Author(s): Calabrich, Simone L; Oppenheim, Gary M.; Jones, Manon W | Abstract: When learning to bind visual symbols to sounds, to what extent do beginning readers track seemingly irrelevant information such as a symbol’s position within a visual display? In this study, we used adult typical readers’ own webcams to track their eye movements during a paired associate learning task that arbitrarily bound unfamiliar characters with monosyllabic pseudowords. Overall, participants’ error rate in recognition (Phase 1) decreased as a function of exposure, but was not modulated by the episodic memory-based effect of ‘looking-at-nothing’. Moreover, participants’ lowest error rate in both recognition and recall (Phases 1 and 2) was associated with item consistency across multiple exposures, in terms of spatial and contextual properties (i.e., stimulus’ screen location and co-occurrences with specific distractor items during encoding). Taken together, our findings suggest that normally developing readers extract statistical regularities in the input during visual-phonological associative learning, leading to rapid acquisition of these pre-orthographic representations.
Author(s): Ma, Ili; Ma, Wei Ji; Gureckis, Todd M | Abstract: From navigation in unfamiliar environments to career planning, people typically first sample information before committing to a plan. However, most studies find that people adopt myopic strategies when sampling information. Here we challenge those findings by investigating whether contingency planning is a driver of information sampling. To this aim, we developed a novel navigation task that is a shortest path finding problem under uncertainty of bridge closures. Participants (n = 109) were allowed to sample information on bridge statuses prior to committing to a path. We developed a computational model in which the agent samples information based on the cost of switching to a contingency plan. We find that this model fits human behavior well and is qualitatively similar to the approximated optimal solution. Together, this suggests that humans use contingency planning as a driver of information sampling.
Although orangutans are closely related to humans, very little is known about their ontogenetic development. In particular, there is a lack of systematic research on the maternal behaviors that mediate skill development in early infancy. To address this topic, we conducted a longitudinal study in which a Sumatran orangutan (Pongo abelii) mother-infant dyad was systematically observed across 28 months, starting with the infant’s birth. Our data revealed several classes of maternal behavior that potentially influenced infant skill development. The timing of these behaviors was contingent upon infant competence level, as active interventions were intense during periods of skill acquisition. The same behaviors were flexibly deployed independent of whether the infant was in the process of acquiring foraging, locomotor or social skills. Our findings suggest that the maternal behaviors that mediate infant skill development in Sumatran orangutans have features reminiscent of human scaffolding, and raise questions about intentionality in such behaviors
Author(s): Bruijns, Sebastian; Laboratory, International Brain; Dayan, Peter | Abstract: Learning the contingencies of a complex experiment is hard, and animals likely revise their strategies multiple times during the process. Individuals learn in an idiosyncratic manner and may even end up with different asymptotic strategies. Modeling such long-run acquisition requires a flexible and extensible structure which can capture radically new behaviours as well as slow changes in existing ones. To this end, we suggest a dynamic input-output infinite hidden Markov model whose latent states capture behaviours. We fit this model to data collected from mice who learnt a contrast detection task over tens of sessions and thousands of trials. Different stages of learning are quantified via the number and psychometric nature of prevalent behavioural states. Our model indicates that initial learning proceeds via drastic changes in behavior (i.e. new states), whereas later learning consists of adaptations to existing states, even if the task structure changes notably at this time.
Author(s): Brandle, Franziska; Allen, Kelsey R; Tenenbaum, Josh; Schulz, Eric | Abstract: Over the last decades, games have become one of the most popular recreational activities, not only among children but also among adults. Consequently, they have also gained popularity as an avenue for studying cognition. Games offer several advantages, such as the possibility to gather big data sets, engage participants to play for a long time, and better resemblance of real world complexities. In this workshop, we will bring together leading researchers from across the cognitive sciences to explore how games can be used to study diverse aspects of intelligent behavior, explore their differences compared to classical lab experiments, and discuss the future of game-based cognitive science research.
Author(s): Elteto, Noemi; Nemeth, Dezso; Janacsek, Karolina; Dayan, Peter | Abstract: Long perceptuo-motor sequences underlie skills from walking to language learning, and are often learned gradually and unconsciously in the face of noise. We used a non-parametric Bayesian n-gram model (Teh, 2006) to characterize the multi-day evolution of human subjects’ implicit representation of a serial reaction time task sequence with second-order contingencies. The reaction time for an element in the sequence depended on zero, one and more preceding elements at the same time, predicting frequency, repetition and higher-order learning effects. Our trial-level dynamic model captured these coexistent facilitation effects by seamlessly combining information from shorter and longer windows onto past events. We show how shifting their priors over window lengths allowed subjects to grow and refine their internal sequence representations week by week.
Author(s): Haridi, Susanne; Wu, Charley M; Dasgupta, Ishita; Schulz, Eric | Abstract: Human cognition can tackle a wide range of problems, producing scalable results within a manageable timeframe. Many cognitive models can predict human behavior but lack such scalability, resulting in rapidly increasing processing times for more complex inputs. We present a task where participants mentally sort sequences of rectangles by size while we measure reaction times (RTs) and accuracy. By manipulating the size of the input and the presence of latent structure in the sequences, we investigate i) how mental sorting scales with input complexity, ii) how latent structure influences scaling, and iii) how mental computations can be captured by plausible cognitive models. Our results reveal RTs scale linearly with sequence length, and participants can learn and actively use latent structure to sort faster. This behavior is in line with a noisy sorting algorithm, which sequentially rules out potential hypotheses about the latent structure, thus reducing complexity while retaining accuracy.
