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Contemporary theories of causal induction have focussed largely on the question of how evidence in the form of covariations between causes and effects is used to compute measures of causal strength. A very important precursor enabling such computations is that the reasoner notices that a cause and effect have co-occurred. Standard laboratory experiments have usually bypassed this problem by presenting participants directly with covariational information. As a result, relatively little is known about how humans identify causal relations in real time. What evidence exists, however, paints a rather unflattering picture of human causal induction and converges to the conclusion that humans cannot identify causal relations if cause and effect are separated by more than a few seconds. Associative learning theory has interpreted these findings to indicate that temporal contiguity is essential to causal inference. I argue instead that contiguity is not essential, but that the influence of time in causal inference is crucially dependent on people's beliefs and expectations about the timeframe of the causal relation in question. First I demonstrate that humans are capable of dissociating temporal contiguity from causal strength; more specifically, they can learn that a given event exerts a stronger causal influence when it is temporally separated from the effect than when it is contiguous with it. Then I re-investigate a paradigm commonly used to study the effects of delay on human causal induction. My experiments employed one crucial additional manipulation regarding participants' awareness of potential delays. This manipulation was sufficient to reduce the detrimental effects of delay. Three other experiments employed a similar strategy, but relied on implicit instructions about the timeframe of the causal relation in question. Overall, results support the hypothesis that knowledge mediates the timeframe of covariation assessment in human causal induction. Implications for associative learning and causal power theories are discussed.

The contextual cueing effect (CCE) is a phenomenon that shows that our brains can take advantage of invariant contextual information in our environment to help us locate targets or relevant information more efficiently. In a seminal study by Chun and Jiang (1998), participants searched for a target letter “T” among “L” distractors. Unbeknownst to the participants, some trials had repeated configurations, while others had novel ones. Participants found the “T” faster in repeated configurations, showing implicit learning. Classical studies demonstrated learning of only single context-target pairing. However, recent research (Wang et al., 2020) shows that learning could also happen for repeated contexts paired with one of multiple (e.g., 4) target locations. In the current study, we intended to examine such learning at the individual scene level by producing matching target eccentricity between a pair of repeated and novel scenes. We varied the magnitude of four target eccentricities by producing equal spacing (in Experiment 1) or variable spacing (in Experiments 2 and 3) of both repeated and novel scenes. Experiment 1 showed comparable learning for different target locations with different eccentricities except for targets with the smallest eccentricity. In Experiment 2, we compared conditions with targets concentrated on the larger versus smaller eccentricity range in a between-subject design, and we found that at least when the target appeared in a large eccentricity, CCE was larger when the target appeared in the distribution condition with larger eccentricity bias than distribution with low eccentricity bias. However, this trend appeared present even in the classical contextual cueing paradigm with one target paired with one repeated context. In Experiment 3, we performed the same manipulation of eccentricity distribution in the classical contextual cueing paradigm and found the effect seen in Experiment 2 was not robust. These results suggest that when a given target could be paired with multiple repeated contexts, the learning of target-context association is more flexible and can be modulated by the target's location probability. / Thesis / Master of Science (MSc) / Through our daily interactions with the environment, we learn consistent relations between objects. For example, in a classroom with a fixed seating arrangement, the teacher can find a particular student with greater ease after repeated exposures. This type of learning can be demonstrated in laboratory settings through the contextual cueing effect (CCE). When participants encounter repeated search displays, they locate targets faster than if the display was novel. This learning occurs implicitly. Even when the repeated context is paired with multiple possible target locations learning still occurs (Wang et al., 2020). The present studies investigate this multiple target location–context pairing using a paradigm similar to that used by Wang et al.. We ask whether each target location is learned equally well when multiple targets are paired with repeated contexts, and explore if location probability cueing influences the CCE. Our results suggest that participants can learn multiple target locations equally well and may also learn the target's location probability. Humans interact with their environment daily, learning consistent relationships between objects. For example, in a classroom with a fixed seating arrangement, finding a particular student becomes easier. This type of learning is known as the contextual cueing effect (CCE) in laboratory settings. When participants encounter repeated search displays, they locate targets faster than in novel ones, and this learning occurs implicitly. Recent findings (Wang et al., 2020) show that learning also occurs when a repeated context is paired with one of multiple possible targets, as long as those targets pair with other repeated contexts. The goals of the current studies are to investigate, in the paradigm used by Wang et al. 2020, whether each target location is learned equally well when multiple targets are paired with repeated contexts and to determine if location probability cueing can influence CCE. Our results suggest that participants can learn target locations equally well and may also learn the target's location probability.

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No description available.

The central purpose of this study is to ascertain the function of immediate verbal feedback after each response on learning a paired associative task with normal and retarded subjects.

<p>Human learning and memory relies on a broad network of neural substrates, and is sensitive to a range of environmental factors and behaviors. The present studies are designed to investigate the modulation of active exploration behaviors in humans. In the current work, we operationalize exploration in two ways: participants’ spatial navigation (using a computer mouse) in environments containing rewarding and informative stimuli, and participants’ eyegaze activity while viewing images on a computer screen. The study described in Study 1 investigates the relationship between spatial exploration and reward, using participants’ reported anxiety levels to predict between-subject variability in vigor and information-seeking. The study described in Study 2 investigates the relationship between cue-outcome predictive validity and eyegaze behavior during learning; additionally, we test the extent to which differing states of expectation drive differences in eyegaze behavior to novel images. The study described in Study 3 expands on the questions raised in Study 2: using functional imaging and eyetracking, we investigate the relationship between predictive validity, gaze, and the neural systems supporting active exploration. Taken together, the findings in the present study suggest that emerging certainty in reward outcomes, rather than uncertainty, drives exploration and associative learning for events and their outcomes as well as encoding into long-term memory.</p> / Dissertation

Recent research has found that subjects learning a list of paired-associates under conditions requiring one-trial learning are capable of learning a list of paired items in as few a number of trials as subject learning similar lists of paired-associates under a condition using repetition.

We review some computational aspects of motor control. The problem of trajectory control is phrased in terms of an efficient representation of the operator connecting joint angles to joint torques. Efficient look-up table solutions of the inverse dynamics are related to some results on the decomposition of function of many variables. In a biological perspective, we emphasize the importance of the constraints coming from the properties of the biological hardware for determining the solution to the inverse dynamic problem.