Accumulating evidence for myriad alternatives: Modeling the generation of free association

The associative manner by which thoughts follow one another has intrigued scholars for decades. The process by which an association is generated in response to a cue can be explained by classic models of semantic processing through distinct computational mechanisms. Distributed attractor networks implement rich-get-richer dynamics and assume that stronger associations can be reached with fewer steps. Conversely, spreading activation models assume that a cue distributes its activation, in parallel, to all associations at a constant rate. Despite these models’ huge influence, their intractability together with the unconstrained nature of free association have restricted their few previous uses to qualitative predictions. To test these computational mechanisms quantitatively, we conceptualize free association as the product of internal evidence accumulation and generate predictions concerning the speed and strength of people’s associations. To this end, we first develop a novel approach to mapping the personalized space of words from which an individual chooses an association to a given cue. We then use state-of-the-art evidence accumulation models to demonstrate the function of rich-get-richer dynamics on the one hand and of stochasticity in the rate of spreading activation on the other hand, in preventing an exceedingly slow resolution of the competition among myriad potential associations. Furthermore, whereas our results uniformly indicate that stronger associations require less evidence, only in combination with rich-get-richer dynamics does this explain why weak associations are slow yet prevalent. We discuss implications for models of semantic processing and evidence accumulation and offer recommendations for practical applications and individual-differences research. (PsycInfo Database Record (c) 2022 APA, all rights reserved

Hierarchical inference as a source of human biases

The finding that human decision-making is systematically biased continues to have an immense impact on both research and policymaking. Prevailing views ascribe biases to limited computational resources, which require humans to resort to less costly resource-rational heuristics. Here, we propose that many biases in fact arise due to a computationally costly way of coping with uncertainty—namely, hierarchical inference—which by nature incorporates information that can seem irrelevant. We show how, in uncertain situations, Bayesian inference may avail of the environment’s hierarchical structure to reduce uncertainty at the cost of introducing bias. We illustrate how this account can explain a range of familiar biases, focusing in detail on the halo effect and on the neglect of base rates. In each case, we show how a hierarchical-inference account takes the characterization of a bias beyond phenomenological description by revealing the computations and assumptions it might reflect. Furthermore, we highlight new predictions entailed by our account concerning factors that could mitigate or exacerbate bias, some of which have already garnered empirical support. We conclude that a hierarchical inference account may inform scientists and policy makers with a richer understanding of the adaptive and maladaptive aspects of human decision-making

Theory-Driven Analysis of Natural Language Processing Measures of Thought Disorder Using Generative Language Modeling

BACKGROUND: Natural language processing (NLP) holds promise to transform psychiatric research and practice. A pertinent example is the success of NLP in the automatic detection of speech disorganization in formal thought disorder (FTD). However, we lack an understanding of precisely what common NLP metrics measure and how they relate to theoretical accounts of FTD. We propose tackling these questions by using deep generative language models to simulate FTD-like narratives by perturbing computational parameters instantiating theory-based mechanisms of FTD. METHODS: We simulated FTD-like narratives using Generative-Pretrained-Transformer-2 by either increasing word selection stochasticity or limiting the model's memory span. We then examined the sensitivity of common NLP measures of derailment (semantic distance between consecutive words or sentences) and tangentiality (how quickly meaning drifts away from the topic) in detecting and dissociating the 2 underlying impairments. RESULTS: Both parameters led to narratives characterized by greater semantic distance between consecutive sentences. Conversely, semantic distance between words was increased by increasing stochasticity, but decreased by limiting memory span. An NLP measure of tangentiality was uniquely predicted by limited memory span. The effects of limited memory span were nonmonotonic in that forgetting the global context resulted in sentences that were semantically closer to their local, intermediate context. Finally, different methods for encoding the meaning of sentences varied dramatically in performance. CONCLUSIONS: This work validates a simulation-based approach as a valuable tool for hypothesis generation and mechanistic analysis of NLP markers in psychiatry. To facilitate dissemination of this approach, we accompany the paper with a hands-on Python tutorial

Doubting what you already know:Uncertainty regarding state transitions is associated with obsessive compulsive symptoms

Obsessive compulsive (OC) symptoms involve excessive information gathering (e.g., checking, reassurance-seeking), and uncertainty about possible, often catastrophic, future events. Here we propose that these phenomena are the result of excessive uncertainty regarding state transitions (transition uncertainty): a computational impairment in Bayesian inference leading to a reduced ability to use the past to predict the present and future, and to oversensitivity to feedback (i.e. prediction errors). Using a computational model of Bayesian learning under uncertainty in a reversal learning task, we investigate the relationship between OC symptoms and transition uncertainty. Individuals high and low in OC symptoms performed a task in which they had to detect shifts (i.e. transitions) in cue-outcome contingencies. Modeling subjects' choices was used to estimate each individual participant's transition uncertainty and associated responses to feedback. We examined both an optimal observer model and an approximate Bayesian model in which participants were assumed to attend (and learn about) only one of several cues on each trial. Results suggested the participants were more likely to distribute attention across cues, in accordance with the optimal observer model. As hypothesized, participants with higher OC symptoms exhibited increased transition uncertainty, as well as a pattern of behavior potentially indicative of a difficulty in relying on learned contingencies, with no evidence for perseverative behavior. Increased transition uncertainty compromised these individuals' ability to predict ensuing feedback, rendering them more surprised by expected outcomes. However, no evidence for excessive belief updating was found. These results highlight a potential computational basis for OC symptoms and obsessive compulsive disorder (OCD). The fact the OC symptoms predicted a decreased reliance on the past rather than perseveration challenges preconceptions of OCD as a disorder of inflexibility. Our results have implications for the understanding of the neurocognitive processes leading to excessive uncertainty and distrust of past experiences in OCD

If you don't let it in, you don't have to get it out: Thought preemption as a method to control unwanted thoughts.