People often test changes to see if the change is producing the desired result (e.g., does taking an antidepressant improve my mood, or does keeping to a consistent schedule reduce a child’s tantrums?). Despite the prevalence of such decisions in everyday life, it is unknown how well people can assess whether the change has influenced the result. According to interrupted time series analysis (ITSA), doing so involves assessing whether there has been a change to the mean (‘level’) or slope of the outcome, after versus before the change. Making this assessment could be hard for multiple reasons. First, people may have difficulty understanding the need to control the slope prior to the change. Additionally, one may need to remember events that occurred prior to the change, which may be a long time ago. In Experiments 1 and 2, we tested how well people can judge causality in 9 ITSA situations across 4 presentation formats in which participants were presented with the data simultaneously or in quick succession. We also explored individual differences. In Experiment 3, we tested how well people can judge causality when the events were spaced out once per day, mimicking a more realistic timeframe of how people make changes in their lives. We found that participants were able to learn accurate causal relations when there is a zero pre-intervention slope in the time series but had difficulty controlling for nonzero pre-intervention slopes. We discuss these results in terms of 2 heuristics that people might use.
Author(s): Hafri, Alon; Gleitman, Lila; Landau, Barbara; Trueswell, John | Abstract: Symmetry is ubiquitous in nature, in logic and mathematics, and in perception, language, and thought. Although humans are exquisitely sensitive to visual symmetry (e.g., of a butterfly), linguistic symmetry goes far beyond visuospatial properties: Many words refer to abstract, logically symmetrical concepts (e.g., equal, marry). This raises a question: Do representations of symmetry correspond across language and vision, and if so, how? To address this question, we used a cross-modal paradigm. On each trial, adult participants observed a visual stimulus (either symmetrical or non-symmetrical) and had to choose between a symmetrical and non-symmetrical English predicate unrelated to the stimulus (e.g., negotiate vs. propose). In a first study with visual events (symmetrical collision or asymmetrical launch), participants reliably chose the predicate the event's symmetry. A second study showed that this matching generalized to static objects, and was weakened when the stimuli's binary-relational nature was made less apparent (i.e., one object with a symmetrical contour, rather than two symmetrically configured objects). Taken together, our findings support the existence of an abstract relational concept of symmetry which humans access via both perceptual and linguistic means. More broadly, this work sheds light on the rich, structured nature of the language-cognition interface, and points towards a possible avenue for acquisition of word-to-world mappings for the seemingly inaccessible logical symmetry of linguistic terms.
Adaptive generation of spacing intervals in learning using response times improves learning relative to both adaptive systems that do not use response times and fixed spacing schemes (Mettler, Massey & Kellman, 2016). Studies have often used limited presentations (e.g., 4) of each learning item. Does adaptive practice benefit learning if items are presented until attainment of objective mastery criteria? Does it matter if mastered items drop out of the active learning set? We compared adaptive and non-adaptive spacing under conditions of mastery and dropout. Experiment 1 compared random presentation order with no dropout to adaptive spacing and mastery using the ARTS (Adaptive Response-time-based Sequencing) system. Adaptive spacing produced better retention than random presentation. Experiment 2 showed clear learning advantages for adaptive spacing compared to random schedules that also included dropout. Adaptive spacing performs better than random schedules of practice, including when learning proceeds to mastery and items drop out when mastered.
Individuals are readily able to extract and encode statistical information from their environment (or statistical learning). However, the bulk of the literature has primarily focused on conditional statistical learning (i.e. the ability to learn joint and conditional relationships between stimuli), and has largely neglected distributional statistical learning (i.e. the ability to learn the frequency and variability of distributions). In this paper, we investigate how and how well distributional learning can be measured by exploring the relationship between and psychometric properties of two measures: discrimination judgements and frequency estimates. Reliable performance was observed in both measures across two different distributional learning tasks (natural and artificial). Discrimination judgements and frequency estimates also significantly correlated with one another in both tasks, and performance on all tasks accounted for the majority of variance across tasks (55%). These results suggest that distributional learning can be measured reliably, and may tap into both the ability to discriminate between relative frequencies and to explicitly estimate them.
CogSci 2020: 42nd Annual Conference of the Cognitive Science Society (29 July - 1 August 2020)
CogSci 2020: 42nd Annual Conference of the Cognitive Science Society (29 July - 1 August 2020)
Internet of Vehicles (IoV) importance has been arisen to reduce the current road accidents and provide smarter, greener, and safer Intelligent Transport System (ITS). The wide deployment of 5G cellular network, supported by device-to-device (D2D) technology, can afford the infrastructure to enhance IoV.This paper investigates the resource assignment performance issues and proposes a Maximum Power Allocation algorithm (MAX-PA) for both Vehicles to Infrastructure (V2I) and Vehicles to Vehicles (V2V); where multiple V2V links can share the same channel with a V2I link. Furthermore, the proposed algorithm is provided to guarantee reliability for V2V links while maximizing the V2I links ergodic capacity. Finally, the MAX-PA algorithm performance is evaluated and the simulation results are presented and compared with other existing algorithms to show the enhancement.
