Recent work has shown that the regulatory fit between global approach/avoidance goals and the local approach/avoidance mechanisms of goal pursuit influence cognition and behavior in predictable ways. A regulatory fit leads to an increase in motivation and engagement relative to a regulatory mismatch. The increase in engagement can lead to an increase in cognitive flexibility on cognitively demanding tasks. This work is composed of three inter-related studies that examine how the fit between global performance incentives and local mechanisms of goal pursuit influence decision-making and categorization. In Study 1 I examine how the interaction between global performance incentives and local goal pursuit mechanisms influences decision-making strategies in an experience-based decision-making paradigm. In this paradigm decision-making strategies can be classified as more exploratory or more exploitative. I find that participants in a regulatory fit would exhibit more exploratory decision-making patterns than participants in a regulatory mismatch. In Study 2 I examine how social pressure is related to approach and avoidance-based performance incentives using two types of category-learning tasks. I test the hypothesis that increasing performance pressure will induce an avoidance-based prevention focus which then interacts with the local mechanism of goal pursuit employed in the task (maximizing points gained or minimizing points lost). Participants either perform an explicit, rule-based category-learning task, or an implicit information-integration category-learning task. Behavioral and model-based analyses support the hypothesis that social pressure induces a prevention focus. When the pressure-induced prevention focus aligns with the local goal-pursuit mechanism participants perform better on the rule-based task, but worse on the information-integration task. Study 3 examines the effects of social pressure on categorization in highly-trained participants. Participants performed over 2500 training trials of either a rule-based or an information-integration category-learning task, and then performed another 640 trials after half received a manipulation designed to raise social pressure. Performance was worse on both the rule-based and information-integration task for participants who were under high social pressure compared to participants under low social pressure. The results from all three projects suggest that motivational incentives have a large effect on cognitively demanding tasks. / text

This thesis investigates the neurobiological pathways that underpin learning of visual categories, and the behaviour associated with these neural systems. The work contains two strands. The first assesses the neural and behavioural predictions of the COmpetition between Verbal and Implicit Systems (COVIS) account of category learning. The second aims to examine the brain regions implicated in the prototype effect after transcranial Direct Current Stimulation (tDCS) to the left dorsolateral prefrontal cortex (DLPFC). COVIS predicts there are separate explicit and implicit category learning systems. According to COVIS, the explicit system optimally learns rule-based (RB) categories and relies upon the frontal lobes for working memory (WM) and executive functioning processes, and the medial temporal lobes (MTL) to store decision boundaries. In contrast, the implicit system employs the basal ganglia to procedurally learn information-integration (II) categories through stimulus-response associations. Experiment 1 found little evidence of separable implicit or explicit systems in an fMRI study that investigated category decision making processes during RB and II category learning using conditions matched in difficulty, category separation and number of relevant stimulus dimensions. Contrary to the predictions of COVIS, the MTL was more active during the II condition compared to the RB condition, an area that should be more engaged by the explicit system. There was also extensive neural activation overlap found between RB and II learning. Experiments 2 and 3 aimed to generalise these neural findings to activation during feedback processing in RB and II conditions. Experiment 2 was a behavioural study which showed that adding a feedback delay necessary for fMRI data analysis did not differentially impact RB or II learning. Experiment 3, including this feedback delay, found the same neural pattern of results as Experiment 1 offering further support that the MTL is more engaged in II learning than RB learning. There was also again considerable overlap in the regions involved in the two tasks. Taken together, Experiments 1 to 3 found no evidence for the neurally dissociable category learning systems predicted by COVIS. Experiments 4, 5 and 6 investigated the behavioural dissociation reported by Smith et al. (2014) that deferring feedback to the end of a six trial block selectively impairs II learning compared to a unidimensional RB condition. Experiment 4 replicated this result. However, when equating the number of dimensions relevant for RB and II learning in Experiment 5, both conditions were hindered by deferring feedback, with Experiment 6 confirming that conjunctive RB learning was impaired by deferred feedback compared to immediate feedback. I concluded that the dissociation reported by Smith et al. is attributed to the use of a unidimensional category as a comparison for II performance, and that when the number of relevant stimulus dimensions between conditions are controlled there is little evidence for the separable systems of COVIS. Experiment 7 used tDCS to investigate if RB or II learning was differentially affected by anodal stimulation to the left DLPFC. Although there was no significant difference in learning between category conditions, during anodal stimulation participants improved less across blocks than those receiving sham stimulation. While the results suggest that the effect of tDCS on RB and II learning may be more tangible during stimulation, the numerical pattern of the data warrants further research into the possibility that RB participants are more affected by tDCS than II participants after stimulation to the left DLPFC. Strand 2 of this thesis aimed to further previous work that suggests anodal stimulation to the DLPFC during a prototype distortion task induced a prototype effect (better responding to unseen prototype trials than other category exemplars derived from this prototype) that was not present in sham participants. Contrary to this past work, Experiments 8 and 9 found that anodal stimulation to the left DLPFC inhibited a prototype effect that was present in sham participants. Experiment 10 implemented a combined tDCS and fMRI task and found that anodal participants engaged the stimulated DLPFC and the MTL more than sham participants in measures of the prototype effect. Based on these findings, this thesis argues that anodal tDCS to the left DLPFC inhibits perceptual learning by disrupting error prediction processes. Anodal participants are also considered to use generalization more than sham participants when perceiving category exemplars, a process attributed to the MTL.