To attain goals, people must proactively prevent interferences and react to interferences once they occur. Whereas most research focuses on how people deal with external interferences, here we investigate the use of proactive and reactive control in dealing with unwanted thoughts. To examine this question, we asked people to generate an association to each of several repeating cue words, while forbidding the repetition of associations. Reactively rejecting and replacing unwanted repeated associations after they occur entails slower response times. Conversely, proactive control entails constricting the search space and thus faster response times. To gain further insight into different potential proactive thought control mechanisms, we augmented the analysis of raw response times with a novel, hypothesis-based, tractable computational model describing how people serially sample associations. Our results indicate that people primarily react to unwanted thoughts after they occur. Yet, we found evidence for two latent proactive control mechanisms: one that allows people to mitigate the episodic strengthening of repeated thoughts, and another that helps avoid looping in a repetitive thought. Exploratory analysis showed a relationship between model parameters and self-reported individual differences in the control over unwanted thoughts in daily life. The findings indicate the novel task and model can advance our understanding of how people can and cannot control their thoughts and memories, and benefit future research on the mechanisms responsible for unwanted thought in different psychiatric conditions. Finally, we discuss implications concerning the involvement of associative thinking and various control processes in semantic fluency, decision-making and creativity

Are stronger memories forgotten more slowly? No evidence that memory strength influences the rate of forgetting

<div><p>Information stored in visual short-term memory is used ubiquitously in daily life; however, it is forgotten rapidly within seconds. When more items are to be remembered, they are forgotten faster, potentially suggesting that stronger memories are forgotten less rapidly. Here we tested this prediction with three experiments that assessed the influence of memory strength on the rate of forgetting of visual information without manipulating the number of items. Forgetting rate was assessed by comparing the accuracy of reports in a delayed-estimation task following relatively short and long retention intervals. In the first experiment, we compared the forgetting rate of items that were directly fixated, to items that were not. In Experiments 2 and 3 we manipulated memory strength by extending the exposure time of one item in the memory array. As expected, direct fixation and longer exposure led to better accuracy of reports, reflecting stronger memory. However, in all three experiments, we did not find evidence that increased memory strength moderated the forgetting rate.</p></div

How computational psychiatry can advance the understanding and treatment of obsessive-compulsive disorder

The behavioral repertoires of patients with obsessive-compulsive disorder (OCD) often appear puzzling and irrational. For example, an OCD patient who just locked a door might repeatedly return and check that it is locked. Similarly, a patient might continue washing and rewashing his hands, waiting for a vague “just-right” feeling before deciding to stop

Method and results of Experiment 2.

<p>(A) Sample trial. One bar appeared before the rest of the memory array. After a blank retention interval in variable durations, a probe appeared and participants were required to rotate it to match the orientation of the target bar. (B) Average error (and SEM across participants) for each memory strength condition and retention interval condition. (C) The posterior distributions of the simple effects of retention interval (long–short) for the two memory strength conditions. Horizontal bars represent the 95% high posterior density intervals (HDI). The overlap in the two distributions supports the notion that there is no difference in forgetting rate between the conditions.</p

Bayesian parameter estimation and aggregation of evidence across experiments.

<p>Violin plots depicting the posterior distributions for the main effects of memory strength and retention interval as well as the two-way interaction. Light gray violins depict posterior distributions for the parameters given non-informative priors and data from the separate experiments. Dark gray violins depict posterior distributions for the parameters when aggregating evidence across experiments using the posterior of the previous experiments as the prior for the later experiments. White dots represent the center of the posterior distribution, and black rectangles depict 95% high posterior density intervals. The dashed red line marks the zero effect line. (A) Posterior distributions of the main effect of memory strength. (B) Posterior distributions of the main effect of retention interval (i.e. forgetting). (C) Posterior distributions for the retention interval and memory strength interaction. The distributions of the interaction effect overlap the zero effect line, supporting the notion that there was no difference in forgetting rate between the conditions.</p

Method and results of Experiment 3.

<p>(A) Sample trial. One bar appeared before the rest of the memory array. After a blank retention interval in variable durations, a probe appeared and participants were required to rotate it to match the orientation of the target bar. (B) Average error (and SEM across participants) for each memory strength condition and retention interval condition. (C) The posterior distributions of the simple effects of retention interval (long–short) for the two memory strength conditions. Horizontal bars represent the 95% high posterior density intervals (HDI). The overlap in the two distributions supports the notion that there is no difference in forgetting rate between the conditions.</p