Table 3
Overview of Cognitive Functions of ACTR Model.
 Cognitive function  Overview of operation 
 Centroid generation Tasks: 1–3  Buffers implicated: blending, imaginal, and goal Biases instantiated: baserate neglect, anchoring and adjustment The model generates a category centroid by aggregating overall of the perceived events (SIGACTs) in memory via the blended memory retrieval mechanism. Judgments are based on generating a centroidofcentroids by performing a blended retrieval over all previously generated centroids, resulting to a tendency to anchor to early judgments. Because there is an equal number of centroids per category, this mechanism explicitly neglects base rate 
 Path planning Tasks: 34  Buffers implicated: retrieval, imaginal, and goal Biases instantiated: anchoring and adjustment The model parses the roads into a set of intersections and road segments. The model hillclimbs by starting at the category centroid and appends contiguous road segments until the probe event is reached. Road segment lengths are perceived veridically; however, when recalled the lengths are influenced by bottomup perceptual mechanisms (e.g., curve complexity and length) simulated by a power law with an exponent less than unity. This leads to underestimation of longer and curvier segments, resulting in a tendency to anchor when perceiving long segments 
 Probability adjustment Tasks: 1–6  Buffers implicated: blending, imaginal, and goal Biases instantiated: anchoring in weighing evidence, confirmation bias The model represents the prior probability and multiplicative factor rule and then attempts to estimate the correct posterior by performing a blended retrieval over similar chunks in memory in a form of instancebased learning. The natural tendency towards regression to the mean in blended retrievals leads to anchoring bias in higher probabilities and confirmation bias in lower probabilities. The partial matching mechanism is used to allow for matches between the prior and similar values in DM 
 Resource allocation Tasks: 1–6  Buffers implicated: blending, imaginal, and goal Biases instantiated: probability matching The model takes the probability assigned to a category and then estimates an expected outcome by performing a blended retrieval using the probability as a cue. The outcome value of the retrieved chunk is the expected outcome for the trial. Next, an additional blended retrieval is performed based on both the probability and expected outcome, whose output is the resources allocation After feedback, the model stores the leading category probability, the resources allocated, and the actual outcome of the trial. Up to two counterfactuals are learned, representing what would have happened if a winnertakeall or pure probability matching resources allocation had occurred. Negative feedback on forced winnertakeall assignments in Tasks 1–3 leads to probability matching in Tasks 4–6 
 Layer selection Task: 4–6  Buffers implicated: blending, goal Biases instantiated: confirmation bias In Task 6, the model uses partial matching to find chunks representing past layerselection experiences that are similar to the current situation (the distribution of probabilities over hypotheses). If that retrieval succeeds, the model attempts to estimate the utility of each potential layer choice by performing a blended retrieval over the utilities of past layerchoice outcomes in similar situations. The layer choice that has the highest utility is selected. If the model fails to retrieve past experiences similar to the current situations, it performs a “lookahead” search by calculating the expected utility for some feature layers. The number of moves mentally searched will not often be exhaustive The blended retrieval mechanism will tend to average the utility of different feature layers based on prior experiences from Tasks 4 and 5 (where feature layers were provided to participants), in addition to prior trials on Task 6. 

