skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Evaluative Conditioning From the Perspective of the Associative-Propositional Evaluation Model
Evaluative conditioning (EC) is defined as the change in the evaluation of a conditioned stimulus (CS) due to its pairing with a positive or negative unconditioned stimulus (US). According to the associative-propositional evaluation (APE) model, EC effects can be the result of two functionally distinct learning mechanisms: associative and propositional learning. The current article reviews the core assumptions of the APE model regarding (1) the defining features of associative and propositional learning, (2) the mental representations resulting from the two learning mechanisms, (3) the processes involved in the behavioral expression of these representations, and (4) the automatic versus controlled nature of the processes underlying EC effects. In addition to reviewing the core assumptions of the APE model, the article reviews relevant evidence to illustrate the theory’s main hypotheses, its explanatory and predictive power, as well as empirical challenges for the theory.  more » « less
Award ID(s):
1649900
PAR ID:
10184960
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Social Psychological Bulletin
Volume:
13
Issue:
3
ISSN:
1896-1800
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Dual-process theories of evaluative learning suggest that evaluative representations can be formed via two functionally distinct mechanisms: automatic formation of associative links between co-occurring events (associative learning) and non-automatic generation and truth assessment of mental propositions about the relation between stimuli (propositional learning). Single-process propositional theories reject the idea of automatic association formation, attributing all instances of evaluative learning to propositional processes. A central question in the debate between the two theories concerns the mechanisms underlying unqualified effects of stimulus co-occurrence when the relation between the co-occurring stimuli suggests an evaluation that is opposite to the one implied by the observed co-occurrence (e.g., sunscreen prevents skin cancer). Addressing interpretational ambiguities in previous research on the differential impact of co-occurrence and relational information on implicit and explicit measures, the current research used a multinomial modeling approach to investigate the functional properties of the effects of co-occurrence and relational information on a single measure of evaluative responses. Although the moderating effects obtained for relational information are consistent with the predictions of the two theories, the obtained properties of co-occurrence effects pose an explanatory challenge to both dual-process and single-process propositional theories. The findings demonstrate the value of multinomial modeling in providing deeper insights into the functional properties of the effects of co-occurrence and relational information, which impose stronger empirical constraints on extant theories of evaluative learning. 
    more » « less
  2. Abstract Catastrophic forgetting remains an outstanding challenge in continual learning. Recently, methods inspired by the brain, such as continual representation learning and memory replay, have been used to combat catastrophic forgetting. Associative learning (retaining associations between inputs and outputs, even after good representations are learned) plays an important function in the brain; however, its role in continual learning has not been carefully studied. Here, we identified a two-layer neural circuit in the fruit fly olfactory system that performs continual associative learning between odors and their associated valences. In the first layer, inputs (odors) are encoded using sparse, high-dimensional representations, which reduces memory interference by activating nonoverlapping populations of neurons for different odors. In the second layer, only the synapses between odor-activated neurons and the odor’s associated output neuron are modified during learning; the rest of the weights are frozen to prevent unrelated memories from being overwritten. We prove theoretically that these two perceptron-like layers help reduce catastrophic forgetting compared to the original perceptron algorithm, under continual learning. We then show empirically on benchmark data sets that this simple and lightweight architecture outperforms other popular neural-inspired algorithms when also using a two-layer feedforward architecture. Overall, fruit flies evolved an efficient continual associative learning algorithm, and circuit mechanisms from neuroscience can be translated to improve machine computation. 
    more » « less
  3. Deep learning accomplishes remarkable success through training with massively labeled datasets. However, the high demands on the datasets impede the feasibility of deep learning in edge computing scenarios and suffer the data scarcity issue. Rather than relying on labeled data, animals learn by interacting with their surroundings and memorizing the relationship between concurrent events. This learning paradigm is referred to as associative memory. The successful implementation of associative memory potentially achieves self-learning schemes analogous to animals to resolve the challenges of deep learning. The state-of-the-art implementations of associative memory are limited to small-scale and offline paradigms. Thus, in this work, we implement associative memory learning with an Unmanned Ground Vehicle (UGV) and neuromorphic chips (Intel Loihi) for an online learning scenario. Our system reproduces the classic associative memory in rats. In specific, our system successfully reproduces the fear conditioning with no pretraining procedure and labeled datasets. In our experiments, the UGV serves as a substitute for the rats. Our UGV autonomously memorizes the cause-and-effect of the light stimulus and vibration stimulus, then exhibits a movement response. During associative memory learning, the synaptic weights are updated by Hebbian learning. The Intel Loihi chip is integrated with our online learning system for processing visual signals. Its average power usages for computing logic and memory are 30 mW and 29 mW, respectively. 
    more » « less
  4. null (Ed.)
    User and item reviews are valuable for the construction of recommender systems. In general, existing review-based methods for recommendation can be broadly categorized into two groups: the siamese models that build static user and item representations from their reviews respectively, and the interaction-based models that encode user and item dynamically according to the similarity or relationships of their reviews. Although the interaction-based models have more model capacity and fit human purchasing behavior better, several problematic model designs and assumptions of the existing interaction-based models lead to its suboptimal performance compared to existing siamese models. In this paper, we identify three problems of the existing interaction-based recommendation models and propose a couple of solutions as well as a new interaction-based model to incorporate review data for rating prediction. Our model implements a relevance matching model with regularized training losses to discover user relevant information from long item reviews, and it also adapts a zero attention strategy to dynamically balance the item-dependent and item-independent information extracted from user reviews. Empirical experiments and case studies on Amazon Product Benchmark datasets show that our model can extract effective and interpretable user/item representations from their reviews and outperforms multiple types of state-of-the-art review-based recommendation models. 
    more » « less
  5. Relational reasoning is a complex form of human cognition involving the evaluation of relations between mental representations of information. Prior studies have modified stimulus properties of relational reasoning problems and examined differences in difficulty between different problem types. While subsets of these stimulus properties have been addressed in separate studies, there has not been a comprehensive study, to our knowledge, which investigates all of these properties in the same set of stimuli. This investigative gap has resulted in different findings across studies which vary in task design, making it challenging to determine what stimulus properties make relational reasoning—and the putative formation of mental models underlying reasoning—difficult. In this article, we present the Multidimensional Relational Reasoning Task (MRRT), a task which systematically varied an array of stimulus properties within a single set of relational reasoning problems. Using a mixed-effects framework, we demonstrate that reasoning problems containing a greater number of the premises as well as multidimensional relations led to greater task difficulty. The MRRT has been made publicly available for use in future research, along with normative data regarding the relative difficulty of each problem. 
    more » « less