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The Where's Waldo problem concerns how individuals can rapidly scan a scene to detect a target object in it. This dissertation develops the ARTSCAN Search neural model to clarify how brain mechanisms that govern spatial and object attention, spatially-invariant object learning and recognition, reinforcement learning, and eye movement search are coordinated to enable learning and directed search for desired objects at specific locations in a cluttered scene. In the model, interactions from the Where cortical processing stream to the What cortical processing stream modulate invariant category learning of a desired object, whereas interactions from the What cortical processing stream to the Where cortical processing stream support search for the object. In particular, when an invariant object category representation is activated top-down by a cognitive plan or by an active motivational source in the model's What stream, it can shift spatial attention in the Where stream and thereby selectively activate the locations of sought-after object exemplars. These combined What-to-Where and Where-to-What interactions clarify how the brain's solution of the Where's Waldo problem overcomes the complementary deficiencies of What and Where stream processes taken individually by using inter-stream interactions that allow both invariant object recognition and spatially selective attention and action to occur.

<p>According to the heuristics and biases approach, the representativeness heuristic (RH) is one of the heuristics available for assessing subjective probabilities (A. Tversky & D. Kahneman, 1974). A subjective probability assessed by the RH is determined by how representative the target object is of the target category. Several aspects of the RH are argued to cause systematic biases, for example: (<i>i</i>) When the RH is used, the category is represented by one single prototypical exemplar. This feature is argued to cause biases such as misperception of chance and insensitivity to sample size. (<i>ii</i>) The RH assesses the inverse rather than the conditional probability. This feature is argued to cause biases such as the conjunction fallacy and base-rate neglect.</p><p>The present thesis focuses on the cognitive aspects of the RH. Three studies were conducted. Overall, data indicated that the RH does not play a major role when subjective probabilities are assessed. Study I indicated that subjective probabilities are not typically determined by how representative the target object is of the target category. Study II indicated that the category is not represented by one single prototypical exemplar when subjective probabilities are assessed. Study III indicated that conjunction fallacies are not caused by the RH. </p><p>The results presented in Studies I-III cast serious doubts on the claim that subjective probabilities are routinely assessed using the RH. Rather, Studies I-II suggested that subjective probabilities are based on exemplar memory and Study III suggested that the conjunction fallacy is caused by people combining component probabilities in a an inappropriate way. In the General Discussion, it is suggested that people use a weighted average rule when combining component probabilities into conjunction probabilities. A simulation showing the ecological relevance of the weighted average rule is presented.</p>

