cover training of face recognition and training of different kinds of perceptual comparisons where we are of the opinion that the methodologies or findings of such studies are informative. The majority of studies of face processing have examined face recognition, which relies heavily on memory. This may be memory for a face that was learned recently (e.g. minutes or hours previously) or for a face learned longer ago, perhaps after many exposures (e.g. friends, family members, celebrities). Successful face recognition, irrespective of the type of face, relies on the ability to retrieve the to-berecognised face from long-term memory. This memory is then compared to the physically present image to reach a recognition decision. In contrast, in face matching task two physical representations of a face (live, photographs, movies) are compared and so long-term memory is not involved. Because the comparison is between two present stimuli rather than between a present stimulus and a memory, one might expect that face matching, even if not an easy task, would be easier to do and easier to learn than face recognition. In support of this, there is evidence that judgment tasks where a presented stimulus must be judged by a remembered standard are generally more cognitively demanding than judgments that require comparing two presented stimuli Davies & Parasuraman, 1982; Parasuraman & Davies, 1977; Warm and Dember, 1998). Is there enough overlap between face recognition and matching that it is useful to look at the literature recognition? No study has directly compared face recognition and face matching, so we turn to research in which people decided whether two non-face stimuli were the same or different. In these studies, accuracy of comparison is not always better when the comparator is present than when it is remembered. Further, all perceptual factors that were found to affect comparisons of simultaneously presented objects also affected comparisons of successively presented objects in qualitatively the same way. Those studies involved judgments about colour (Newhall, Burnham & Clark, 1957; Romero, Hita & Del Barco, 1986), and shape (Larsen, McIlhagga & Bundesen, 1999; Lawson, Bülthoff & Dumbell, 2003; Quinlan, 1995). Although one must be cautious in generalising from studies of object processing to studies of face processing (see, e.g., section comparing face processing to object processing), from these kinds of studies there is no evidence to suggest that there are qualitative differences in the perceptual aspects of how recognition and matching are done. As a result, this review will include studies of face recognition skill as well as face matching skill. The distinction between face recognition involving memory and face matching not involving memory is clouded in many recognition studies which require observers to decide which of many presented faces matches a remembered face (e.g., eyewitness studies). And of course there are other forensic face-matching tasks that will require comparison to both presented and remembered comparators (e.g., deciding whether any person in a video showing a crowd is the target person). For this reason, too, we choose to include studies of face recognition as well as face matching in our revie

    Individual variability in the perceptual learning of L2 speech sounds and its cognitive correlates

    Get PDF
    This study explored which cognitive processes are related to individual variability in the learning of novel phonemic contrasts in a second language. 25 English participants were trained to perceive a Korean stop voicing contrast which is novel for English speakers. They were also presented with a large battery of tests which investigated different aspects of their perceptual and cognitive abilities, as well as pre- and posttraining tests of their ability to discriminate this novel consonant contrast. The battery included: adaptive psychoacoustic tasks to determine frequency limens, a paired-association task looking at the ability to memorise the pairing of two items, a backward digit span task measuring working memory span, a sentence perception in noise task that quantifies the effect of top-down information as well as signal detection ability, a sorting task investigating the attentional filtering of the key acoustic features. The general measures that were the most often correlated with the ability to learn the novel phonetic contrast were measures of attentional switching (i.e. the ability to reallocate attention), the ability to sort stimuli according to a particular dimension, which is also somewhat linked to allocation of attention, frequency acuity and the ability to associate two unrelated events

    Perceptual impairment in face identification with poor sleep

    Get PDF
    Previous studies have shown impaired memory for faces following restricted sleep. However, it is not known whether lack of sleep impairs performance on face identification tasks that do not rely on recognition memory, despite these tasks being more prevalent in security and forensic professions—for example, in photo-ID checks at national borders. Here we tested whether poor sleep affects accuracy on a standard test of face-matching ability that does not place demands on memory: the Glasgow Face-Matching Task (GFMT). In Experiment 1, participants who reported sleep disturbance consistent with insomnia disorder show impaired accuracy on the GFMT when compared with participants reporting normal sleep behaviour. In Experiment 2, we then used a sleep diary method to compare GFMT accuracy in a control group to participants reporting poor sleep on three consecutive nights—and again found lower accuracy scores in the short sleep group. In both experiments, reduced face-matching accuracy in those with poorer sleep was not associated with lower confidence in their decisions, carrying implications for occupational settings where identification errors made with high confidence can have serious outcomes. These results suggest that sleep-related impairments in face memory reflect difficulties in perceptual encoding of identity, and point towards metacognitive impairment in face matching following poor sleep

    Perceptual grouping abilities in individuals with Autism Spectrum Disorder: exploring patterns of ability in relation to grouping type and levels of development

    Get PDF
    This study further investigates findings of impairment in Gestalt, but not global processing in Autism Spectrum Disorder (ASD) [Brosnan, Scott, Fox, & Pye, 2004]. Nineteen males with ASD and nineteen typically developing (TD) males matched by nonverbal ability, took part in five Gestalt perceptual grouping tasks. Results showed that performance differed according to grouping type. The ASD group showed typical performance for grouping by proximity and by alignment, impairment on low difficulty trials for orientation and luminance similarity, and general impairment for grouping by shape similarity. Group differences were also observed developmentally; for the ASD group, with the exception of grouping by shape similarity, perceptual grouping performance was poorer at lower than higher levels of nonverbal ability. In contrast, no developmental progression was observed in the TD controls
    corecore