According to the heuristics and biases approach, the representativeness heuristic (RH) is one of the heuristics available for assessing subjective probabilities (A. Tversky &amp; D. Kahneman, 1974). A subjective probability assessed by the RH is determined by how representative the target object is of the target category. Several aspects of the RH are argued to cause systematic biases, for example: (i) When the RH is used, the category is represented by one single prototypical exemplar. This feature is argued to cause biases such as misperception of chance and insensitivity to sample size. (ii) The RH assesses the inverse rather than the conditional probability. This feature is argued to cause biases such as the conjunction fallacy and base-rate neglect. The present thesis focuses on the cognitive aspects of the RH. Three studies were conducted. Overall, data indicated that the RH does not play a major role when subjective probabilities are assessed. Study I indicated that subjective probabilities are not typically determined by how representative the target object is of the target category. Study II indicated that the category is not represented by one single prototypical exemplar when subjective probabilities are assessed. Study III indicated that conjunction fallacies are not caused by the RH. The results presented in Studies I-III cast serious doubts on the claim that subjective probabilities are routinely assessed using the RH. Rather, Studies I-II suggested that subjective probabilities are based on exemplar memory and Study III suggested that the conjunction fallacy is caused by people combining component probabilities in a an inappropriate way. In the General Discussion, it is suggested that people use a weighted average rule when combining component probabilities into conjunction probabilities. A simulation showing the ecological relevance of the weighted average rule is presented.

Judgements of and sensitivity to style are ubiquitous. People become sensitive to the structural regularities of complex or “polymorphous” categories through exposure to individual examples, which allows them respond to new items that are of the same style as those previously experienced. This thesis investigates whether a dimension reduction mechanism could account for how people learn about the structure of complex categories. That is, whether through experience, people extract the primary dimensions of variation in a category and use these to analyse and categorise subsequent instances. We used Singular Value Decomposition (SVD) as the method of dimension reduction, which yields the main dimensions of variation of pixel-based stimuli (eigenvectors). We then tested whether a simple autoassociative network could learn to distinguish paintings by Picasso and Braque which were reconstructed from only these primary dimensions of variation. The network could correctly classify the stimuli, and its performance was optimal with reconstructions based on just the first few eigenvectors. Then we reconstructed the paintings using either just the first 10 (early reconstructions) or all 1,894 eigenvectors (full reconstructions), and asked human participants to categorise the images. We found that people could categorise the images with either the early or full reconstructions. Therefore, people could learn to distinguish category membership based on the reduced set of dimensions obtained from SVD. This suggests that a dimension reduction mechanism analogous to SVD may be operating when people learn about the structure and regularities in complex categories.

Judgements of and sensitivity to style are ubiquitous. People become sensitive to the structural regularities of complex or “polymorphous” categories through exposure to individual examples, which allows them respond to new items that are of the same style as those previously experienced. This thesis investigates whether a dimension reduction mechanism could account for how people learn about the structure of complex categories. That is, whether through experience, people extract the primary dimensions of variation in a category and use these to analyse and categorise subsequent instances. We used Singular Value Decomposition (SVD) as the method of dimension reduction, which yields the main dimensions of variation of pixel-based stimuli (eigenvectors). We then tested whether a simple autoassociative network could learn to distinguish paintings by Picasso and Braque which were reconstructed from only these primary dimensions of variation. The network could correctly classify the stimuli, and its performance was optimal with reconstructions based on just the first few eigenvectors. Then we reconstructed the paintings using either just the first 10 (early reconstructions) or all 1,894 eigenvectors (full reconstructions), and asked human participants to categorise the images. We found that people could categorise the images with either the early or full reconstructions. Therefore, people could learn to distinguish category membership based on the reduced set of dimensions obtained from SVD. This suggests that a dimension reduction mechanism analogous to SVD may be operating when people learn about the structure and regularities in complex categories.

Thesis (Ph. D.)--Miami University, Dept. of Psychology, 2009. / Title from second page of PDF document. Includes bibliographical references (p. 26-31).